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A Comprehensive Methodology of Field-Oriented Control Design With Parameter Variation Analysis for Interior Permanent Magnet Synchronous Machine Drives

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IEEE Access 2025年 13卷 89524-89541页

作者： Tiago Davi Curi Busarello Abdullah Bubshait Oruganti V. S. R. Varaprasad Abdulhakeem Alsaleem Marcelo Godoy Simões Department of Control Automation and Computation Engineering Federal University of Santa Catarina Florianopolis Brazil Department of Electrical Engineering College of Engineering King Faisal University Al Ahsa Saudi Arabia Smart Transportation Electrification and Energy Research (STEER) Group Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada Department of Electrical Engineering College of Engineering Qassim University Buraydah Saudi Arabia School of Technology and Innovations University of Vaasa Vaasa Finland

This paper proposes a comprehensive methodology for Field-Oriented Control (FOC) with parameter variation analysis for Interior Permanent Magnet Synchronous Machines (IPMSM). The modeling approach for an IPMSM is first presented, followed by a step-by-step procedure for designing a vector-controlled strategy. The formulation is based on a dq-rotating reference frame aligned with the rotor shaft position. A key distinction of this methodology from traditional approaches is that the current controller is designed in the time domain based on desired time constants, while the speed control is formulated within a frequency response domain framework. The proposed hybrid approach enables accurate tuning of the Proportional-Integrator (PI) controllers for both current and speed control loops. Additionally, a parameter variation analysis is conducted to enhance the proposed methodology. The validity region for the design procedure is presented, ensuring that for any wide speed variation of the machine, loop gains are properly tuned. One of the main advantages of the proposed methodology is that it provides a fast, reliable, and accurate technique for implementing IPMSM drive systems. Results from a Controller Hardware-in-the-Loop (C-HIL) setup with an external microcontroller are presented. The comprehensive design approach is validated under two different IPMSM parameter sets, demonstrating its effectiveness.

关键词： PI control Stators Tuning Mathematical models Velocity control Rotors Vectors Stator windings Robustness Microcontrollers

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Heart Sound Classification using Residual Neural Network and Convolution Block Attention Module

Heart Sound Classification using Residual Neural Network and...

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Enoch Adjei Frimpong Qin Zhiguang Tenagyei Edwin Kwadwo Patamia Agbeshi Rutherford Edward Y. Baagyere Regina Esi Turkson School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu P.R. China Department of Computer Science and Technology Cape Coast Technical University Cape Coast Ghana Department of Computer Science C. K. Tedam University of Technology and Applied Sciences Navrongo Ghana Department of Computer Science and Information Technology University of Cape Coast PMB University Post Office Cape Coast Ghana

ISBN: (纸本)9781665493901

Listening to the heart sound with digital or manual stethoscopes has become one of the practical ways to identify heart diseases in recent years. It's still difficult because of its manual approach and the fact that only experienced healthcare practitioners can use it to diagnose anomalies. The automatic extraction of heart sound features to aid in classification has been explored, however there is still potential for improvement. This paper proposes a residual neural network integrated with a convolutional block attention module (CBAM) for heart sound analysis, using generated Mel-spectrograms as input for our network. We tested our model using the Pascal Heart Sound Challenge dataset, and it performed favorably to other cutting-edge models.

关键词： Heart Convolution Manuals Stethoscope Medical services Media Feature extraction

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Application of intelligent computing to develop performance index algorithm as a multicriteria decision making tool

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International Journal of Intelligent Networks 2020年 1卷 85-91页

作者： Saleem, Muhammad Asim Bhardwaj, Rashmi Datta, Debabrata School of Information and Software Engineering Univeristy of Electronic Science and Technology of China China University School of Basic & Applied Sciences Nonlinear Dynamics Research Lab Guru Gobind Singh Indraprastha University Delhi India Radiological Physics & Advisory Division Bhabha Atomic Research Centre Mumbai400085 India

Multi-criteria-based group decision-making methods are important in the area of data science. Many of such works are focused on multi criteria with crisp inputs. However, in practice approximate reasoning methodology is used to address multi criteria. In general decision-making problems are categorized as problems under uncertain environment where uncertainty is due to fuzziness of criteria for the set of substitutes. In order to select the best among the available feasible alternatives, it is mandatory to execute multi-criteria decision-making problem using soft computing approach. In this study, we have developed performance index algorithm to implement the methodology of fuzzy multi-criteria decision-making methodologies. A fuzzy likeness between evaluation alternatives and extreme solutions of a decision matrix is the backbone of performance index algorithm. The computation steps of proposed fuzzy multi-criteria decision-making method are quite simple, efficient & it is also one of the goals of this paper to reduce computational loads for a multi-criteria decision-making problem under fuzzy environment. Proposed method is compared with a standard method for order preference through comparison to ideal outcomes. © 2020 The Author(s)

关键词： Soft computing

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Training Differentially Private Models with Secure Multiparty Computation

arXiv

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arXiv 2022年

作者： Pentyala, Sikha Railsback, Davis Maia, Ricardo J.M. Melanson, David Dowsley, Rafael Nascimento, Anderson De Cock, Martine School of Engineering and Technology University of Washington Tacoma United States Department of Computer Science University of Brasilia Brazil Department of Software Systems and Cybersecurity Monash University Australia Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Mila - Quebec Ai Institute Canada

We address the problem of learning a machine learning model from training data that originates at multiple data owners, while providing formal privacy guarantees regarding the protection of each owner's data. Existing solutions based on Differential Privacy (DP) achieve this at the cost of a drop in accuracy. Solutions based on Secure Multiparty Computation (MPC) do not incur such accuracy loss but leak information when the trained model is made publicly available. We propose an MPC solution for training DP models. Our solution relies on an MPC protocol for model training, and an MPC protocol for perturbing the trained model coefficients with Laplace noise in a privacy-preserving manner. The resulting MPC+DP approach achieves higher accuracy than a pure DP approach, while providing the same formal privacy guarantees. Our work obtained first place in the iDASH2021 Track III competition on confidential computing for secure genome analysis. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Asynchronous Filtering for Takagi-Sugeno Fuzzy Neural Networks with General Transition Probabilities and Missing Measurements 5

Asynchronous Filtering for Takagi-Sugeno Fuzzy Neural Networ...

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5th IEEE International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020

作者： Shan, Yaonan She, Kun Zhong, Shouming Hu, Taotao Hao, Shiqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China University of Electronic Science and Technology of China Department of Applied Mathematics Chengdu China MediaTek Software Design Co. Ltd. Chengdu China Si Chuan Association of Automobile Industry Chengdu China

ISBN: (纸本)9781728160245

This paper focuses on the issue of asynchronous filtering for Markov jump Takagi-Sugeno (T-S) fuzzy neural networks (MJTSFNNs). We firstly consider the general transition probabilities (GTPs) including both uncertain parts and unknown ones. Then, by introducing stochastic variables, the filtering system involves not only packet loss but also quantization. After that, through Lyapunov stability theory and stochastic process knowledge, we design an asynchronous fuzzy filter and obtain a sufficient criterion to ensure the MJTSFNNs stochastically mean square stable with some performance. Finally, an example verifies the validity of our results. © 2020 IEEE.

关键词： Fuzzy neural networks

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UX Definitions: The Missing Components and the Need for the Context and Time of Use 13

UX Definitions: The Missing Components and the Need for the ...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Mkpojiogu, Emmanuel O.C. Okeke-Uzodike, Obianuju E. Emmanuel, Emelda I. Eze, Chika Universiti Utara Malaysia School of Computing Sintok06010 UUM Malaysia Veritas University Department of Software Engineering Abuja Nigeria Durban University of Technology Faculty of Management Sciences Department of Applied Management KwaZulu-Natal Pietermaritzburg South Africa Ibrahim Badamasi Babangida University Faculty of Management and Social Science Department of Sociology Niger State Lapai Nigeria Veritas University Abuja Faculty of Education Department of Educational Foundations Guidance and Counselling Unit Abuja Nigeria

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

There are several definitions for user experience (UX). However, this study unveiled that there are missing components in those definitions. An analysis of user experience definitions shows that the definitions had the elements of time of use and context of use absent in them. These missing components limit and obscure the understanding of user experience. Nevertheless, the inclusion of these components improves the specificity, robustness, uniqueness, and meaningfulness of UX and will provide illumination for the conceptualization, design, evaluation, and modeling of UX. As UX is understood the way it is defined, an all-encompassing definition that comprises the components of time and context of use will therefore be worthwhile. This study reviewed extant related literature and unraveled these gaps and suggested their inclusion in user experience definitions. © 2022 IEEE.

关键词： User experience

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An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

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Computational Intelligence and Neuroscience 2022年第0期2022卷 6096289页

作者： Wahab, Fazal Zhao, Yuhai Javeed, Danish Al-Adhaileh, Mosleh Hmoud Almaaytah, Shahab Ahmad Khan, Wasiat Saeed, Muhammad Shahid Kumar Shah, Rajeev College of Computer Science and Technology Northeastern University Shenyang110169 China Software College Northeastern University Shenyang110169 China Deanship of E-Learning and Distance Education King Faisal University P.O. Box 400 Al-Ahsa Saudi Arabia Applied College in Abqaq King Faisal University Al-Ahsa Saudi Arabia Department of Software Engineering University of Science and Technology Bannu Bannu Pakistan Dalian University of Technology Dalian116024 China Sunway International Business School Kathmandu Nepal

E-health has grown into a billion-dollar industry in the last decade. Its device's high throughput makes it an obvious target for cyberattacks, and these environments desperately need protection. In this scientific study, we presented an artificial intelligence (AI)-driven software-defined networking (SDN)-enabled intrusion detection system (IDS) to address increasing cyber threats in the E-health and internet of medical things (IoMT) environments. AI's success in various fields, including big data and intrusion detection systems, has prompted us to develop a flexible and cost-effective approach to protect such critical environments from cyberattacks. We present a hybrid model consisting of long short-term memory (LSTM) and gated recurrent unit (GRU). The proposed model was thoroughly evaluated using the publicly available CICDDoS2019 dataset and conventional evaluation measures. Furthermore, for proper validation, the proposed framework is compared with relevant classifiers, such as cu-GRU+ DNN and cu-BLSTM. We have further compared the proposed model with existing literature to prove its efficacy. Lastly, 10-fold cross-validation is also used to verify that our results are unbiased. The proposed approach has bypassed the current literature with extraordinary performance ramifications such as 99.01% accuracy, 99.04% precision, 98.80 percent recall, and 99.12% F1-score. © 2022 Fazal Wahab et al.

关键词： Intrusion detection

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A Glove CNN-Bilstm Sentiment Classification

A Glove CNN-Bilstm Sentiment Classification

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International Conference on Wavelet Active Media technology and Information Processing (ICWAMTIP)

作者： Peter Atandoh Zhang Feng Daniel Adu-Gyamfi Habte Lejebo Leka Paul Hakeem Atandoh School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computing Information Sciences University of Technology and Applied Sciences Navrongo Ghana Western Michigan University Kalamazoo MI

ISBN: (纸本)9781665413657

Reviewing products online has become an increasingly popular way for consumers to voice their opinions and feelings about a product or service. Analyzing this Big data of online reviews would help to discern and extract useful facts and information that could provide a competitive and economic advantage to merchants and other organizations that are interested. Text classification organizes documents according to a variety of predefined categories. In other to solve the aforementioned problems, we employed Glove embeddings for our review sentiment analysis. We further integrate this embedding layer into a deep convolutional neural network (CNN)-bidirectional LSTM model. We further train our model on the IMDB and movie review dataset to extract the polarity as positive or negative and subsequently compare our model with other state-of- the-art models. The aforementioned experiments validate the efficacy and superiority of our proposed approach.

关键词： Sentiment analysis Computational modeling Text categorization Organizations Predictive models Media Wavelet analysis

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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Chebyshev Polynomial Broad Learning System

Chebyshev Polynomial Broad Learning System

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International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

作者： Shuang Feng Bingshu Wang C. L. Philip Chen School of Applied Mathematics Beijing Normal University Zhuhai China School of Software Northwestern Polytechnical University Suzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781665402460

The broad learning system (BLS) has been attracting more and more attention due to its excellent property in the field of machine learning. A great deal of variants and hybrid structures of BLS have also been designed and developed for better performance in some specialized tasks. In this paper, the Chebyshev polynomials are introduced into the BLS to take advantage of their powerful approximation capability, where the feature windows are replaced by a set of Chebyshev polynomials. This new variant, named Chebyshev polynomial BLS (CPBLS), has a light structure with a reduction in computational complexity since the sparse autoencoder is removed. Instead, the dimension of each input sample is expended by n + 1 Chebyshev polynomials, mapping the original feature into a new feature space with higher dimension, which helps to classify the patterns in training. The proposed CPBLS is evaluated by some popular datasets from UCI and KEEL repositories, and it outperforms some representative neural networks and neuro-fuzzy models in terms of classification accuracy. The CPBLS also show some advantages over the recent developed compact fuzzy BLS (CFBLS) which indicates its great potential in future research and real-world applications.

关键词： Learning systems Training Computational modeling Neural networks Machine learning Chebyshev approximation Feature extraction

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