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9th International Conference on control Science and Systems engineering, ICCSSE 2023

作者： Zhang, Boheng Xu, Kun Cong, Haibo Sun, Mingjian Harbin Institute of Technology Department of Control Science and Engineering Harbin China School of Information Science and Engineering Harbin Institute of Technology Weihai China Shandong First Medical University Department of Orthopedics Jinan China

ISBN: (纸本)9798350339055

Ultrasonography is the preferred method for detecting lung lesions, which has been widely recognized and adopted. In order to reduce the work stress and risk of infection for health care workers, lung ultrasound (LUS) scanning robots can be used to perform this task instead of doctors. For the LUS scanning robot, the first critical step is the detection of the scanned areas of the patient's lungs. The algorithms using computer vision and deep learning have made significant progress in the field of target detection, which are being applied more and more frequently to robotic autonomous decision-making. However, some of the performance of the traditional convolutional neural networks (CNN) still needs to be improved due to the more stringent requirements for real-Time, accuracy and hardware cost of LUS scanning robots. The effectiveness of CNN combined with computer vision for detecting scanned sites in LUS scanning robots is explored and a lightweight Yolo network based on the attention mechanism is proposed. We use Yolo V4-Tiny model as the backbone network. Then the depth feature information of the feature layer output from the backbone network are extracted by adding space and channel attention mechanism, and the prediction results are output by using Yolo head model. Meanwhile, we also optimize the loss function of the network to further improve the performance of the network. Compared to several classical algorithms, the proposed method improves the target detection performance of the LUS scanning robot, which achieves 98.83% average precision, 96.87% precision, and 91.00% F1 score. We apply the proposed algorithm to the designed LUS scanning robot system and conduct clinical experiments. The results have shown that the proposed method has a 3D localization accuracy of 7.53 ± 0.37mm for the lung scanned sites, which has the potential to be applied to the target detection of the LUS scanning robot. © 2023 IEEE.

关键词： Ultrasonic applications

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Facial Expression Recognition Model Depending on Optimized Support Vector Machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector Machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Development of a user interface for parking lot exploration 20

Development of a user interface for parking lot exploration

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Kocsany, Laszlo Gincsaine Szadeczky-Kardoss, Emese Budapest University of Technology and Economics Department of Control Engineering and Information Technology BudapestH-1111 Hungary

ISBN: (纸本)9781665497046

This paper presents a user interface developed for a parking aid system. The task of the autonomous parking system is to provide an end-to-end solution for the parking space search problem. However, the system requires user inputs, from which an autonomous vehicle can be navigated through an exploration path, where the vehicle, with its sensors, is able to detect free parking spaces. These user inputs are used to extract the map, including the actual location of the user, the desired destination, and the streets within a maximal desired range measured from the destination, where possible parking spaces are located. © 2022 IEEE.

关键词： Space vehicles Technological innovation Navigation User interfaces Search problems Sensor systems Sensors

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A Hybrid Compensation Topology with Constant Voltage and Constant Current for EV Bidirectional Wireless Charging 26

A Hybrid Compensation Topology with Constant Voltage and Con...

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26th International Conference on Industrial technology, ICIT 2025

作者： Tian, Shuo Wang, Helin Zhang, Hailong Qingdao University of Technology Department of Information and Control Engineering Qingdao China

ISBN: (纸本)9798331521950

A constant current (CC) and constant voltage (CV) charging topology applied to the bidirectional wireless power transfer system for electric vehicles (EVs) is presented in this paper. It only needs to adjust the switching state of a relay to realize the CC output of inductor-capacitor-capacitor - inductor-capacitor-capacitor (LCC-LCC) and CV output of inductor-capacitor-capacitor-series (LCC-S) and series- inductor-capacitor-capacitor (S-LCC). Compared with the other topologies, redundant components are not required, and the switching frequency is fixed under the operating process. Under the misalignment condition, the CC and CV output can be regulated by applying the phase-shift control. The proposed reconfigurable topology can realize bidirectional CC and CV charging without communication between primary and secondary side. A 6-KW prototype is built in the laboratory to verify the effectiveness of the topology, and the maximum effective is 94%. © 2025 IEEE.

关键词： Wireless power transfer

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Wind Power Prediction Based on Machine Learning and Deep Learning Models

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Computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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A Hybrid Compensation Topology with Constant Voltage and Constant Current for EV Bidirectional Wireless Charging

A Hybrid Compensation Topology with Constant Voltage and Con...

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IEEE International Conference on Industrial technology (ICIT)

作者： Shuo Tian Helin Wang Hailong Zhang Department of Information and Control Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

关键词： Analytical models Voltage measurement Switching frequency Current measurement Inductive charging Prototypes Switches Wireless power transfer Topology Relays

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Robust Consensus Tracking control of Uncertain Multi-Agent Systems With Local Disturbance Rejection

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 427-438页

作者： Pan Yu Kang-Zhi Liu Xudong Liu Xiaoli Li Min Wu Jinhua She the Faculty of Information Technology and the Beijing Institute of Artificial Intelligence Beijing University of TechnologyBeijing 100124China IEEE the Department of Electrical and Electronic Engineering Chiba UniversityChiba 263-8522Japan the Beijing Aerospace Automatic Control Institute Beijing 100854China the School of Automation China University of GeosciencesWuhan 430074China the School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan

In this paper,a new distributed consensus tracking protocol incorporating local disturbance rejection is devised for a multi-agent system with heterogeneous dynamic uncertainties and disturbances over a directed *** is of two-degree-of-freedom ***,a robust distributed controller is designed for consensus tracking,while a local disturbance estimator is designed for each agent without requiring the input channel information of *** condition for asymptotic disturbance rejection is ***,even when the disturbance model is not exactly known,the developed method also provides good disturbance-rejection ***,a robust stabilization condition with less conservativeness is derived for the whole multi-agent ***,a design algorithm is ***,comparisons with the conventional one-degree-of-freedombased distributed disturbance-rejection method for mismatched disturbances and the distributed extended-state observer for matched disturbances validate the developed method.

关键词： Directed graph distributed control disturbance rejection dynamic uncertainties multi-agent systems robust control

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Energy Efficient and Intelligent Mosquito Repellent Fuzzy control System

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Computers, Materials & Continua 2023年第10期77卷 699-715页

作者： Aaqib Inam Zhu Li Salah-ud-din Khokhar Zubia Zafar Muhammad Imran School of Software Institute of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’anChina School of Intelligent Manufacturing and Control Engineering Qilu Institute of TechnologyJinan250200China Department of Medicine Islam Medical CollegeSialkotPakistan Department of Electronics G.C.UniversityLahorePunjab54000Pakistan

Mosquitoes are of great concern for occasionally carrying noxious diseases(dengue,malaria,zika,and yellow fever).To control mosquitoes,it is very crucial to effectively monitor their behavioral trends and *** mosquito repellent works by heating small pads soaked in repellant,which then diffuses a protected area around you,a great alternative to spraying yourself with *** they have limitations,including the range,turning them on manually,and then waiting for the protection to kick in when the mosquitoes may find *** research aims to design a fuzzy-based controller to solve the above issues by automatically determining a mosquito repellent’s speed and active *** speed and active time depend on the repellent cartridge and the number of *** Mamdani model is used in the proposed fuzzy system(FS).The FS consists of identifying unambiguous inputs,a fuzzification process,rule evaluation,and a defuzzification process to produce unambiguous *** input variables used are the repellent cartridge and the number of mosquitoes,and the speed of mosquito repellent is used as the output *** whole FS is designed and simulated using MATLAB Simulink *** proposed FS is executed and verified utilizing a microcontroller using its pulse width modulation *** simulations of the proposed model are performed in many nonlinear ***,a comparative analysis of the outcomes under similar conditions confirms the higher accuracy of the FS,yielding a maximum relative error of 10%.The experimental outcomes show that the root mean square error is reduced by 67.68%,and the mean absolute percentage error is reduced by 52.46%.Using a fuzzy-based mosquito repellent can help maintain the speed of mosquito repellent and control the energy used by the mosquito repellent.

关键词： Fuzzy logic mosquito repellent relative error root mean square error

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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Rethinking Guidance information to Utilize Unlabeled Samples: A Label Encoding Perspective 41

Rethinking Guidance Information to Utilize Unlabeled Samples...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yulong Yao, Yuan Chen, Shuhao Jin, Pengrong Zhang, Yu Jin, Jian Lu, Jiangang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China College of Control Science and Engineering Zhejiang University Hangzhou China Beijing Teleinfo Technology Company Ltd. China Academy of Information and Communications Technology Beijing China Research Institute of Industrial Internet of Things China Academy of Information and Communications Technology Beijing China

Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples.A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which employs the soft-labels of unlabeled samples to guide their ***, EntMin emphasizes prediction discriminability while neglecting prediction *** alleviate this issue, in this paper, we rethink the guidance information to utilize unlabeled *** analyzing the learning objective of ERM, we find that the guidance information for labeled samples in a specific category is the corresponding label *** by this finding, we propose a Label-Encoding Risk Minimization (LERM).It first estimates the label encodings through prediction means of unlabeled samples and then aligns them with their corresponding ground-truth label *** a result, the LERM ensures both prediction discriminability and diversity, and it can be integrated into existing methods as a ***, we analyze the relationships between LERM and ERM as well as ***, we verify the superiority of the LERM under several label insufficient *** codes are available at https://***/zhangyl660/LERM. Copyright 2024 by the author(s)

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