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Biomedical Imaging Challenges (ISBIC), IEEE International Symposium on

作者： Moritz Bö hland Oliver Neumann Marcel P. Schilling Markus Reischl Ralf Mikut Katharina Lö ffler Tim Scherr Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany

ISBN: (数字)9781665451727

ISBN: (纸本)9781665451734

Comprehensible and high-quality automated cell nucleus segmentation and classification are required to assist pathol-ogists in their decision making. Commonly, cell nucleus segmentation and classification are either treated as separate tasks or split into different branches of a convolutional neural network. In this contribution, we present our joint cell nu-cleus instance segmentation and classification convolutional neural network ciscNet. In contrast to the state-of-the-art convolutional neural network HoVer-Net that uses multi-ple branches, ciscNet uses a single branch to segment and classify cell nuclei. Our single-branch approach outper-forms HoVer-Net on the histopathological Lizard dataset. In addition, we show that training our approach with the Ranger optimizer yields better results than using the Adam optimizer. Furthermore, we participate as team ciscNet in the Colon Nuclei Identification and Counting Challenge 2022 (CoNIC Challenge 2022). Our code is available at https://***/ciscnet/ciscnet-conic-2022.

关键词： Training Image segmentation Codes Biological system modeling Decision making Colon Convolutional neural networks

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Real-time Pricing Mechanism for V2G Using Distributed Bilevel Optimization

Real-time Pricing Mechanism for V2G Using Distributed Bileve...

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IEEE Conference on Decision and Control

作者： Jiawei You Xinliang Dai Yuning Jiang Haoyu Yin Yuanming Shi Colin N. Jones School of Information Science and Technology ShanghaiTech University China EPFL Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Automatic Control Laboratory EPFL Switzerland Department of Electrical and Systems Engineering Washington University in St. Louis USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper explores the impact of the burgeoning electric vehicle (EV) presence on distribution grid operations, highlighting the challenges they present to conventional pricing strategies due to their dual role as power consumers and suppliers, coupled with their energy storage capabilities. We propose an advanced real-time pricing model for the electricity market, employing a novel distributed bilevel optimization framework. This framework distinguishes between the distribution system operator (DSO) at the upper level and the EVs at the lower level, each aiming to optimize profit margins. The optimization includes power flow constraints at the upper level to ensure efficient operation within safe system limits, while model predictive control (MPC) is used to optimize lower-level EV responses. Additionally, we provide a rigorous convergence analysis of the proposed bilevel optimization method. Detailed convergence studies and simulation results demonstrate the effectiveness and superiority of the proposed algorithm.

关键词： Vehicle-to-grid Simulation Pricing Prediction algorithms Real-time systems Robustness Convergence Load flow Energy storage Predictive control

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On Optimal Grid Partitioning for Distributed Optimization of Reactive Power Dispatch

On Optimal Grid Partitioning for Distributed Optimization of...

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Innovative Smart Grid Technologies (ISGT)

作者： Alexander Murray Michael Kyesswa Hüseyin Çakmak Veit Hagenmeyer Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781728188973

ISBN: (纸本)9781728188980

Distributed optimization has been shown to be one promising method for tackling reactive power dispatch, however the performance of distributed algorithms is known to be dependent on how the given problem is partitioned. The question of how to optimally partition a power grid for use in distributed optimization remains open in the literature. In the present paper, we test partitions generated by the graph partitioned KaFFPa, METIS, and spectral clustering using five edge-weighting metrics. The standard IEEE 14, 30, and 57 bus models are used as benchmark case studies and the Augmented Lagrangian Alternating Direction Inexact Newton algorithm is used as the distributed optimization algorithm. It is shown that performance varies drastically depending on which partitioner and weighting is used. Overall, KaFFPa with weightings given by the Y-bus matrix yields the best results.

关键词： Measurement Reactive power Heuristic algorithms Clustering algorithms Partitioning algorithms Optimization Convergence

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Comparing human and algorithmic anomaly detection for HVAC systems applications 8

Comparing human and algorithmic anomaly detection for HVAC s...

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8th IEEE International Conference on Big Data Computing Service and Applications, BigDataService 2022

作者： Borrison, Reuben Syndicus, Marc Orth, Andre Markovic, Romana Dix, Marcel Liguori, Antonio Berning, Matthias Wagner, Andreas Van Treeck, Christoph Corporate Research ABB AG Ladenburg Germany RWTH Aachen University Energy Efficiency and Sustainable Building Aachen Germany Institute for Automation and Applied Informatics KIT Karlsruhe Institute of Technology Karlsruhe Germany Building Science Group fbta KIT Karlsruhe Institute of Technology Karlsruhe Germany Bauphysik und Technischer Ausbau KIT Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9781665458900

ISBN: (纸本)9781665458900

This paper reports the first results of a comparison of human and algorithmic anomaly detection. We are interested in how human and automated anomaly detection can be combined in the most beneficial way to improve how fault detection is practiced in building maintenance. Open source datasets with sensor data were annotated by persons with low subject matter experience, and compared with a Convolutional Autoencoder Neural Network (CAE) as well as a dedicated time series algorithm (DeepAnT), and detection metrics of human and algorithmic procedures are compared. Future comparisons will include higher levels of expertise on the human side, and more sophistication/training amount on the algorithm side. We close by discussing the advantages and caveats of our approach. © 2022 IEEE.

关键词： Anomaly detection

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems Engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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Enhanced Flexibility Aggregation Using LinDistFlow Model with Loss Compensation

arXiv

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arXiv 2025年

作者： Jiang, Yanlin Dai, Xinliang Zahn, Frederik Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe76131 Germany

With the increasing integration of renewable energy resources and the growing need for data privacy between system operators, flexibility aggregation methods have emerged as a promising solution to coordinate integrated transmission-distribution (ITD) systems with limited information exchange. However, existing methods face significant challenges due to the nonlinearity of AC power flow models, and therefore mostly rely on linearized models. This paper examines the inherent errors in the LinDistFlow model, a linearized approximation, and demonstrates their impact on flexibility aggregation. To address these issues, we propose an intuitive compensation approach to refine the LinDistFlow-based flexibility set. Simulation results demonstrate the effectiveness of the proposed method in efficiently coordinating ITD systems. © 2025, CC BY.

关键词： Linearization

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ON AUTOREGRESSIVE DEEP LEARNING MODELS FOR DAY-AHEAD WIND POWER FORECASTING WITH IRREGULAR SHUTDOWNS DUE TO REDISPATCHING

arXiv

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arXiv 2024年

作者： Meisenbacher, Stefan Selzer, Silas Aaron Dado, Mehdi Beichter, Maximilian Martin, Tim Zdrallek, Markus Bretschneider, Peter Hagenmeyer, Veit Mikut, Ralf Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany University of Wuppertal Wuppertal42119 Germany Technische Universität Ilmenau Ilmenau98693 Germany Fraunhofer IOSB Ilmenau98693 Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

Renewable energies and their operation are becoming increasingly vital for the stability of electrical power grids since conventional power plants are progressively being displaced, and their contribution to redispatch interventions is thereby diminishing. In order to consider renewable energies like Wind Power (WP) for such interventions as a substitute, day-ahead forecasts are necessary to communicate their availability for redispatch planning. In this context, automated and scalable forecasting models are required for the deployment to thousands of locally-distributed onshore WP turbines. Furthermore, the irregular interventions into the WP generation capabilities due to redispatch shutdowns pose challenges in the design and operation of WP forecasting models. Since state-of-the-art forecasting methods consider past WP generation values alongside day-ahead weather forecasts, redispatch shutdowns may impact the forecast. Therefore, the present paper highlights these challenges and analyzes state-of-the-art forecasting methods on data sets with both regular and irregular shutdowns. Specifically, we compare the forecasting accuracy of three autoregressive Deep Learning (DL) methods to methods based on WP curve modeling. Interestingly, the latter achieve lower forecasting errors, have fewer requirements for data cleaning during modeling and operation while being computationally more efficient, suggesting their advantages in practical applications. © 2024, CC BY.

关键词： Wind forecasting

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ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles

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Energy and Buildings 2022年 265卷 1页

作者： Quintana, Matias Stoeckmann, Till Park, June Young Turowski, Marian Hagenmeyer, Veit Miller, Clayton College of Design and Engineering National University of Singapore Singapore Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Department of Civil Engineering University of Texas at Arlington United States

Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction. The ASHRAE Great Energy Predictor III (GEPIII) machine learning competition used an extensive meter data set to crowdsource the most accurate machine learning workflow for whole building energy prediction. A significant component of the winning solutions was the pre-processing phase to remove anomalous training data. Contemporary pre-processing methods focus on filtering statistical threshold values or deep learning methods requiring training data and multiple hyper-parameters. A recent method named ALDI (Automated Load profile Discord Identification) managed to identify these discords using matrix profile, but the technique still requires user-defined parameters. We develop ALDI++, a method based on the previous work that bypasses user-defined parameters and takes advantage of discord similarity. We evaluate ALDI++ against a statistical threshold, variational auto-encoder, and the original ALDI as baselines in classifying discords and energy forecasting scenarios. Our results demonstrate that while the classification performance improvement over the original method is marginal, ALDI++ helps achieve the best forecasting error improving 6% over the winning's team approach with six times less computation time. © 2022 Elsevier B.V.

关键词： Matrix algebra

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Analysis for the joining of light alloys by numerical experiment

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Materials Science Forum 2020年 994卷 248-255页

作者： Kravarikova, Helena Faculty of Materials Science and Technology in Trnava Institute of Applied Informatics Automation and Mechatronics J.Bottu 25 Trnava917 24 Slovakia

The current paper deals with the application of numerical experiment of joining light alloys. Modelling and numerical simulation and finite element method in the ANSYS program were used to investigate the course of thermal cycles, the joining process of light alloys by welding. Joining process of light alloys by welding is defined as a moving point source of heat, which generates temperature fields of various kinds, depending on the time and thickness of the material being welded. The paper is therefore devoted to: *Thermal energy transfer and solution to differential equation of heat conduction, *Initial and boundary conditions for temperatures distribution of a moving point source of heat, *Generation (definition) of thermal cycle. Another part of the paper deals with the analysis of the heat-affected zone. Of the result of the solution will be expressed as the temperature field generated in the base material during the welding process. © 2020 Trans Tech Publications Ltd, Switzerland.

关键词： Finite element method

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Analyzing Spatio-Temporal Dynamics of Dissolved Oxygen for the River Thames using Superstatistical Methods and Machine Learning

arXiv

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arXiv 2025年

作者： He, Hankun Boehringer, Takuya Schäfer, Benjamin Heppell, Kate Beck, Christian Centre for Complex Systems Queen Mary University of London United Kingdom University College London United Kingdom Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Germany Chilterns National Landscape United Kingdom School of Geography Queen Mary University of London United Kingdom

By employing superstatistical methods and machine learning, we analyze time series data of water quality indicators for the River Thames (UK). The indicators analyzed include dissolved oxygen, temperature, electrical conductivity, pH, ammonium, turbidity, and rainfall, with a specific focus on the dynamics of dissolved oxygen. After detrending, the probability density functions of dissolved oxygen fluctuations exhibit heavy tails that are effectively modeled using q-Gaussian distributions. Our findings indicate that the multiplicative Empirical Mode Decomposition method stands out as the most effective detrending technique, yielding the highest log-likelihood in nearly all fittings. We also observe that the optimally fitted width parameter of the q-Gaussian shows a negative correlation with the distance to the sea, highlighting the influence of geographical factors on water quality dynamics. In the context of same-time prediction of dissolved oxygen, regression analysis incorporating various water quality indicators and temporal features identify the Light Gradient Boosting Machine as the best model. SHapley Additive exPlanations reveal that temperature, pH, and time of year play crucial roles in the predictions. Furthermore, we use the Transformer, a state-of-the-art machine learning model, to forecast dissolved oxygen concentrations. For long-term forecasting, the Informer model consistently delivers superior performance, achieving the lowest Mean Absolute Error and Symmetric Mean Absolute Percentage Error with the 192 historical time steps that we used. This performance is attributed to the Informer’s ProbSparse self-attention mechanism, which allows it to capture long-range dependencies in time-series data more effectively than other machine learning models. It effectively recognizes the half-life cycle of dissolved oxygen, with particular attention to critical periods such as morning to early afternoon, late evening to early morning, and key intervals between t

关键词： Aquatic ecosystems

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