Anak Agung, Surya Pradhana, S. D. Astuti, Fauziah, Perwira Annissa Dyah, Permatasari, Riskia Agustina, A. K. Yaqubi, H. Setyawati, Cendra Devayana Putra
The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%.
{"title":"Sensor Array System Based on Electronic Nose to Detect Borax in Meatballs with Artificial Neural Network","authors":"Anak Agung, Surya Pradhana, S. D. Astuti, Fauziah, Perwira Annissa Dyah, Permatasari, Riskia Agustina, A. K. Yaqubi, H. Setyawati, Cendra Devayana Putra","doi":"10.1155/2023/8847929","DOIUrl":"https://doi.org/10.1155/2023/8847929","url":null,"abstract":"The categorization of odors utilizing gas sensor arrays with various meatball borax concentrations has been studied. The samples included meatballs with a borax content of 0.05%, 0.10%, 0.15%, 0.20%, and 0.25% (%mm) and meatballs without any borax. Six TGS gas sensors with a baseline of 10 seconds, a detecting period of 120 seconds, and a purging period of 250 seconds make up the gas sensor array used in this work. Artificial neural networks (ANNs) and principal component analysis (PCA), which are beneficial for feature extraction and classification, are used to handle the collected data based on machine learning approaches. Two models were produced by the data analysis: model 1, which only used the PCA approach, and model 2, which only used the ANN methodology. 90.33% is the total variance value of PC from model 1. In addition, the multilayer perceptron artificial neural network (ANN-MLP) technique for model 2 yielded accuracy values of 95%.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74686259","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}
Synchronous reluctance motor drives (SynRMs) are the best promising machines utilized in modern industries and electric vehicles, according to the current study. Research on new SynRMs drive systems has increased as a result. This review article disseminates the most recent developments in these technologies’ design, modeling, and controlling. First, a simple comparison between the main motor technologies and SynRMs is made. To aid researchers in selecting the appropriate motor controller for their motor drive systems, the most common motor control approaches are examined and classed.
{"title":"Comprehensive Overview of Modern Controllers for Synchronous Reluctance Motor","authors":"S. Angayarkanni, K. R. Kumar, A. Senthilnathan","doi":"10.1155/2023/1345792","DOIUrl":"https://doi.org/10.1155/2023/1345792","url":null,"abstract":"Synchronous reluctance motor drives (SynRMs) are the best promising machines utilized in modern industries and electric vehicles, according to the current study. Research on new SynRMs drive systems has increased as a result. This review article disseminates the most recent developments in these technologies’ design, modeling, and controlling. First, a simple comparison between the main motor technologies and SynRMs is made. To aid researchers in selecting the appropriate motor controller for their motor drive systems, the most common motor control approaches are examined and classed.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83745153","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}
For the mixed traffic flow, obtaining the distribution of connected vehicles (CVs) and regular vehicles (RVs) is of great significance for road network analysis and cooperative control in intelligent transportation systems (ITSs). However, whether it is based on fixed sensors or based on CVs and traffic mechanism to estimate the spatial distribution of RVs, the implementation complexity and low estimation accuracy are the points that need to be improved. This paper proposes a regular vehicle spatial distribution estimation method using adjacent connected vehicles as mobile sensors. First, to investigate the hidden relationship between the interaction information of adjacent CVs and the spatial distribution of RVs among CVs, the Gaussian mixture model-hidden Markov model (GMM-HMM) is selected as the identification method. Then, three sets of experiments were designed to study the influence of observed features on the identification capability of the model, generalization capability validation, and comparison with other methods, respectively. Finally, the proposed method is verified by the dataset generated by the car-following model. The experimental results show that selecting the relative position and time headway as observed features can effectively reflect the regular vehicle spatial distribution between adjacent CVs. The average accuracy of the proposed method to identify the regular vehicle spatial distribution is over 93.7%, which can provide valuable suggestions for the Internet of Vehicles application.
{"title":"Regular Vehicle Spatial Distribution Estimation Based on Machine Learning","authors":"Lin Liu, Bin Wang, Yongfu Li, Nenglong Hu","doi":"10.1155/2023/4954035","DOIUrl":"https://doi.org/10.1155/2023/4954035","url":null,"abstract":"For the mixed traffic flow, obtaining the distribution of connected vehicles (CVs) and regular vehicles (RVs) is of great significance for road network analysis and cooperative control in intelligent transportation systems (ITSs). However, whether it is based on fixed sensors or based on CVs and traffic mechanism to estimate the spatial distribution of RVs, the implementation complexity and low estimation accuracy are the points that need to be improved. This paper proposes a regular vehicle spatial distribution estimation method using adjacent connected vehicles as mobile sensors. First, to investigate the hidden relationship between the interaction information of adjacent CVs and the spatial distribution of RVs among CVs, the Gaussian mixture model-hidden Markov model (GMM-HMM) is selected as the identification method. Then, three sets of experiments were designed to study the influence of observed features on the identification capability of the model, generalization capability validation, and comparison with other methods, respectively. Finally, the proposed method is verified by the dataset generated by the car-following model. The experimental results show that selecting the relative position and time headway as observed features can effectively reflect the regular vehicle spatial distribution between adjacent CVs. The average accuracy of the proposed method to identify the regular vehicle spatial distribution is over 93.7%, which can provide valuable suggestions for the Internet of Vehicles application.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86176111","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}
This paper presents an improved whale optimization algorithm (IWOA) for optimizing the model predictive torque control (MPTC) of brushless DC motor (BLDCM) to further reduce the problems of strong torque pulsation and high ripple caused by the special structure of BLDCM. IWOA adds a randomized convergence factor strategy to the original algorithm, enabling the parameter weights to be adjusted in time. The relative error between the training set and the predicted values is reduced, and a suitable interval is selected for the target. The proposed method takes into account the switching frequency loss factor in the MPTC system of BLDCM, discarding the traditional trial-and-error method and choosing to control the parameter adjustment by the degree of deviation. The IWOA is compared with the popular whale optimization algorithm (WOA), dragonfly algorithm (DA), ant colony optimization (ACO) algorithm, and grey wolf optimization (GWO) algorithm on the MATLAB SIMULINK platform to verify the effectiveness of the method in dealing with improved chain tracking, reduced torque pulsation, and reduced speed error. The simulation results show that IWOA performs well, with an efficiency of 94.32%.
{"title":"Optimized Model Torque Prediction Control Strategy for BLDCM Torque Error and Speed Error Reduction System","authors":"Ye Yuan, Cheng Liu, Siyu Chen, Zhenxiong Zhou","doi":"10.1155/2023/5563242","DOIUrl":"https://doi.org/10.1155/2023/5563242","url":null,"abstract":"This paper presents an improved whale optimization algorithm (IWOA) for optimizing the model predictive torque control (MPTC) of brushless DC motor (BLDCM) to further reduce the problems of strong torque pulsation and high ripple caused by the special structure of BLDCM. IWOA adds a randomized convergence factor strategy to the original algorithm, enabling the parameter weights to be adjusted in time. The relative error between the training set and the predicted values is reduced, and a suitable interval is selected for the target. The proposed method takes into account the switching frequency loss factor in the MPTC system of BLDCM, discarding the traditional trial-and-error method and choosing to control the parameter adjustment by the degree of deviation. The IWOA is compared with the popular whale optimization algorithm (WOA), dragonfly algorithm (DA), ant colony optimization (ACO) algorithm, and grey wolf optimization (GWO) algorithm on the MATLAB SIMULINK platform to verify the effectiveness of the method in dealing with improved chain tracking, reduced torque pulsation, and reduced speed error. The simulation results show that IWOA performs well, with an efficiency of 94.32%.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86570355","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}
This paper proposes the technique of using low loss on-chip inductors in the design of low noise amplifier (LNA) that offers high gain and lower noise figure. Upon the substrate of octagonal spiral inductors, a surface of patterned ground shield is inserted that significantly reduces the substrate loss. This effect limits the penetration of electric filed into the substrate, thereby improving the inductor’s Quality (Q) factor and decouples the substrate parasitic that results with smaller series resistance. These effects result with improved gain and noise figure of LNA at 60 GHz when the designed inductors are included in it to serve as gate, source, and load inductances. The proposed work uses an inductively degenerated 3-stage common-source LNA in a 65-nm CMOS process. Simulation results show that the LNA using custom designed inductors achieves the peak gain of 17.02 dB at 56 GHz with a noise figure of 5 dB at 60 GHz for the power consumption of 10 mW. The figure-of-merit (FOM) is 14.56 which is 0.8 times more than the LNA design using off-chip inductors. A complete LNA layout using custom designed inductor footprints has been presented and analyzed.
{"title":"Low Noise Amplifier at 60 GHz Using Low Loss On-Chip Inductors","authors":"K. Balamurugan, M. N. Devi, M. Jayakumar","doi":"10.1155/2023/2469673","DOIUrl":"https://doi.org/10.1155/2023/2469673","url":null,"abstract":"This paper proposes the technique of using low loss on-chip inductors in the design of low noise amplifier (LNA) that offers high gain and lower noise figure. Upon the substrate of octagonal spiral inductors, a surface of patterned ground shield is inserted that significantly reduces the substrate loss. This effect limits the penetration of electric filed into the substrate, thereby improving the inductor’s Quality (Q) factor and decouples the substrate parasitic that results with smaller series resistance. These effects result with improved gain and noise figure of LNA at 60 GHz when the designed inductors are included in it to serve as gate, source, and load inductances. The proposed work uses an inductively degenerated 3-stage common-source LNA in a 65-nm CMOS process. Simulation results show that the LNA using custom designed inductors achieves the peak gain of 17.02 dB at 56 GHz with a noise figure of 5 dB at 60 GHz for the power consumption of 10 mW. The figure-of-merit (FOM) is 14.56 which is 0.8 times more than the LNA design using off-chip inductors. A complete LNA layout using custom designed inductor footprints has been presented and analyzed.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89142167","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}
Mustapha Es-Semyhy, A. Ba-Razzouk, Mustapha Elharoussi, M. Madark
Feedback linearization technique (FLT) linearizes the model of induction machine (IM). This actuator suffers from the variation of its inductances due to saturation and resistances due to joule and skin effects. Sliding-mode control (SMC) is widely recognized as a robust technique against parametric variations of IM. This control strategy has the advantage of being simple to implement and requires only a simple flux observer. This explains the use of FLT and SMC in this work to control an IM while taking into account the magnetic saturation and heating of the IM. Simulation results, conducted in a MATLAB/Simulink environment, demonstrate the relevance and efficiency of the proposed control schemes.
{"title":"Comparative Study of Two Nonlinear Control Strategies of Induction Motors considering Heating and Magnetic Saturation","authors":"Mustapha Es-Semyhy, A. Ba-Razzouk, Mustapha Elharoussi, M. Madark","doi":"10.1155/2023/7907782","DOIUrl":"https://doi.org/10.1155/2023/7907782","url":null,"abstract":"Feedback linearization technique (FLT) linearizes the model of induction machine (IM). This actuator suffers from the variation of its inductances due to saturation and resistances due to joule and skin effects. Sliding-mode control (SMC) is widely recognized as a robust technique against parametric variations of IM. This control strategy has the advantage of being simple to implement and requires only a simple flux observer. This explains the use of FLT and SMC in this work to control an IM while taking into account the magnetic saturation and heating of the IM. Simulation results, conducted in a MATLAB/Simulink environment, demonstrate the relevance and efficiency of the proposed control schemes.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83883230","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}
A. Moustafa, Mohd Shafry Mohd Rahim, B. Bouallegue, M. Khattab, Amr Mohmed Soliman, Gamal Tharwat, Abdelmoty M. Ahmed
Deaf and dumb people struggle with communicating on a day-to-day basis. Current advancements in artificial intelligence (AI) have allowed this communication barrier to be removed. A letter recognition system for Arabic sign language (ArSL) has been developed as a result of this effort. The deep convolutional neural network (CNN) structure is used by the ArSL recognition system in order to process depth data and to improve the ability for hearing-impaired to communicate with others. In the proposed model, letters of the hand-sign alphabet and the Arabic alphabet would be recognized and identified automatically based on user input. The proposed model should be able to identify ArSL with a rate of accuracy of 97.1%. In order to test our approach, we carried out a comparative study and discovered that it is able to differentiate between static indications with a higher level of accuracy than prior studies had achieved using the same dataset.
{"title":"Integrated Mediapipe with a CNN Model for Arabic Sign Language Recognition","authors":"A. Moustafa, Mohd Shafry Mohd Rahim, B. Bouallegue, M. Khattab, Amr Mohmed Soliman, Gamal Tharwat, Abdelmoty M. Ahmed","doi":"10.1155/2023/8870750","DOIUrl":"https://doi.org/10.1155/2023/8870750","url":null,"abstract":"Deaf and dumb people struggle with communicating on a day-to-day basis. Current advancements in artificial intelligence (AI) have allowed this communication barrier to be removed. A letter recognition system for Arabic sign language (ArSL) has been developed as a result of this effort. The deep convolutional neural network (CNN) structure is used by the ArSL recognition system in order to process depth data and to improve the ability for hearing-impaired to communicate with others. In the proposed model, letters of the hand-sign alphabet and the Arabic alphabet would be recognized and identified automatically based on user input. The proposed model should be able to identify ArSL with a rate of accuracy of 97.1%. In order to test our approach, we carried out a comparative study and discovered that it is able to differentiate between static indications with a higher level of accuracy than prior studies had achieved using the same dataset.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72738201","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}
In the process of charging and using electric vehicles, lithium battery may cause hazards such as fire or even explosion due to thermal runaway. Therefore, a target detection model based on the improved YOLOv5 (You Only Look Once) algorithm is proposed for the features generated by lithium battery combustion, using the K-means algorithm to cluster and analyse the target locations within the dataset, while adjusting the residual structure and the number of convolutional kernels in the network and embedding a convolutional block attention module (CBAM) to improve the detection accuracy without affecting the detection speed. The experimental results show that the improved algorithm has an overall mAP evaluation index of 94.09%, an average F1 value of 90.00%, and a real-time detection FPS (frames per second) of 42.09, which can meet certain real-time monitoring requirements and can be deployed in various electric vehicle charging stations and production platforms for safety detection and will provide a guarantee for the safe production and development of electric vehicles in the future.
电动汽车在充电和使用过程中，锂电池可能会因热失控而引发火灾甚至爆炸等危险。因此，针对锂电池燃烧产生的特征，提出了一种基于改进的YOLOv5 (You Only Look Once)算法的目标检测模型，使用K-means算法对数据集中的目标位置进行聚类和分析，同时调整网络中的残差结构和卷积核数，并嵌入卷积块注意模块(CBAM)，在不影响检测速度的前提下提高检测精度。实验结果表明，改进后的算法mAP总体评价指标为94.09%，平均F1值为90.00%，实时检测FPS(帧/秒)为42.09，能够满足一定的实时监控要求，可部署在各类电动汽车充电站和生产平台进行安全检测，将为未来电动汽车的安全生产和发展提供保障。
{"title":"Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring","authors":"Yuchen Gao, Qing Yang, Shiyu Zhang, D. Gao","doi":"10.1155/2023/9215528","DOIUrl":"https://doi.org/10.1155/2023/9215528","url":null,"abstract":"In the process of charging and using electric vehicles, lithium battery may cause hazards such as fire or even explosion due to thermal runaway. Therefore, a target detection model based on the improved YOLOv5 (You Only Look Once) algorithm is proposed for the features generated by lithium battery combustion, using the K-means algorithm to cluster and analyse the target locations within the dataset, while adjusting the residual structure and the number of convolutional kernels in the network and embedding a convolutional block attention module (CBAM) to improve the detection accuracy without affecting the detection speed. The experimental results show that the improved algorithm has an overall mAP evaluation index of 94.09%, an average F1 value of 90.00%, and a real-time detection FPS (frames per second) of 42.09, which can meet certain real-time monitoring requirements and can be deployed in various electric vehicle charging stations and production platforms for safety detection and will provide a guarantee for the safe production and development of electric vehicles in the future.","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90446736","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":"Retracted: Environmental Landscape Design Based on Artificial Intelligence and Digital Space Technology","authors":"Journal of Electrical and Computer Engineering","doi":"10.1155/2023/9873094","DOIUrl":"https://doi.org/10.1155/2023/9873094","url":null,"abstract":"<jats:p />","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85630852","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":"Retracted: Vehicle Detection Algorithm Based on Embedded Video Image Processing in the Background of Information Technology","authors":"Journal of Electrical and Computer Engineering","doi":"10.1155/2023/9856928","DOIUrl":"https://doi.org/10.1155/2023/9856928","url":null,"abstract":"<jats:p />","PeriodicalId":23352,"journal":{"name":"Turkish J. Electr. Eng. Comput. Sci.","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89647907","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}
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