2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)最新文献_第4页

A Study on Mechanical Power Transmission of the Human Body Using an All Four Limbs Based Energy Harvester 基于四肢能量采集器的人体机械动力传输研究

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664740

G. A. P. Jaiiitli, R. Wijesiriwardana, W. Wijayapala

{"title":"A Study on Mechanical Power Transmission of the Human Body Using an All Four Limbs Based Energy Harvester","authors":"G. A. P. Jaiiitli, R. Wijesiriwardana, W. Wijayapala","doi":"10.1109/ICECIE52348.2021.9664740","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664740","url":null,"abstract":"Four limbs-based energy harvester is an equipment that converts human mechanical power into electrical power using a DC generator-based electrical system. The study was conducted to identify the human mechanical power transmission of a four limbs-based energy harvester and to determine the minimum, maximum and average electrical power, a person can generate through upper and lower limbs of the body. This paper discusses the working principle of the energy harvester, the power generation of the lower limbs, the power generation of the upper limbs, the instantaneous change in power with phase difference in hand and leg crank angles and the average change in power with crank angular frequency. The variation in input power given by the four limbs (hands and legs) of a person and the variation in electrical power output with time are analyzed using simulation models, while changing the pedal cycle time and phase difference between the hand pedal crank and the leg pedal crank. The results of the study is used to determine how much power a person can generate with their upper and lower limbs separately and how to achieve maximum stable electrical power output from the all four limbs-based energy harvester.","PeriodicalId":309754,"journal":{"name":"2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127139340","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}

引用次数: 0

Real-time Face Mask Detection Using Deep Learning on Embedded Systems 基于嵌入式系统的深度学习实时人脸检测

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664684

Vidal Wyatt M. Lopez, P. Abu, M. R. Estuar

{"title":"Real-time Face Mask Detection Using Deep Learning on Embedded Systems","authors":"Vidal Wyatt M. Lopez, P. Abu, M. R. Estuar","doi":"10.1109/ICECIE52348.2021.9664684","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664684","url":null,"abstract":"Coronavirus disease (COVID-19) is an infectious disease, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that was identified in December 2019 in Wuhan, China [1], [2]. It is a pandemic that causes respiratory disorder and is transmitted through sneezing droplets of infected individuals. These droplets can fall on the objects around the effected and enter a healthy individual through contact. Major symptoms of this disease include lethargy, dry cough, followed by fever [3]. The number of cases is surging dramatically, raping developed and undeveloped countries together [3]. According to the World Health Organization (WHO) COVID-19 weekly epidemiological Update for 29th of December there are 79 million infected cases and 1.7 million deaths globally. This pandemic not only affects our health but also affects our livelihood. In the absence of specific treatment or a vaccine, non-pharmaceutical interventions (NPI) form the backbone of the response to the COVID-19 pandemic. These NPI includes physical distancing, regular hand washing, and wearing a face mask. This study aims to help with the monitoring of these NPIs specifically wearing face masks using deep learning. This study implements face mask detection and recognition system that automatically detects and recognizes if a person is wearing a Medically approved face mask, Non-Medically approved face mask, or not wearing a mask at all. This study has determined that MobileNetV1 model has shown the best performance regarding classification (79%) and processing speed up to 3.25 fps.","PeriodicalId":309754,"journal":{"name":"2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131041142","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}

引用次数: 2

Designing Augmented Reality and Chatbot as tourist assistants: Case Study West Manggarai 增强现实和聊天机器人作为旅游助理的设计:以西曼加莱为例

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664732

Yohanes Eudes Hugo Maur, Suyoto

引用次数: 1

Low-Cost Automatic Test Equipment for Digital ICs Using DE0-Nano - Altera Cyclone IV FPGA 基于DE0-Nano - Altera Cyclone IV FPGA的数字集成电路低成本自动测试设备

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664695

Lahiru Nawarathna, Nalith Udugampola, Yasara Yasawardhana, Thilina W. Weerasinghe, S. Thayaparan

{"title":"Low-Cost Automatic Test Equipment for Digital ICs Using DE0-Nano - Altera Cyclone IV FPGA","authors":"Lahiru Nawarathna, Nalith Udugampola, Yasara Yasawardhana, Thilina W. Weerasinghe, S. Thayaparan","doi":"10.1109/ICECIE52348.2021.9664695","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664695","url":null,"abstract":"With the advancement of technology, the digital Integrated Circuit (IC) design process has become more complex and denser. Hence, the IC testing procedure requires high-end test equipment to validate the accuracy and reliability of the manufactured components. Testers with such capabilities usually cost millions of dollars. In this paper, the authors have presented a low-cost hardware and software solution for digital IC testing. Digital ICs which operate under the 100MHz range can be easily tested in the digital domain with the FPGA-based test environment. The presented design comprises of a scalable architecture with a set of clock synchronized Altera DE0-Nano Field Programmable Gate Arrays (FPGAs) which handles the digital testing of Device Under Test (DUT) at a low cost. The digital test patterns are generated inside a computer, which transfers them to the FPGA environment and feeds them to DUT. The resulting patterns captured by the FPGAs are sent back to the computer, where they are compared with the expected results. The design prototype made by the authors of this paper consists of 48 digital input/ output channels which can source and capture bit streams parallelly to test digital ICs up to 100MHz frequency. Furthermore, the prototyped tester consists of electrical measuring instruments that can measure voltages with a 10mV accuracy and currents with a 10µA accuracy.","PeriodicalId":309754,"journal":{"name":"2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126305509","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}

引用次数: 1

Non-Intrusive Load Monitoring for High Power Consuming Appliances using Neural Networks 基于神经网络的高功耗电器非侵入式负荷监测

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664681

W. Wickramarachchi, P. H. Panawenna, J. Majuran, V. Logeeshan, S. Kumarawadu

{"title":"Non-Intrusive Load Monitoring for High Power Consuming Appliances using Neural Networks","authors":"W. Wickramarachchi, P. H. Panawenna, J. Majuran, V. Logeeshan, S. Kumarawadu","doi":"10.1109/ICECIE52348.2021.9664681","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664681","url":null,"abstract":"The topic of Energy Conservation requires urgent attention worldwide to avoid the impending energy crisis and reduce the impact on the environment through emissions. A crucial step in energy conservation is to motivate individual consumers to reduce their consumption. Itemized energy consumption feedback on each appliance helps users to plan their consumption patterns in an optimum way. Non-intrusive load monitoring is a low-cost and low-maintenance method for identifying consumptions of individual devices from the aggregate data of the mains supply. However, high power-consuming devices with power patterns with varying states are generally difficult to identify, despite them making a huge impact on the overall consumption of a household. Research shows that machine learning techniques are a promising approach for this disaggregation process. This paper focuses on developing data preprocessing methods and neural network algorithms to accurately disaggregate four common household appliances including ones with multistate power patterns.","PeriodicalId":309754,"journal":{"name":"2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134122737","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}

引用次数: 2

Detecting Fraudulent Transactions using Hybrid Fusion Techniques 利用混合融合技术检测欺诈交易

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664719

Yashowardhan Shinde, Akalbir Singh Chadha, Ajitkumar Shitole

引用次数: 1

Optimization of Reconfigurable Islanded Microgrids using Random Forest Classifier 基于随机森林分类器的可重构孤岛微电网优化

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664672

Nestor Vazquez, Manou Rosenberg, Tat Kei Chau, Xinan Zhang, T. Fernando, Herbert Ho Ching Iu

{"title":"Optimization of Reconfigurable Islanded Microgrids using Random Forest Classifier","authors":"Nestor Vazquez, Manou Rosenberg, Tat Kei Chau, Xinan Zhang, T. Fernando, Herbert Ho Ching Iu","doi":"10.1109/ICECIE52348.2021.9664672","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664672","url":null,"abstract":"In this paper, a classifier is developed as an approach to find the optimal configuration of islanded microgrids. In islanded microgrids with high penetration of renewable sources, the power generation may be intermittent and unpredictable. Moreover, even when forecast information is available, the non-dispatchable nature of these generation units further limits the control variables needed to formulate and address an optimization problem. In this regard, reconfigurable microgrids allow controlled changes in the grid topology to redirect and redistribute the power flow, in order to optimize and/or improve the system resiliency. In these scenarios, the optimization variables are the binary status (closed/open) of the controllable switches, which makes the problem particularly suitable to be addressed by decision classification trees. In this study, the optimization objective is power loss minimization, subject to the system constraints of power flow and supply/demand balance. Initially, a decision tree classifier is introduced and tested on a simple 9bus islanded system, to identify and categorize different generation and loading level profiles of the system and learn from them the optimal configurations. After that, a random forest classifier is designed as an ensemble of decision trees with enhanced capabilities. A time-series learning component is also implemented to boost the time-related learning characteristics of the classifier, such as trend and seasonality, which are inherent to the power generation levels of renewable energy sources. The proposed random forest classifier is tested on the modified IEEE 33bus islanded microgrid test system. Simulation results show the random forest classifier, when sufficiently trained, is able to find the optimal configuration of the microgrid to any new generation and loading profile.","PeriodicalId":309754,"journal":{"name":"2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134207810","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}

引用次数: 1

Heart Rate Variability and Breathing Rate Variability Analysis During Four Limbs Exercise 四肢运动时心率变异性和呼吸变异性分析

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664687

K. Bandara, R. Wijesiriwardana

引用次数: 2

Cholera Disease Detection using Fuzzy Logic Technique 用模糊逻辑技术检测霍乱

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664703

Swati Jayade, D. Ingole, M. D. Ingole, Aditya Tohare

引用次数: 4

A Fault Detection Method Based on SIFT Algorithm 基于SIFT算法的故障检测方法

2021 3rd International Conference on Electrical, Control and Instrumentation Engineering (ICECIE) Pub Date : 2021-11-27 DOI: 10.1109/ICECIE52348.2021.9664688

Qi Huan, Hongyu Yi, Daolin Qu, Chunyuan Wang, Dianchun Bai

引用次数: 0