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Sigma-Mixed Unscented Kalman Filter-Based Fault Detection for Traction Systems in High-Speed Trains

Chinese Journal of Electronics 2023年第5期32卷 982-991页

作者： CHENG Chao WANG Weijun MENG Xiangxi SHAO Haidong CHEN Hongtian Department of Computer Science and Technology Changchun University of Technology Department of Mathematics and Statistics Changchun University of Technology Institute of System Research China Industrial Control Systems Cyber Emergency Response Team Department of Mechanical and Vehicle Engineering Hunan University Department of Chemical and Materials Engineering University of Alberta

Fault detection(FD) for traction systems is one of the active topics in the railway and academia because it is the initial step for the running reliability and safety of high-speed trains. Heterogeneity of data and complexity of systems have brought new challenges to the traditional FD methods. For addressing these challenges, this paper designs an FD algorithm based on the improved unscented Kalman filter(UKF) with consideration of performance degradation. It is derived by incorporating a degradation process into the state-space *** network topology of traction systems is taken into consideration for improving the performance of state estimation. We first obtain the mixture distribution by the mixture of sigma points in UKF. Then, the Lévy process with jump points is introduced to construct the degradation model. Finally, the moving average interstate standard deviation(MAISD) is designed for detecting *** the proposed methods via a traction systems in a certain type of trains obtains satisfactory results.

关键词： Degradation Network topology Fault detection Rail transportation State-space methods Safety Kalman filters

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture 1

Optimization of Neural Networks using Swarm Intelligence Tec...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Raja, M. Karthick Shermatova, Makhbuba Al Ansari, Mohammed Saleh Abdufattokhov, Shokhjakhon Rao, Vuda Sreenivasa Raj, I. Infant Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Turin Polytechnic University in Tashkent Civil Engineering and Architecture Department Tashkent Uzbekistan University of Bahrain College of Engineering Department of Chemical Engineering Bahrain Turin Polytechnic University Automatic Control and Computer Engineering Dept Italy Tashkent Intl. University of Education Dept of Information Technologies Tashkent Uzbekistan Koneru Lakshmaiah Education Fou. Green Fields Department of Computer Science and Engineering A.P Vaddeswaram522302 India K. Ramakrishnan College of Engineering Department of Computer Science and Engineering Tamil Nadu Trichy621 112 India

ISBN: (纸本)9798350372748

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches. © 2024 IEEE.

关键词： Energy efficiency

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A Generalized Current Balancing control for Series-Capacitor Buck Converter with Interleaved Phase Angle 24

A Generalized Current Balancing Control for Series-Capacitor...

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24th IEEE Workshop on control and Modeling for Power Electronics, COMPEL 2023

作者： Li, Chung-Yi Nien, Chin-Fu Lin, Li Chen, Hung-Chi Chang Gung University Department of Electronics Engineering Taiwan Chang Gung University Department of Computer Science and Information Engineering Taiwan National Yang Ming Chiao Tung University Institution of Electrical Control Engineering Department of Electrical and Computer Engineering Taiwan

ISBN: (纸本)9798350316186

In addition to increasing the output current, an interleaved buck converter can significantly reduce the current ripple at the output. However, the bottleneck of the interleaved buck converter application is the unbalanced inductor current. In order to achieve the current balance in the entire section of the duty cycle, this paper proposes a control architecture with interleaved alignment, using the adjustment of the duty cycle and phase offset, to analyze and realize a high output current and current balanced in three-arm series capacitor interleaved buck converters (SCIBCs) in this work. By analyzing and modeling the SCIBC and using a single-loop control architecture that feedbacks the output voltage, the proposed current-balancing method balances all inductors' current within the full duty cycle without any current sensor. In addition, the small-signal transfer function of the control signal to the output voltage and design of the controller parameters are derived according to the specification, and the SCIBC with the proposed current-balancing control with PI controller of the alignment of interleaved phase angle is constructed. The high output current and current-balancing characteristics of the proposed circuit control architecture are validated in the simulation tool SIMPLIS. © 2023 IEEE.

关键词： controllers

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Probabilistic Tube-based control Synthesis of Stochastic Multi-Agent Systems under Signal Temporal Logic 63

Probabilistic Tube-based Control Synthesis of Stochastic Mul...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Vlahakis, Eleftherios E. Lindemann, Lars Sopasakis, Pantelis Dimarogonas, Dimos V. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden University of Southern California Viterbi School of Engineering Department of Computer Science Los AngelesCA90089 United States Queen's University School of Electronics Electrical Engineering and Computer Science Northern Ireland Belfast United Kingdom

ISBN: (纸本)9798350316339

We consider the control design of stochastic discrete-time linear multi-agent systems (MASs) under a global signal temporal logic (STL) specification to be satisfied at a predefined probability. By decomposing the dynamics into deterministic and error components, we construct a probabilistic reachable tube (PRT) as the Cartesian product of reachable sets of the individual error systems driven by disturbances lying in confidence regions (CRs) with a fixed probability. By bounding the PRT probability with the specification probability, we tighten all state constraints induced by the STL specification by solving tractable optimization problems over segments of the PRT, and relax the underlying stochastic problem with a deterministic one. This approach reduces conservatism compared to tightening guided by the STL structure. Additionally, we propose a recursively feasible algorithm to attack the resulting problem by decomposing it into agent-level subproblems, which are solved iteratively according to a scheduling policy. We demonstrate our method on a ten-agent system, where existing approaches are impractical. © 2024 IEEE.

关键词： Stochastic systems

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Two Performance Indicators Assisted Infill Strategy for Expensive Many-Objective Optimization

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Journal of Harbin Institute of Technology(New series) 2025年

作者： Yi Zhao Jianchao Zeng Ying Tan School of Economics and Management Taiyuan University of Science and Technology Department of Computer Science and Control Engineering North University of China Department of Computer Science and Technology Taiyuan University of Science and Technology

In recent years,surrogate models derived from genuine data samples have proven to be efficient in addressing optimization challenges that are costly or time-intensive. However,the individuals in the population become indistinguishable as the curse of dimensionality increase in the objective space and the accumulation of surrogate approximated errors. Therefore,in this paper,each objective function is modeled using a radial basis function approach,and the optimal solution set of the surrogate model is located by the multi-objective evolutionary algorithm of strengthened dominance relation. The original objective function values of the true evaluations are converted to two indicator values,and then the surrogate models are set up for the two performance indicators. Finally,an adaptive infill sampling strategy that relies on approximate performance indicators is proposed to assist in selecting individuals for real evaluations from the potential optimal solution set. The algorithm is contrasted against several advanced surrogate-assisted evolutionary algorithms on two suites of test cases,and the experimental findings prove that the approach is competitive in solving expensive many-objective optimization problems.

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Al-Biruni Based Optimization of Rainfall Forecasting in Ethiopia

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computer Systems science & engineering 2023年第6期45卷 2885-2899页

作者： El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Fadwa Alrowais Mostafa Abotaleb Abdelhameed Ibrahim Doaa Sami Khafaga Department of Communications and Elecronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science F aculty of Computer and Infomation SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Progamming South Ual State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Rainfall plays a significant role in managing the water level in the *** unpredictable amount of rainfall due to the climate change can cause either overflow or dry in the *** individuals,especially those in the agricultural sector,rely on rain *** rainfall is challenging because of the changing nature of the *** area of Jimma in southwest Oromia,Ethiopia is the subject of this research,which aims to develop a rainfall forecasting *** estimate Jimma's daily rainfall,we propose a novel approach based on optimizing the parameters of long short-term memory(LSTM)using Al-Biruni earth radius(BER)optimization algorithm for boosting the fore-casting accuracy.N ash-Sutcliffe model eficiency(NSE),mean square error(MSE),root MSE(RMSE),mean absolute error(MAE),and R2 were all used in the conducted experiments to assess the proposed approach,with final scores of(0.61),(430.81),(19.12),and(11.09),***,we compared the proposed model to current machine-learning regression models;such as non-optimized LSTM,bidirectional LSTM(BiLSTM),gated recurrent unit(GRU),and convolutional LSTM(ConvLSTM).It was found that the proposed approach achieved the lowest RMSE of(19.12).In addition,the experimental results show that the proposed model has R-with a value outperforming the other models,which confirms the superiority of the proposed *** the other hand,a statistical analysis is performed to measure the significance and stability of the proposed approach and the recorded results proved the expected perfomance.

关键词： Rainfall prediction long short-term memory Al-Biruni earth radius algorithm meta-heuristic optimization

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Deep Learning for EEG-Based Motor Imagery Classification: Towards Enhanced Human-Machine Interaction and Assistive Robotics 9

Deep Learning for EEG-Based Motor Imagery Classification: To...

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9th Scholar's Yearly Symposium of Technology, engineering and Mathematics, SYSTEM 2023

作者： Boutarfaia, Nejia Russo, Samuele Tibermacine, Ahmed Tibermacine, Imad Eddine Department of Computer Science University Mohamed Khider of Biskra Algeria Department of Psychology Sapienza University of Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Italy

This study presents a comprehensive exploration of EEG-based motor imagery classification using advanced deep learning architectures. Focusing on six distinct motor imagery classes, we investigate the performance of convolutional neural networks (CNN), CNN with Long Short-Term Memory (CNN-LSTM), and CNN with Bidirectional LSTM (CNN-BILSTM) models. The CNN architecture excels with a remarkable accuracy of 99.86%, while the CNN-LSTM and CNN-BILSTM models achieve 98.39% and 99.27%, respectively, showcasing their effectiveness in decoding EEG signals associated with imagined *** results underscore the potential applications of this research in fields such as assistive robotics and automation, showcasing the ability to translate cognitive intent into robotic actions. This study offers valuable insights into the realm of deep learning for EEG analysis, setting the stage for advancements in brain-computer interfaces and human-machine interaction. © 2023 Copyright for this paper by its authors.

关键词： Brain computer interface

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An iterative algorithm for phase hologram generation with overlap constraints 8

An iterative algorithm for phase hologram generation with ov...

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8th International Conference on Speckle Metrology, Speckle 2023

作者： Lin, Angyi Chen, Rui Lin, Huixiang You, Jiangfeng Zhang, Fucai The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China School of Control and Computer Engineering North China Electric Power University Baoding071003 China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Currently, the dominant encoding method for computer-generated holograms is phase-only holography. The Gerchberg-Saxton (GS) algorithm is a commonly used approach for hologram generation. However, it suffers from issues such as poor convergence and low efficiency. In this paper, we introduce an iterative hologram algorithm that addresses these challenges. Our proposed algorithm divides the target object into segments with a certain degree of data redundancy. It computes the hologram iteratively, giving extra weight to each iteration. We also incorporate overlap constraints to enhance hologram quality and accelerate convergence by leveraging data redundancy. What sets our algorithm apart from others is that it varies the order in which segments are processed during iterations, resulting in different final holograms. By randomly selecting the order in each iteration, we can rapidly generate multiple diverse holograms. Importantly, objects reconstructed from these holograms exhibit consistency, allowing for the quick generation of multiple phase-only holograms that help mitigate the speckle effect. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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Multi-Agent Coordination for a Partially Observable and Dynamic Robot Soccer Environment with Limited Communication 10

Multi-Agent Coordination for a Partially Observable and Dyna...

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10th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2023

作者： Affinita, Daniele Volpi, Flavio Spagnoli, Valerio Suriani, Vincenzo Nardi, Daniele Bloisi, Domenico D. Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Italy Faculty of Political Science and Sociopsychological Dynamics UNINT University Rome Italy

RoboCup represents an International testbed for advancing research in AI and robotics, focusing on a definite goal: developing a robot team that can win against the human world soccer champion team by the year 2050. To achieve this goal, autonomous humanoid robots' coordination is crucial. This paper explores novel solutions within the RoboCup Standard Platform League (SPL), where a reduction in WiFi communication is imperative, leading to the development of new coordination paradigms. The SPL has experienced a substantial decrease in network packet rate, compelling the need for advanced coordination architectures to maintain optimal team functionality in dynamic environments. Inspired by market-based task assignment, we introduce a novel distributed coordination system to orchestrate autonomous robots' actions efficiently in low communication scenarios. This approach has been tested with NAO robots during official RoboCup competitions and in the SimRobot simulator, demonstrating a notable reduction in task overlaps in limited communication settings. © 2024 Copyright for this paper by its authors.

关键词： Anthropomorphic robots

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