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Co-operation of the control logic of the T-shape road intersection and the near railway level crossing

Transportation Research Procedia 2019年 40卷 1465-1472页

作者： Roman Michalík Aleš Janota Dušan Nemec Marián Hruboš Department of Control and Information Systems Faculty of Electrical Engineering and Information Technology University of Žilina Univerzitná 8215/1 010 26 Žilina Slovakia

The paper deals with the problem of traffic control in the situation when controlled road intersection of the T-shape is situated close to the railway level crossing. That configuration very often brings some additional risks resulting from the phenomenon known as “blocking back”, i.e. blocking movement of road vehicles found at the level crossing when a train is approaching and passing. There are various approaches how to eliminate that risk, one of them consisting in establishing co-operation between both installations and proper modification of signal control phasing of the near controlled road intersection. The authors briefly analyze and discuss the problem, its potential solutions and present applied approach in the form of the animated UML-based model.

关键词： road railway traffic control model risk UML

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A New CNN Occlusion Masking Method for IRT Imaging in Neurosurgery

A New CNN Occlusion Masking Method for IRT Imaging in Neuros...

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European Conference on Circuit Theory and Design, ECCTD

作者： Yahya Moshaei-Nezhad Juliane Müller Christian Schnabel Matthias Kirsch Ronald Tetzlaff Institute of Circuits and Systems Faculty of Electrical and Computer Engineering TU Dresden Dresden Germany Carl Gustav Carus Faculty of Medicine Anesthesiology and Intensive Care Medicine Clinical Sensoring and Monitoring TU Dresden Dresden Germany Carl Gustav Carus Faculty of Medicine TU Dresden Dresden Germany Department of Neurosurgery Asklepios Kliniken Schildautal Seesen Germany

ISBN: (数字)9781728171838

ISBN: (纸本)9781728171845

In this paper, a new occlusion masking method based on Cellular Nonlinear Networks (CNN) for a 2-dimensional (2D) InfraRed Thermography (IRT) brain image is proposed. IRT imaging is one of the noteworthy technique in the medical analysis due to capturing those activities which cannot be seen with unarmed eyes. In neurosurgery, the image occlusion can be classified roughly into two fashions namely, surgery tools and reflection of surgery tools (or cover by the ice-cold saline solution) over the brain cortex surface. In the proposed method, firstly, a CNN texture segmentation is carried out to separate the image textures by the local ratio of the black and white pixels. Thereafter, a CNN dilation is employed to increase the black and white pixels separation. Next, a CNN remove-small-object and filling-holes are exerted for revamping the texture segmentation. Afterwards, the obtained mask is applied to remove the occluded area in the image. To overcome the issues related to reconstruction after masking the image, a solution is given by a 5×5 overlapping sliding window with respect to a reference image, in order to substitute the masked area (pixel values) in the target image. Our proposed method is evaluated on real IRT brain cortex images and compared with other methods. The proposed method shows promising results for IRT brain images in comparison to those other mentioned methods.

关键词： Image segmentation Image reconstruction Surgery Imaging Brain Tools Image texture

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A spectral small-gain condition for input-to-state stability of infinite networks

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IFAC-PapersOnLine 2020年第2期53卷 5303-5308页

作者： Christoph Kawan Andrii Mironchenko Abdalla Swikir Navid Noroozi Majid Zamani Institute of Informatics Ludwig Maximilian University Munich Germany Faculty of Computer Science and Mathematics University of Passau Germany Department of Electrical and Computer Engineering Technical University of Munich Germany Otto-von-Guericke University Magdeburg Laboratory for Systems Theory and Automatic Control Germany Computer Science Department University of Colorado Boulder USA

This paper presents a tight small-gain theorem for networks composed of infinitely many finite-dimensional subsystems. Assuming that each subsystem is exponentially input-to-state stable, we show that if the gain operator, collecting all the information about the internal Lyapunov gains, has a spectral radius less than one, the overall infinite network is exponentially input-to-state stable. We illustrate the effectiveness of our result by applying it to traffic networks.

关键词： Nonlinear systems small-gain theorems infinite-dimensional systems input-to-state stability Lyapunov methods large-scale systems

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Assessment of machine and deep learning models integrated with variational mode decomposition for photovoltaic power forecasting using real-world data from the semi-arid region of Djelfa, Algeria

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Energy Conversion and Management: X 2025年 27卷

作者： Ferial El Robrini Badia Amrouche Umit Cali Taha Selim Ustun Laboratory of Electrical Systems and Remote Control (LabSET) Renewable Energies Department Faculty of Technology Blida 1 University 09000 Blida Algeria Norwegian University of Science and Technology Department of Electric Energy O. S. Bragstads plass 2E 7034 Trondheim Norway University of York School of Physics Engineering and Technology Heslington York YO10 5DD United Kingdom Fukushima Renewable Energy Institute AIST Japan

Accurate photovoltaic power forecasting is essential for grid stability and efficient energy management. While Deep Learning (DL) and Machine Learning (ML) models are widely used, the extent to which each can be effectively leveraged remains an open question. This study thoroughly compares several ML and DL models, applied to both short-term (30-minute) and medium-term (3-hour) horizon. The research is built upon real-world data from a 53 MW PV plant located in Algeria, offering practical insights under realistic conditions. Another critical element of the study is the incorporation of Variational Mode Decomposition (VMD) for feature processing, which mainly enhances information extraction. The study also includes a monthly performance analysis investigating the climatological variability on forecasting accuracy. Among standalone models, CNN performs best with an nMAE of 2.9 %, nRMSE of 5.45 %, and R 2 of 0.9678 at 30 min ahead, and nMAE of 3.15 %, RMSE of 4.15 %, and R 2 of 0.9839 at 3 h forecasting. When combined with VMD, ML models, particularly ANN, SVM, and Random Forest frequently outperform DL-VMD counterparts. For instance, ANN achieves an nMAE of 1.08 %, nRMSE of 1.89 %, and R 2 of 0.9961 at 30 min, and maintains excellent accuracy at 3 h with nMAE of 1.1 %, nRMSE of 1.4 %, and R 2 of 0.9982. Collectively, this research serves as a reference for a multidimensional evaluation of forecasting performance. The analysis highlights the importance of selecting appropriate models and preprocessing techniques in PV power forecasting, tailored to location and climatological conditions, contributing to effectively addressing abrupt fluctuations and facilitating large-scale integration.

关键词： Grid-connected photovoltaic power plant Long short-term memory Deep learning Prediction

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Conceptual Foundations of the Use of Formal Models and Methods for the Rapid Creation of Web Applications

Conceptual Foundations of the Use of Formal Models and Metho...

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IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems

作者： Sergii Telenyk Grzegorz Nowakowski Eduard Zharikov Jewhenii Vovk Department of Theoretical Electrical Engineering and Computer Science Faculty of Electrical and Computer Engineering Cracow University of Technology Department of Automatic Control and Information Technology Faculty of Electrical and Computer Engineering Cracow University of Technology Department of Automation and Control in Technical Systems National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

ISBN: (纸本)9781728140704

The problem of the rapid creation of effective web applications of one class using formal means is considered. A conceptual approach to its solution is offered based on an analysis of the peculiarities of the construction of web applications. Our approach is based on defining the standard web application architecture and selecting its components using formal methods in accordance with user requirements. A formal logical system is proposed that uses the design of web applications as a process of outputting a formula specified according to the needs of the user, which defines the schemes of execution of the modules of the system. An important feature of this approach is the ability to visualise the process of designing the system in 3D, which creates the conditions for effective interaction between developers and machine development tools.

关键词： Web-applications Application construction Business processes Mathematical logic web applications Mathematical logic conceptual User needs Developers

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Data augmentation for improving deep learning in image classification problem

Data augmentation for improving deep learning in image class...

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2018 International Interdisciplinary PhD Workshop, IIPhDW 2018

作者： Mikolajczyk, Agnieszka Grochowski, Michal Department of Control Systems Engineering Faculty of Electrical and Control Engineering Gdańsk University of Technology Narutowicza 11/12 Gdańsk80-233 Poland

ISBN: (纸本)9781538661437

These days deep learning is the fastest-growing field in the field of Machine Learning (ML) and Deep Neural Networks (DNN). Among many of DNN structures, the Convolutional Neural Networks (CNN) are currently the main tool used for the image analysis and classification purposes. Although great achievements and perspectives, deep neural networks and accompanying learning algorithms have some relevant challenges to tackle. In this paper, we have focused on the most frequently mentioned problem in the field of machine learning, that is the lack of sufficient amount of the training data or uneven class balance within the datasets. One of the ways of dealing with this problem is so called data augmentation. In the paper we have compared and analyzed multiple methods of data augmentation in the task of image classification, starting from classical image transformations like rotating, cropping, zooming, histogram based methods and finishing at Style Transfer and Generative Adversarial Networks, along with the representative examples. Next, we presented our own method of data augmentation based on image style transfer. The method allows to generate the new images of high perceptual quality that combine the content of a base image with the appearance of another ones. The newly created images can be used to pre-train the given neural network in order to improve the training process efficiency. Proposed method is validated on the three medical case studies: skin melanomas diagnosis, histopathological images and breast magnetic resonance imaging (MRI) scans analysis, utilizing the image classification in order to provide a diagnose. In such kind of problems the data deficiency is one of the most relevant issues. Finally, we discuss the advantages and disadvantages of the methods being analyzed. © 2018 IEEE.

关键词： Image classification

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Time-Varying Convex Optimization: Time-Structured Algorithms and Applications

arXiv

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arXiv 2020年

作者： Simonetto, Andrea Dall’Anese, Emiliano Paternain, Santiago Leus, Geert Giannakis, Georgios B. Optimization and Control Group of IBM Research Ireland Dublin Ireland College of Engineering and Applied Science University of Colorado BoulderCO80309 United States Department of Electrical and Systems Engineering University of Pennsylvania Faculty of Electrical Mathematics and Computer Science Delft University of Technology Delft2628CD Netherlands Digital Technology Center University of Minnesota MinneapolisMN55455 United States

Optimization underpins many of the challenges that science and technology face on a daily basis. Recent years have witnessed a major shift from traditional optimization paradigms grounded on batch algorithms for medium-scale problems to challenging dynamic, time-varying, and even huge-size settings. This is driven by technological transformations that converted infrastructural and social platforms into complex and dynamic networked systems with even pervasive sensing and computing capabilities. The present paper reviews a broad class of state-of-the-art algorithms for time-varying optimization, with an eye to both algorithmic development and performance analysis. It offers a comprehensive overview of available tools and methods, and unveils open challenges in application domains of broad interest. The real-world examples presented include smart power systems, robotics, machine learning, and data analytics, highlighting domain-specific issues and solutions. The ultimate goal is to exempify wide engineering relevance of analytical tools and pertinent theoretical foundations. Copyright © 2020, The Authors. All rights reserved.

关键词： Data Analytics

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Efficiency fairness tradeoff in battery sharing

arXiv

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arXiv 2019年

作者： Chadha, Karan N. Kulkarni, Ankur A. Nair, Jayakrishnan Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai400076 India Faculty at Systems and Control Engineering Indian Institute of Technology Bombay Mumbai400076 India Faculty at Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai400076 India

The increasing presence of decentralized renewable generation in the power grid has motivated consumers to install batteries to save excess energy for future use. The high price of energy storage calls for a shared storage system, but careful battery management is required so that the battery is operated in a manner that is fair to all and as efficiently as possible. In this paper, we study the tradeoffs between efficiency and fairness in operating a shared battery. We develop a framework based on constrained Markov decision processes to study both regimes, namely, optimizing efficiency under a hard fairness constraint and optimizing fairness under hard efficiency constraint. Our results show that there are fundamental limits to efficiency under fairness and vice-versa, and, in general, the two cannot be achieved simultaneously. We characterize these fundamental limits via absolute bounds on these quantities, and via the notion of price of fairness that we introduce in this *** Codes 60J27, 90B22, 60F10 Copyright © 2019, The Authors. All rights reserved.

关键词： Battery management systems

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Analysis of H-Bridge STATCOM with Fault Phase controlled by Modulated Predictive Current control

Analysis of H-Bridge STATCOM with Fault Phase Controlled by ...

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CHILEAN Conference on electrical, Electronics engineering, Information and Communication Technologies (CHILECON)

作者： Alfredo Renault Magno Ayala Julio Pacher Leonardo Comparatore Raul Gregor Marco Rivera Laboratory of Power and Control Systems Faculty of Engineering Universidad Nacional de Asunción Paraguay Department of Electrical Engineering Universidad de Talca Campus Curicó Chile

This paper presents the model-based predictive current control (MPC) applied to a four-wire three-phase STATCOM based on the three-level H-Bridge converter. The main advantages of MPC are its simplicity, easy inclusion of restrictions and fast dynamic response. However, MPC uses a variable switching frequency and this causes a lot of current ripple and a high total harmonic distortion (THD). This article proposes a modulation strategy, at a fixed switching frequency, in order to mitigate the propagation of the THD of the current injected in the point of common coupling (PCC). It is considered a critical operation of the STATCOM operating with a fault on one of the phases of the electrical network. Theoretical results based on simulations are provided to show the performance of the proposed control.

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Preface

Lecture Notes in Control and Information Sciences

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Lecture Notes in control and Information Sciences 2021年 485卷 v-viii页

作者： Faulwasser, Timm Müller, Matthias A. Worthmann, Karl Department of Electrical Engineering and Information Technology Institute of Energy Systems Energy Efficiency and Energy Economics TU Dortmund University Dortmund Germany Faculty of Electrical Engineering and Computer Science Institute of Automatic Control Leibniz University Hannover Hannover Germany Faculty of Mathematics and Natural Sciences Institute for Mathematics Optimization-based Control Technische Universität Ilmenau Ilmenau Germany

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