Christian Walter Peter Omlin

• Vishwanath, Ajay & Omlin, Christian Walter Peter (2024). Exploring Affinity-Based Reinforcement Learning for Designing Artificial Virtuous Agents in Stochastic Environments. In Farmanbar, Mina; Tzamtzi, Maria P.; Verma, Ajit Kumar & Chakravorty, Antorweep (Ed.), Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications. FAIEMA 2023. Springer. ISSN 978-981-99-9835-7. p. 25–38. doi: 10.1007/978-981-99-9836-4_3.
• Sadiq, Touseef & Omlin, Christian Walter Peter (2023). Scene Retrieval in Traffic Videos with Contrastive Multimodal Learning, 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI 2023). IEEE Computer Society Digital Library. ISSN 9798350342734. p. 1020–1025. doi: 10.1109/ICTAI59109.2023.00153.
• Fernandes, Fara Aninha; Chaltikyan, Georgi; Gerdes, Martin & Omlin, Christian Walter Peter (2023). Bias – The Achilles Heel of Artificial Intelligence in Healthcare. Journal of applied interdisciplinary research. ISSN 2940-8199. p. 90–101. doi: 10.25929/5qxh-nt21.
• Darowski, Michal; Aftab, Muhammad Faisal; Li, Hongyu; Walker, David; Yu, Guoyu & An, Chenghui [Show all 7 contributors for this article] (2023). Towards Data-Driven Material Removal Rate Estimation in Bonnet Polishing. International Conference on Control, Mechatronics and Automation. ISSN 2837-5114. p. 473–479. doi: 10.1109/ICCMA59762.2023.10375024.
• Asres, Mulugeta Weldezgina; Omlin, Christian Walter Peter; Wang, Long; Yu, David; Parygin, Pavel & Dittmann, Jay [Show all 14 contributors for this article] (2023). Spatio-Temporal Anomaly Detection with Graph Networks for Data Quality Monitoring of the Hadron Calorimeter. Sensors. ISSN 1424-8220. 23(24). doi: 10.3390/s23249679. Full text in Research Archive Show summary

  The Compact Muon Solenoid (CMS) experiment is a general-purpose detector for high-energy collision at the Large Hadron Collider (LHC) at CERN. It employs an online data quality monitoring (DQM) system to promptly spot and diagnose particle data acquisition problems to avoid data quality loss. In this study, we present a semi-supervised spatio-temporal anomaly detection (AD) monitoring system for the physics particle reading channels of the Hadron Calorimeter (HCAL) of the CMS using three-dimensional digi-occupancy map data of the DQM. We propose the GraphSTAD system, which employs convolutional and graph neural networks to learn local spatial characteristics induced by particles traversing the detector and the global behavior owing to shared backend circuit connections and housing boxes of the channels, respectively. Recurrent neural networks capture the temporal evolution of the extracted spatial features. We validate the accuracy of the proposed AD system in capturing diverse channel fault types using the LHC collision data sets. The GraphSTAD system achieves production-level accuracy and is being integrated into the CMS core production system for real-time monitoring of the HCAL. We provide a quantitative performance comparison with alternative benchmark models to demonstrate the promising leverage of the presented system.

• Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter (2023). Stiction detection in industrial control valves using Poincaré plot-based convolutional neural networks. IFAC-PapersOnLine. ISSN 2405-8963. 56(2), p. 11687–11692. doi: 10.1016/j.ifacol.2023.10.523.
• Sadiq, Touseef & Omlin, Christian Walter Peter (2023). NLP-based Traffic Scene Retrieval via Representation Learning. In Benedicenti, Luigi (Eds.), Proceedings of the 9th World Congress on Electrical Engineering and Computer Systems and Science (EECSS 2023) . Avestia Publishing. ISSN 978-1-990800-26-9. doi: 10.11159/mvml23.108. Show summary

  Many automated systems require the interpretation of visual information, i.e., images, videos, and natural language input, i.e., speech or text, to comprehend their surroundings and communicate with interacting humans. One such hybrid application of computer vision using images and videos and natural language processing (NLP) recognizes traffic scenes, a crucial and challenging problem in automated transportation systems. Scene classification is just one of many areas where recent convolutional neural network (CNN) frameworks have proven to be highly effective. Still to be fully explored for application to problem-solving in the real world is CNN’s impressive, truly representative learning capability. However, newer CNN implementations, such as YOLO and DeepSort, show promise for object detection. The BERT model is the benchmark for text embeddings and the most efficient method currently available. Hence, we aim to retrieve the vehicles from the traffic videos using natural language-based description, i.e., text. The paper proposes a novel approach that combines YOLOv7, the recent version of YOLO, DeepSort algorithms for object detection, i.e., detecting the vehicles from the traffic scene from the frames of the videos and the transfer learning model, i.e., BERT model for text embeddings. Additionally, a Kalman filter is utilized to track the cars by providing the id and will retain them in the other frames of the videos. The machine learning model performs the similarity checking, i.e., siamese neural networks. The experiments are performed on the standard dataset of AI city challenge 2022. Moreover, the results depict that the proposed approach achieves 28.49 % of Recall@5, 42.08 % of Recall@10, and 20.73 % of MRR, indicating the proposed method’s effective approach

• Sharif, Md Haidar; Lei, Jiao & Omlin, Christian Walter Peter (2023). CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection. Sensors. ISSN 1424-8220. 23(18). doi: 10.3390/s23187734. Full text in Research Archive
• Mathew, Manuel Sathyajith; Kolhe, Mohan Lal; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2023). Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles. Journal of Cleaner Production. ISSN 0959-6526. 421. doi: 10.1016/j.jclepro.2023.138467. Full text in Research Archive Show summary

  With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

• Sharif, Md Haidar; Lei, Jiao & Omlin, Christian Walter Peter (2023). Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks. Electronics. ISSN 2079-9292. 12(7). doi: 10.3390/electronics12071517. Full text in Research Archive Show summary

  Abnormal event detection is one of the most challenging tasks in computer vision. Many existing deep anomaly detection models are based on reconstruction errors, where the training phase is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. It is assumed that the reconstruction error gap between frames of normal and abnormal scores is high for abnormal events during the testing phase. Yet, this assumption may not always hold due to superior capacity and generalization of deep neural networks. In this paper, we design a generalized framework (rpNet) for proposing a series of deep models by fusing several options of a reconstruction network (rNet) and a prediction network (pNet) to detect anomaly in videos efficiently. In the rNet, either a convolutional autoencoder (ConvAE) or a skip connected ConvAE (AEc) can be used, whereas in the pNet, either a traditional U-Net, a non-local block U-Net, or an attention block U-Net (aUnet) can be applied. The fusion of both rNet and pNet increases the error gap. Our deep models have distinct degree of feature extraction capabilities. One of our models (AEcaUnet) consists of an AEc with our proposed aUnet has capability to confirm better error gap and to extract high quality of features needed for video anomaly detection. Experimental results on UCSD-Ped1, UCSD-Ped2, CUHK-Avenue, ShanghaiTech-Campus, and UMN datasets with rigorous statistical analysis show the effectiveness of our models.

• Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter (2023). Online detrended fluctuation analysis and improved empirical wavelet transform for real-time oscillations detection in industrial control loops. Computers and Chemical Engineering. ISSN 0098-1354. 172. doi: 10.1016/j.compchemeng.2023.108173. Show summary

  Detrended Fluctuation Analysis (DFA) is a reliable and assumption-free approach for gauging the complexity of a time series. In this paper, an online oscillations detection paradigm is presented, which integrates the potential of DFA in detecting abnormal coherent fluctuations with the Empirical Wavelet Transform (EWT) efficiency in extracting the characteristics of oscillations. However, the standard EWT fails to separate modes oscillating at close frequencies, resulting in an incorrect decomposition. Furthermore, the lack of an appropriate stopping criterion frequently results in the signal being over-decomposed into several inconsequential components. Therefore, owing to the capability of DFA to differentiate between fluctuations stemming from noise and coherent fluctuations arising from genuine oscillations, an Improved EWT (IEWT) is presented to mitigate these issues and accurately extract only compelling oscillating modes. The proposed DFA-based IEWT framework is verified on simulated applications and data from real industrial processes, illustrating its effectiveness.

• Saeed, Waddah & Omlin, Christian Walter Peter (2023). Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities. Knowledge-Based Systems. ISSN 0950-7051. 263. doi: 10.1016/j.knosys.2023.110273. Show summary

  The past decade has seen significant progress in artificial intelligence (AI), which has resulted in algorithms being adopted for resolving a variety of problems. However, this success has been met by increasing model complexity and employing black-box AI models that lack transparency. In response to this need, Explainable AI (XAI) has been proposed to make AI more transparent and thus advance the adoption of AI in critical domains. Although there are several reviews of XAI topics in the literature that have identified challenges and potential research directions of XAI, these challenges and research directions are scattered. This study, hence, presents a systematic meta-survey of challenges and future research directions in XAI organized in two themes: (1) general challenges and research directions of XAI and (2) challenges and research directions of XAI based on machine learning life cycle’s phases: design, development, and deployment. We believe that our meta-survey contributes to XAI literature by providing a guide for future exploration in the XAI area.

• Dutt, Micheal; Redhu, Surender; Goodwin, Morten & Omlin, Christian Walter Peter (2022). Sleep Stage Identification based on Single-Channel EEG Signals using 1-D Convolutional Autoencoders, 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE conference proceedings. ISSN 978-1-6654-8016-1. p. 94–99. doi: 10.1109/HealthCom54947.2022.9982775. Show summary

  Automatic sleep stage classification can play a vital role when measuring sleep quality and diagnosing different sleep-related ailments. Several automated sleep stage identification algorithms have been proposed using various physiological signals. However, most of these methods use hand-crafted features or multiple Electroencephalography (EEG) signals. This work proposes a one-dimensional convolutional autoencoder (1D-CAE) based on a single-channel EEG signal for sleep stage identification. A total of five 1-D CAEs models are implemented, and each model is trained to reconstructs a specific sleep stage with the lowest reconstruction error, thus enabling the sleep stage identification based on this error. Furthermore, the proposed approach is evaluated on the Sleep EDF expanded datasets and achieved an overall classification accuracy of 87.2% using a single-channel EEG FPz-Cz signal. Also, our approach demonstrated the highest sleep stage identification accuracy compared with the recent algorithms, especially for sleep stage N1, a short period that transitions between sleep stages during a sleep cycle.

• Vishwanath, Ajay; Bøhn, Einar Duenger; Granmo, Ole-Christoffer; Maree, Charl & Omlin, Christian Walter Peter (2022). Towards artificial virtuous agents: games, dilemmas and machine learning. AI and Ethics. ISSN 2730-5953. doi: 10.1007/s43681-022-00251-8.
• Dutt, Micheal; Redhu, Surender; Goodwin, Morten & Omlin, Christian Walter Peter (2022). SleepXAI: An explainable deep learning approach for multi-class sleep stage identification. Applied intelligence (Boston). ISSN 0924-669X. doi: 10.1007/s10489-022-04357-8. Full text in Research Archive Show summary

  Extensive research has been conducted on the automatic classification of sleep stages utilizing deep neural networks and other neurophysiological markers. However, for sleep specialists to employ models as an assistive solution, it is necessary to comprehend how the models arrive at a particular outcome, necessitating the explainability of these models. This work proposes an explainable unified CNN-CRF approach (SleepXAI) for multi-class sleep stage classification designed explicitly for univariate time-series signals using modified gradient-weighted class activation mapping (Grad-CAM). The proposed approach significantly increases the overall accuracy of sleep stage classification while demonstrating the explainability of the multi-class labeling of univariate EEG signals, highlighting the parts of the signals emphasized most in predicting sleep stages. We extensively evaluated our approach to the sleep-EDF dataset, and it demonstrates the highest overall accuracy of 86.8% in identifying five sleep stage classes. More importantly, we achieved the highest accuracy when classifying the crucial sleep stage N1 with the lowest number of instances, outperforming the state-of-the-art machine learning approaches by 16.3%. These results motivate us to adopt the proposed approach in clinical practice as an aid to sleep experts.

• Bounoua, Wahiba; Aftab, Muhammad Faisal & Omlin, Christian Walter Peter (2022). Controller performance monitoring: A survey of problems and a review of approaches from a data-driven perspective with a focus on oscillations detection and diagnosis. Industrial & Engineering Chemistry Research. ISSN 0888-5885. 61(49), p. 17735–17765. doi: 10.1021/acs.iecr.2c02785. Show summary

  Optimal operations of industrial control systems require rigorous monitoring to ensure safety, increase profitability, and minimize plant maintenance downtime. Thus, controller performance monitoring has been actively pursued by the research community, resulting in increased research publications over the past two decades. The availability of large data sets, the so-called big data era, has led impetus to the data-driven domain of controller performance monitoring. In this paper, a comprehensive review of the data-driven research in the CPM domain is presented. To illustrate the rationale behind the research efforts, a succinct explanation of the faults observed in control loops and their influence on overall controller performance is also presented. The paper then provides the publicly available data repositories adopted by most researchers to assess and compare their performance monitoring techniques realistically. Moreover, a review of the most eminent techniques proposed so far concerning both detection and diagnosis phases is presented.

• Maree, Charl & Omlin, Christian Walter Peter (2022). Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation, 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr). IEEE conference proceedings. ISSN 978-1-6654-4234-3. doi: 10.1109/CIFEr52523.2022.9776210. Full text in Research Archive
• Maree, Charl & Omlin, Christian Walter Peter (2022). Balancing Profit, Risk, and Sustainability for Portfolio Management, 2022 IEEE Symposium on Computational Intelligence for Financial Engineering and Economics (CIFEr). IEEE conference proceedings. ISSN 978-1-6654-4234-3. doi: 10.1109/CIFEr52523.2022.9776048. Full text in Research Archive
• Maree, Charl & Omlin, Christian Walter Peter (2022). Reinforcement learning with intrinsic affinity for personalized prosperity management. Digital Finance. ISSN 2524-6984. doi: 10.1007/s42521-022-00068-4. Full text in Research Archive Show summary

  The purpose of applying reinforcement learning (RL) to portfolio management is commonly the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other preferences or constraints. We have developed a regularization method that ensures that strategies have global intrinsic affinities, i.e., different personalities may have preferences for certain asset classes which may change over time. We capitalize on these intrinsic policy affinities to make our RL model inherently interpretable. We demonstrate how RL agents can be trained to orchestrate such individual policies for particular personality profiles and still achieve high returns.

• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2022). Estimation of Wind Turbine Performance Degradation with Deep Neural Networks. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). ISSN 2325-016X. 7(1), p. 351–359. doi: 10.36001/phme.2022.v7i1.3328. Full text in Research Archive Show summary

  In this paper, we estimate the age-related performance degradation of a wind turbine working under Norwegian environment, based on a deep neural network model. Ten years of high-resolution operational data from a 2 MW wind turbine were used for the analysis. Operational data of the turbine, between cut-in and rated wind velocities, were considered, which were pre-processed to eliminate outliers and noises. Based on the SHapley Additive exPlanations of a preliminary performance model, a benchmark performance model for the turbine was developed with deep neural networks. An efficiency index is proposed to gauge the agerelated performance degradation of the turbine, which compares measured performances of the turbine over the years with corresponding bench marked performance. On an average, the efficiency index of the turbine is found to decline by 0.64 percent annually, which is comparable with the degradation patterns reported under similar studies from the UK and the US.

• Asres, Mulugeta Weldezgina; Cummings, Grace; Khukhunaishvili, Aleko; Parygin, Pavel; Cooper, Seth I. & Yu, David [Show all 8 contributors for this article] (2022). Long Horizon Anomaly Prediction in Multivariate Time Series with Causal Autoencoders. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). ISSN 2325-016X. 7(1), p. 21–31. doi: 10.36001/phme.2022.v7i1.3367. Full text in Research Archive Show summary

  Predictive maintenance is essential for complex industrial systems to foresee anomalies before major system faults or ultimate breakdown. However, the existing efforts on Industry 4.0 predictive monitoring are directed at semi-supervised anomaly detection with limited robustness for large systems, which are often accompanied by uncleaned and unlabeled data. We address the challenge of predicting anomalies through data-driven end-to-end deep learning models using early warning symptoms on multivariate time series sensor data. We introduce AnoP, a long multi-timestep anomaly prediction system based on unsupervised attention-based causal residual networks, to raise alerts for anomaly prevention. The experimental evaluation on large data sets from detector health monitoring of the Hadron Calorimeter of the CMS Experiment at LHC CERN demonstrates the promising efficacy of the proposed approach. AnoP predicted around 60% of the anomalies up to seven days ahead, and the majority of the missed anomalies are abnormalities with unpredictable noisy-like behavior. Moreover, it has discovered previously unknown anomalies in the calorimeter's sensors.

• Maree, Charl & Omlin, Christian Walter Peter (2022). Can Interpretable Reinforcement Learning Manage Prosperity Your Way? AI. 3(2), p. 526–537. doi: 10.3390/ai3020030. Full text in Research Archive
• Maree, Charl & Omlin, Christian Walter Peter (2022). Reinforcement Learning Your Way: Agent Characterization through Policy Regularization. AI. 3(2), p. 250–259. doi: 10.3390/ai3020015. Full text in Research Archive
• Maree, Charl & Omlin, Christian Walter Peter (2021). Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality. In Haddow, Pauline (Eds.), IEEE Symposium Series on Computational Intelligence 2021. IEEE Press. ISSN 978-1-7281-9048-8. doi: 10.1109/SSCI50451.2021.9659905. Full text in Research Archive
• Asres, Mulugeta Weldezgina; Cummings, Grace; Parygin, Pavel; Khukhunaishvili, Aleko; Toms, Maria & Campbell, Alan [Show all 10 contributors for this article] (2021). Unsupervised Deep Variational Model for Multivariate Sensor Anomaly Detection, 2021 IEEE International Conference on Progress in Informatics and Computing (PIC). IEEE conference proceedings. ISSN 978-1-6654-2655-8. p. 364–371. doi: 10.1109/PIC53636.2021.9687034. Show summary

  The ever-increasing detector complexity at CERN triggers a call for an increasing level of automation. Since the quality of collected physics data hinges on the quality of the detector components at the time of data-taking, the rapid identification and resolution of detector system anomalies will result in a better amount of high-quality particle data. Therefore, this study proposes CGVAE, a data-driven unsupervised anomaly detection using a deep learning model, for detector system monitoring from multivariate time series sensor data. The CGVAE model is composed of a variational autoencoder with convolutional and gated recurrent unit networks for fast localized feature extraction, long temporal characteristics capturing, and descriptive representation learning. Furthermore, to mitigate signal reconstruction overfitting on anomalous patterns, the CGVAE employs encoded latent feature- and reconstruction-based metrics for anomaly detection. Moreover, the model integrates feature attribution algorithms to explain the contribution of the input sensors to the detected anomalies. The experimental evaluation on large sensor data sets of the Hadron Calorimeter of the CMS experiment demonstrates the efficacy of the proposed model in capturing temporal anomalies.

• Dutt, Micheal; Goodwin, Morten & Omlin, Christian Walter Peter (2021). Automatic Sleep Stage Identification with Time Distributed Convolutional Neural Network. Proceedings of the International Joint Conference on Neural Networks. ISSN 2161-4393. doi: 10.1109/IJCNN52387.2021.9533542.
• Maree, Charl; Modal, Jan Erik & Omlin, Christian Walter Peter (2020). Towards Responsible AI for Financial Transactions, 2020 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE (Institute of Electrical and Electronics Engineers). ISSN 978-1-7281-2547-3. p. 16–21. doi: 10.1109/SSCI47803.2020.9308456. Full text in Research Archive
• Holen, Martin; Saha, Rupsa; Goodwin, Morten; Omlin, Christian Walter Peter & Sandsmark, Knut Eivind (2020). Road Detection for Reinforcement Learning Based Autonomous Car, ICISS 2020: Proceedings of the 2020 The 3rd International Conference on Information Science and System. ACM Publications. ISSN 978-1-4503-7725-6. p. 67–71. doi: 10.1145/3388176.3388199. Show summary

  Human mistakes in traffic often have terrible consequences. The long-awaited introduction of self-driving vehicles may solve many of the problems with traffic, but much research is still needed before cars are fully autonomous. In this paper, we propose a new Road Detection algorithm using online supervised learning based on a Neural Network architecture. This algorithm is designed to support a Reinforcement Learning algorithm (for example, the standard Proximal Policy Optimization or PPO) by detecting when the car is in an adverse condition. Specifically, the PPO gets a penalty whenever the virtual automobile gets stuck or drives off the road with any of its four wheels. Initial experiments show significantly improved results for PPO when using our Road Detection algorithm, as compared to not using any form of Road Detection. In fact, without this detection algorithm, the vehicle often gets into non-terminating loops (for example, driving into the dividers, getting stuck, or driving into a pit).

• Zimudzi, Edward; Sanders, Ian; Rollings, Nicholas & Omlin, Christian Walter Peter (2019). Remote sensing of mangroves using unmanned aerial vehicles: current state and future directions. Journal of Spatial Science. ISSN 1449-8596. doi: 10.1080/14498596.2019.1627252.

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• Vishwanath, Ajay & Omlin, Christian Walter Peter (2023). Exploring Affinity-based Reinforcement Learning for Designing Artificial Virtuous Agents in Stochastic Environments.
• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2023). Predicting the capacity factor of wind farms using deep neural networks for estimating age related performance degradation. . Show summary

  Performance of wind energy conversion systems gradually decline over its life cycle. Understanding this age-related performance degradation is essential in efficiently maintaining and managing these systems. In case of wind farms where sufficient time series performance data are available through SCADA, the decline in the wind farm level performance can be modelled though data driven methods. In this work, we propose such a deep neural network (DNN) model for predicting the capacity factor, which can further be used in analysing the age-related performance degradation of wind farms.

• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2023). Predicting the capacity factor of wind farms using deep neural networks for estimating age related performance degradation. . Show summary

  Performance of wind energy conversion systems gradually decline over its life cycle. Understanding this age-related performance degradation is essential in efficiently maintaining and managing these systems. In case of wind farms where sufficient time series performance data are available through SCADA, the decline in the wind farm level performance can be modelled though data driven methods. In this work, we propose such a deep neural network (DNN) model for predicting the capacity factor, which can further be used in analysing the age-related performance degradation of wind farms.

• Asres, Mulugeta Weldezgina; Wang, Long; Yu, David & Omlin, Christian Walter Peter (2022). Spatio-Temporal Anomaly Detection for the DQM of the CMS Experiment via Graph Networks. Show summary

  he Data Quality Monitoring (DQM) is in place to spot and diagnose particle physics data problems as promptly as possible to avoid data loss in the CMS experiment of CERN. Several studies have proposed to leverage the DQM automation using machine learning algorithms. However, only a few efforts explored temporal characteristics to underpin system monitoring automation of the CMS detectors via anomaly detection models thus far. Moreover, the DQM for the HCAL detector of the CMS experiment poses multidimensional challenges, yet it is relatively unexplored with machine learning models. In this study, we propose a time-aware deep learning model for anomaly detection on the multidimensional spatial quantity of the DQM for the HCAL detector. The model employs convolutional, recurrent and graph networks to learn three-dimensional spatial characteristics, exploit physical connections of the QIE channels into RBX of the detector systems, and temporal evolution, respectively. Performance evaluation on artificially generated anomalies such as dead, hot and degrading channels has demonstrated the efficacy of the proposed model in detecting and robustly localizing anomalies in temporal and spatial contexts on digioccupacy histograms. Finally, we have carried-out comparison among several models that shows the substantial gain of the proposed model architecture and temporal modeling.

• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2021). Analytics for Wind Farm Asset Management.
• Maree, Charl & Omlin, Christian Walter Peter (2023). Affinity-Based Reinforcement Learning: A New Paradigm for Agent Interpretability. Universitetet i Agder. ISSN 978-82-8427-108-8. Full text in Research Archive

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