Journal of Electrical Systems最新文献

{"title":"Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks","authors":"Qianqian Hu","doi":"10.52783/jes.3733","DOIUrl":"https://doi.org/10.52783/jes.3733","url":null,"abstract":"With the expansion of Speech Emotion Recognition in the consumer domain, several devices, particularly those designed for managing smart home personal assistants for the elderly, have been widely available on the market. The increasing processing power and connection, together with the growing need to facilitate longer residency through technological interventions, highlight the potential benefits of smart home assistants. Enabling these assistants to recognize human emotions would greatly improve user-assistant communication, allowing the assistant to deliver more constructive and customized feedback to the user. In this research work, Modeling and Sentiment Analysis of Social Relationships in Elderly Smart Homes Based on Graph Neural Networks (SASR-MBHNN-BBOA) is proposed. The input data are collected from Social Recommendation Dataset. Then, input data are pre-processed utilizing Inverse Optimal Safety Filters (IOSF) for cleaning the data and removing the background noise. Then the pre-processed data are given to Memristive Bi-neuron Hopfield Neural Network (MBHNN) for predicting the sentiments like positive, negative and neutral. In general, MBHNN doesn’t express some adoption of optimization approaches for determining optimal parameters to predicting the sentiments accurately. Hence BBOA is proposed to optimize MBHNN classifier which precisely predicts the sentiments in elderly smart home. The proposed SASR-MBHNN-BBOA method is implemented in Python, and it assessed with numerous performance metrics such as accuracy, precision, recall, F1-score, ROC. The outcomes show SASR-MBHNN-BBOA attains 20.8%, 19.5%, and 29.6% higher Accuracy, 28.8%, 22.5%, and 32.6% higher Precision, 15.5%, 27.4%, and 18.2% higher Recall are analysed with existing methods such as, Emotional speech analysis in real time for smart home assistants.(SASR-CNN-SHA), Machine Learning to Investigate Elderly Care Requirements in China via the Lens of Family Caregivers (SASR-ML-IECR),Identifying User Emotions via Audio Conversations with Smart Assistants (SASR-DNN-EASA) methods respectively.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrical Properties of Porous Nano Modified Lithium-ion Battery Negative Electrode Materials","authors":"Guigang Cheng","doi":"10.52783/jes.3739","DOIUrl":"https://doi.org/10.52783/jes.3739","url":null,"abstract":"The negative electrode material of lithium-ion batteries is one of the key factors affecting the overall performance of the battery. Traditional carbon/graphite materials are prone to safety issues such as capacity loss and metal precipitation during application. Due to its high conductivity and flexibility, as well as its excellent properties in battery energy density, cycle life, and other aspects, Ti3C2 nanomaterials have brought new opportunities for the lithium-ion batteries development. The development of convenient electronic products and power equipment has put forward higher requirements for the performance of lithium-ion batteries. On the basis of introducing modification technologies for lithium-ion battery electrode materials (such as carbon coating, nanocomposition, etc.), this study further reviews and analyzes the electrochemical performance characteristics of batteries under porous nanomaterials. The purpose is to provide reference value for the selection and design synthesis of battery negative electrode materials.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation Design of Three Phase Grid Integrated EV Charging Station with Renewables","authors":"Manish D Patel, Dr. Rajendra R. Aparnathi","doi":"10.52783/jes.3740","DOIUrl":"https://doi.org/10.52783/jes.3740","url":null,"abstract":"The potential for improving sustainability in the transportation sector is considerable when renewable energy sources are combined with electric vehicle (EV) charging stations. The design methodology for a three-phase grid-integrated EV charging station that integrates renewable energy sources is presented in this paper using simulation-based design. The suggested system seeks to reduce grid dependency, maximize the use of renewable energy sources, and optimize energy management. Utilizing cutting-edge software tools, the simulation model is created to evaluate the integrated system's viability and performance under various operating scenarios. The study offers insightful information for the planning and development of sustainable transportation solutions and emphasizes the possible advantages of incorporating renewable energy sources into EV charging infrastructure.In this paper, a power predictive model was presented to forecast EVs power demand and to enhance the CS performance, in order to avoid an extra burden on the grid during peak hours. PV array with storage battery supported by the grid increased the reliability and flexibility of the system.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artwork Traceability and Anti-Counterfeiting Model Based on Block Chain Technology","authors":"Rui Yin","doi":"10.52783/jes.3727","DOIUrl":"https://doi.org/10.52783/jes.3727","url":null,"abstract":"The Chinese art market fostered the advancement of culture and spirituality. Nevertheless, false and counterfeit artwork is not uncommon. The art technology and security introduce the innovative method to determine Artwork Traceability and Anti-Counterfeiting. The aim of the work is to prevent the creation and circulation of fake or unauthorized copies of artworks. Because of the cryptographic security and immutability of block chains, it would be challenging to falsify artwork records or make accurate duplicates. This would foster confidence in the art market and shield collectors and artists from monetary losses and harm to their reputations. The data on mobile Application Data Center (ADC) platform and the cleansing the data using sub-aperture keystone transform matched filtering (SAKTMF) and the preprocessed data is fed into the Hierarchically Gated Recurrent Neural Network (HGRNN) classify the art work and Anti-Counterfeiting. The Bald Eagle Search Optimization (BES) Algorithm is used to optimize the weight parameter of HGRNN. The proposed model is implemented in MATLAB/ Simulink platform and the accuracy is compared to various existing approaches such as Dual-Branch Multi-Scale Feature Fusion network (DMF-Net), Region Convolutional Neural Network (R-CNN) and practical Byzantine fault tolerance (PBFT) algorithm. The gained results of the proposed HGRNN-BES method attains higher accuracy 97%, 96%, and 99%, higher precision 98%, 99%, and 97%.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Product Design Interaction and Experience Based on Virtual Reality Technology","authors":"Qianqian Hu","doi":"10.52783/jes.3735","DOIUrl":"https://doi.org/10.52783/jes.3735","url":null,"abstract":"Artificial intelligence involves imitating human thought, consciousness, and other aspects. This is similar to having machine with human brain that can think, produce independently. However, unlike human brain, it has a speed, memory advantage. A virtual reality interactive glove is built using a nine-axis inertial sensor, an artificial intelligence deep learning algorithm. In this research work, Product Design Interaction and Experience Based on Virtual Reality Technology (PDIE-VRT-SCGAN) were proposed. Initially, input gestures data are gathered from the Virtual Reality Experiences (VRE) dataset. The input gestures data is then pre-processed using Multi-Window Savitzky-Golay Filter (MWSGF) to reduce noises, increase overall quality of the gestures data. In order to improving overall user engagement in product design interactions on virtual reality (VR) technology, the pre-processed gestures data are then fed into an adversarial network called a Semi-Cycled Generative Adversarial Network (SCGAN). In general, SCGAN does not express some adaption of optimization strategies for determining optimal parameters to promise exact to improving overall user engagement in product design interactions using VR technology. Therefore, FOX-inspired Optimization (FIO) is proposed to enhance weight parameter of SCGAN method, which precisely improving the user experience in product design interaction. The efficacy of PDIE-VRT-SCGAN method is assessed using a number of performance criteria, including tracking accuracy, frame rate, latency, rendering time, error rate, and user error. The proposed PDIE-VRT-SCGAN method attains 22.36%, 25.42% and 18.17% higher tracking accuracy, 21.26%, 15.42% and 19.27% higher latency, 28.36%, 25.32% and 28.27% higher frame rate compared with existing methods, such as design and implementation of virtual reality interactive product software depend on artificial intelligence deep learning algorithm(DVRI-PS-AI-DL), virtual evaluation system for product designing utilizing virtual reality (VES-PD-VR), and analysis of unsatisfying user experiences with unmet psychological needs for virtual reality exergames utilizing deep learning approach (AUUE-UP-VRE-DLA) respectively.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Design and Performance Evaluation of Network Intrusion Detection System Based on Deep Learning","authors":"Lin Yang","doi":"10.52783/jes.3728","DOIUrl":"https://doi.org/10.52783/jes.3728","url":null,"abstract":"Today’s internets are made up of nearly half million various networks. In any network connection, detecting attacks by their kinds is challenging task as various attacks may have several connections, their number vary from few to hundreds of network connections. In this paper, Design and Performance Evaluation of Network Intrusion Detection System Based on Deep Learning (NID-SPGAN-STO) is proposed.  Initially, input data are collected by NSL-KDD dataset. Afterward, data are fed to preprocessing. In preprocessing, Distributed Set-Membership Fusion Filtering is used to remove redundant and biased records from input data. The pre-processed output is given to feature selection for selecting optimal features utilizing Piranha foraging Optimization Algorithm. Finally the selected features are transferred into the Semantic-Preserved Generative Adversarial Network (SPGAN) for detecting Network Intrusion, like DoS, Probe, R2L, U2R and Normal. Generally SPGAN doesn’t reveal some adoption of optimization techniques for computing optimal parameters for promising precise network intrusion detection. Hence Siberian Tiger Optimisation (STO) is used to enhance weight parameters of SPGAN. The proposed NID-SPGAN-STO method is implemented using Python. To detect network intrusion detection, performance metrics likes precision, sensitivity, FI-score, specificity, accuracy, RoC, computational time are considered. The NID-SPGAN-STO method attains 30.58%, 28.73% and 25.62%, higher precision, 20.48%, 24.73%, 29.32% higher specificity and 30.98%, 26.66% and 21.32% higher F-score, 26.78%, 34.47%, and 22.86% higher recall  analysed, with existing techniques likes improved binary gray wolf optimizer with SVM for intrusion detection system in WSNs (NID-SVM-IDS), network intrusion detection system utilizing deep learning (NID-DNN), design with improvement of efficient network intrusion detection system utilizing ML methods (NID-IDS-ANN) respectively.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"English Speaking Assessment Algorithm Based on Deep Learning","authors":"Xiaoli Hu","doi":"10.52783/jes.3729","DOIUrl":"https://doi.org/10.52783/jes.3729","url":null,"abstract":"English as a Foreign Language (EFL) students perform when speaking in public. An increasingly globalized world, effective public speaking is critical, but EFL students struggle to perform it, despite importance of qualities such as eye contact, speech pauses, there is presently no objective examination of such elements. A summative assessment has historically been the predominant form of evaluation in college English speaking assessments. Exam-centric teaching has considerable negative effect on foreign language training. In this research work, English Speaking Assessment Algorithm Based on Deep Learning (ESA-NEGCN-NBOA) is proposed. Initially, input video data are gathered from the multiple video dataset (MVD).The input video data is then pre-processed using Deep Attentional Guided Image Filtering (DAGIF) to remove presence of signal-dependent noise and improve lack of pixels from the regions and enhanced the video data. The data that has been pre-processed is utilized to Feature extraction using New General Double Integral Transform (NGDIT), which extract the significant attributes such as mel-frequency cepstral coefficients, energy, speech rate and pitch. Then NEGCN is proposed to improve students spoken English performance by assessmenting the English speakers. In general, NEGCN doesn’t express some adaption of optimization approaches for determining optimal parameters to promise exact improvement of assessment. Therefore, NBOA is proposed to enhance weight parameter of NEGCN for English speaking assessments, which precisely assess the English speaking. Performance measures such as accuracy, assessment error, evaluation time, pretest and posttest are examined when the proposed ESA-NEGCN-NBOA method is put into practice. The proposed ESA-NEGCN-NBOA method attains21.36%, 23.42% and 19.29% higher accuracy, 23.36%, 18.42% and 28.27% lower evaluation error, 20.36%, 27.42%, 28.17% lesser evaluation time analysed with existing techniques, likes innovative strategy towards oral English assessment utilizing machine learning, data mining,  blockchain methods(IST-OEA-ML), machine learning assessment system for spoken English depend on linear predictive coding (AS-SE-LPC-ML), multimodal transfer learning for oral presentation assessment (MM-TL-OPA) respectively.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Music Teaching Mode of Colleges and Universities Based On Hierarchically Gated Recurrent Neural Network (HGRNN) and Lyrebird Optimization Algorithm (LOA)","authors":"Kegang Lu, Honghui Zhu","doi":"10.52783/jes.3734","DOIUrl":"https://doi.org/10.52783/jes.3734","url":null,"abstract":"Colleges and universities play a crucial role in nurturing talent and providing highly skilled individuals for various sectors of society. Through modifications over time, the model of music education at colleges and universities has advanced. However, there are still numerous issues that demand careful consideration. This manuscript proposes a hierarchically gated recurrent neural network (HGRNN) optimized with the lyrebird Optimization Algorithm (LOA) for predicting music teaching mode of colleges and universities (MTM-HGRNN-LOA). Initially, the data is collected via real time basis. Afterward, the data is fed to an unscented trainable kalman filter (UTKF) based pre-processing process. In the pre-processing segment, it enhances training rate and eliminates the of batch size dependency. The pre-processing output is given to modified spline-kernelled chirp let transform (MSKCT). The input signal undergoes feature extraction to derive the primary features, which are subsequently combined to yield more comprehensive features in an efficient manner. After that, the extracted features are given to a hierarchically gated recurrent neural network and lyrebird optimization algorithm for effectively classifying the music teaching mode for best, good, normal, satisfactory and poor. The weight parameters of hierarchically gated recurrent neural network are optimized using the lyrebird optimization algorithm. The proposed method is implemented in python and evaluated their performance with existing methods. The performance metrics, like precision, F1-score, accuracy, specificity, sensitivity, and ROC is analysed to the proposed method's performance. The proposed MTM-HGRNN-LOA methods of accuracy are provide 97% best, 98% good, 95% normal, 98% satisfactory and 97% poor music teaching mode. The existing methods MTM-CNN, MTM-BPNN and MTM-GNN, the specificity becomes 90%, 70%, 79% best, 77%, 75%, 65% good, 66%, 85%, 84% normal, 59%, 58%, 70% satisfactory, 61%, 79%, 81% poor music teaching mode. The results show that the proposed MTM-HGRNN-LOA method outperforms other existing techniques, such as online vocal music teaching quality using Back Propagation neural network and convolutional neural network based College-Level Music Teaching Quality Evaluation.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141127865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Industrial Reliability: A Comparative Study of Hot and Cold Standby Configurations in Three-Unit Parallel Systems","authors":"Prawar","doi":"10.52783/jes.3677","DOIUrl":"https://doi.org/10.52783/jes.3677","url":null,"abstract":"This comparative research study explores the efficacy of two parallel systems, Model A employing a hot standby configuration and Model B utilizing a cold standby setup, each comprising three units operating based on demand. The investigation focuses on evaluating the reliability and cost-effectiveness of these systems, employing metrics such as Mean Time to System Failure, availability at full and reduced capacity, repairer busy periods, downtime, and profit analysis using Semi-Markov and regenerative point techniques. Results reveal that Model B (cold standby) outperforms Model A (hot standby) across multiple parameters, including Mean Time to System Failure, availability, repairer busy periods, downtime, and profitability. Specifically, Model B demonstrates higher values for Mean Time to System Failure, Availability at full and reduced capacity, Busy period for repairmen, and Profit in Rs. Consequently, it is inferred that Model B proves to be more reliable and efficient than Model A. These findings underscore the potential advantages of employing cold standby configurations in similar machinery setups, offering insights to enhance system performance and minimize downtime, thus suggesting avenues for optimizing cold standby parallel systems based on the study's outcomes.","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140979475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Solar Photo Voltaic Connected Quasi Z Source Inverter for Grid and utility Application","authors":"Muneeb Ahmad","doi":"10.52783/jes.3678","DOIUrl":"https://doi.org/10.52783/jes.3678","url":null,"abstract":"This paper discusses the use of quasi-Z-source inverters, in utility grid systems. These inverters enhanced dependability and one-stage buck and boost abilities make them appropriate for photovoltaic power conditioning applications. The small signal sate space method utilized for the proposed system using Quasi ZSI for utility and grid integration application. Shoot through state also implemented on dc side to enhanced the voltage gain. The Controlling of dc side and ac side also implemented in MATLAB using state space transfer function. A two-stage controlling technique implemented on two level 6 switch inverters with SPWM with P & O MPPT and PI controller. The proposed system has solar PV array rating of 5.5kW and utilized in distribution purpose. A simple boost controlling technique has been implemented to obtained the desired gain and less ripples are present in the current due to the presence of inductor in the QZSI impedance network. During light loading the surplus power injected to the grid with the help of PLL system and when load demand increases the power take from the grid smoothly without loss of synchronism. The system has better stability and fast dynamic response during transient. The THD has been observed at the inverter and load side with LC filter, THD within the permissible limit as per IEEE std 1547. ","PeriodicalId":44451,"journal":{"name":"Journal of Electrical Systems","volume":null,"pages":null},"PeriodicalIF":0.4,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}