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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Lin, Yujun Yan, Linfang Hui, Hongxun Chen, Yin Chen, Xia Wen, Jinyu School of Electrical and Electronic Engineering Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology Wuhan China City & Energy Research Institute Suzhou China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China University of Strathclyde Department of Electronic and Electrical Engineering Glasgow United Kingdom

ISBN: (纸本)9798350366945

In this study, the home energy management problem, which can be regarded as a high-dimensional optimization problem, for numerous residential houses, is addressed. The concept of the aggregator is utilized to reduce the state and action space and to handle the high dimensionality. A two-stage deep reinforcement learning (DRL)-based approach is proposed for the aggregators to track the schedule from a superior grid and guarantee the operation constraints. In the first stage, a DRL control agent is set to learn the optimal scheduling strategy interacting with the environment based on the soft-actor-critic framework and generate the aggregate control actions. In the second stage, the aggregate control actions are disaggregated to individual appliances considering the users' behaviors. The uncertainty of an electric vehicle's charging demand is quantitatively expressed based on the driver's experience. An aggregate anxiety concept is introduced to characterize the driver's anxiety on the electric vehicle's range and uncertain events. Finally, simulations are conducted to verify the effectiveness of the proposed approach under dynamic user behaviors, and comparisons show the superiority of the proposed approach over other benchmark methods. © 2024 IEEE.

关键词： Deep reinforcement learning

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Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbreak: A Comprehensive study in Healthcare

Monitoring Body Mass Index (BMI) Pre & Post Covid-19 Outbrea...

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MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon)

作者： Pritam Ramesh Ahire K. Ulaga Priya Computer Science & Engineering Department Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350362879

ISBN: (纸本)9798350362886

A comprehensive health monitoring & analyzing system that provides personalized recommendations, goal setting, and tracking based on relevant factors related to BMI & COVID-19. System aims to analyze attack obesity through prediction, prevention and ongoing monitoring through appropriate predictive analytical system. The system's Risk Assessment component categorizes individuals by their risk levels using factors like age, gender, medical history, and behavior related to pre & post covid-19 related to BMI, then relates appropriate connections in human body. The wellness solution includes features such as fitness tracking, data integration, personalized metrics, and predictive analytics to create a holistic approach for predictive analytics health management. The system healthcare monitoring and analyzing system is a groundbreaking solution that leverages predictive analytics to promote a healthy lifestyle, combat obesity, and enhance overall well-being. By considering a range of factors and employing cutting-edge technology, we empower individuals to take control of their health, achieve their fitness objectives, and mitigate health risks.

关键词： COVID-19 Obesity Pandemics Urban areas Sociology Medical services Mental health

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Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan Ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi Syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication

Advancements in Speech Recognition: Exploring Feature Extrac...

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Advances in electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： R. Bhavani Kalaivani Kathirvelu Department of Computer Science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy.

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The effect of electrical stress and thermal annealing on swift heavy ion irradiated SiGe HBTs

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Nuclear and Particle Physics Proceedings 2023年 336-338卷 37-40页

作者： Hegde, Vinayakprasanna N. Ceressler, J.D. Gnana Prakash, A.P. Department of Physics Vidyavardhaka College of Engineering Mysuru 570002 India School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta 30303 United States Department of Studies in Physics University of Mysore Manasagangotri Mysuru 570006 India

The nano-engineered silicon–germanium heterojunction bipolar transistors (SiGe HBTs) were irradiated with 100 MeV phosphorous ions up to a high total dose of 100 Mrad. The irradiated SiGe HBTs were subjected to mixed mode (MM) electrical stress and isochronal annealing. The DC electrical characteristics were studied for ion irradiated, electrically stressed and thermally annealed SiGe HBTs. The important parameters such as base current (I_B), current gain (h_FE) and output characteristics of the irradiated SiGe HBTs were significantly degraded after 100 Mrad of the total dose. This is due to high energy ion induced damages in Emitter-Base (E-B) and Shallow Trench Isolation (STI) oxides. A significant recovery in device parameters was observed after both MM stress and isochronal annealing. The recovery in electrical characteristics of the irradiated SiGe HBTs is mainly due to the raise in the junction temperature and de-trapping of trapped charges. © 2023 Elsevier B.V.

关键词： Ion irradiation Isochronal annealing Mixed-mode electrical stress SiGe HBTs

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Empowered Artificial Intelligence Approach using Intuitionistic Fuzzy based Deep Neural Network for Chronic Diseases Prediction

Empowered Artificial Intelligence Approach using Intuitionis...

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Advances in Modern Age Technologies for Health and engineering Science (AMATHE), International Conference on

作者： D. Karthik S. Arun Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai

ISBN: (数字)9798350371567

ISBN: (纸本)9798350371574

Managing the increasing prevalence of chronic illnesses is a major worldwide health concern. It's important to understand the medical problem, its stages, types, and available treatments. The most important component in the early diagnosis of chronic diseases might enhance the diagnostic procedure more successfully. Medical professionals can diagnose a process with the help of numerous artificial intelligence tools. However, ambiguity, irrelevance, and inconsistency in the disease dataset have an impact on the classification accuracy. Conventional machine learning techniques are inadequate for managing scenarios of uncertainty that arise in datasets related to chronic illnesses. To anticipate chronic diseases, each characteristic in the suggested work is represented with a hesitation index. In order to compute the hesitation degree as a more informative procedure in determining the association among features, the belongingness and non-belongingness aspects of the intuitionistic fuzzy concept are induced in the prediction process. This work effectively manages the large chronic illness dataset by developing an intuitionistic fuzzy hesitation index-based similarity measure to weed out unimportant and strongly associated characteristics and enhance the classification model. Utilizing the scored feature information, the deep neural network modifies its hyper parameter to address the overfitting issues that frequently arise in DNNs. The performance of the empowered intuitionistic fuzzy-based deep neural network (EIF-DNN) is analyzed using five distinct chronic conditions, including hypertension, diabetes, hepatitis, cancer, and stroke. Comparing the proposed EIF-DNN against the current versions of multi-layered networks, the simulation results show that it produces promising results in predicting chronic diseases at an early stage.

关键词： Training Uncertainty Accuracy Artificial neural networks Stroke (medical condition) Prediction algorithms Indexes

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Aging decreases the precision of visual working memory

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Aging, Neuropsychology, and Cognition 2024年第4期31卷 762-776页

作者： Esfahan, Shahrzad M. Nili, Mohammad-Hossein H.K Hatami, Javad Sanayei, Mehdi Rezayat, Ehsan Institute for Cognitive Science Studies (ICSS) Tehran Iran Department of Psychology Psychology and Educational Science Faculty University of Tehran Tehran Iran School of Electrical & Computer Engineering College of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran

Objectives: As individuals age, cognitive abilities such as working memory (WM), decline. In the current study, we investigated the effect of age on WM, and elucidated sources of errors. Method: A total of 102 healthy individuals, aged 18 to 71, participated in this research. We designed and administered a face-based visual WM task, collecting responses via a graded scale in a delayed match-to-sample reproduction task. Results: The error of participants increased significantly as they aged. Our analysis revealed a significant age-related rise in the standard deviation of error distribution. However, there was no significant change in uniform probability with age. Conclusion: We found that WM performance declines through the lifespan. Investigating the sources of error, we found that the precision of WM decreased monotonously with age. The results also indicated that the probability of guessing the response as a measure of random response is not affected by age. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Aging continuous error face working memory working memory task

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Understanding Trends, Patterns, and Dynamics in Global Company Acquisitions: A Network Perspective

arXiv

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arXiv 2024年

作者： Kalhor, Ghazal Bahrak, Behnam School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Institute for Advanced Studies Tehran Iran

Studying acquisitions offers invaluable insights into startup trends, aiding informed investment decisions for businesses. However, the scarcity of studies in this domain prompts our focus on shedding light in this area. Employing Crunchbase data, our study delves into the global network of company acquisitions using diverse network analysis techniques. Our findings unveil an acquisition network characterized by a primarily sparse structure comprising localized dense connections. We reveal a prevalent tendency among organizations to acquire companies within their own country and industry, as well as those within the same age bracket. Furthermore, we show that the country, region, city, and category of the companies can affect the formation of acquisition relationships between them. Our temporal analysis indicates a growth in the number of weakly connected components of the network over time, accompanied by a trend toward a sparser network. Through centrality metrics computation in the cross-city acquisition network, we identify New York, London, and San Francisco as pivotal and central hubs in the global economic landscape. Finally, we show that the United States, United Kingdom, and Germany are predominant countries in international acquisitions. The insights from our research assist policymakers in crafting better regulations to foster global economic growth, and aid businesses in deciding which startups to acquire and which markets to target for expansion. © 2024, CC BY.

关键词： Investments

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A LESO Based Backstepping Controller Considering Coal Seam Hardness for Rotary Speed in Coal Mine Tunnel Drilling Process

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Journal of Systems Science & Complexity 2024年第5期37卷 1789-1808页

作者： CHEN Luefeng LIU Xiao WU Min LU Chengda PEDRYCZ Witold HIROTA Kaoru School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China Department of Electrical and Computer Engineering University of AlbertaEdmontonAB T6R2G7Canada The Tokyo Institute of Technology Yokohama 226-8502Japan

In the process of coal mine drilling,controlling the rotary speed is important as it determines the efficiency and safety of *** this paper,a linear extended state observer(LESO)based backstepping controller for rotary speed is proposed,which can overcome the impact of changes in coal seam hardness on rotary ***,the influence of coal seam hardness on the drilling rig’s rotary system is considered for the first time,which is reflected in the numerical variation of load torque,and a dynamic model for the design of rotary speed controller is *** an LESO is designed to observe the load torque,and feedforward compensation is carried out to overcome the influence of coal seam *** on the model of the compensated system,a backstepping method is used to design a controller to achieve tracking control of the rotary ***,the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments,the steady-state error of the rotary speed in field is 1 r/min,and the overshoot is reduced to 5.8%.This greatly improves the stability and security,which is exactly what the drilling process requires.

关键词： Backstepping extended state observer hydraulic tunnel drilling machine rotary speed

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Real-Time Tunable Gas Sensing Platform Based on SnO_(2) Nanoparticles Activated by Blue Micro-Light-Emitting Diodes

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Nano-Micro Letters 2024年第12期16卷 103-119页

作者： Gi Baek Nam Jung-El Ryu Tae Hoon Eom Seung Ju Kim Jun Min Suh Seungmin Lee Sungkyun Choi Cheon Woo Moon Seon Ju Park Soo Min Lee Byungsoo Kim Sung Hyuk Park Jin Wook Yang Sangjin Min Sohyeon Park Sung Hwan Cho Hyuk Jin Kim Sang Eon Jun Tae Hyung Lee Yeong Jae Kim Jae Young Kim Young Joon Hong Jong-In Shim Hyung-Gi Byun Yongjo Park Inkyu Park Sang-Wan Ryu Ho Won Jang Department of Materials Science and Engineering Seoul National UniversitySeoul 08826Republic of Korea Research Laboratory of Electronics Department of Mechanical EngineeringMassachusetts Institute of TechnologyCambridgeMA 02139USA Ming Hsieh Department of Electrical and Computer Engineering University of Southern CaliforniaLos AngelesCA 90089USA Department of Display Materials Engineering Soonchunhyang UniversityAsan 31538Republic of Korea Department of Photonics and Nanoelectronics BK21 FOUR ERICA-ACE CenterHanyang University ERICAAnsan 15588Republic of Korea Department of Nanotechnology and Advanced Materials Engineering Sejong UniversitySeoul 05006Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok 25913Republic of Korea Advance Institute of Convergence Technology Seoul National UniversitySuwon 16229Republic of Korea Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Physics Chonnam National UniversityGwangju 500-757Republic of Korea

Micro-light-emitting diodes(μLEDs)have gained significant interest as an activation source for gas sensors owing to their advantages,including room temperature operation and low power ***,despite these benefits,challenges still exist such as a limited range of detectable gases and slow *** this study,we present a blueμLED-integrated light-activated gas sensor array based on SnO_(2)nanoparticles(NPs)that exhibit excellent sensitivity,tunable selectivity,and rapid detection with micro-watt level power *** optimal power forμLED is observed at the highest gas response,supported by finite-difference time-domain ***,we first report the visible light-activated selective detection of reducing gases using noble metal-decorated SnO_(2)*** noble metals induce catalytic interaction with reducing gases,clearly distinguishing NH3,H2,and ***-time gas monitoring based on a fully hardwareimplemented light-activated sensing array was demonstrated,opening up new avenues for advancements in light-activated electronic nose technologies.

关键词： Micro-LED Gas sensor array Low power consumption Metal decoration Real-time detection

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