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4th IEEE KhPI Week on Advanced Technology, KhPI Week 2023

作者： Chorna, Olha Kurliak, Petro Busher, Victor Chornyi, Oleksii Tytiuk, Valerii Zachepa, Iurii Institute of Education and Science in Electrical Engineering and Information Technologies Kremenchuk Mykhailo Ostrogradskyi National University Kremenchuk Ukraine Department of Electrical Power Engineering Electrical Engineering And Electromechanics Ivano-Frankivsk National Technical University of Oil and Gas Ivano-Frankivsk Ukraine Dept. of Electrical Engineering and Electronics National University "Odessa Maritime Academy" Odessa Ukraine Department of Electromechanics Kryvyi Rih National University Kryvyi Rih Ukraine Department of Systems of Automatic Control and Electric Drive Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

ISBN: (纸本)9798350395532

This paper presents client-server mobile system for diagnosis of asynchronous motors. The system is created as a mobile application for smartphones and tablets. The system provides processing of the measured values of the currents and voltages of the stator phases of the asynchronous motor. According to the processing results, the results of diagnostics and forecasting of the current state of asynchronous motors are calculated. The software runs on the Android OS. The main purpose is to predict the current state of AD, which has a parametric electromagnetic asymmetry of the stator phases. © 2023 IEEE.

关键词： Induction motors

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Industrial Scenario of Renewable Energy-Based Electromobility 3rd

Industrial Scenario of Renewable Energy-Based Electromobilit...

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3rd International Conference on Energy Systems, Drives and Automations, ESDA 2020

作者： Szymanski, Jerzy Zurek-Mortka, Marta Faculty of Transport Electrical Engineering and Computer Science Kazimierz Pulaski University of Technology and Humanities Radom Poland Department of Control Systems Lukasiewicz Research Network Institute for Sustainable Technology Radom Poland

ISBN: (纸本)9789811672736

The article presents the concept of a hybrid network topology in the enterprises with the use of a solar power plant and energy storage as well as a drive frequency converter for charging of transportation battery of wheels vehicles and powering of electric caterpillar mining machines. Two important functionalities of the drive frequency converter as a unidirectional EV battery charger and for powering electric motors are presented. The obtained results of the simulation and experimental tests of the presented model confirm the possibility of controlling the DC current value of connected to drive frequency converter of the diode rectifier unit charging the EV battery. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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Falcon Optimized CNN for the Detection of Alzheimer's Disease in MRI Image

Falcon Optimized CNN for the Detection of Alzheimer's Diseas...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： S. Febeena Ezhil Jothi L. Mahalakshmi P. Elamurugan Naveenkumar Kaliyan J. Swapna R. Rajeshwari Department of Artificial Intelligence and Data Science Adhi College of Engineering And Technology Kanchipuram Department of Biomedical Engineering Kongunadu College of Engineering and Technology Trichy Department of Instrumentation and Control Engineering Sri Manakula Vinayagar Engineering College Puducherry Department of Computer science and Engineering Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electrical and Electronics Engineering J.P. College of Engineering Ayikudi Tenkasi

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Degenerative illnesses that lead to the eventual death of brain neurons afflict a large number of people. Compared to the traditional Machine Learning (ML) approach, the recent success in the medical community of classifying Alzheimer's Disease (AD) utilized a Deep Learning (DL) technique eliminates the necessity for manual feature extraction techniques. Although DL methods are widely used, there is currently a dearth of trustworthy and comprehensive research on the quantitative analysis of brain Magnetic Resonance Image (MRI) data. In this paper, an effective AD classification approach is proposed that use MRI to classify brain structure and diagnose AD. Gaussian filter is used to eliminate noises at different degrees of complexity, noisy backgrounds, low contrast, and partial volume effect in the original input image. Next, K-Means clustering segmentation is useful for dividing image into different regions based on pixel intensity values. Key features for categorization are extracted using the Gray Level Co-Occurrence Matrix (GLCM) method. A Convolutional Neural Network (CNN), enhanced by Falcon Optimization Algorithm (FOA), is employed for AD categorization. The proposed system, implemented on the Python platform using the best Alzheimer's MRI dataset, demonstrates an enhanced accuracy of 93.21 %. Additionally, this model-based approach provides a capable avenue for advancing research in the early detection of AD and holds substantial potential in differentiating AD patients from healthy individuals.

关键词： Training Accuracy Magnetic resonance imaging Neurons Magnetic resonance Feature extraction Convolutional neural networks Alzheimer's disease Optimization Testing

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Semi-supervised graphical autoencoder regression modeling for soft sensors

Semi-supervised graphical autoencoder regression modeling fo...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Xiaoyu Wang Li Zhu Junghui Chen Xirong Xu School of Control Science and Engineering Dalian University of Technology Dalian China School Control Science and Engineering Dalian University of Technology Dalian China Department of Chemical Engineering Chung Yuan Christian University Chung Li R.O.C School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

For soft sensor in chemical processes, process variables are usually measured at a higher frequency while quality variables are often measured at a lower rate. This discrepancy in measurement frequencies results in a large number of unlabeled data points. In addition, there are instances where the daily recorded component variables are more readily available, contrasting with the difficulty in obtaining data for the quality variable data. To address these issues, this paper proposes a novel semi-supervised learning framework, TGAE-STN(Transfer entropy based Graph Autoencoder-Spatiotemporal Networks), which employs an encoder-decoder architecture to efficiently handle both unlabeled and labeled data. For unlabeled data, transfer entropy is computed from the original sampling data to facilitate the conversion into graph data and subsequent reconstruction of graph structure. For labeled data, the latent variable obtained from the reconstruction process of the codec is used as the input to the prediction model, and the adjacency matrix is dynamically constructed during the training process. The prediction network uses a spatiotemporal graph model to extract features across both temporal and spatial dimensions. Moreover, the encoder part is updated asynchronously based on the performance of the decoder and the prediction network, facilitating effective utilization of the information from both unlabeled and labeled data. The effectiveness of the proposed method is verified through a numerical case and a chemical process. The results show that the proposed TGAE-STN method provides a promising solution for soft sensor technology under semi-supervised conditions.

关键词： Training Soft sensors Predictive models Semisupervised learning Feature extraction Entropy Frequency measurement

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Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning

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Neural Computing and Applications 2024年第30期36卷 18841-18862页

作者： Mamalakis, Michail Banerjee, Abhirup Ray, Surajit Wilkie, Craig Clayton, Richard H. Swift, Andrew J. Panoutsos, George Vorselaars, Bart Department of Psychiatry University of Cambridge Herchel Smith Building Robinson Way CambridgeCB2 0SZ United Kingdom Department of Computer Science University of Sheffield 211 Portobello SheffieldS1 4DP United Kingdom Insigneo Institute for in silico Medicine University of Sheffield The Pam Liversidge Building SheffieldS1 3JD United Kingdom School of Mathematics and Statistics University of Glasgow GlasgowG12 8QQ United Kingdom Department of Infection Immunity and Cardiovascular Disease University of Sheffield SheffieldS10 2HQ United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS10 2TN United Kingdom School of Mathematics and Physics University of Lincoln Brayford Pool LincolnLN6 7TS United Kingdom Department of Engineering Science Institute of Biomedical Engineering University of Oxford OxfordOX3 7DQ United Kingdom Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford OxfordOX3 9DU United Kingdom

The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyperparameter tuning strategies is more than crucial to deliver the highest performance of the deep learning networks in large and demanding datasets. In our study, we have developed and evaluated a new training methodology named deep multi-metric training (DMMT) for enhanced training performance. The DMMT delivers a state of robust learning for deep networks using a new important criterion of multi-metric performance evaluation. We have tested the DMMT methodology in multi-class (three, four, and ten), multi-vendors (different X-ray imaging devices), and multi-size (large, medium, and small) datasets. The validity of the DMMT methodology has been tested in three different classification problems: (i) medical disease classification, (ii) environmental classification, and (iii) ecological classification. For disease classification, we have used two large COVID-19 chest X-rays datasets, namely the BIMCV COVID-19+ and Sheffield hospital datasets. The environmental application is related to the classification of weather images in cloudy, rainy, shine or sunrise conditions. The ecological classification task involves a classification of three animal species (cat, dog, wild) and a classification of ten animals and transportation vehicles categories (CIFAR-10). We have used state-of-the-art networks of DenseNet-121, ResNet-50, VGG-16, VGG-19, and DenResCov-19 (DenRes-131) to verify that our novel methodology is applicable in a variety of different deep learning networks. To the best of our knowledge, this is the first work that proposes a training methodology to deliver robust learning, over a variety of deep learning networks and multi-field classification problems. © Crown 2024.

关键词： COVID-19

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Reinforcement Learning of Threshold Policy for Dynamic Wireless Charging with Distributed Energy Resources

Reinforcement Learning of Threshold Policy for Dynamic Wirel...

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IEEE General Meeting Power& Energy Society

作者： Liangliang Hao Yunjian Xu Lang Tong Department of Control Science and Engineering Shandong University Jinan China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong SAR School of Electrical and Computer Engineering Cornell University Ithaca NY USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

We study dynamic wireless charging for Electric Vehicles (EVs) on electrified highways where EVs are charged while in motion. Our focus is on the cost-minimization scheduling of EV charging based on EV mobility states, charging demands, travel plans, available distributed renewables, and power constraints of the distribution systems. As a Markov decision process with uncountable action space that couples stochasticities in charging demands, vehicle mobilities, and renewable resources, standard dynamic programming and approximation approaches face exponentially growing complexity and modeling uncertainties. By exploiting the linear network topology when EVs are on a highway, this work reveals a threshold structure of the optimal charging policy and develops a reinforcement learning approach for uncertainty models, resulting in a scalable EV charging solution. The proposed EV charging algorithm outperforms multiple charging benchmarks by 4.7-52.4% cost reduction.

关键词： Road transportation Renewable energy sources Uncertainty Costs Markov decision processes Inductive charging Dynamic scheduling Electric vehicle charging Distributed power generation Vehicle dynamics

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LiDAR-based drone navigation with reinforcement learning

arXiv

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arXiv 2023年

作者： Miera, Pawel Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest. © 2023, CC BY.

关键词： Robot Operating System

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Policy and Value Deep RL for Temporal Language-Agnostic Street Image Captioning 3

Policy and Value Deep RL for Temporal Language-Agnostic Stre...

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3rd IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2021

作者： Chan, Christopher Chun Ki Chen, Chih-Cheng Delaney, Steven Yang, Cheng-Fu Department of Information Management Chaoyang University of Technology Taichung Taiwan Department of Automatic Control Engineering Feng Chia University Taichung40724 Taiwan Department of Aeronautical Engineering Chaoyang University of Technology Taichung Taiwan Department of Computer Science Ryerson University Canada Department of Chemical and Materials Engineering National University of Kaohsiung Kaohsiung Taiwan

ISBN: (纸本)9781728193045

Dashcams and street cameras capture a huge amount of street and traffic video data. Temporal image captioning for such video data has been approached with an encoder-decoder framework and achieved substantial success in captioning accuracy. Most recently, policy and value deep reinforcement learning (PVRL) emerged as an outperformer over other decision-making frameworks for image captioning. In this paper, we design a framework that utilizes PVRL on an in-house dataset containing temporal images of East Asia streets as a step towards designing a language-agnostic street image captioning framework that is capable of captioning temporal images of any street regardless of location. For language-invariance, the framework includes cross-modal retrieval at the character level so that similar words in different languages but in the same word-embedding space are grouped together. Our results show that PVRL can be applied successfully to temporal video captured the streets and achieve natural semantic captions;preliminary studies on our dataset suggest that the framework is capable of use in multi-language scenarios. © 2021 ECBIOS 2021. All rights reserved.

关键词： Deep learning

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Wi-Wheat+:Contact-free wheat moisture sensing with commodity WiFi based on entropy

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Digital Communications and Networks 2023年第3期9卷 698-709页

作者： Weidong Yang Erbo Shen Xuyu Wang Shiwen Mao Yuehong Gong Pengming Hu College of Information Science and Engineering Henan University of TechnologyZhengzhou450001China Henan Key Laboratory of Grain Photoelectric Detection and Control Zhengzhou450001China College of International Education Kaifeng UniversityKaifengHenan475004China Knight Foundation School of Computing&Information Sciences Florida International UniversityMiamiFL33199USA Department of Electrical and Computer Engineering Auburn UniversityAuburnAL36849-5201USA

In this paper,we propose a contact-free wheat moisture monitoring system,termed Wi-Wheatþ,to address the several limitations of the existing grain moisture detection technologies,such as time-consuming process,expensive equipment,low accuracy,and difficulty in real-time *** proposed system is based on Commodity WiFi and is easy to *** WiFi CSI data,this paper proposes a feature extraction method based on multi-scale and multi-channel *** feasibility and stability of the system are validated through experiments in both Line-Of-Sight(LOS)and Non-Line-Of-Sight(NLOS)scenarios,where ten types of wheat moisture content are tested using multi-class Support Vector Machine(SVM).Compared with the Wi-Wheat system proposed in our prior work,Wi-Wheatþhas higher efficiency,requiring only a simple training process,and can sense more wheat moisture content levels.

关键词： Channel state information(CSI) WiFi Multi-scale entropy Multi-class support vector machine(SVM) Radio frequency(RF)sensing

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Application of Hybrid Variable Admittance Force Tracking and Fixed-time Position control for Robot Interaction Tasks

Application of Hybrid Variable Admittance Force Tracking and...

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International Conference on control, Mechatronics and Automation (ICCMA)

作者： Hong Zhan Chao Zeng Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798331517519

ISBN: (纸本)9798331517526

This paper applies the proposed hybrid force and position control method to the physical robot system with interaction tasks to further improve our previous study. In the control scheme, the variable stiffness based on proportional integral derivative(PID) admittance control is adopted for interaction force tracking and the radial basis function neural network(RBFNN) based fixed-time control is designed to ensure position tracking. We have performed interaction tasks based on a Baxter robot for drawing on the plane and slope plane with different expected interaction forces and position trajectories. The experiment results indicate that the method performs well in terms of interaction force and trajectory tracking.

关键词： Mechatronics Automation Trajectory tracking Force Dynamics Position control Trajectory Robots Admittance control Convergence

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