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2nd International Conference on Electrical, Electronics, and Information engineering, EEIE 2023

作者： Wang, Mini Han Huang, Lina Hou, Guanghui Yang, Jie Xing, Lumin Yuan, Qiting Chong, Kelvin Kam-Lung Lin, Zhiyuan Zeng, Peijin Fang, Xiaoxiao Yao, Xiaoping Li, Qingqian Liu, Jiang Lin, Chen Department of Ophthalmology & Visual Sciences Faculty of Medicine Chinese University of Hong Kong Hong Kong999077 Hong Kong The faculty of Data Science City University of Macau 999078 China Zhuhai People's Hospital Zhuhai Hospital Affiliated with Jinan University Zhuhai519000 China The Department of AI and big data application Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China Perspective Technology Group Zhuhai519000 China Jinan University Affiliated Shenzhen Eye Hospital Shenzhen518000 China Aier Eye Hospital of Zhuhai Zhuhai519000 China College of Artificial Intelligence Chongqing Industry & Trade Polytechnic Chongqing408000 China The First Affiliated Hospital Shandong First Medical University Shandong Provincial Qianfoshan Hospital Jinan250014 China Business School Hong Kong University of Science and Technology Hong Kong999077 Hong Kong Company Ltd. Guangdong Shenzhen518000 China School of Computer Beijing Institute of Technology Zhuhai Zhuhai519000 China Union Hospital of Fujian Medical University Fuzhou353000 China Department of Ophthalmology People's Hospital of Yantian District Shenzhen518000 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518000 China Department of Ophthalmology Shenzhen People's Hospital Shenzhen518000 China

ISBN: (纸本)9781510672840

Macula fovea detection is a crucial molecular biological prerequisite for screening and diagnosing macular diseases. Without early detection and proper treatment, any abnormality involving the macula may lead to blindness. However, with the ophthalmologist shortage and time-consuming artificial evaluation, neither the accuracy nor effectiveness of the diagnosis process could be guaranteed. In this project, we proposed a light-weighted deep learning model based on ultra-widefield fundus (UWF) images for macula fovea detection tasks. This study collected 2300 ultra-widefield fundus images from Shenzhen Aier Eye Hospital in China. A light-weighted method based on a U-shape network (Unet) and Fully Convolution Network (FCN) approach is implemented on 1800 (before amplifying process) training fundus images, 400 (before amplifying process) validation images, and 100 test images. Three professional ophthalmologists were invited to mark the fovea. A method from the anatomy perspective is investigated. This approach is derived from the spatial relationship between the macula fovea and optic disc center in UWF. A set of parameters of this method is set based on the experience of ophthalmologists and verified to be effective. The ultra-widefield swept-source optical coherence tomography (UWF-OCT) approach is the grounded method. Through a comparison of proposed methods, we conclude that the proposed light-weighted Unet method outperformed other methods on macula fovea detection tasks. © 2024 SPIE.

关键词： Convolution

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Post-storm vehicle routing for distribution grid restoration: An OLUCT based learning approach

Post-storm vehicle routing for distribution grid restoration...

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2020 IEEE Power and Energy Society General Meeting, PESGM 2020

作者： Shuai, Hang He, Haibo Wen, Jinyu University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI02881 United States Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Wuhan China

ISBN: (纸本)9781728155081

Natural disasters such as storms usually bring significant damages to distribution grids. Many distribution grids are not equipped with sensors that can pinpoint the location of actual faults. This further complicated the outage restoration process in distribution grids. This paper focuses on the optimal routing of repair vehicles to restore outages in the grid as fast as possible after a storm. First, the vehicle routing problem is formulated as a sequential stochastic optimization problem. In the optimization model, the belief state of the power grid is updated according to the phone calls from customers and the information collected by the repair vehicles. Second, an Open Loop Upper Confidence Bound for Trees (OLUCT) algorithm based stochastic lookahead policy is utilized to achieve the realtime dispatching of the repair vehicles. Simulation results show that the proposed approach can effectively navigate the vehicle to repair all outages. © 2020 IEEE.

关键词： Optimization

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An Improved Direct Torque Control with Battery Power Management of Open-End Winding Induction Motor Drive for Electric Vehicles 12

An Improved Direct Torque Control with Battery Power Managem...

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12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020

作者： Muduli, Utkal Ranjan Beig, Abdul R. Al Jaafari, Khaled Alsawalhi, Jamal Y. Behera, Ranjan Kumar Indian Institute of Technology Patna Dept. of Electrical Engineering Patna India Khalifa University Advanced Power and Energy Center Dept. of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9781728158266

A dual two-level inverter fed open-end winding induction motor (OEWIM) drive with isolated batteries for electric vehicle application is presented in this paper. For uniform state-of-charge (SOC) distribution, the power flow from each isolated batteries need to be balanced. The conventional direct torque control (DTC) includes torque and stator flux control objectives without taking care of dc-link power management. An improved DTC with proper vector selection based on state of charge of batteries is proposed to regulate the power shared by each dc-link. Proper tuning is essential for controlling the SOC level of both the battery. The superiority of the proposed method in terms of power sharing based on battery SOC is demonstrated by simulation and experimental verification, and the results are presented. An experimental validation of proposed method has been demonstrated and compared with that of the conventional DTC. © 2020 IEEE.

关键词： Electric vehicles

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Stereo centernet based 3D object detection for autonomous driving

arXiv

引用

arXiv 2021年

作者： Shi, Yuguang Guo, Yu Mi, Zhenqiang Li, Xinjie School of Computer and Communication Engineering University of Science and Technology Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science University of Science and Technology Beijing China Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing China Shunde Graduate School University of Science and Technology Guangdong China

Recently, three-dimensional (3D) detection based on stereo images has progressed remarkably;however, most advanced methods adopt anchor-based two-dimensional (2D) detection or depth estimation to address this problem. Nevertheless, high computational cost inhibits these methods from achieving real-time performance. In this study, we propose a 3D object detection method, Stereo CenterNet (SC), using geometric information in stereo imagery. SC predicts the four semantic key points of the 3D bounding box of the object in space and utilizes 2D left and right boxes, 3D dimension, orientation, and key points to restore the bounding box of the object in the 3D space. Subsequently, we adopt an improved photometric alignment module to further optimize the position of the 3D bounding box. Experiments conducted on the KITTI dataset indicate that the proposed SC exhibits the best speed-accuracy trade-off among advanced methods without using extra data. Copyright © 2021, The Authors. All rights reserved.

关键词： Object recognition

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Telemedicine platform for health assessment remotely by an integrated nanoarchitectonics FePS_(3)/rGO and Ti_(3)C_(2)-based wearable device

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npj Flexible Electronics 2022年第1期6卷 735-744页

作者： Jayraj V.Vaghasiya Carmen C.Mayorga-Martinez Martin Pumera Center for Advanced Functional Nanorobots Department of Inorganic ChemistryFaculty of Chemical TechnologyUniversity of Chemistry and Technology PragueTechnická516628 PragueCzech Republic Energy Research Institute@NTU(ERI@N) Research Techno PlazaX-Frontier BlockLevel 550 Nanyang Drive637553 SingaporeSingapore Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava17.listopadu 2172/1570800 OstravaCzech Republic Department of Medical Research China MedicalUniversity HospitalChina Medical UniversityNo.91 Hsueh-Shih RoadTaichung 40402Taiwan

Due to the emergence of various new infectious(viral/bacteria)diseases,the remote surveillance of infected persons has become most important,especially if hospitals need to isolate infected patients to prevent the spreading of pathogens to health care ***,we develop a remote health monitoring system by integrating a stretchable asymmetric supercapacitor(SASC)as a portable power source with sensors that can monitor the human physical health condition in real-time and *** abnormal body temperature and breathing rate could indicate a person’s sickness/infection *** we integrated FePS3@graphene-based strain sensor and SASC into an all-in-one textile system and wrapped it around the abdomen to continuously monitor the breathing cycle of the *** real body temperature was recorded by integrating the temperature sensor with the *** proposed system recorded physiological parameters in real-time and when monitored remotely could be employed as a screening tool for monitoring pathogen infection status.

关键词： integrating wear portable

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Synthesis of churros-like V2Se9 nanostructures for ultrasensitive electrochemical detection of oxalic acid in urine with portable point-of-care diagnostic devices

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Biosensors and Bioelectronics 2025年 285卷 117589页

作者： Shi, Peizheng Ye, Chen Sun, Zhuang Sun, Kaiqiang Zhao, Ningbin Pourfakhr, Mahan Cai, Tao Nishimura, Kazuhito Jiang, Nan Tsai, Hsu-Sheng Wu, Lidong Wu, Wenqi Yu, Jiancheng Fu, Li Xu, Lixin Chen, Jun Lin, Cheng-Te College of Materials Science and Engineering Zhejiang University of Technology Hangzhou310014 China Qianwan Institute of CNITECH Ningbo315336 China State Key Laboratory of Advanced Marine Materials Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Department of Bioengineering University of California Los AngelesCA90095 United States School of Physics Harbin Institute of Technology Harbin150001 China Key Laboratory of Control of Quality and Safety for Aquatic Products Chinese Academy of Fishery Sciences Beijing100141 China Department of Urology The Second Affiliated Hospital of Guangzhou Medical University Guangdong 510260 China Guangdong Key Laboratory of Urology Guangzhou Medical University Guangdong 511495 China Institute of Mass Spectrometry Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application Ningbo University Ningbo315211 China Zhenhai Institute of Mass Spectrometry Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China College of Materials and Environmental Engineering Hangzhou Dianzi University Hangzhou310018 China

The binding of oxalic acid (OA) with metal ions, particularly calcium ions, is a key factor in kidney stone formation, as it produces insoluble calcium oxalate, which crystallizes and accumulates in the kidneys. Consequently, OA in urine is commonly considered a significant biomarker for clinical diagnosis and early screening of kidney stones. This study reports churros-like V2Se9 nanostructures synthesized using a solvothermal method (high pressure and temperature) for sensitive and selective electrochemical detection of OA in urine. The electrodes made of churros-like V2Se9 hybridized with reduced graphene oxide (rGO) for OA recognition, demonstrated a linear range from 20 nM to 4 mM, with an extremely low detection limit of 9.7 nM. To enable fast, user friendly, and accurate point-of-care testing (POCT) for OA in urine samples, we designed and developed a portable workstation integrated with smartphone for data visualization. This is the first study to utilize the V2Se9 system for highly sensitive biosensor applications, highlighting its potential to advance screening techniques for key indicators of kidney stones by incorporating novel transition metal chalcogenide nanomaterials in POCT devices. © 2025 Elsevier B.V.

关键词： Electrochemical biosensors

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Telemonitoring Parkinson’s disease using machine learning by combining tremor and voice analysis

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Brain Informatics 2020年第1期7卷 12页

作者： Sajal, Md. Sakibur Rahman Ehsan, Md. Tanvir Vaidyanathan, Ravi Wang, Shouyan Aziz, Tipu Mamun, Khondaker Abdullah Al Department of Computer Science and Engineering United International University Dhaka Bangladesh Institute of Advanced Research United International University Dhaka Bangladesh Department of Mechanical Engineering Imperial College London London United Kingdom Fudan University Shanghai China Functional Neurosurgery and Experimental Neurology Group University of Oxford Oxford United Kingdom

Background: With the growing number of the aged population, the number of Parkinson’s disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients’ symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries. Method: This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients’ feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress. Result: The highest accuracy in PD detection using offline data was 98.3 % from voice data and 98.5 % from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient’s gender, we could improve the detection accuracy. This study’s novelty is the appli

关键词： Accelerometers

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Applications of MXenes in human-like sensors and actuators

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Nano Research 2023年第4期16卷 5767-5795页

作者： Jinbo Pang Songang Peng Chongyang Hou Xiao Wang Ting Wang Yu Cao Weijia Zhou Ding Sun Kai Wang Mark H.Rümmeli Gianaurelio Cuniberti Hong Liu Institute for Advanced Interdisciplinary Research(iAIR) Collaborative Innovation Center of Technology and Equipment for Biological Diagnosis and Therapy in Universities of ShandongUniversity of JinanJinan 250022China Institute for Materials Science and Max Bergmann Center of Biomaterials Technische Universität DresdenDresden 01069Germany Center for Advancing Electronics Dresden Technische Universität DresdenDresden 01069Germany Dresden Center for Computational Materials Science Technische Universität DresdenDresden 01062Germany Dresden Center for Intelligent Materials(GCL DCIM) Technische Universität DresdenDresden 01062Germany High-Frequency High-Voltage Device and Integrated Circuits R&D Center Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Microelectronic Devices&Integrated Technology Institute of MicroelectronicsChinese Academy of SciencesBeijing 100029China Shenzhen Key Laboratory of Nanobiomechanics Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China State Key Laboratory of Biobased Material and Green Papermaking Qilu University of TechnologyShandong Academy of SciencesJinan 250353China School of Bioengineering Qilu University of TechnologyShandong Academy of ScienceJinan 250353China Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology(Ministry of Education) Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Northeast Electric Power UniversityJilin 132012China School of Electrical Engineering Weihai Innovation Research InstituteQingdao UniversityQingdao 266000China School of Electrical and Computer Engineering Jilin Jianzhu UniversityChangchun 130118China Institute for Complex Materials Leibniz Institute for Solid State and Materials Research Dresden(IFW Dresden)20 Helmholtz StrasseDresden 01069Germany College of Energy Soochow Institute for Energy and Materials Innovations Soochow UniversitySuzhou 215006China Key L

Human beings perceive the world through the senses of sight,hearing,smell,taste,touch,space,and *** first five senses are prerequisites for people to *** sensing organs upload information to the nervous systems,including the brain,for interpreting the surrounding ***,the brain sends commands to muscles reflexively to react to stimuli,including light,gas,chemicals,sound,and ***,as an emerging two-dimensional material,has been intensively adopted in the applications of various sensors and *** this review,we update the sensors to mimic five primary senses and actuators for stimulating muscles,which employ MXene-based film,membrane,and composite with other functional ***,a brief introduction is delivered for the structure,properties,and synthesis methods of ***,we feed the readers the recent reports on the MXene-derived image sensors as artificial retinas,gas sensors,chemical biosensors,acoustic devices,and tactile sensors for electronic ***,the actuators of MXene-based composite are ***,future opportunities are given to MXene research based on the requirements of artificial intelligence and humanoid robot,which may induce prospects in accompanying healthcare and biomedical engineering applications.

关键词： MXenes sensors actuators gas sensors biosensors tactile sensors sound sensors artificial retina

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Personal fixations-based object segmentation with object localization and boundary preservation

arXiv

引用

arXiv 2021年

作者： Li, Gongyang Liu, Zhi Shi, Ran Hu, Zheng Wei, Weijie Wu, Yong Huang, Mengke Ling, Haibin Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science Stony Brook University Stony BrookNY11794 United States

As a natural way for human-computer interaction, fixation provides a promising solution for interactive image segmentation. In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction. In particular, we first construct a new PFOS dataset by carefully collecting pixel-level binary annotation data over an existing fixation prediction dataset, such dataset is expected to greatly facilitate the study along the line. Then, considering characteristics of personal fixations, we propose a novel network based on Object Localization and Boundary Preservation (OLBP) to segment the gazed objects. Specifically, the OLBP network utilizes an Object Localization Module (OLM) to analyze personal fixations and locates the gazed objects based on the interpretation. Then, a Boundary Preservation Module (BPM) is designed to introduce additional boundary information to guard the completeness of the gazed objects. Moreover, OLBP is organized in the mixed bottom-up and top-down manner with multiple types of deep supervision. Extensive experiments on the constructed PFOS dataset show the superiority of the proposed OLBP network over 17 state-of-the-art methods, and demonstrate the effectiveness of the proposed OLM and BPM components. The constructed PFOS dataset and the proposed OLBP network are available at https://***/MathLee/OLBPNet4PFOS. © 2021, CC BY.

关键词： Object recognition

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Towards Automated Multiclass Severity Prediction Approach for COVID-19 Infections Based on Combinations of Clinical Data

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Mobile Information Systems 2022年第1期2022卷

作者： Dinar, Ahmed M. Raheem, Enas A. Abdulkareem, Karrar Hameed Mohammed, Mazin Abed Oleiwie, Marwan Ghazi Zayr, Fawzi Hasan Al-Boridi, Omar Al-Mhiqani, Mohammed Nasser Al-Andoli, Mohammed Nasser Computer Engineering Department University of Technology Baghdad Iraq College of Agriculture Al-Muthanna University Samawah66001 Iraq College of Computer Science and Information Technology University of Anbar Anbar31001 Iraq Azizia Primary Health Care Sector Wasit Health Directorate Ministry of Health Al Aziziyah Saudi Arabia Department of Biochemistry College of Medicine University of Wasitc Wasit Iraq School of Engineering RMIT University Melbourne Australia Center for Advanced Computing Technology Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Malacca Malaysia Computer Science and Information Systems Department Faculty of Science Sa'Adah University Sa'adah Yemen

The recent dramatic expansion of the COVID-19 outbreak is placing enormous strain on human society as a whole. Numerous biomarkers are being investigated in an effort to track the condition of the patient. This could interfere with signs of many other illnesses, making it more difficult for a specialist to diagnose or predict the severity level of the case. As a result, the focus of this research was on the development of a multiclass prediction system capable of dealing with three severity cases (severe, moderate, and mild). The lymphocyte to CRP ratio (C-reactive protein blood test) and SpO2 (blood oxygen saturation level) indicators were ranked and used as prediction system attributes. A machine learning model based on SVMs is created. A total of 78 COVID-19 patients were recruited from the Azizia primary health care sector/Wasit Health Directorate/Ministry of Health to form different combinations of COVID-19 clinical dataset. The outcomes demonstrate that the proposed approach had an average accuracy of 82%. The established prediction system allows for the early identification of three severity cases, which reduces deaths. © 2022 Ahmed M. Dinar et al.

关键词： COVID-19

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