Pub Date : 2023-11-21DOI: 10.1080/00051144.2023.2284026
Thon-Da Nguyen
{"title":"An approach to improve the accuracy of rating prediction for recommender systems","authors":"Thon-Da Nguyen","doi":"10.1080/00051144.2023.2284026","DOIUrl":"https://doi.org/10.1080/00051144.2023.2284026","url":null,"abstract":"","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-21DOI: 10.1080/00051144.2023.2284031
G. Arun, C. N. Marimuthu
{"title":"Diabetes classification using MapReduce-based capsule network","authors":"G. Arun, C. N. Marimuthu","doi":"10.1080/00051144.2023.2284031","DOIUrl":"https://doi.org/10.1080/00051144.2023.2284031","url":null,"abstract":"","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-21DOI: 10.1080/00051144.2023.2269514
Mohamed Safiyur Rahman, V. Sumathy
{"title":"Forecasting failure-prone air pressure systems (FFAPS) in vehicles using machine learning","authors":"Mohamed Safiyur Rahman, V. Sumathy","doi":"10.1080/00051144.2023.2269514","DOIUrl":"https://doi.org/10.1080/00051144.2023.2269514","url":null,"abstract":"","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-21DOI: 10.1080/00051144.2023.2277492
Yaowen Zhang, Chunjun Chen
{"title":"Robust predictive compensation control for lateral magnetorheological semi-active suspension of high-speed trains with time delay","authors":"Yaowen Zhang, Chunjun Chen","doi":"10.1080/00051144.2023.2277492","DOIUrl":"https://doi.org/10.1080/00051144.2023.2277492","url":null,"abstract":"","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139252658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-21DOI: 10.1080/00051144.2023.2280875
V. Chamundeeswari, R. Seyezhai
{"title":"Design and Implementation of Fuzzy sliding mode control (FSMC) approach for a Modified Negative Output Luo DC-DC Converter with its comparative analysis","authors":"V. Chamundeeswari, R. Seyezhai","doi":"10.1080/00051144.2023.2280875","DOIUrl":"https://doi.org/10.1080/00051144.2023.2280875","url":null,"abstract":"","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139254176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/00051144.2023.2269515
S. V. Evangelin Sonia, R. Nedunchezhian, M. Rajalakshmi
Heart disease is a leading cause of mortality and illness worldwide. Heart disease identification and prediction may considerably improve patient outcomes. We use deep neural networks (DNNs) and heart rate variability (HRV) data to construct a deep learning strategy for diagnosing cardiovascular abnormalities in diabetic men. The non-invasive HRV test shows how the autonomic nervous system affects heart function. It show promise for diagnosing heart dysfunction. DNNs, noted for their ability to interpret complex data patterns, are useful for prediction and diagnosis. Our unique system, DNHRV (Deep Neural Network with HRV Features), integrates two networks using DNN and DCNN methods (Deep Convolutional Neural Network). Our DNN analyses clinical risk variables using powerful deep learning architecture, while the DCNN trains. We integrate HRV signals, medical pictures, and other clinical parameters with deep neural network computing power in the suggested technique (DNNs). This multimodal technique gives us a complete picture of each patient's cardiovascular health by utilising physiological and imaging-based indicators. Our DNHRV model outperformed earlier models in accuracy, precision, F1-score, and other parameters. Our prediction model was evaluated using SHAREEDB, proving its accuracy and stability. The DNHRV model exceeds state-of-the-art CVD prediction methods by a large margin, with 98.8% accuracy, according to extensive SHAREEDB dataset tests. By highlighting CVD predicting data points, the suggested technique increased interpretability and accuracy.
{"title":"A multi-modal integrated deep neural networks for the prediction of cardiovascular disease in type-2 diabetic males","authors":"S. V. Evangelin Sonia, R. Nedunchezhian, M. Rajalakshmi","doi":"10.1080/00051144.2023.2269515","DOIUrl":"https://doi.org/10.1080/00051144.2023.2269515","url":null,"abstract":"Heart disease is a leading cause of mortality and illness worldwide. Heart disease identification and prediction may considerably improve patient outcomes. We use deep neural networks (DNNs) and heart rate variability (HRV) data to construct a deep learning strategy for diagnosing cardiovascular abnormalities in diabetic men. The non-invasive HRV test shows how the autonomic nervous system affects heart function. It show promise for diagnosing heart dysfunction. DNNs, noted for their ability to interpret complex data patterns, are useful for prediction and diagnosis. Our unique system, DNHRV (Deep Neural Network with HRV Features), integrates two networks using DNN and DCNN methods (Deep Convolutional Neural Network). Our DNN analyses clinical risk variables using powerful deep learning architecture, while the DCNN trains. We integrate HRV signals, medical pictures, and other clinical parameters with deep neural network computing power in the suggested technique (DNNs). This multimodal technique gives us a complete picture of each patient's cardiovascular health by utilising physiological and imaging-based indicators. Our DNHRV model outperformed earlier models in accuracy, precision, F1-score, and other parameters. Our prediction model was evaluated using SHAREEDB, proving its accuracy and stability. The DNHRV model exceeds state-of-the-art CVD prediction methods by a large margin, with 98.8% accuracy, according to extensive SHAREEDB dataset tests. By highlighting CVD predicting data points, the suggested technique increased interpretability and accuracy.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135902592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/00051144.2023.2262016
Qixiang Wang, Fei Long, Lipo Mo, Jing Yang
In this paper, we address the almost sure stability problem of Caputo fractional-order switched linear systems with deterministic and stochastic switching signals (DS-CFLSs). Firstly, due to the non-locality and memory of fractional-order switched systems, an inequality is proposed to solve the difficulties in the discussion of stability. Then, for DS-CFLSs, a deterministic switching strategy is predesigned, and stochastic switching signals are generated by the Markov process. After that, for the globally asymptotic stability almost surely (GAS a.s.) and exponential stability almost surely (ES a.s.) of DS-CFLSs, some sufficient conditions are proposed by using the multi-Lyapunov function and probability analysis methods. Finally, some numerical examples show that our results are effective.
{"title":"Almost sure stability of Caputo fractional-order switched linear systems with deterministic and stochastic switching signals","authors":"Qixiang Wang, Fei Long, Lipo Mo, Jing Yang","doi":"10.1080/00051144.2023.2262016","DOIUrl":"https://doi.org/10.1080/00051144.2023.2262016","url":null,"abstract":"In this paper, we address the almost sure stability problem of Caputo fractional-order switched linear systems with deterministic and stochastic switching signals (DS-CFLSs). Firstly, due to the non-locality and memory of fractional-order switched systems, an inequality is proposed to solve the difficulties in the discussion of stability. Then, for DS-CFLSs, a deterministic switching strategy is predesigned, and stochastic switching signals are generated by the Markov process. After that, for the globally asymptotic stability almost surely (GAS a.s.) and exponential stability almost surely (ES a.s.) of DS-CFLSs, some sufficient conditions are proposed by using the multi-Lyapunov function and probability analysis methods. Finally, some numerical examples show that our results are effective.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/00051144.2023.2241772
J. Jayalakshmi, M. Mary Linda
WPREs (wind power ramp events) are one of the most critical factors affecting the security and protection of the electrical system. Accurate ramp event detection may help power systems better manage extreme events and reduce financial damage. In this study, We present an improved piecewise linear approximation for recognizing wind ramps in Kanyakumari district. In practise, wind power ramps can be decreased by properly managing and dispatching flexible reserve and associated services. This necessitates the use of proper ramp detection techniques as well as precise ramp forecasts. The method’s plan to break down wind power signal into increasing with increasing ramps, making ramp identification easier and ensuring that all conceivable ramps of varying lengths are identified. Using observed wind power data, the ramp detection method is used to assess the performance of an energy wind farm. The results reveal that identifying wind power ramps using the segmentation method is equivalent to optical ramp identification.
{"title":"Piecewise linear approximation for identifying wind power ramp events","authors":"J. Jayalakshmi, M. Mary Linda","doi":"10.1080/00051144.2023.2241772","DOIUrl":"https://doi.org/10.1080/00051144.2023.2241772","url":null,"abstract":"WPREs (wind power ramp events) are one of the most critical factors affecting the security and protection of the electrical system. Accurate ramp event detection may help power systems better manage extreme events and reduce financial damage. In this study, We present an improved piecewise linear approximation for recognizing wind ramps in Kanyakumari district. In practise, wind power ramps can be decreased by properly managing and dispatching flexible reserve and associated services. This necessitates the use of proper ramp detection techniques as well as precise ramp forecasts. The method’s plan to break down wind power signal into increasing with increasing ramps, making ramp identification easier and ensuring that all conceivable ramps of varying lengths are identified. Using observed wind power data, the ramp detection method is used to assess the performance of an energy wind farm. The results reveal that identifying wind power ramps using the segmentation method is equivalent to optical ramp identification.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-02DOI: 10.1080/00051144.2023.2261088
T. R. Dinesh Kumar, A. Karthikeyan
3D ICs, a novel technology, might significantly impact multicore NoCs with hundreds or thousands of processing components on a single chip. Multiple 2D chips can be stacked vertically to create multiple active processing elements at various levels. Adding active device layers to 3D ICs can enhance system performance, increase functionality, and increase packing density. New architectural and IC technology advancements hinder energy-efficient design research. Achieving a balance between chip power and performance is crucial. This paper describes the “Dynamic Low Power Management Method in 3DWiNoC” (DLPM 3DWiNoC) architecture, which enables self-organized, centrally managed service management using Smart Master Agents. The approach utilizes SMA's ODA DD module for self-organized, centrally managed service management. To improve power regulation, data flow across vertical interconnects (TSVs) is reconfigured based on a dynamic evaluation of channel link use. SMA aims to reduce congestion by increasing connection utilization through high-frequency, bi-directional vertical channels via TSVs. The suggested system is modeled in MATLAB Simulink. Compared to 3D stacking, TSV stacking of vertical interconnects with the SMA method ensures low parasitic (latency and power) and higher bandwidth with higher vertical wire densities. Experimental results show that the proposed architecture decreases area overhead by 5%-7%, network latency by 12%-15%, and NoC power consumption by 15%-20% compared to the present multi-NoC design.
{"title":"Dynamic low power management technique for decision directed inter-layer communication in three dimensional wireless network on chip","authors":"T. R. Dinesh Kumar, A. Karthikeyan","doi":"10.1080/00051144.2023.2261088","DOIUrl":"https://doi.org/10.1080/00051144.2023.2261088","url":null,"abstract":"3D ICs, a novel technology, might significantly impact multicore NoCs with hundreds or thousands of processing components on a single chip. Multiple 2D chips can be stacked vertically to create multiple active processing elements at various levels. Adding active device layers to 3D ICs can enhance system performance, increase functionality, and increase packing density. New architectural and IC technology advancements hinder energy-efficient design research. Achieving a balance between chip power and performance is crucial. This paper describes the “Dynamic Low Power Management Method in 3DWiNoC” (DLPM 3DWiNoC) architecture, which enables self-organized, centrally managed service management using Smart Master Agents. The approach utilizes SMA's ODA DD module for self-organized, centrally managed service management. To improve power regulation, data flow across vertical interconnects (TSVs) is reconfigured based on a dynamic evaluation of channel link use. SMA aims to reduce congestion by increasing connection utilization through high-frequency, bi-directional vertical channels via TSVs. The suggested system is modeled in MATLAB Simulink. Compared to 3D stacking, TSV stacking of vertical interconnects with the SMA method ensures low parasitic (latency and power) and higher bandwidth with higher vertical wire densities. Experimental results show that the proposed architecture decreases area overhead by 5%-7%, network latency by 12%-15%, and NoC power consumption by 15%-20% compared to the present multi-NoC design.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135901204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.1080/00051144.2023.2253067
V. Karpagam, N. Narmadhai
An Innovative Modular multilevel matrix converter (M3C) is proposed with reduced number of switching device owing to the improved efficiency, reduced cost and minimizes the size. Offshore Low-Frequency AC (LFAC) transmissions are economical with greater reliability for short and intermediate distance transmissions. Similar to HVDC, it increases the transmission capacity and also distance can be increased in LFAC.M3C is proposed as frequency converters for LFAC transmissions which link AC systems operating at 16.7 and 50 Hz. The double αβ0 transform control technique has been the most often used approach for decoupling control of input, output and circulating currents in such applications. The performances of this work’s proposed modular multilevel matrix converters are analysed using simulation in MATLAB/SIMULINK software.
{"title":"Seven levels highly efficient modular multilevel matrix converter (M3C) for low frequency three-phase AC-AC conversion","authors":"V. Karpagam, N. Narmadhai","doi":"10.1080/00051144.2023.2253067","DOIUrl":"https://doi.org/10.1080/00051144.2023.2253067","url":null,"abstract":"An Innovative Modular multilevel matrix converter (M3C) is proposed with reduced number of switching device owing to the improved efficiency, reduced cost and minimizes the size. Offshore Low-Frequency AC (LFAC) transmissions are economical with greater reliability for short and intermediate distance transmissions. Similar to HVDC, it increases the transmission capacity and also distance can be increased in LFAC.M3C is proposed as frequency converters for LFAC transmissions which link AC systems operating at 16.7 and 50 Hz. The double αβ0 transform control technique has been the most often used approach for decoupling control of input, output and circulating currents in such applications. The performances of this work’s proposed modular multilevel matrix converters are analysed using simulation in MATLAB/SIMULINK software.","PeriodicalId":55412,"journal":{"name":"Automatika","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136314457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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