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MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis

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arXiv 2023年

作者： Sitcheu, Angelo Yamachui Friederich, Nils Baeuerle, Simon Neumann, Oliver Reischl, Markus Mikut, Ralf Institute for Automation and Applied Informatics Germany Institute of Biological and Chemical Systems Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Robert Bosch GmbH Markwiesenstraße 46 Reutlingen72770 Germany

Nowadays, Machine Learning (ML) is experiencing tremendous popularity that has never been seen before. The operationalization of ML models is governed by a set of concepts and methods referred to as Machine Learning Operations (MLOps). Nevertheless, researchers, as well as professionals, often focus more on the automation aspect and neglect the continuous deployment and monitoring aspects of MLOps. As a result, there is a lack of continuous learning through the flow of feedback from production to development, causing unexpected model deterioration over time due to concept drifts, particularly when dealing with scarce data. This work explores the complete application of MLOps in the context of scarce data analysis. The paper proposes a new holistic approach to enhance biomedical image analysis. Our method includes: a fingerprinting process that enables selecting the best models, datasets, and model development strategy relative to the image analysis task at hand;an automated model development stage;and a continuous deployment and monitoring process to ensure continuous learning. For preliminary results, we perform a proof of concept for fingerprinting in microscopic image datasets. © 2023, CC BY.

关键词： Machine learning

Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow

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arXiv 2022年

作者： Bauer, Rebecca Mühlpfordt, Tillmann Ludwig, Nicole Hagenmeyer, Veit Institute for Automation und applied Informatics Karlsruhe Institute of Technology Germany Cluster of Excellence "Machine Learning: New Perspectives for Science" University of Tübingen Germany

The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained DC optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. Storage absorbed RES uncertainty, and reduced generation cost. © 2022, CC BY-NC-ND.

关键词： Electric power transmission networks

Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow

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SSRN

SSRN 2022年

The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. Storage absorbed RES uncertainty, and reduced generation cost. © 2022, The Authors. All rights reserved.

关键词： Acoustic generators

Analytical Uncertainty Propagation for Multi-Period Stochastic Optimal Power Flow

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arXiv 2022年

作者： Bauer, Rebecca Mühlpfordt, Tillmann Ludwig, Nicole Hagenmeyer, Veit Institute for Automation und Applied Informatics Karlsruhe Institute of Technology Germany Cluster of Excellence Machine Learning: New Perspectives for Science University of Tübingen Germany

The increase in renewable energy sources (RESs), like wind or solar power, results in growing uncertainty also in transmission grids. This affects grid stability through fluctuating energy supply and an increased probability of overloaded lines. One key strategy to cope with this uncertainty is the use of distributed energy storage systems (ESSs). In order to securely operate power systems containing renewables and use storage, optimization models are needed that both handle uncertainty and apply ESSs. This paper introduces a compact dynamic stochastic chance-constrained optimal power flow (CC-OPF) model, that minimizes generation costs and includes distributed ESSs. Assuming Gaussian uncertainty, we use affine policies to obtain a tractable, analytically exact reformulation as a second-order cone problem (SOCP). We test the new model on five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes and include complexity analysis. The results show that the model is computationally efficient and robust with respect to constraint violation risk. The distributed energy storage system leads to more stable operation with flattened generation profiles. Storage absorbed RES uncertainty, and reduced generation cost. © 2022, CC BY.

关键词： Acoustic generators

Method for Planning a Coverage Trajectory for a Group of UAVs Marking Out Zones for Installing Seismic Modules 7th

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Method for Planning a Coverage Trajectory for a Group of UAV...

7th International Conference on Interactive Collaborative Robotics, ICR 2022

作者： Lebedeva, Valeriia Iakovlev, Roman Bryksin, Vitaliy Agafonov, Vadim St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences 14th Line VI 39 St. Petersburg199178 Russia Institute of Applied Informatics and Mathematical Geophysics I. Kant BFU Nevskogo Ul. 14 A Kaliningrad Oblast Kaliningrad236016 Russia Seismic Instruments and Software Likhachevsky Proezd 4/1 Office 101 Moscow Region Dolgoprudny141701 Russia

ISBN: (纸本)9783031236082

Today, the task of planning the trajectory of covering the area with a group of unmanned aerial vehicles (UAVs) remains relevant. This paper presents a method for planning the coverage trajectory when a group of UAVs performs aerial monitoring of the terrain in order to mark out the zones for installing seismic modules. The developed method allows solving the problem of constructing high-precision three-dimensional maps of vast territories by a group of UAVs due to the effective distribution of group agents over various parts of the global trajectory of terrain coverage. The proposed solution not only takes into account the current parameters of the UAV, the distance to the segments of the covering trajectories, but also ensures the minimization of the total time of the aerial monitoring mission. According to the results of experiments, using the proposed method, the average error in reconstructing maps of simulated areas using the ADNN metric was 13.01 cm. In the future, the proposed solution can be modified by introducing new algorithms for the decomposition of the global coverage trajectory, as well as methods for smoothing the covering trajectories. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs

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Probabilistic Forecasting of Day-Ahead Electricity Prices an...

IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Julius Trebbien Sebastian Pütz Benjamin Schäfer Heidi S. Nygård Leonardo Rydin Gorjão Dirk Witthaut Institute of Energy and Climate Research (IEK-10) Forschungszentrum Jülich Jülich Germany Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen Germany Faculty of Science and Technology Norwegian University of Life Sciences Ås Norway

Accurate forecasts of electricity prices are crucial for the management of electric power systems and the development of smart applications. European electricity prices have risen substantially and became highly volatile after the Russian invasion of Ukraine, challenging established forecasting methods. Here, we present a Long Short-Term Memory (LSTM) model for the German-Luxembourg day-ahead electricity prices addressing these challenges. The recurrent structure of the LSTM allows the model to adapt to trends, while the joint prediction of both mean and standard deviation enables a probabilistic prediction. Using a physics-inspired approach–superstatistics–to derive an explanation for the statistics of prices, we show that the LSTM model faithfully reproduces both prices and their volatility.

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Data-driven copy-paste imputation for energy time series

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arXiv 2021年

作者： Weber, Moritz Turowski, Marian Çakmak, Hüseyin K. Mikut, Ralf Kühnapfel, Uwe Hagenmeyer, Veit Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Eggenstein-Leopoldshafen76344 Germany

A cornerstone of the worldwide transition to smart grids are smart meters. Smart meters typically collect and provide energy time series that are vital for various applications, such as grid simulations, fault-detection, load forecasting, load analysis, and load management. Unfortunately, these time series are often characterized by missing values that must be handled before the data can be used. A common approach to handle missing values in time series is imputation. However, existing imputation methods are designed for power time series and do not take into account the total energy of gaps, resulting in jumps or constant shifts when imputing energy time series. In order to overcome these issues, the present paper introduces the new Copy-Paste Imputation (CPI) method for energy time series. The CPI method copies data blocks with similar properties and pastes them into gaps of the time series while preserving the total energy of each gap. The new method is evaluated on a real-world dataset that contains six shares of artificially inserted missing values between 1 and 30%. It outperforms by far the three benchmark imputation methods selected for comparison. The comparison furthermore shows that the CPI method uses matching patterns and preserves the total energy of each gap while requiring only a moderate run-time. Copyright © 2021, The Authors. All rights reserved.

关键词： Time series

CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault response

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arXiv 2023年

作者： Müller, Nils Bao, Kaibin Matthes, Jörg Heussen, Kai Wind and Energy Systems Department Technical University of Denmark Building 330 Risø campus Roskilde4000 Denmark Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Building 445 Campus North Eggenstein-Leopoldshafen76344 Germany

Cyber-physical systems (CPSs) constitute the backbone of critical infrastructures such as power grids or water distribution networks. Operating failures in these systems can cause serious risks for society. To avoid or minimize downtime, operators require real-time awareness about critical incidents. However, online event identification in CPSs is challenged by the complex interdependency of numerous physical and digital components, requiring to take cyber attacks and physical failures equally into account. The online event identification problem is further complicated through the lack of historical observations of critical but rare events, and the continuous evolution of cyber attack strategies. This work introduces and demonstrates CyPhERS, a Cyber-Physical Event Reasoning System. CyPhERS provides real-time information pertaining the occurrence, location, physical impact, and root cause of potentially critical events in CPSs, without the need for historical event observations. Key novelty of CyPhERS is the capability to generate informative and interpretable event signatures of known and unknown types of both cyber attacks and physical failures. The concept is evaluated and bench-marked on a demonstration case that comprises a multitude of attack and fault events targeting various components of a CPS. The results demonstrate that the event signatures provide relevant and inferable information on both known and unknown event types. © 2023, CC BY.

关键词： Cyber Physical System

An Explainable Framework for Machine learning-Based Reactive Power Optimization of Distribution Network

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arXiv 2023年

作者： Liao, Wenlong Schäfer, Benjamin Qin, Dalin Zhang, Gonghao Wang, Zhixian Yang, Zhe Lausanne Switzerland Institute for Automation and Applied Informatics Karlsruhe Institute of Technology Karlsruhe Germany Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong

To reduce the heavy computational burden of reactive power optimization of distribution networks, machine learning models are receiving increasing attention. However, most machine learning models (e.g., neural networks) are usually considered as black boxes, making it challenging for power system operators to identify and comprehend potential biases or errors in the decision-making process of machine learning models. To address this issue, an explainable machine-learning framework is proposed to optimize the reactive power in distribution networks. Firstly, a Shapley additive explanation framework is presented to measure the contribution of each input feature to the solution of reactive power optimizations generated from machine learning models. Secondly, a model-agnostic approximation method is developed to estimate Shapley values, so as to avoid the heavy computational burden associated with direct calculations of Shapley values. The simulation results show that the proposed explainable framework can accurately explain the solution of the machine learning model-based reactive power optimization by using visual analytics, from both global and instance perspectives. Moreover, the proposed explainable framework is model-agnostic, and thus applicable to various models (e.g., neural networks). © 2023, CC BY-NC-ND.

关键词： Electric power distribution