Dr. Christian Gruhl

Team Leader: Self-Aware Microsystems (SAM)

Telephone: +49 561 804-6186
Email: cgruhl[at]uni-kassel[dot]de

Location: Wilhelmshöher Allee 73
34121 Kassel

Room: WA-altes Gebäude (WA 73), ohne Raumangabe

Publications

[ 2024 ]
[ 2023 ]
[ 2022 ]
[ 2021 ]
[ 2020 ]
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[ 2017 ]
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2024[ to top ]

Time-Series Representation Learning via Heterogeneous Spatial-Temporal Contrasting for Remaining Useful Life Prediction. Huang, Zhixin; He, Yujiang; Nivarthi, Chandana Priya; Gruhl, Christian; Sick, Bernhard. In International Conference on Pattern Recognition (ICPR), bll 1–21. Springer, 2024.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Classical contrastive learning paradigms rely on manual augmentations like cropping, masking, dropping, or adding noise randomly to create divergent sample views from original data. However, the choice of which method to manipulate samples is often subjective and may destroy the latent pattern of the sample. In response, this paper introduces a novel contrastive learning paradigm without choosing sample view augmentation methods, termed Heterogeneous Spatial-Temporal Representation Contrasting (HSTRC). Instead of sample view augmentation, we employ dual branches with a heterogeneous spatial-temporal flipped structure to extract two distinct hidden feature views from the same source data, which avoids disturbing the original time series. Leveraging a combination of cross-branch spatial-temporal contrastive and projected feature contrastive loss functions, HSTRC can effectively extract robust representations from unlabeled time series data. Remarkably, by only fine-tuning the fully connected layers on top of extracted representations by HSTRC, we achieve the best performance across several Remaining Useful Life prediction datasets, showing up to 19.2% improvements over the state-of-the-art supervised learning methods and classical contrastive learning paradigms. Besides, further intensive experiments demonstrate HSTRC's effectiveness in active learning, out-of-distribution testing, and transfer learning scenarios.
@inproceedings{huang2024time, abstract = {Classical contrastive learning paradigms rely on manual augmentations like cropping, masking, dropping, or adding noise randomly to create divergent sample views from original data. However, the choice of which method to manipulate samples is often subjective and may destroy the latent pattern of the sample. In response, this paper introduces a novel contrastive learning paradigm without choosing sample view augmentation methods, termed Heterogeneous Spatial-Temporal Representation Contrasting (HSTRC). Instead of sample view augmentation, we employ dual branches with a heterogeneous spatial-temporal flipped structure to extract two distinct hidden feature views from the same source data, which avoids disturbing the original time series. Leveraging a combination of cross-branch spatial-temporal contrastive and projected feature contrastive loss functions, HSTRC can effectively extract robust representations from unlabeled time series data. Remarkably, by only fine-tuning the fully connected layers on top of extracted representations by HSTRC, we achieve the best performance across several Remaining Useful Life prediction datasets, showing up to 19.2% improvements over the state-of-the-art supervised learning methods and classical contrastive learning paradigms. Besides, further intensive experiments demonstrate HSTRC's effectiveness in active learning, out-of-distribution testing, and transfer learning scenarios.}, author = {Huang, Zhixin and He, Yujiang and Nivarthi, Chandana Priya and Gruhl, Christian and Sick, Bernhard}, booktitle = {International Conference on Pattern Recognition (ICPR)}, keywords = {itegpub}, pages = {1–21}, publisher = {Springer}, title = {Time-Series Representation Learning via Heterogeneous Spatial-Temporal Contrasting for Remaining Useful Life Prediction}, year = 2024 }
%0 Conference Paper %1 huang2024time %A Huang, Zhixin %A He, Yujiang %A Nivarthi, Chandana Priya %A Gruhl, Christian %A Sick, Bernhard %B International Conference on Pattern Recognition (ICPR) %D 2024 %I Springer %P 1--21 %R 10.1007/978-3-031-78398-2_1 %T Time-Series Representation Learning via Heterogeneous Spatial-Temporal Contrasting for Remaining Useful Life Prediction %X Classical contrastive learning paradigms rely on manual augmentations like cropping, masking, dropping, or adding noise randomly to create divergent sample views from original data. However, the choice of which method to manipulate samples is often subjective and may destroy the latent pattern of the sample. In response, this paper introduces a novel contrastive learning paradigm without choosing sample view augmentation methods, termed Heterogeneous Spatial-Temporal Representation Contrasting (HSTRC). Instead of sample view augmentation, we employ dual branches with a heterogeneous spatial-temporal flipped structure to extract two distinct hidden feature views from the same source data, which avoids disturbing the original time series. Leveraging a combination of cross-branch spatial-temporal contrastive and projected feature contrastive loss functions, HSTRC can effectively extract robust representations from unlabeled time series data. Remarkably, by only fine-tuning the fully connected layers on top of extracted representations by HSTRC, we achieve the best performance across several Remaining Useful Life prediction datasets, showing up to 19.2% improvements over the state-of-the-art supervised learning methods and classical contrastive learning paradigms. Besides, further intensive experiments demonstrate HSTRC's effectiveness in active learning, out-of-distribution testing, and transfer learning scenarios.
Spatial-Temporal Attention Graph Neural Network with Uncertainty Estimation for Remaining Useful Life Prediction. Huang, Zhixin; Nivarthi, Chandana Priya; Gruhl, Christian; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN), bll 1–9. IEEE, 2024.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In the increasingly complex industrial system health management domain, accurate prediction of remaining useful life plays an essential role. This paper analyzes the methods to improve the predictive performance of remaining useful life from three aspects: optimizing model structures, augmenting uncertainty estimation in predictions, and transitioning normalization methods. Based on our analysis, we propose a novel model, the Uncertainty Spatial-Temporal Attention Graph Neural Network (USTAGNN), which consists of three primary components: sensor graph construction, a spatio-temporal feature extractor, and a probabilistic prediction module. The feature extractor leverages graph neural networks and temporal convolutional networks as a foundation to extract spatial and temporal features, further enhanced by attention mechanisms, spectral normalization, and residual connections to bolster its distance awareness. Following extensive experimental comparisons, we utilized the parameter-driven dynamic adjacency matrix for sensor graph construction and the deep kernel Gaussian process for precise uncertainty estimation. USTAGNN tries to resolve issues not thoroughly addressed in existing research, such as comparative analyses of sensor graph construction methods, accurate uncertainty estimation, and the model’s generalization under different preprocessing conditions. The proposed model demonstrated state-of-the-art performance on various subsets of the C-MAPSS dataset, achieving up to a 35.9% improvement in prediction score.
@inproceedings{huang2024spatial, abstract = {In the increasingly complex industrial system health management domain, accurate prediction of remaining useful life plays an essential role. This paper analyzes the methods to improve the predictive performance of remaining useful life from three aspects: optimizing model structures, augmenting uncertainty estimation in predictions, and transitioning normalization methods. Based on our analysis, we propose a novel model, the Uncertainty Spatial-Temporal Attention Graph Neural Network (USTAGNN), which consists of three primary components: sensor graph construction, a spatio-temporal feature extractor, and a probabilistic prediction module. The feature extractor leverages graph neural networks and temporal convolutional networks as a foundation to extract spatial and temporal features, further enhanced by attention mechanisms, spectral normalization, and residual connections to bolster its distance awareness. Following extensive experimental comparisons, we utilized the parameter-driven dynamic adjacency matrix for sensor graph construction and the deep kernel Gaussian process for precise uncertainty estimation. USTAGNN tries to resolve issues not thoroughly addressed in existing research, such as comparative analyses of sensor graph construction methods, accurate uncertainty estimation, and the model’s generalization under different preprocessing conditions. The proposed model demonstrated state-of-the-art performance on various subsets of the C-MAPSS dataset, achieving up to a 35.9% improvement in prediction score.}, author = {Huang, Zhixin and Nivarthi, Chandana Priya and Gruhl, Christian and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, pages = {1–9}, publisher = {IEEE}, title = {Spatial-Temporal Attention Graph Neural Network with Uncertainty Estimation for Remaining Useful Life Prediction}, year = 2024 }
%0 Conference Paper %1 huang2024spatial %A Huang, Zhixin %A Nivarthi, Chandana Priya %A Gruhl, Christian %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %D 2024 %I IEEE %P 1--9 %R 10.1109/IJCNN60899.2024.10650622 %T Spatial-Temporal Attention Graph Neural Network with Uncertainty Estimation for Remaining Useful Life Prediction %X In the increasingly complex industrial system health management domain, accurate prediction of remaining useful life plays an essential role. This paper analyzes the methods to improve the predictive performance of remaining useful life from three aspects: optimizing model structures, augmenting uncertainty estimation in predictions, and transitioning normalization methods. Based on our analysis, we propose a novel model, the Uncertainty Spatial-Temporal Attention Graph Neural Network (USTAGNN), which consists of three primary components: sensor graph construction, a spatio-temporal feature extractor, and a probabilistic prediction module. The feature extractor leverages graph neural networks and temporal convolutional networks as a foundation to extract spatial and temporal features, further enhanced by attention mechanisms, spectral normalization, and residual connections to bolster its distance awareness. Following extensive experimental comparisons, we utilized the parameter-driven dynamic adjacency matrix for sensor graph construction and the deep kernel Gaussian process for precise uncertainty estimation. USTAGNN tries to resolve issues not thoroughly addressed in existing research, such as comparative analyses of sensor graph construction methods, accurate uncertainty estimation, and the model’s generalization under different preprocessing conditions. The proposed model demonstrated state-of-the-art performance on various subsets of the C-MAPSS dataset, achieving up to a 35.9% improvement in prediction score.
Self-Aware Control for Autonomous Underwater Vehicles. Decke, Jens; Gerland, Florian; Schomberg, Thomas; Wünsch, Olaf; Sick, Bernhard; Gruhl, Christian. In Workshop on self-improving system integration (SISSY), ACSOS, bll 13–14. IEEE, 2024.
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We present a concept to research the implementation of a self-aware control system for underwater cleaning robots. Multiple robots cooperate as a swarm to clean the hull of large container ships while in transit. We utilise physics-informed neural networks to implement an autonomous steering system and combine it with a deep reinforcement learning-based decision and scheduling system. This approach ensures efficient navigation and cleaning operations, adapting to dynamic environmental conditions in real-time. The proposed control system allows the cleaner robots, to enhance fuel efficiency, reduce CO2 emissions, and minimize the environmental impact of maritime transportation.
@inproceedings{decke2024self, abstract = {We present a concept to research the implementation of a self-aware control system for underwater cleaning robots. Multiple robots cooperate as a swarm to clean the hull of large container ships while in transit. We utilise physics-informed neural networks to implement an autonomous steering system and combine it with a deep reinforcement learning-based decision and scheduling system. This approach ensures efficient navigation and cleaning operations, adapting to dynamic environmental conditions in real-time. The proposed control system allows the cleaner robots, to enhance fuel efficiency, reduce CO2 emissions, and minimize the environmental impact of maritime transportation.}, author = {Decke, Jens and Gerland, Florian and Schomberg, Thomas and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian}, booktitle = {Workshop on self-improving system integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {13–14}, publisher = {IEEE}, title = {Self-Aware Control for Autonomous Underwater Vehicles}, year = 2024 }
%0 Conference Paper %1 decke2024self %A Decke, Jens %A Gerland, Florian %A Schomberg, Thomas %A Wünsch, Olaf %A Sick, Bernhard %A Gruhl, Christian %B Workshop on self-improving system integration (SISSY), ACSOS %D 2024 %I IEEE %P 13--14 %R 10.1109/ACSOS-C63493.2024.00022 %T Self-Aware Control for Autonomous Underwater Vehicles %X We present a concept to research the implementation of a self-aware control system for underwater cleaning robots. Multiple robots cooperate as a swarm to clean the hull of large container ships while in transit. We utilise physics-informed neural networks to implement an autonomous steering system and combine it with a deep reinforcement learning-based decision and scheduling system. This approach ensures efficient navigation and cleaning operations, adapting to dynamic environmental conditions in real-time. The proposed control system allows the cleaner robots, to enhance fuel efficiency, reduce CO2 emissions, and minimize the environmental impact of maritime transportation.
Multi-Task Representation Learning with Temporal Attention for Zero-Shot Time Series Anomaly Detection. Nivarthi, Chandana Priya; Huang, Zhixin; Gruhl, Christian; Sick, Bernhard. In International Joint Conference on Neural Networks (IJCNN), bll 1–10. IEEE, 2024.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Ensuring the reliability of critical industrial systems across various sectors is crucial. It is essential to detect deviations from regular behaviour to mitigate disruptions and preserve infrastructure integrity. However, accurately labelling anomaly datasets is challenging due to their rarity and manual annotation subjectivity. The conventional approach of training separate models for each dataset entity further complicates model development. This paper presents a novel Multi-task Learning framework combining LSTM Autoencoder with temporal attention mechanism (MTL-LATAM) for effective time series anomaly detection. Multitask learning models improve adaptability and generalizability, leading to reduced runtime and compute power while supporting zero-shot evaluation. These models offer flexibility in detecting emerging anomalies. Additionally, we introduce a dynamic thresholding mechanism to incorporate temporal context for anomaly detection and provide visualizations of attention weights to enhance interpretability. The study compares MTL- LATAM, with other multi-task models, evaluates multi-task versus single-task models and assesses the performance of the proposed frame- work in zero-shot learning scenarios. The findings indicate MTL- LATAM’s effectiveness across real-world and open-source datasets, achieving 95% and 97% task synergy. The results underscore the superior performance of multi-task models in zero-shot tasks compared to individual models trained exclusively on their respective datasets.
@inproceedings{nivarthi2024multi, abstract = {Ensuring the reliability of critical industrial systems across various sectors is crucial. It is essential to detect deviations from regular behaviour to mitigate disruptions and preserve infrastructure integrity. However, accurately labelling anomaly datasets is challenging due to their rarity and manual annotation subjectivity. The conventional approach of training separate models for each dataset entity further complicates model development. This paper presents a novel Multi-task Learning framework combining LSTM Autoencoder with temporal attention mechanism (MTL-LATAM) for effective time series anomaly detection. Multitask learning models improve adaptability and generalizability, leading to reduced runtime and compute power while supporting zero-shot evaluation. These models offer flexibility in detecting emerging anomalies. Additionally, we introduce a dynamic thresholding mechanism to incorporate temporal context for anomaly detection and provide visualizations of attention weights to enhance interpretability. The study compares MTL- LATAM, with other multi-task models, evaluates multi-task versus single-task models and assesses the performance of the proposed frame- work in zero-shot learning scenarios. The findings indicate MTL- LATAM’s effectiveness across real-world and open-source datasets, achieving 95% and 97% task synergy. The results underscore the superior performance of multi-task models in zero-shot tasks compared to individual models trained exclusively on their respective datasets.}, author = {Nivarthi, Chandana Priya and Huang, Zhixin and Gruhl, Christian and Sick, Bernhard}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, pages = {1–10}, publisher = {IEEE}, title = {Multi-Task Representation Learning with Temporal Attention for Zero-Shot Time Series Anomaly Detection}, year = 2024 }
%0 Conference Paper %1 nivarthi2024multi %A Nivarthi, Chandana Priya %A Huang, Zhixin %A Gruhl, Christian %A Sick, Bernhard %B International Joint Conference on Neural Networks (IJCNN) %D 2024 %I IEEE %P 1--10 %R 10.1109/IJCNN60899.2024.10651344 %T Multi-Task Representation Learning with Temporal Attention for Zero-Shot Time Series Anomaly Detection %X Ensuring the reliability of critical industrial systems across various sectors is crucial. It is essential to detect deviations from regular behaviour to mitigate disruptions and preserve infrastructure integrity. However, accurately labelling anomaly datasets is challenging due to their rarity and manual annotation subjectivity. The conventional approach of training separate models for each dataset entity further complicates model development. This paper presents a novel Multi-task Learning framework combining LSTM Autoencoder with temporal attention mechanism (MTL-LATAM) for effective time series anomaly detection. Multitask learning models improve adaptability and generalizability, leading to reduced runtime and compute power while supporting zero-shot evaluation. These models offer flexibility in detecting emerging anomalies. Additionally, we introduce a dynamic thresholding mechanism to incorporate temporal context for anomaly detection and provide visualizations of attention weights to enhance interpretability. The study compares MTL- LATAM, with other multi-task models, evaluates multi-task versus single-task models and assesses the performance of the proposed frame- work in zero-shot learning scenarios. The findings indicate MTL- LATAM’s effectiveness across real-world and open-source datasets, achieving 95% and 97% task synergy. The results underscore the superior performance of multi-task models in zero-shot tasks compared to individual models trained exclusively on their respective datasets.
LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction. Huang, Zhixin; Gruhl, Christian; Sick, Bernhard. In IEEE Conference on Industrial Electronics and Applications (ICIEA), bll 1–7. IEEE, 2024.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.
@inproceedings{huang2024list, abstract = {In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.}, author = {Huang, Zhixin and Gruhl, Christian and Sick, Bernhard}, booktitle = {IEEE Conference on Industrial Electronics and Applications (ICIEA)}, keywords = {itegpub}, pages = {1–7}, publisher = {IEEE}, title = {LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction}, year = 2024 }
%0 Conference Paper %1 huang2024list %A Huang, Zhixin %A Gruhl, Christian %A Sick, Bernhard %B IEEE Conference on Industrial Electronics and Applications (ICIEA) %D 2024 %I IEEE %P 1--7 %R 10.1109/ICIEA61579.2024.10665236 %T LiST: An All-Linear-Layer Spatial-Temporal Feature Extractor with Uncertainty Estimation for RUL Prediction %X In the context of Remaining Useful Life (RUL) prediction for industrial systems, the pursuit of prediction accuracy must be balanced against the hardware costs of model operation and the reliability of prediction results. To resolve these challenges, we introduce LiST, an all-linear-layer spatial-temporal feature extractor integrated with uncertainty estimation, specifically designed for processing sensor multivariate time series (MTS) data. Unlike traditional linear models that flatten MTS and thus neglect their spatial-temporal dependencies, LiST's linear layers act on both the sensor and time dimensions of MTS that can extract spatial and temporal features like GNN and RNN models. Through performance comparisons on four RUL prediction datasets, LiST uses only 66.1% of the parameters, achieves comparable accuracy to state-of-the-art GNN and RNN models, obtains the best results on two datasets with up to a 21.6% improvement in Score, and enhances training efficiency by 3.2 times. Additionally, LiST can predict RUL with uncertainty estimation and precisely disentangle epistemic and aleatoric uncertainties, thus enhancing the model's practicality and reliability in real-world industrial applications.
From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs. Decke, Jens; Wünsch, Olaf; Sick, Bernhard; Gruhl, Christian. In International Conference on Architecture of Computing Systems (ARCS), bll 82–96. Springer, 2024.
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This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.
@inproceedings{decke2024structured, abstract = {This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.}, author = {Decke, Jens and Wünsch, Olaf and Sick, Bernhard and Gruhl, Christian}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {82–96}, publisher = {Springer}, title = {From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs}, year = 2024 }
%0 Conference Paper %1 decke2024structured %A Decke, Jens %A Wünsch, Olaf %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2024 %I Springer %P 82--96 %R 10.1007/978-3-031-66146-4_6 %T From Structured to Unstructured: A Comparative Analysis of Computer Vision and Graph Models in solving Mesh-based PDEs %X This article investigates the application of computer vision and graph-based models in solving mesh-based partial differential equations within high-performance computing environments. Focusing on structured, graded structured, and unstructured meshes, the study compares the performance and computational efficiency of three computer vision-based models against three graph-based models across three datasets. The research aims to identify the most suitable models for different mesh topographies, particularly highlighting the exploration of graded meshes, a less studied area. Results demonstrate that computer vision-based models, notably U-Net, outperform the graph models in prediction performance and efficiency in two (structured and graded) out of three mesh topographies. The study also reveals the unexpected effectiveness of computer vision-based models in handling unstructured meshes, suggesting a potential shift in methodological approaches for data-driven partial differential equation learning. The article underscores deep learning as a viable and potentially sustainable way to enhance traditional high-performance computing methods, advocating for informed model selection based on the topography of the mesh.
An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing. Decke, Jens; Jenß, Arne; Sick, Bernhard; Gruhl, Christian. In International Conference on Architecture of Computing Systems (ARCS), bll 51–66. Springer, 2024.
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This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.
@inproceedings{decke2024efficient, abstract = {This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.}, author = {Decke, Jens and Jenß, Arne and Sick, Bernhard and Gruhl, Christian}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {51–66}, publisher = {Springer}, title = {An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing}, year = 2024 }
%0 Conference Paper %1 decke2024efficient %A Decke, Jens %A Jenß, Arne %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2024 %I Springer %P 51--66 %R 10.1007/978-3-031-66146-4_4 %T An Efficient Multi Quantile Regression Network with Ad Hoc Prevention of Quantile Crossing %X This article presents the Sorting Composite Quantile Regression Neural Network (SCQRNN), an advanced quantile regression model designed to prevent quantile crossing and enhance computational efficiency. Integrating ad hoc sorting in training, the SCQRNN ensures non-intersecting quantiles, boosting model reliability and interpretability. We demonstrate that the SCQRNN not only prevents quantile crossing and reduces computational complexity but also achieves faster convergence than traditional models. This advancement meets the requirements of high-performance computing for sustainable, accurate computation. In organic computing, the SCQRNN enhances self-aware systems with predictive uncertainties, enriching applications across finance, meteorology, climate science, and engineering.

2023[ to top ]

Self-Integration and Agent Compatibility. Gruhl, Christian; Sick, Bernhard. In Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), bll 71–73. IEEE, 2023.
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This position paper addresses the issue of agent compatibility in the context of self-aware microsystems (SAMs), which are SISSY systems with self-awareness capabilities. SAMs are designed to adapt their behaviour and collaborate with other agents in multi-agent settings. Achieving compatibility and effective collaboration between agents pose significant challenges, such as detecting incompatibility and discerning it from malicious behaviour. We present our concept of agent compatibility, highlight future challenges, and propose directions for future research in this area. By focusing on agent compatibility, we aim to advance the understanding of SAMs and promote successful collaboration among agents.
@inproceedings{gruhl2023self, abstract = {This position paper addresses the issue of agent compatibility in the context of self-aware microsystems (SAMs), which are SISSY systems with self-awareness capabilities. SAMs are designed to adapt their behaviour and collaborate with other agents in multi-agent settings. Achieving compatibility and effective collaboration between agents pose significant challenges, such as detecting incompatibility and discerning it from malicious behaviour. We present our concept of agent compatibility, highlight future challenges, and propose directions for future research in this area. By focusing on agent compatibility, we aim to advance the understanding of SAMs and promote successful collaboration among agents.}, author = {Gruhl, Christian and Sick, Bernhard}, booktitle = {Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)}, keywords = {itegpub}, pages = {71–73}, publisher = {IEEE}, title = {Self-Integration and Agent Compatibility}, year = 2023 }
%0 Conference Paper %1 gruhl2023self %A Gruhl, Christian %A Sick, Bernhard %B Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C) %D 2023 %I IEEE %P 71--73 %R 10.1109/ACSOS-C58168.2023.00039 %T Self-Integration and Agent Compatibility %U https://ieeexplore.ieee.org/abstract/document/10336212 %X This position paper addresses the issue of agent compatibility in the context of self-aware microsystems (SAMs), which are SISSY systems with self-awareness capabilities. SAMs are designed to adapt their behaviour and collaborate with other agents in multi-agent settings. Achieving compatibility and effective collaboration between agents pose significant challenges, such as detecting incompatibility and discerning it from malicious behaviour. We present our concept of agent compatibility, highlight future challenges, and propose directions for future research in this area. By focusing on agent compatibility, we aim to advance the understanding of SAMs and promote successful collaboration among agents.
Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges. Esterle, Lukas; Dutt, Nikil; Gruhl, Christian; Lewis, Peter R.; Marcenaro, Lucio; Regazzoni, Carlo; Jantsch, Axel. In Design, Automation & Test in Europe Conference & Exhibition (DATE), bll 1–6. IEEE, 2023.
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Self-aware computing systems enable computing systems to reflect on their actions and behavior. This becomes even more relevant in Cyber-Physical Systems where computing systems have to control and interact with elements in the real world. This paper reports on recent advances made in computational self-awareness for cyber-physical systems.
@inproceedings{esterle2023self, abstract = {Self-aware computing systems enable computing systems to reflect on their actions and behavior. This becomes even more relevant in Cyber-Physical Systems where computing systems have to control and interact with elements in the real world. This paper reports on recent advances made in computational self-awareness for cyber-physical systems.}, author = {Esterle, Lukas and Dutt, Nikil and Gruhl, Christian and Lewis, Peter R. and Marcenaro, Lucio and Regazzoni, Carlo and Jantsch, Axel}, booktitle = {Design, Automation & Test in Europe Conference & Exhibition (DATE)}, keywords = {itegpub}, pages = {1–6}, publisher = {IEEE}, title = {Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges}, year = 2023 }
%0 Conference Paper %1 esterle2023self %A Esterle, Lukas %A Dutt, Nikil %A Gruhl, Christian %A Lewis, Peter R. %A Marcenaro, Lucio %A Regazzoni, Carlo %A Jantsch, Axel %B Design, Automation & Test in Europe Conference & Exhibition (DATE) %D 2023 %I IEEE %P 1--6 %R 10.23919/DATE56975.2023.10137197 %T Self-awareness in Cyber-Physical Systems: Recent Developments and Open Challenges %U https://ieeexplore.ieee.org/abstract/document/10137197 %X Self-aware computing systems enable computing systems to reflect on their actions and behavior. This becomes even more relevant in Cyber-Physical Systems where computing systems have to control and interact with elements in the real world. This paper reports on recent advances made in computational self-awareness for cyber-physical systems.
DADO – Low-Cost Query Strategies for Deep Active Design Optimization. Decke, Jens; Gruhl, Christian; Rauch, Lukas; Sick, Bernhard. In International Conference on Machine Learning and Applications (ICMLA), bll 1611–1618. IEEE, 2023.
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In this work, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations widely used in industry and engineering. We are interested in optimizing the design of structural components, where a set of parameters describes the shape. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two query strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our query strategies in accelerating design optimization. Furthermore, the introduced method is easily transferable to other self-optimization problems in industry and engineering.
@inproceedings{decke2023dado, abstract = {In this work, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations widely used in industry and engineering. We are interested in optimizing the design of structural components, where a set of parameters describes the shape. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two query strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our query strategies in accelerating design optimization. Furthermore, the introduced method is easily transferable to other self-optimization problems in industry and engineering.}, author = {Decke, Jens and Gruhl, Christian and Rauch, Lukas and Sick, Bernhard}, booktitle = {International Conference on Machine Learning and Applications (ICMLA)}, keywords = {itegpub}, pages = {1611–1618}, publisher = {IEEE}, title = {DADO – Low-Cost Query Strategies for Deep Active Design Optimization}, year = 2023 }
%0 Conference Paper %1 decke2023dado %A Decke, Jens %A Gruhl, Christian %A Rauch, Lukas %A Sick, Bernhard %B International Conference on Machine Learning and Applications (ICMLA) %D 2023 %I IEEE %P 1611--1618 %R 10.1109/ICMLA58977.2023.00244 %T DADO – Low-Cost Query Strategies for Deep Active Design Optimization %X In this work, we apply deep active learning to the field of design optimization to reduce the number of computationally expensive numerical simulations widely used in industry and engineering. We are interested in optimizing the design of structural components, where a set of parameters describes the shape. If we can predict the performance based on these parameters and consider only the promising candidates for simulation, there is an enormous potential for saving computing power. We present two query strategies for self-optimization to reduce the computational cost in multi-objective design optimization problems. Our proposed methodology provides an intuitive approach that is easy to apply, offers significant improvements over random sampling, and circumvents the need for uncertainty estimation. We evaluate our strategies on a large dataset from the domain of fluid dynamics and introduce two new evaluation metrics to determine the model's performance. Findings from our evaluation highlights the effectiveness of our query strategies in accelerating design optimization. Furthermore, the introduced method is easily transferable to other self-optimization problems in industry and engineering.

2022[ to top ]

The Vision of Self-Management in Cognitive Organic Power Distribution Systems. Loeser, Inga; Braun, Martin; Gruhl, Christian; Menke, Jan-Hendrik; Sick, Bernhard; Tomforde, Sven. In Energies, 15(3), bl 881. MDPI, 2022.
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Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.
@article{loeser2022vision, abstract = {Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.}, author = {Loeser, Inga and Braun, Martin and Gruhl, Christian and Menke, Jan-Hendrik and Sick, Bernhard and Tomforde, Sven}, journal = {Energies}, keywords = {self-healing}, number = 3, pages = 881, publisher = {MDPI}, title = {The Vision of Self-Management in Cognitive Organic Power Distribution Systems}, volume = 15, year = 2022 }
%0 Journal Article %1 loeser2022vision %A Loeser, Inga %A Braun, Martin %A Gruhl, Christian %A Menke, Jan-Hendrik %A Sick, Bernhard %A Tomforde, Sven %D 2022 %I MDPI %J Energies %N 3 %P 881 %R 10.3390/en15030881 %T The Vision of Self-Management in Cognitive Organic Power Distribution Systems %U https://www.mdpi.com/1996-1073/15/3/881 %V 15 %X Due to the ongoing trend towards a decarbonisation of energy use, the power system is expected to become the backbone of all energy sectors and thus the fundamental critical infrastructure. High penetration with distributed energy resources demands the coordination of a large number of prosumers, partly controlled by home energy management systems (HEMS), to be designed in such a way that the power system’s operational limits are not violated. On the grid level, distribution management systems (DMS) seek to keep the power system in the normal operational state. On the prosumer level, distributed HEMS optimise the internal power flows by setpoint specification of batteries, photovoltaic generators, or flexible loads. The vision of the ODiS (Organic Distribution System) initiative is to develop an architecture to operate a distribution grid reliably, with high resiliency, and fully autonomously by developing “organic” HEMS and DMS which possess multiple self-x capabilities, collectively referred to as self-management. Thus, ODiS seeks answers to the following question: How can we create the most appropriate models, techniques, and algorithms to develop novel kinds of self-configuring, self-organising, self-healing, and self-optimising DMS that are integrally coupled with the distributed HEMS? In this concept paper, the vision of ODiS is presented in detail based on a thorough review of the state of the art.
Social Machines. Draude, Claude; Gruhl, Christian; Hornung, Gerrit; Kropf, Jonathan; Lamla, Jörn; Leimeister, Jan Marco; Sick, Bernhard; Stumme, Gerd. In Informatik Spektrum, 45(1), bll 38–42. Springer, 2022.
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Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.
@article{draude2022social, abstract = {Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.}, author = {Draude, Claude and Gruhl, Christian and Hornung, Gerrit and Kropf, Jonathan and Lamla, Jörn and Leimeister, Jan Marco and Sick, Bernhard and Stumme, Gerd}, journal = {Informatik Spektrum}, keywords = {itegpub}, number = 1, pages = {38–42}, publisher = {Springer}, title = {Social Machines}, volume = 45, year = 2022 }
%0 Journal Article %1 draude2022social %A Draude, Claude %A Gruhl, Christian %A Hornung, Gerrit %A Kropf, Jonathan %A Lamla, Jörn %A Leimeister, Jan Marco %A Sick, Bernhard %A Stumme, Gerd %D 2022 %I Springer %J Informatik Spektrum %N 1 %P 38--42 %R https://doi.org/10.1007/s00287-021-01421-4 %T Social Machines %V 45 %X Social Machines sind ein Paradigma für die Gestaltung soziotechnischer Systeme, die unter Verwendung von Web- und Plattformlösungen das Potenzial digitaler Technologien mit der Eigenlogik sozialer Interaktion, Organisation und Struk- turbildung auf neue Weise zusammenführen. Im Folgenden diskutieren wir das Paradigma der Social Machine aus den Perspektiven der Informatik, der Wirtschaftsinformatik, der Soziologie und des Rechts, um Orientierungspunkte für seine Gestaltung zu identifizieren. Der Begriff ist in der Literatur jedoch bisher nicht abschließend definiert sondern nur durch Beispiele illustriert. In diesem Artikel stellen wir zunächst die folgende Definition zur Diskussion: Social Machines sind soziotechnische Sys- teme, in denen die Prozesse sozialer Interaktion hybrid zwischen menschlichen und maschinellen Akteuren ablaufen und teilweise algorithmisiert sind. Im Anschluss beleuchten wir drei aktuelle, sich gegenseitig bedingende Entwicklungen von Social Machines: die immer stärkere Verschmelzung von Sozialität und Maschine, die Vermessung von Nutzeraktivitäten als Grundstoff gesellschaftlichen Zusammenhalts und die zunehmende Algorithmisierung gesellschaftlicher Prozesse. Ab- schließend diskutieren wir, dass eine teilhabeorientierte, demokratischen Werten folgende Gestaltung von Social Machines die Perspektiven der Nutzungsakzeptanz, der gesellschaftlichen Akzeptabilität und der nachhaltigen Wirtschaftlichkeit adressieren und umsetzen muss.
Self-Aware Microsystems. Gruhl, Christian; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 126–127. IEEE, 2022.
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Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.
@inproceedings{gruhl2022self, abstract = {Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.}, author = {Gruhl, Christian and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {126–127}, publisher = {IEEE}, title = {Self-Aware Microsystems}, year = 2022 }
%0 Conference Paper %1 gruhl2022self %A Gruhl, Christian %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2022 %I IEEE %P 126--127 %R 10.1109/ACSOSC56246.2022.00045 %T Self-Aware Microsystems %U https://ieeexplore.ieee.org/abstract/document/9934876/ %X Besides infrastructure-based solutions, small-scale microsystems are increasingly connected and interoperating with others in changing environments. Following the same motivation as for self-adaptive systems, this demands novel attempts to counter complexity and provide autonomous decision freedom that paves the path towards resilient and flexible behaviour. In this article, we outline a vision for research on self-aware microsystems (SAM) that aims at providing technical solutions that are especially relevant for initiatives such as self-improving system integration.
Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario. Krupitzer, Christian; Gruhl, Christian; Sick, Bernhard; Tomforde, Sven. In Information and Software Technology, 145, bl 106826. Elsevier, 2022.
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Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
@article{krupitzer2022proactive, abstract = {Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.}, author = {Krupitzer, Christian and Gruhl, Christian and Sick, Bernhard and Tomforde, Sven}, journal = {Information and Software Technology}, keywords = {itegpub}, pages = 106826, publisher = {Elsevier}, title = {Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario}, volume = 145, year = 2022 }
%0 Journal Article %1 krupitzer2022proactive %A Krupitzer, Christian %A Gruhl, Christian %A Sick, Bernhard %A Tomforde, Sven %D 2022 %I Elsevier %J Information and Software Technology %P 106826 %R 10.1016/j.infsof.2022.106826 %T Proactive hybrid learning and optimisation in self-adaptive systems: The swarm-fleet infrastructure scenario %U https://www.sciencedirect.com/science/article/abs/pii/S0950584922000052 %V 145 %X Context: Smart and adaptive Systems, such as self-adaptive and self-organising (SASO) systems, typically consist of a large set of highly autonomous and heterogeneous subsystems that are able to adapt their behaviour to the requirements of ever-changing, dynamic environments. Their successful operation is based on appropriate modelling of the internal and external conditions. Objective: The control problem for establishing a near-to-optimal coordinated behaviour of systems with multiple, potentially conflicting objectives is either approached in a distributed (i.e., fully autonomous by the autonomous subsystems) or in a centralised way (i.e. one instance controlling the optimisation and planning process). In the distributed approach, selfish behaviour and being limited to local knowledge may lead to sub-optimal system behaviour, while the centralised approach ignores the autonomy and the coordination efforts of parts of the system. Method: In this article, we present a concept for a hybrid (i.e., integrating a central optimisation with a distributed decision-making process) system management that combines local reinforcement learning and self-awareness mechanisms of fully autonomous subsystems with external system-wide planning and optimisation of adaptation freedom that steers the behaviour dynamically by issuing plans and guidelines augmented with incentivisation schemes. Results: This work addresses the inherent uncertainty of the dynamic system behaviour, the local autonomous and context-aware learning of subsystems, and proactive control based on adaptiveness. We provide the ‘swarm-fleet infrastructure’—a self-organised taxi service established by autonomous, privately-owned cars—as a testbed for structured comparison of systems. Conclusion: The ‘swarm-fleet infrastructure’ supports the advantages of a proactive hybrid self-adaptive and self-organising system operation. Further, we provide a system model to combine the system-wide optimisation while ensuring local decision-making through reinforcement learning for individualised configurations.
NDNET: A Unified Framework for Anomaly and Novelty Detection. Decke, Jens; Schmeißing, Jörn; Botache, Diego; Bieshaar, Maarten; Sick, Bernhard; Gruhl, Christian. In International Conference on Architecture of Computing Systems (ARCS), bll 197–210. Springer, 2022.
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We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.
@inproceedings{decke2022ndnet, abstract = {We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.}, author = {Decke, Jens and Schmeißing, Jörn and Botache, Diego and Bieshaar, Maarten and Sick, Bernhard and Gruhl, Christian}, booktitle = {International Conference on Architecture of Computing Systems (ARCS)}, keywords = {itegpub}, pages = {197–210}, publisher = {Springer}, title = {{NDNET}: {A} Unified Framework for Anomaly and Novelty Detection}, year = 2022 }
%0 Conference Paper %1 decke2022ndnet %A Decke, Jens %A Schmeißing, Jörn %A Botache, Diego %A Bieshaar, Maarten %A Sick, Bernhard %A Gruhl, Christian %B International Conference on Architecture of Computing Systems (ARCS) %D 2022 %I Springer %P 197--210 %R 10.1007/978-3-031-21867-5_13 %T NDNET: A Unified Framework for Anomaly and Novelty Detection %X We introduce NDNET (https://novelty-detection.net/p/ndnet), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.

2021[ to top ]

The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models. Gruhl, Christian; Hannan, Abdul; Huang, Zhixin; Nivarthi, Chandana; Vogt, Stephan. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 204–209. IEEE, 2021.
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@inproceedings{gruhl2021problem, author = {Gruhl, Christian and Hannan, Abdul and Huang, Zhixin and Nivarthi, Chandana and Vogt, Stephan}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {204–209}, publisher = {IEEE}, title = {The Problem with Real-World Novelty Detection – Issues in Multivariate Probabilistic Models}, year = 2021 }
%0 Conference Paper %1 gruhl2021problem %A Gruhl, Christian %A Hannan, Abdul %A Huang, Zhixin %A Nivarthi, Chandana %A Vogt, Stephan %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 204--209 %R 10.1109/acsos-c52956.2021.00055 %T The Problem with Real-World Novelty Detection -- Issues in Multivariate Probabilistic Models
Self-improving system integration: Mastering continuous change. Bellman, Kirstie; Botev, Jean; Diaconescu, Ada; Esterle, Lukas; Gruhl, Christian; Landauer, Christopher; Lewis, Peter R.; Nelson, Phyllis R.; Pournaras, Evangelos; Stein, Anthony; Tomforde, Sven. In Future Generation Computer Systems, 117, bll 29–46. Elsevier, 2021.
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The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.
@article{bellman2021self, abstract = {The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.}, author = {Bellman, Kirstie and Botev, Jean and Diaconescu, Ada and Esterle, Lukas and Gruhl, Christian and Landauer, Christopher and Lewis, Peter R. and Nelson, Phyllis R. and Pournaras, Evangelos and Stein, Anthony and Tomforde, Sven}, journal = {Future Generation Computer Systems}, keywords = {System}, pages = {29–46}, publisher = {Elsevier}, title = {Self-improving system integration: Mastering continuous change}, volume = 117, year = 2021 }
%0 Journal Article %1 bellman2021self %A Bellman, Kirstie %A Botev, Jean %A Diaconescu, Ada %A Esterle, Lukas %A Gruhl, Christian %A Landauer, Christopher %A Lewis, Peter R. %A Nelson, Phyllis R. %A Pournaras, Evangelos %A Stein, Anthony %A Tomforde, Sven %D 2021 %I Elsevier %J Future Generation Computer Systems %P 29--46 %R 10.1016/j.future.2020.11.019 %T Self-improving system integration: Mastering continuous change %U http://www.sciencedirect.com/science/article/pii/S0167739X20330430 %V 117 %X The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.
OHODIN -- Online Anomaly Detection for Data Streams. Gruhl, Christian; Tomforde, Sven. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 193–197. IEEE, 2021.
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@inproceedings{gruhl2021ohodin, author = {Gruhl, Christian and Tomforde, Sven}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {193–197}, publisher = {IEEE}, title = {OHODIN – Online Anomaly Detection for Data Streams}, year = 2021 }
%0 Conference Paper %1 gruhl2021ohodin %A Gruhl, Christian %A Tomforde, Sven %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 193--197 %R 10.1109/acsos-c52956.2021.00046 %T OHODIN -- Online Anomaly Detection for Data Streams
Novelty detection in continuously changing environments. Gruhl, Christian; Sick, Bernhard; Tomforde, Sven. In Future Generation Computer Systems, 114, bll 138–154. Elsevier, 2021.
- [ Abstract ]
- [ BibTeX ]
- [ EndNote ]
Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.
@article{gruhl2021novelty, abstract = {Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.}, author = {Gruhl, Christian and Sick, Bernhard and Tomforde, Sven}, journal = {Future Generation Computer Systems}, keywords = {itegpub}, pages = {138–154}, publisher = {Elsevier}, title = {Novelty detection in continuously changing environments}, volume = 114, year = 2021 }
%0 Journal Article %1 gruhl2021novelty %A Gruhl, Christian %A Sick, Bernhard %A Tomforde, Sven %D 2021 %I Elsevier %J Future Generation Computer Systems %P 138--154 %R 10.1016/j.future.2020.07.037 %T Novelty detection in continuously changing environments %V 114 %X Self-improving system integration (SISSY) aims at mastering the challenges of system organisation decisions for subsystems with highly dynamic behaviours. This is achieved by increased decision freedom of these subsystems, i.e., by moving the corresponding design decisions from design-time to run-time and from engineers to the systems themselves. As a result, a more robust and efficient constellation of the overall system organisation is maintained even under the presence of continuous change and disturbed conditions. Appropriate integration decisions require a solid foundation of assessing the current conditions and their dynamics using identifying triggers for a necessary evaluation of their own integration status. With this article, we present mCANDIES, a modular novelty detection technique especially suited for SISSY systems that fulfils exactly these identification tasks. mCANDIES tackles an anomaly or novelty detection problem in a modular way by combining the advantages of different detection approaches by exploiting locality in different regions of the input space — depending on, e.g. the density or location of expected observations. This is augmented with information about changes in the underlying model of the observations, i.e., mCANDIES identifies concept drifts. The corresponding information can then be used to trigger online learning behaviour. We analyse the behaviour of mCANDIES on artificial and real-world data sets and compare it to other techniques from the state of the art.
Novelty based Driver Identification on RR Intervals from ECG Data. Heidecker, Florian; Gruhl, Christian; Sick, Bernhard. In Workshop on Integrated Artificial Intelligence in Data Science, ICPR, bll 407–421. IEEE, Milan, Italy, 2021.
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@inproceedings{heidecker2021novelty, address = {Milan, Italy}, author = {Heidecker, Florian and Gruhl, Christian and Sick, Bernhard}, booktitle = {Workshop on Integrated Artificial Intelligence in Data Science, ICPR}, keywords = {itegpub}, pages = {407–421}, publisher = {IEEE}, title = {Novelty based Driver Identification on RR Intervals from ECG Data}, year = 2021 }
%0 Conference Paper %1 heidecker2021novelty %A Heidecker, Florian %A Gruhl, Christian %A Sick, Bernhard %B Workshop on Integrated Artificial Intelligence in Data Science, ICPR %C Milan, Italy %D 2021 %I IEEE %P 407--421 %R 10.1007/978-3-030-68799-1_29 %T Novelty based Driver Identification on RR Intervals from ECG Data
Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling. Al-Falouji, Ghassan; Gruhl, Christian; Tomforde, Sven. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 166–171. IEEE, 2021.
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@inproceedings{alfalouji2021digital, author = {Al-Falouji, Ghassan and Gruhl, Christian and Tomforde, Sven}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {166–171}, publisher = {IEEE}, title = {Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling}, year = 2021 }
%0 Conference Paper %1 alfalouji2021digital %A Al-Falouji, Ghassan %A Gruhl, Christian %A Tomforde, Sven %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2021 %I IEEE %P 166--171 %R 10.1109/acsos-c52956.2021.00047 %T Digital Shadows in Self-Improving System Integration: A Concept Using Generative Modelling
Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders. Hannan, Abdul; Gruhl, Christian; Sick, Bernhard. In IEEE International Conference on Cyber Security and Resilience (CSR), bll 1–7. IEEE, 2021.
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@inproceedings{hannan2021anomaly, author = {Hannan, Abdul and Gruhl, Christian and Sick, Bernhard}, booktitle = {IEEE International Conference on Cyber Security and Resilience (CSR)}, keywords = {itegpub}, pages = {1–7}, publisher = {IEEE}, title = {{Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders}}, year = 2021 }
%0 Conference Paper %1 hannan2021anomaly %A Hannan, Abdul %A Gruhl, Christian %A Sick, Bernhard %B IEEE International Conference on Cyber Security and Resilience (CSR) %D 2021 %I IEEE %P 1--7 %R 10.1109/csr51186.2021.9527980 %T Anomaly based Resilient Network Intrusion Detection using Inferential Autoencoders

2020[ to top ]

Normal-Wishart clustering for novelty detection. Gruhl, Christian; Schmeißing, Jörn; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 64–69. IEEE, 2020.
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@inproceedings{gruhl2020normal, author = {Gruhl, Christian and Schmei{\ss}ing, J{\"o}rn and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {64–69}, publisher = {IEEE}, title = {{Normal-Wishart} clustering for novelty detection}, year = 2020 }
%0 Conference Paper %1 gruhl2020normal %A Gruhl, Christian %A Schmeißing, Jörn %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2020 %I IEEE %P 64--69 %R 10.1109/acsos-c51401.2020.00032 %T Normal-Wishart clustering for novelty detection
Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. Pham Minh, T.; Kottke, D.; Tsarenko, A.; Gruhl, C.; Sick, B. In International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.
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@inproceedings{phamminh2020improving, author = {{Pham Minh}, T. and {Kottke}, D. and {Tsarenko}, A. and {Gruhl}, C. and {Sick}, B.}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, keywords = {itegpub}, publisher = {IEEE}, title = {Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning}, year = 2020 }
%0 Conference Paper %1 phamminh2020improving %A Pham Minh, T. %A Kottke, D. %A Tsarenko, A. %A Gruhl, C. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2020 %I IEEE %R 10.1109/ijcnn48605.2020.9206873 %T Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning
Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?. Tomforde, Sven; Gruhl, Christian. In Workshop on Self-Improving System Integration (SISSY), ACSOS, bll 54–59. IEEE, 2020.
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@inproceedings{tomforde2020fairness, author = {Tomforde, Sven and Gruhl, Christian}, booktitle = {Workshop on Self-Improving System Integration (SISSY), ACSOS}, keywords = {itegpub}, pages = {54–59}, publisher = {IEEE}, title = {Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?}, year = 2020 }
%0 Conference Paper %1 tomforde2020fairness %A Tomforde, Sven %A Gruhl, Christian %B Workshop on Self-Improving System Integration (SISSY), ACSOS %D 2020 %I IEEE %P 54--59 %R 10.1109/acsos-c51401.2020.00029 %T Fairness, performance, and robustness: is there a cap theorem for self-adaptive and self-organising systems?
A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour. Tomforde, Sven; Gruhl, Christian; Sick, Bernhard. In Workshop on Self -Aware Computing (SeAC), ACSOS, bll 166–169. IEEE, 2020.
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@inproceedings{tomforde2020swarm, author = {Tomforde, Sven and Gruhl, Christian and Sick, Bernhard}, booktitle = {Workshop on Self -Aware Computing (SeAC), ACSOS}, keywords = {itegpub}, pages = {166–169}, publisher = {IEEE}, title = {A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour}, year = 2020 }
%0 Conference Paper %1 tomforde2020swarm %A Tomforde, Sven %A Gruhl, Christian %A Sick, Bernhard %B Workshop on Self -Aware Computing (SeAC), ACSOS %D 2020 %I IEEE %P 166--169 %R 10.1109/acsos-c51401.2020.00050 %T A swarm-fleet infrastructure as a scenario for proactive, hybrid adaptation of system behaviour

2019[ to top ]

Self-Improving System Integration -- On a Definition and Characteristics of the Challenge. Bellman, K. L.; Gruhl, C.; Landauer, C.; Tomforde, S. In Workshop on Self-Improving System Integration (SISSY), FAS*W, bll 1–3. IEEE, 2019.
- [ Abstract ]
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The self-improving system integration (SISSY) initiative-sometimes also called self-integration-started over six years ago. Recently, we have seen contributions approaching parts of the scope from different directions. In order to provide a better understanding how these attempts support the development of novel SISSY technology, this paper presents a definition of the term. In addition, it discusses the fundamental challenges stemming from the increasingly interconnected character of large-scale distributed and adaptive systems typically referred to as "Interwoven Systems", with these systems describing the basic motivation for SISSY research.
@inproceedings{bellman2019self, abstract = {The self-improving system integration (SISSY) initiative-sometimes also called self-integration-started over six years ago. Recently, we have seen contributions approaching parts of the scope from different directions. In order to provide a better understanding how these attempts support the development of novel SISSY technology, this paper presents a definition of the term. In addition, it discusses the fundamental challenges stemming from the increasingly interconnected character of large-scale distributed and adaptive systems typically referred to as "Interwoven Systems", with these systems describing the basic motivation for SISSY research.}, author = {{Bellman}, K. L. and {Gruhl}, C. and {Landauer}, C. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {integration;self}, pages = {1–3}, publisher = {IEEE}, title = {Self-Improving System Integration – {O}n a Definition and Characteristics of the Challenge}, year = 2019 }
%0 Conference Paper %1 bellman2019self %A Bellman, K. L. %A Gruhl, C. %A Landauer, C. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 1--3 %R 10.1109/FAS-W.2019.00014 %T Self-Improving System Integration -- On a Definition and Characteristics of the Challenge %X The self-improving system integration (SISSY) initiative-sometimes also called self-integration-started over six years ago. Recently, we have seen contributions approaching parts of the scope from different directions. In order to provide a better understanding how these attempts support the development of novel SISSY technology, this paper presents a definition of the term. In addition, it discusses the fundamental challenges stemming from the increasingly interconnected character of large-scale distributed and adaptive systems typically referred to as "Interwoven Systems", with these systems describing the basic motivation for SISSY research.
Explicit Consideration of Resilience in Organic Computing Design Processes. Tomforde, S.; Gelhausen, P.; Gruhl, C.; Haering, I.; Sick, B. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, bll 1–6. VDE, 2019.
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@inproceedings{tomforde2019explicit, author = {{Tomforde}, S. and {Gelhausen}, P. and {Gruhl}, C. and {Haering}, I. and {Sick}, B.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–6}, publisher = {VDE}, title = {Explicit Consideration of Resilience in Organic Computing Design Processes}, year = 2019 }
%0 Conference Paper %1 tomforde2019explicit %A Tomforde, S. %A Gelhausen, P. %A Gruhl, C. %A Haering, I. %A Sick, B. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2019 %I VDE %P 1--6 %T Explicit Consideration of Resilience in Organic Computing Design Processes %U https://ieeexplore.ieee.org/document/8836197
CHARIOT -- Towards a Continuous High-Level Adaptive Runtime Integration Testbed. Barnes, C. M.; Bellman, K.; Botev, J.; Diaconescu, A.; Esterle, L.; Gruhl, C.; Landauer, C.; Lewis, P. R.; Nelson, P. R.; Stein, A.; Stewart, C.; Tomforde, S. In Workshop on Self-Improving System Integration (SISSY), FAS*W, bll 52–55. IEEE, 2019.
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@inproceedings{barnes2019chariot, author = {{Barnes}, C. M. and {Bellman}, K. and {Botev}, J. and {Diaconescu}, A. and {Esterle}, L. and {Gruhl}, C. and {Landauer}, C. and {Lewis}, P. R. and {Nelson}, P. R. and {Stein}, A. and {Stewart}, C. and {Tomforde}, S.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {52–55}, publisher = {IEEE}, title = {{CHARIOT} – {T}owards a Continuous High-Level Adaptive Runtime Integration Testbed}, year = 2019 }
%0 Conference Paper %1 barnes2019chariot %A Barnes, C. M. %A Bellman, K. %A Botev, J. %A Diaconescu, A. %A Esterle, L. %A Gruhl, C. %A Landauer, C. %A Lewis, P. R. %A Nelson, P. R. %A Stein, A. %A Stewart, C. %A Tomforde, S. %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2019 %I IEEE %P 52--55 %R 10.1109/FAS-W.2019.00026 %T CHARIOT -- Towards a Continuous High-Level Adaptive Runtime Integration Testbed

2018[ to top ]

Novelty detection with CANDIES: a holistic technique based on probabilistic models. Gruhl, Christian; Sick, Bernhard. In International Journal of Machine Learning and Cybernetics, 9(6), bll 927–945. Springer, 2018.
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@article{gruhl2018novelty, author = {Gruhl, Christian and Sick, Bernhard}, journal = {International Journal of Machine Learning and Cybernetics}, keywords = {imported}, number = 6, pages = {927–945}, publisher = {Springer}, title = {Novelty detection with {CANDIES:} a holistic technique based on probabilistic models}, volume = 9, year = 2018 }
%0 Journal Article %1 gruhl2018novelty %A Gruhl, Christian %A Sick, Bernhard %D 2018 %I Springer %J International Journal of Machine Learning and Cybernetics %N 6 %P 927--945 %R 10.1007/s13042-016-0618-8 %T Novelty detection with CANDIES: a holistic technique based on probabilistic models %V 9
Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime. Gruhl, Christian; Tomforde, Sven; Sick, Bernhard. In Workshop on Self-Improving System Integration (SISSY), FAS*W, bll 198–203. IEEE, 2018.
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@inproceedings{gruhl2018aspects, author = {Gruhl, Christian and Tomforde, Sven and Sick, Bernhard}, booktitle = {Workshop on Self-Improving System Integration (SISSY), FAS*W}, keywords = {imported}, pages = {198–203}, publisher = {IEEE}, title = {Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime}, year = 2018 }
%0 Conference Paper %1 gruhl2018aspects %A Gruhl, Christian %A Tomforde, Sven %A Sick, Bernhard %B Workshop on Self-Improving System Integration (SISSY), FAS*W %D 2018 %I IEEE %P 198--203 %R 10.1109/fas-w.2018.00047 %T Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime

2017[ to top ]

Highly Autonomous Learning in Collaborative, Technical Systems. Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2017, S. Tomforde, B. Sick (reds.). kassel university press, Kassel, Germany, 2017.
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@incollection{gruhl2017highly, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2017}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, publisher = {kassel university press}, series = {Intelligent Embedded Systems}, title = {Highly Autonomous Learning in Collaborative, Technical Systems}, year = 2017 }
%0 Book Section %1 gruhl2017highly %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2017 %C Kassel, Germany %D 2017 %E Tomforde, S. %E Sick, B. %I kassel university press %T Highly Autonomous Learning in Collaborative, Technical Systems
A Concept for Intelligent Collaborative Network Intrusion Detection. Gruhl, C.; Beer, F.; Heck, H.; Sick, B.; Bühler, U.; Wacker, A.; Tomforde, S. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. VDE, 2017.
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@inproceedings{gruhl2017concept, author = {Gruhl, C. and Beer, F. and Heck, H. and Sick, B. and B{\"u}hler, U. and Wacker, A. and Tomforde, S.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, publisher = {VDE}, title = {A Concept for Intelligent Collaborative Network Intrusion Detection}, year = 2017 }
%0 Conference Paper %1 gruhl2017concept %A Gruhl, C. %A Beer, F. %A Heck, H. %A Sick, B. %A Bühler, U. %A Wacker, A. %A Tomforde, S. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %D 2017 %I VDE %T A Concept for Intelligent Collaborative Network Intrusion Detection

2016[ to top ]

Towards Autonomous Self-tests at Runtime. Heck, H.; Wacker, A.; Rudolph, S.; Gruhl, C.; Sick, B.; Tomforde, S. In IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W, bll 98–99. IEEE, 2016.
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@inproceedings{heck2016towards, author = {Heck, H. and Wacker, A. and Rudolph, S. and Gruhl, C. and Sick, B. and Tomforde, S.}, booktitle = {IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W}, keywords = {imported}, pages = {98–99}, publisher = {IEEE}, title = {Towards Autonomous Self-tests at Runtime}, year = 2016 }
%0 Conference Paper %1 heck2016towards %A Heck, H. %A Wacker, A. %A Rudolph, S. %A Gruhl, C. %A Sick, B. %A Tomforde, S. %B IEEE International Workshop on Quality Assurance for Self-Adaptive, Self-Organising Systems (QA4SASO), FAS*W %D 2016 %I IEEE %P 98--99 %R 10.1109/fas-w.2016.32 %T Towards Autonomous Self-tests at Runtime
Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers. Fisch, D.; Gruhl, C.; Kalkowski, E.; Sick, B.; Ovaska, S. J. In Information Sciences, 370--371, bll 476–496. Elsevier, 2016.
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@article{fisch2016towards, author = {Fisch, D. and Gruhl, C. and Kalkowski, E. and Sick, B. and Ovaska, S. J.}, journal = {Information Sciences}, keywords = {imported}, pages = {476–496}, publisher = {Elsevier}, title = {Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers}, volume = {370–371}, year = 2016 }
%0 Journal Article %1 fisch2016towards %A Fisch, D. %A Gruhl, C. %A Kalkowski, E. %A Sick, B. %A Ovaska, S. J. %D 2016 %I Elsevier %J Information Sciences %P 476--496 %R 10.1016/j.ins.2016.08.016 %T Towards Automation of Knowledge Understanding: An Approach for Probabilistic Generative Classifiers %V 370--371
Probabilistic Obsoleteness Detection for Gaussian Mixture Models. Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2016, S. Tomforde, B. Sick (reds.), bll 45–56. kassel university press, Kassel, Germany, 2016.
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@incollection{gruhl2016probabilistic, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2016}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {45–56}, publisher = {kassel university press}, title = {Probabilistic Obsoleteness Detection for Gaussian Mixture Models}, year = 2016 }
%0 Book Section %1 gruhl2016probabilistic %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2016 %C Kassel, Germany %D 2016 %E Tomforde, S. %E Sick, B. %I kassel university press %P 45--56 %T Probabilistic Obsoleteness Detection for Gaussian Mixture Models
Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems. Heck, H.; Gruhl, C.; Rudolph, S.; Wacker, A.; Sick, B.; Hähner, J. In International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS, bll 1–8. VDE, Nuremberg, Germany, 2016.
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@inproceedings{heck2016multi, address = {Nuremberg, Germany}, author = {Heck, H. and Gruhl, C. and Rudolph, S. and Wacker, A. and Sick, B. and H{\"a}hner, J.}, booktitle = {International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS}, keywords = {imported}, pages = {1–8}, publisher = {VDE}, title = {Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems}, year = 2016 }
%0 Conference Paper %1 heck2016multi %A Heck, H. %A Gruhl, C. %A Rudolph, S. %A Wacker, A. %A Sick, B. %A Hähner, J. %B International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS %C Nuremberg, Germany %D 2016 %I VDE %P 1--8 %T Multi-k-Resilience in Distributed Adaptive Cyber-Physical Systems

2015[ to top ]

On the Application Possibilities of Organic Computing Principles in Socio-technical Systems. Heck, H.; Edenhofer, S.; Gruhl, C.; Lund, A.; Shuka, R.; Hähner, J. In Organic Computing -- Doctoral Dissertation Colloquium 2015, S. Tomforde, B. Sick (reds.), bll 165–170. kassel university press, Kassel, Germany, 2015.
- [ BibTeX ]
- [ EndNote ]
@incollection{heck2015application, address = {Kassel, Germany}, author = {Heck, H. and Edenhofer, S. and Gruhl, C. and Lund, A. and Shuka, R. and H{\"a}hner, J.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {165–170}, publisher = {kassel university press}, title = {On the Application Possibilities of Organic Computing Principles in Socio-technical Systems}, year = 2015 }
%0 Book Section %1 heck2015application %A Heck, H. %A Edenhofer, S. %A Gruhl, C. %A Lund, A. %A Shuka, R. %A Hähner, J. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 165--170 %T On the Application Possibilities of Organic Computing Principles in Socio-technical Systems
Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods. Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2015, S. Tomforde, B. Sick (reds.), bll 95–107. kassel university press, Kassel, Germany, 2015.
- [ BibTeX ]
- [ EndNote ]
@incollection{gruhl2015anomalies, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2015}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {95–107}, publisher = {kassel university press}, title = {Anomalies in Generative Trajectory Models – Discovering Suspicious Traces with Novelty Detection Methods}, year = 2015 }
%0 Book Section %1 gruhl2015anomalies %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2015 %C Kassel, Germany %D 2015 %E Tomforde, S. %E Sick, B. %I kassel university press %P 95--107 %T Anomalies in Generative Trajectory Models -- Discovering Suspicious Traces with Novelty Detection Methods
A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection. Gruhl, C.; Sick, B.; Wacker, A.; Tomforde, S.; Hähner, J. In IEEE International Conference on Awareness Science and Technology (iCAST), bll 194–200. IEEE, Qinhuangdao, China, 2015.
- [ BibTeX ]
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@inproceedings{gruhl2015building, address = {Qinhuangdao, China}, author = {Gruhl, C. and Sick, B. and Wacker, A. and Tomforde, S. and H{\"a}hner, J.}, booktitle = {IEEE International Conference on Awareness Science and Technology (iCAST)}, keywords = {imported}, pages = {194–200}, publisher = {IEEE}, title = {A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection}, year = 2015 }
%0 Conference Paper %1 gruhl2015building %A Gruhl, C. %A Sick, B. %A Wacker, A. %A Tomforde, S. %A Hähner, J. %B IEEE International Conference on Awareness Science and Technology (iCAST) %C Qinhuangdao, China %D 2015 %I IEEE %P 194--200 %R 10.1109/icawst.2015.7314046 %T A building block for awareness in technical systems: Online novelty detection and reaction with an application in intrusion detection

2014[ to top ]

Self-Adapting Generative Modeling Techniques -- A Basic Building Block for Many Organic Computing Techniques. Gruhl, C. In Organic Computing -- Doctoral Dissertation Colloquium 2014, S. Tomforde, B. Sick (reds.), bll 99–109. kassel university press, Kassel, Germany, 2014.
- [ BibTeX ]
- [ EndNote ]
@incollection{gruhl2014self, address = {Kassel, Germany}, author = {Gruhl, C.}, booktitle = {Organic Computing – Doctoral Dissertation Colloquium 2014}, editor = {Tomforde, S. and Sick, B.}, keywords = {imported}, pages = {99–109}, publisher = {kassel university press}, title = {Self-Adapting Generative Modeling Techniques – A Basic Building Block for Many Organic Computing Techniques}, year = 2014 }
%0 Book Section %1 gruhl2014self %A Gruhl, C. %B Organic Computing -- Doctoral Dissertation Colloquium 2014 %C Kassel, Germany %D 2014 %E Tomforde, S. %E Sick, B. %I kassel university press %P 99--109 %T Self-Adapting Generative Modeling Techniques -- A Basic Building Block for Many Organic Computing Techniques
"Know thyself" -- Computational Self-Reflection in Intelligent Technical Systems. Tomforde, S.; Hähner, J.; von Mammen, S.; Gruhl, C.; Sick, B.; Geihs, K. In Workshop on Self-Improving System Integration (SISSY), SASO. IEEE, Braunschweig, Germany, 2014.
- [ BibTeX ]
- [ EndNote ]
@inproceedings{tomforde2014know, address = {Braunschweig, Germany}, author = {Tomforde, S. and H{\"a}hner, J. and von Mammen, S. and Gruhl, C. and Sick, B. and Geihs, K.}, booktitle = {Workshop on Self-Improving System Integration (SISSY), SASO}, keywords = {imported}, publisher = {IEEE}, title = {"{K}now thyself" – Computational Self-Reflection in Intelligent Technical Systems}, year = 2014 }
%0 Conference Paper %1 tomforde2014know %A Tomforde, S. %A Hähner, J. %A von Mammen, S. %A Gruhl, C. %A Sick, B. %A Geihs, K. %B Workshop on Self-Improving System Integration (SISSY), SASO %C Braunschweig, Germany %D 2014 %I IEEE %R 10.1109/SASOW.2014.25 %T "Know thyself" -- Computational Self-Reflection in Intelligent Technical Systems