Pub Date : 2024-03-20DOI: 10.24874/pes.si.24.02.005
S. B. Vinay Kumar, B. P. Singh, Raman Batra, Akash Kumar Bhagat
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{"title":"AN AI-POWERED SECURITY SYSTEM FOR CAN BUS ATTACKS IDENTIFICATION IN ELECTRIC AUTOMOBILES","authors":"S. B. Vinay Kumar, B. P. Singh, Raman Batra, Akash Kumar Bhagat","doi":"10.24874/pes.si.24.02.005","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.005","url":null,"abstract":",","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388420","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":"CLIMATE BONDS: THE GREEN ADVANTAGE","authors":"Reepu Reepu","doi":"10.24874/pes06.01.008","DOIUrl":"https://doi.org/10.24874/pes06.01.008","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388586","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}
Pub Date : 2024-03-20DOI: 10.24874/pes.si.24.02.001
Gunawan Budi Wijaya, Benjamin Lumantarna, Fillbert Hanselly Njoko, B. Cahyadi, Daniel Tjandra, Resmana Lim
{"title":"USE OF U-WRAP TO OVERCOME CONCRETE COVER DELAMINATION IN VARIOUS SIZES OF BEAMS USING STRUT AND TIE MODELLING","authors":"Gunawan Budi Wijaya, Benjamin Lumantarna, Fillbert Hanselly Njoko, B. Cahyadi, Daniel Tjandra, Resmana Lim","doi":"10.24874/pes.si.24.02.001","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.001","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 21","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388192","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}
Pub Date : 2024-03-20DOI: 10.24874/pes.si.24.02.016
Shweta Singh, Vivek Ranjan, Padmaja Tripathy, M. N. Nachappa
Finding a balance between the loads and generating demands in power systems is a significant challenge since power producers require important inputs such as security, reliability and quality. In this study, a unique method for balanced load frequency control (BLFC) is proposed in two-area power systems, integrating a customized World Cup Algorithm (CWCA)-Driven PID optimization. The primary objective is to enhance the stability and performance of power systems. For this purpose, the integral time-multiplied absolute error (ITAE) is minimized by evaluating the objective function using time-domain simulation. The proposed methodology aims to improve the overall response of the control system, ensuring a reliable and stable power supply. Within the scope of two-area power systems with BLFC, simulation results show the effectiveness and resilience of the CWCA-Driven PID Optimization. The outcomes of the proposed PID controller-based CWCA approach are contrasted with the existing methods.
{"title":"BALANCED LOAD FREQUENCY CONTROL: CUSTOMIZED WORLD CUP ALGORITHM - DRIVEN PID OPTIMIZATION FOR TWO AREA POWER SYSTEMS","authors":"Shweta Singh, Vivek Ranjan, Padmaja Tripathy, M. N. Nachappa","doi":"10.24874/pes.si.24.02.016","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.016","url":null,"abstract":"Finding a balance between the loads and generating demands in power systems is a significant challenge since power producers require important inputs such as security, reliability and quality. In this study, a unique method for balanced load frequency control (BLFC) is proposed in two-area power systems, integrating a customized World Cup Algorithm (CWCA)-Driven PID optimization. The primary objective is to enhance the stability and performance of power systems. For this purpose, the integral time-multiplied absolute error (ITAE) is minimized by evaluating the objective function using time-domain simulation. The proposed methodology aims to improve the overall response of the control system, ensuring a reliable and stable power supply. Within the scope of two-area power systems with BLFC, simulation results show the effectiveness and resilience of the CWCA-Driven PID Optimization. The outcomes of the proposed PID controller-based CWCA approach are contrasted with the existing methods.","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388632","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":"EXPERIMENTAL STUDY OF SHALLOW FOUNDATION SETTLEMENT UNDER DYNAMIC LOAD IN REINFORCED SANDY SOIL","authors":"Naser Abed Hasen, Jawdat K. Abbas","doi":"10.24874/pes06.01.019","DOIUrl":"https://doi.org/10.24874/pes06.01.019","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 16","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388241","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}
Pub Date : 2024-03-20DOI: 10.24874/pes.si.24.02.011
Sneha Kashyap, P. S. Raghavendra Rao, Pavan Chaudhary, Savita Yadav
The rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a machine learning (ML) approach for rolling element bearing failure identification under a variety of circumstances. This study presents, a new method called Composite coyote optimized resilient linear regression (CCO-RLR) for defect recognition and classification in rolling element bearings. Early rolling bearing failure diagnosis is a crucial and time-sensitive operation that guarantees the dependability and security of mechanical fault systems. Initially, the rolling element bearings dataset is collected and preprocessed using Min-max normalization. For extracting the feature, Fourier transform (FT) is employed. The result shows that the CCO-RLR accuracy is 97.8% when compared with those existing methods. Our suggested method offers an effective means of quantifying flaws and significantly improving classification effectiveness.
{"title":"DEFECT RECOGNITION AND CLASSIFICATION IN ROLLING ELEMENT BEARINGS USING A NOVEL MACHINE LEARNING TECHNIQUE","authors":"Sneha Kashyap, P. S. Raghavendra Rao, Pavan Chaudhary, Savita Yadav","doi":"10.24874/pes.si.24.02.011","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.011","url":null,"abstract":"The rising advancements in Industry 4.0 technologies have made more usual to acquire significant volumes of machine operating data in real time. In response to inconsistent data distribution and label scarcity in target domains, this work suggests a machine learning (ML) approach for rolling element bearing failure identification under a variety of circumstances. This study presents, a new method called Composite coyote optimized resilient linear regression (CCO-RLR) for defect recognition and classification in rolling element bearings. Early rolling bearing failure diagnosis is a crucial and time-sensitive operation that guarantees the dependability and security of mechanical fault systems. Initially, the rolling element bearings dataset is collected and preprocessed using Min-max normalization. For extracting the feature, Fourier transform (FT) is employed. The result shows that the CCO-RLR accuracy is 97.8% when compared with those existing methods. Our suggested method offers an effective means of quantifying flaws and significantly improving classification effectiveness.","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388472","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":"HYBRID APPROACH FOR SECURELY MAXIMIZING SPECTRUM UTILIZATION IN COGNITIVE RADIO NETWORKS: MATCHED FILTER AND SALP SWARM ALGORITHM-OPTIMIZED ENERGY DETECTION","authors":"Imaduddin Imaduddin, Komal Prasad Kanojia, Bharti Chourasia","doi":"10.24874/pes06.01.010","DOIUrl":"https://doi.org/10.24874/pes06.01.010","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 44","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388474","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":"A STUDY ON THE CONSUMPTION PATTERN AND VIEWER EXPERIENCE OF OTT PLATFORMS WITH SPECIAL REFERENCE TO ERNAKULAM CITY","authors":"T. J. Rejo, V. Ramadevi","doi":"10.24874/pes06.01.007","DOIUrl":"https://doi.org/10.24874/pes06.01.007","url":null,"abstract":".","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388718","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":"THE SUCCESS FACTORS FOR THE SUSTAINABLE SMART CITY DEVELOPMENT IN THAILAND","authors":"Natnicha Chotpittayanon, Jetsalid Angsukanjanakul, Vilasinee Jintalikhitdee, Nutcha Phasuk","doi":"10.24874/pes06.01.013","DOIUrl":"https://doi.org/10.24874/pes06.01.013","url":null,"abstract":"","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388799","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}
Pub Date : 2024-03-20DOI: 10.24874/pes.si.24.02.017
Swati Singh, Raman Batra Keerti, Rai S. Sujai, Raman Batra, Keerti Rai, S. Sujai
The proactive exploration and avoidance of errors or variations from quality standards during the manufacturing process is referred to as “early quality detection” in the manufacturing industry. Post-production inspection, which can be expensive and time-consuming, is used in traditional quality control systems. To overcome this, we proposed a Modified gravitational search algorithm-based decision tree (MGSA-DT) to predict the quality of manufacturing processes at an early stage. We gathered sensors data in the manufacturing industry. In order to prepare the data for principal component analysis (PCA), Z-score normalization is used. Then, the essential features are extracted from the preprocessed data. To assess the effectiveness of the suggested approach in terms of accuracy (98.4%), precision (97.6%) and recall (97.2%), respectively. Implementing early quality detection techniques in manufacturing has demonstrated encouraging outcomes in enhancing the overall quality of products and decreasing production expenses.
在生产过程中主动发现和避免错误或质量标准的偏差，在制造业中被称为 "早期质量检测"。在传统的质量控制系统中，生产后检测既昂贵又耗时。为了克服这一问题，我们提出了一种基于修正引力搜索算法的决策树（MGSA-DT）来预测制造过程的早期质量。我们收集了制造业的传感器数据。为了给主成分分析（PCA）准备数据，我们使用了 Z 值归一化。然后，从预处理数据中提取基本特征。分别从准确率（98.4%）、精确率（97.6%）和召回率（97.2%）方面评估建议方法的有效性。在制造业中实施早期质量检测技术在提高产品整体质量和降低生产成本方面取得了令人鼓舞的成果。
{"title":"PROACTIVE QUALITY EVALUATION: A NOVEL STRATEGY-ASSISTED EARLY DETECTION IN MANUFACTURING","authors":"Swati Singh, Raman Batra Keerti, Rai S. Sujai, Raman Batra, Keerti Rai, S. Sujai","doi":"10.24874/pes.si.24.02.017","DOIUrl":"https://doi.org/10.24874/pes.si.24.02.017","url":null,"abstract":"The proactive exploration and avoidance of errors or variations from quality standards during the manufacturing process is referred to as “early quality detection” in the manufacturing industry. Post-production inspection, which can be expensive and time-consuming, is used in traditional quality control systems. To overcome this, we proposed a Modified gravitational search algorithm-based decision tree (MGSA-DT) to predict the quality of manufacturing processes at an early stage. We gathered sensors data in the manufacturing industry. In order to prepare the data for principal component analysis (PCA), Z-score normalization is used. Then, the essential features are extracted from the preprocessed data. To assess the effectiveness of the suggested approach in terms of accuracy (98.4%), precision (97.6%) and recall (97.2%), respectively. Implementing early quality detection techniques in manufacturing has demonstrated encouraging outcomes in enhancing the overall quality of products and decreasing production expenses.","PeriodicalId":33556,"journal":{"name":"Proceedings on Engineering Sciences","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140388997","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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