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Vessels Segmentation in Angiograms Using Convolutional Neural Network: A Deep Learning Based Approach

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 241-255页

作者： Sanjiban Sekhar Roy Ching-Hsien Hsu Akash Samaran Ranjan Goyal Arindam Pande Valentina E.Balas School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Information Engineering Asia UniversityTaichung41354Taiwan Department of Medical Research China Medical University HospitalChina Medical UniversityTaichung406Taiwan Guangdong-Hong Kong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology School of Mathematics and Big DataFoshan UniversityFoshan528000China Medica Superspecialty Hospital Kolkata700099India Automation and Applied Informatics Aurel Vlaicu University of AradArad310130Romania

Coronary arterydisease(CAD)has become a significant causeof heart attack,especially amongthose 40yearsoldor *** is a need to develop new technologies andmethods to deal with this *** researchers have proposed image processing-based solutions for CADdiagnosis,but achieving highly accurate results for angiogram segmentation is still a *** different types of angiograms are adopted for CAD *** paper proposes an approach for image segmentation using ConvolutionNeuralNetworks(CNN)for diagnosing coronary artery disease to achieve state-of-the-art *** have collected the 2D X-ray images from the hospital,and the proposed model has been applied to *** augmentation has been performed in this research as it’s the most significant task required to be initiated to increase the dataset’s ***,the images have been enhanced using noise removal techniques before being fed to the CNN model for segmentation to achieve high *** the output,different settings of the network architecture undoubtedly have achieved different accuracy,among which the highest accuracy of the model is 97.61%.Compared with the other models,these results have proven to be superior to this proposed method in achieving state-of-the-art results.

关键词： Angiogram convolution neural network coronary artery disease diagnosis of CAD image segmentation

Adaptive Feedforward Control for DC/DC Converters in Microgrids - A Power Hardware in the Loop Study

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Adaptive Feedforward Control for DC/DC Converters in Microgr...

International Smart Grid Workshop and Certificate Program (ISGWCP)

作者： Jan Wachter Lutz Gröll Veit Hagenmeyer Institute for Automation and Applied Informatics (IAI) Karlsruhe Institute of Technology Karlsruhe Germany

This paper presents the application of an adaptive feedforward control approach based on radial basis functions for grid-tied Ćuk converters. A stability analysis provides a theoretical foundation for the expected stable operating range as well as bounds for the control signal at the operating points. With a power hardware in the loop implementation of the approach the feasibility for real-time application is shown, which is further illustrated by operating scenarios. Further, practical experience concerning the choice of the control parameters is presented along with the resulting advantages and limitations of the approach.

关键词： Microgrids Hardware Stability analysis Real-time systems Smart grids

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

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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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

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