Pub Date : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664740
G. A. P. Jaiiitli, R. Wijesiriwardana, W. Wijayapala
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.
{"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}
Pub Date : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664684
Vidal Wyatt M. Lopez, P. Abu, M. R. Estuar
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.
{"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}
Pub Date : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664732
Yohanes Eudes Hugo Maur, Suyoto
Indonesia is an archipelagic country that offers various exciting tourist attractions, making Indonesia a beautiful country for foreign tourists and local tourists. To provide a different experience for tourists visiting Indonesia, the authors want to apply Augmented Reality (AR) and Chatbot as assistants for tourists. The concept of the system to be developed is that with AR, users can use the camera on a smartphone to get information about the details of tourist attractions and MSME products seen from magazines or posters in Labuan Bajo. The engineered system is also equipped with a chatbot. With this Chatbot, users can interact with systems related to tourist attractions and MSME products (Micro Small and Medium Enterprises) like Customer Service. The expected result is that with engineered software, tourists can be helped in exploring and exploring tourist attractions in Indonesia and can help MSMEs in promoting their products through an engineered system
{"title":"Designing Augmented Reality and Chatbot as tourist assistants: Case Study West Manggarai","authors":"Yohanes Eudes Hugo Maur, Suyoto","doi":"10.1109/ICECIE52348.2021.9664732","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664732","url":null,"abstract":"Indonesia is an archipelagic country that offers various exciting tourist attractions, making Indonesia a beautiful country for foreign tourists and local tourists. To provide a different experience for tourists visiting Indonesia, the authors want to apply Augmented Reality (AR) and Chatbot as assistants for tourists. The concept of the system to be developed is that with AR, users can use the camera on a smartphone to get information about the details of tourist attractions and MSME products seen from magazines or posters in Labuan Bajo. The engineered system is also equipped with a chatbot. With this Chatbot, users can interact with systems related to tourist attractions and MSME products (Micro Small and Medium Enterprises) like Customer Service. The expected result is that with engineered software, tourists can be helped in exploring and exploring tourist attractions in Indonesia and can help MSMEs in promoting their products through an engineered system","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":"121928706","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 : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664695
Lahiru Nawarathna, Nalith Udugampola, Yasara Yasawardhana, Thilina W. Weerasinghe, S. Thayaparan
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.
{"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}
Pub Date : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664681
W. Wickramarachchi, P. H. Panawenna, J. Majuran, V. Logeeshan, S. Kumarawadu
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.
{"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}
Fraud is one of the most extensive ethical issues in the Financial (Banking) industry. The research aims to create a robust model for predicting fraudulent transactions based on the transactions made by the consumer in the past and present, compare as well as analyse different algorithms that best suit our needs. This paper also focuses on handling the imbalance in the datasets as well as creating a Machine Learning model with high Accuracy, F1-score, AUC, Precision as well as Recall which is achieved using a fusion method in which models are selected from the tested classifiers like Logistic Regression, XGBoost, Random Forest Classifier, Fusion Model, Gaussian NB, and SGDClassifier. Only the models with values of every metric above a certain threshold are selected to churn out maximum performance from the model. The model proposed in this paper uses a probability-based weighted average function for the prediction of fraudulent transactions which yielded a 99% score over all the considered metrics.
{"title":"Detecting Fraudulent Transactions using Hybrid Fusion Techniques","authors":"Yashowardhan Shinde, Akalbir Singh Chadha, Ajitkumar Shitole","doi":"10.1109/ICECIE52348.2021.9664719","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664719","url":null,"abstract":"Fraud is one of the most extensive ethical issues in the Financial (Banking) industry. The research aims to create a robust model for predicting fraudulent transactions based on the transactions made by the consumer in the past and present, compare as well as analyse different algorithms that best suit our needs. This paper also focuses on handling the imbalance in the datasets as well as creating a Machine Learning model with high Accuracy, F1-score, AUC, Precision as well as Recall which is achieved using a fusion method in which models are selected from the tested classifiers like Logistic Regression, XGBoost, Random Forest Classifier, Fusion Model, Gaussian NB, and SGDClassifier. Only the models with values of every metric above a certain threshold are selected to churn out maximum performance from the model. The model proposed in this paper uses a probability-based weighted average function for the prediction of fraudulent transactions which yielded a 99% score over all the considered metrics.","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":"131741345","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 : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664672
Nestor Vazquez, Manou Rosenberg, Tat Kei Chau, Xinan Zhang, T. Fernando, Herbert Ho Ching Iu
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.
{"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}
Pub Date : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664687
K. Bandara, R. Wijesiriwardana
In this study, a four limbs-based exercise equipment was used to examine the effects on heart rate variability (HRV) and breathing rate variability (BRV) during lower limbs, upper limbs, and all four limbs exercise. As a result, a significant reduction of HRV and BRV was observed between the start and the end of each type of exercise. Further comparison between statistics of HRV and BRV information revealed that the minimum coefficient of variance of both HRV and BRV occurred during all limbs exercise, the minimum HRV and BRV were observed during lower limbs exercise and the highest mean of HRV and the highest mean of BRV were detected during upper limbs exercise. Therefore, it is suggested that different variations in HRV and BRV parameters can occur during different types of physical activities which can be useful to distinguish as the lower limbs, upper limbs, or all limbs activities.
{"title":"Heart Rate Variability and Breathing Rate Variability Analysis During Four Limbs Exercise","authors":"K. Bandara, R. Wijesiriwardana","doi":"10.1109/ICECIE52348.2021.9664687","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664687","url":null,"abstract":"In this study, a four limbs-based exercise equipment was used to examine the effects on heart rate variability (HRV) and breathing rate variability (BRV) during lower limbs, upper limbs, and all four limbs exercise. As a result, a significant reduction of HRV and BRV was observed between the start and the end of each type of exercise. Further comparison between statistics of HRV and BRV information revealed that the minimum coefficient of variance of both HRV and BRV occurred during all limbs exercise, the minimum HRV and BRV were observed during lower limbs exercise and the highest mean of HRV and the highest mean of BRV were detected during upper limbs exercise. Therefore, it is suggested that different variations in HRV and BRV parameters can occur during different types of physical activities which can be useful to distinguish as the lower limbs, upper limbs, or all limbs activities.","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":"115693575","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 : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664703
Swati Jayade, D. Ingole, M. D. Ingole, Aditya Tohare
In this research paper, a fuzzy based system is presented for the diagnosis of cholera disease. It provides decision support platform to the scientists, researchers, physicians and healthcare practitioners in cholera disease area. The given fuzzy expert system contains major components as; the Fuzzification, Knowledge base, Inference engine and Defuzzification module. This system is implemented based on observations of patients, medical diagnosis and the expert’s knowledge. The system is developed based on Mamdani's fuzzy inference system. It does the reasoning and inference the data from the rules designed. In this method in order to get the decision results majorly the symptoms considered are like mild, moderate and severe. To do the experimental analysis and study thirty patients of cholera disease are selected and considered. The outcomes are calculated and checked with domain knowledge experts. This system will be helpful for making the cholera diagnosis as the medical practitioners can directly input the symptoms and will get the results to take the decision.
{"title":"Cholera Disease Detection using Fuzzy Logic Technique","authors":"Swati Jayade, D. Ingole, M. D. Ingole, Aditya Tohare","doi":"10.1109/ICECIE52348.2021.9664703","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664703","url":null,"abstract":"In this research paper, a fuzzy based system is presented for the diagnosis of cholera disease. It provides decision support platform to the scientists, researchers, physicians and healthcare practitioners in cholera disease area. The given fuzzy expert system contains major components as; the Fuzzification, Knowledge base, Inference engine and Defuzzification module. This system is implemented based on observations of patients, medical diagnosis and the expert’s knowledge. The system is developed based on Mamdani's fuzzy inference system. It does the reasoning and inference the data from the rules designed. In this method in order to get the decision results majorly the symptoms considered are like mild, moderate and severe. To do the experimental analysis and study thirty patients of cholera disease are selected and considered. The outcomes are calculated and checked with domain knowledge experts. This system will be helpful for making the cholera diagnosis as the medical practitioners can directly input the symptoms and will get the results to take the decision.","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":"127364316","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 : 2021-11-27DOI: 10.1109/ICECIE52348.2021.9664688
Qi Huan, Hongyu Yi, Daolin Qu, Chunyuan Wang, Dianchun Bai
This paper, based on an improved SIFT algorithm, is applied to the vision system of a plug-in machine. The purpose is to judge whether a part of the component to be inserted is damaged or deflected. SIFT algorithm has many good characteristics, but there are a lot of redundant points and mismatched points in the matching results. In this paper, an improved SIFT algorithm is proposed, and a more simple and intuitive method is adopted to filter out a large number of redundant points and mismatched points in the SIFT feature matching results, which is applied to the special case of insert machine to identify the damage and deflection Angle of a part of the component.
{"title":"A Fault Detection Method Based on SIFT Algorithm","authors":"Qi Huan, Hongyu Yi, Daolin Qu, Chunyuan Wang, Dianchun Bai","doi":"10.1109/ICECIE52348.2021.9664688","DOIUrl":"https://doi.org/10.1109/ICECIE52348.2021.9664688","url":null,"abstract":"This paper, based on an improved SIFT algorithm, is applied to the vision system of a plug-in machine. The purpose is to judge whether a part of the component to be inserted is damaged or deflected. SIFT algorithm has many good characteristics, but there are a lot of redundant points and mismatched points in the matching results. In this paper, an improved SIFT algorithm is proposed, and a more simple and intuitive method is adopted to filter out a large number of redundant points and mismatched points in the SIFT feature matching results, which is applied to the special case of insert machine to identify the damage and deflection Angle of a part of the component.","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":"125919942","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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