Pub Date : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619231
A. Shanmuga Sundaram Devi, Awanish Kumar, N. Thanigivelu, L. Ramesh, K. Sundaram
Energy plays a key role in the development of country’s GDP in which energy conservation implementation will reduce the energy demand. Energy audit and management related research carried out across the globe and concluded generalized outcome. This work is to satisfy the energy conservation need by executing detailed energy audit at cable manufacturing industry in Chennai. The proposed procedure adapted to conduct the audit and suggested 5 recommendations. In this paper electrical energy auditing was conducted in an industry and the results were analyzed using ETAP simulation tools. As a result, suitable recommendations have been made to improve the energy efficiency and reduce the industry’s utility tariff bill.
{"title":"Energy Conservation Implementation at Cable Manufacturing Industry through ETAP Analysis","authors":"A. Shanmuga Sundaram Devi, Awanish Kumar, N. Thanigivelu, L. Ramesh, K. Sundaram","doi":"10.1109/ETI4.051663.2021.9619231","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619231","url":null,"abstract":"Energy plays a key role in the development of country’s GDP in which energy conservation implementation will reduce the energy demand. Energy audit and management related research carried out across the globe and concluded generalized outcome. This work is to satisfy the energy conservation need by executing detailed energy audit at cable manufacturing industry in Chennai. The proposed procedure adapted to conduct the audit and suggested 5 recommendations. In this paper electrical energy auditing was conducted in an industry and the results were analyzed using ETAP simulation tools. As a result, suitable recommendations have been made to improve the energy efficiency and reduce the industry’s utility tariff bill.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126228387","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619201
Richa Manglani, Anuja Bokhare
With the advance in the banking space, many individual’s area unit putting up for loans however the banks have its own restricted resources that it must permit to restricted people simply, therefore discovering to whom the advance is conceded which will be a more secure choice for the bank is a normal interaction. Therefore in this study, an attempt to reduce this risk issue behind selecting the protected individual to avoid wasting different bank endeavors and resources. This can be finished by extracting the info of the records of people to whom the credit was conceded antecedently and supported. These records/encounters the machine was ready to utilize the AI model which provides the foremost precise outcome. The main goal of this study to anticipate whether or not delegating the loan to a selected individual are protected or not. During this study foresee the loan knowledge by utilizing machine learning algorithms that area unit logistical regression. Loan prediction is an extremely basic life issue that every genuine bank faces a minimum of once in its period. If done effectively, it will save loads of manhours at the top of a retail bank.
{"title":"Logistic Regression Model for Loan Prediction: A Machine Learning Approach","authors":"Richa Manglani, Anuja Bokhare","doi":"10.1109/ETI4.051663.2021.9619201","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619201","url":null,"abstract":"With the advance in the banking space, many individual’s area unit putting up for loans however the banks have its own restricted resources that it must permit to restricted people simply, therefore discovering to whom the advance is conceded which will be a more secure choice for the bank is a normal interaction. Therefore in this study, an attempt to reduce this risk issue behind selecting the protected individual to avoid wasting different bank endeavors and resources. This can be finished by extracting the info of the records of people to whom the credit was conceded antecedently and supported. These records/encounters the machine was ready to utilize the AI model which provides the foremost precise outcome. The main goal of this study to anticipate whether or not delegating the loan to a selected individual are protected or not. During this study foresee the loan knowledge by utilizing machine learning algorithms that area unit logistical regression. Loan prediction is an extremely basic life issue that every genuine bank faces a minimum of once in its period. If done effectively, it will save loads of manhours at the top of a retail bank.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114748869","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619218
Pratiksha R. Shetgaonkar, Shrameet Nayak, S. Aswale, Saurabh Vernekar, Ashitosh Tilve, Dhanashri Turi
Pneumonia isa lunginfection that usually causes fever, coughing, and difficulty in breathing. Pneumonia is one of the most significant causes of death and morbidity in children under five years of age worldwide. Pneumonia is a very dangerous condition that is very difficult to diagnose at an early stage. This paper focuses on the development of a deep learning model using Convolution Neural Network for detecting Pneumonia disease from X-ray images of the Chest and improve it for efficiency and accuracy by making various hyperparameter optimizations and modifications to achieve better detection and performance accuracy. The model also uses some of the existing models by training them on the required data sets. The research focuses to develop a system that can that detect pneumonia from the chest X-ray images with an improved accuracy which can help to provide an early assistance service at places where the experts are not available easily. Also, this system can be used in the future for the detection of COVID-19 disease.
{"title":"Detection of Pneumonia Using Deep Learning","authors":"Pratiksha R. Shetgaonkar, Shrameet Nayak, S. Aswale, Saurabh Vernekar, Ashitosh Tilve, Dhanashri Turi","doi":"10.1109/ETI4.051663.2021.9619218","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619218","url":null,"abstract":"Pneumonia isa lunginfection that usually causes fever, coughing, and difficulty in breathing. Pneumonia is one of the most significant causes of death and morbidity in children under five years of age worldwide. Pneumonia is a very dangerous condition that is very difficult to diagnose at an early stage. This paper focuses on the development of a deep learning model using Convolution Neural Network for detecting Pneumonia disease from X-ray images of the Chest and improve it for efficiency and accuracy by making various hyperparameter optimizations and modifications to achieve better detection and performance accuracy. The model also uses some of the existing models by training them on the required data sets. The research focuses to develop a system that can that detect pneumonia from the chest X-ray images with an improved accuracy which can help to provide an early assistance service at places where the experts are not available easily. Also, this system can be used in the future for the detection of COVID-19 disease.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124192749","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619217
Ha Thi The Nguyen, Ling-Hsiu Chen, Vani Suthamathi Saravanarajan, H. Pham
Students study in an online environment, the problems relate to reaction based on evaluation of student’s performance and students’ skills to understand the student behavior. In this paper, for students in an online environment, techniques for connecting the students’ skills and the online reactions about behavior via their evaluation are considered. An example about students from a Brazilian University of an introductory class of Algorithms for explorative data analysis is applied, an instrument for XGBoost analysis and RandomForestClassifier. A base for evaluation of student achievement is the analysis of behavior. This idea is based on studies that discussed the use of social features in the actual classroom of the project.
{"title":"Using XG Boost and Random Forest Classifier Algorithms to Predict Student Behavior","authors":"Ha Thi The Nguyen, Ling-Hsiu Chen, Vani Suthamathi Saravanarajan, H. Pham","doi":"10.1109/ETI4.051663.2021.9619217","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619217","url":null,"abstract":"Students study in an online environment, the problems relate to reaction based on evaluation of student’s performance and students’ skills to understand the student behavior. In this paper, for students in an online environment, techniques for connecting the students’ skills and the online reactions about behavior via their evaluation are considered. An example about students from a Brazilian University of an introductory class of Algorithms for explorative data analysis is applied, an instrument for XGBoost analysis and RandomForestClassifier. A base for evaluation of student achievement is the analysis of behavior. This idea is based on studies that discussed the use of social features in the actual classroom of the project.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"1081 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122896492","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619220
B. K. Patel, J. Kanungo
Residue Number System is a carry-free system used in various applications of high speed arithmetic component like digital signal processing, image processing and cryptography. The main work of RNS is to reduce the complexity and to perform operations on overflow detection, sign detection and magnitude comparison. In this paper, we propose the parallel architecture based on parallel prefix tree is helpful for computation at higher speed. This work is carried out to get a regular binary multiplication technique, basically consists of modulo reduction operation. Conversion has been done between binary and diminished-1 representation for a large value of inputs. Proposed work with parallel prefix tree adder improves the speed of multiplication. This modified parallel prefix adder consumes less area as compared to Kogge Stone adder. The proposed work is consisting of a carry-computation unit depends on the carry-generate and carry-propagate terms. In this paper, the area of carry computation unit of modulo adder has been reduced. Then, the proposed adder is used to design the modulo multiplier for less area and higher speed.
{"title":"Efficient Tree Multiplier Design by using Modulo 2n + 1 Adder","authors":"B. K. Patel, J. Kanungo","doi":"10.1109/ETI4.051663.2021.9619220","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619220","url":null,"abstract":"Residue Number System is a carry-free system used in various applications of high speed arithmetic component like digital signal processing, image processing and cryptography. The main work of RNS is to reduce the complexity and to perform operations on overflow detection, sign detection and magnitude comparison. In this paper, we propose the parallel architecture based on parallel prefix tree is helpful for computation at higher speed. This work is carried out to get a regular binary multiplication technique, basically consists of modulo reduction operation. Conversion has been done between binary and diminished-1 representation for a large value of inputs. Proposed work with parallel prefix tree adder improves the speed of multiplication. This modified parallel prefix adder consumes less area as compared to Kogge Stone adder. The proposed work is consisting of a carry-computation unit depends on the carry-generate and carry-propagate terms. In this paper, the area of carry computation unit of modulo adder has been reduced. Then, the proposed adder is used to design the modulo multiplier for less area and higher speed.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129808394","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619202
Swapnil Sinha, S. Anand, K. K
Block chain is a time-stamped series of records of data that cannot be altered and is managed by a cluster of systems not owned by a single person, company, or entity. A chaincode, also known as a smart contract, is a digital code that controls the transfer of cryptocurrencies or assets directly, among the parties under specific conditions. A major drawback with regards to Blockchain as a technology is the fact that its transaction speed is less compared to the centralized system.In the future, for Blockchain technology to be accepted in industries, there is a need for improvement with regards to the speed.To counter this drawback, we have come up with this paper in which an approach to increase the speed of transactions by changing the current Hashing Algorithm (SHA3) in the Hyperledger Fabric to Blake3 has been done.
{"title":"Improving Smart Contract Transaction Performance in Hyperledger Fabric","authors":"Swapnil Sinha, S. Anand, K. K","doi":"10.1109/ETI4.051663.2021.9619202","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619202","url":null,"abstract":"Block chain is a time-stamped series of records of data that cannot be altered and is managed by a cluster of systems not owned by a single person, company, or entity. A chaincode, also known as a smart contract, is a digital code that controls the transfer of cryptocurrencies or assets directly, among the parties under specific conditions. A major drawback with regards to Blockchain as a technology is the fact that its transaction speed is less compared to the centralized system.In the future, for Blockchain technology to be accepted in industries, there is a need for improvement with regards to the speed.To counter this drawback, we have come up with this paper in which an approach to increase the speed of transactions by changing the current Hashing Algorithm (SHA3) in the Hyperledger Fabric to Blake3 has been done.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129978447","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619290
Jayanthi E, V. R
Nowadays, asymmetric multicore architectures become everywhere due to its energy efficiency, QoS, and high performance. Though workload characterization on these architectures become a challenging task due to its heterogeneous pipeline structure and execution process that affects the overall performance of the system. To resolve this issue, BAT_LSTM deep learning predictor has been designed and developed to predict appropriate resource for each workload at runtime. Deep learning algorithms are adopted in several applications such as computer vision, smart vehicles, and medical environment in order to classify and predict the unknown. In this work, BAT_LSTM neural network predictor has been designed and compared with random forest algorithms, decision tree, naive bayes and support vector machine for workload characterization. Cost functions of these algorithms are designed and developed in order to detect the optimal processor for each workload execution at runtime. Core mark workloads are initially executed on quad core multicore hardware to analyze the workload characteristics in terms of memory consumption, I/O, CPU usage, instructions type, cache miss ratios and so on. These characteristics are feed forwarded into machine a learning algorithm that identifies the best processor. Performance of proposed algorithms is evaluated using testing workloads in terms of processor prediction accuracy, execution time metrics. Average of 10% in energy consumption reduction and 96.8% in accuracy is achieved through proposed predictors.
{"title":"Application Workload Characterization using BAT_LSTM Learning algorithm for Asymmetric Architectures","authors":"Jayanthi E, V. R","doi":"10.1109/ETI4.051663.2021.9619290","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619290","url":null,"abstract":"Nowadays, asymmetric multicore architectures become everywhere due to its energy efficiency, QoS, and high performance. Though workload characterization on these architectures become a challenging task due to its heterogeneous pipeline structure and execution process that affects the overall performance of the system. To resolve this issue, BAT_LSTM deep learning predictor has been designed and developed to predict appropriate resource for each workload at runtime. Deep learning algorithms are adopted in several applications such as computer vision, smart vehicles, and medical environment in order to classify and predict the unknown. In this work, BAT_LSTM neural network predictor has been designed and compared with random forest algorithms, decision tree, naive bayes and support vector machine for workload characterization. Cost functions of these algorithms are designed and developed in order to detect the optimal processor for each workload execution at runtime. Core mark workloads are initially executed on quad core multicore hardware to analyze the workload characteristics in terms of memory consumption, I/O, CPU usage, instructions type, cache miss ratios and so on. These characteristics are feed forwarded into machine a learning algorithm that identifies the best processor. Performance of proposed algorithms is evaluated using testing workloads in terms of processor prediction accuracy, execution time metrics. Average of 10% in energy consumption reduction and 96.8% in accuracy is achieved through proposed predictors.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116110138","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619312
Subhra Palit, Shafayet Nur, Zulikha Khatun, Maqsudur Rahman, M. T. Ahmed
The Coronavirus Disease (COVID-19) pandemic has interrupted the education system throughout the world. Bangladesh is no unlike; all educational institutions are shut down across the country. The online teaching method is quite new especially for the developing countries like Bangladesh. Therefore, the main aim of this work is to mine student’s opinions about online class during this COVID-19 pandemic. To achieve this aim, this paper uses a questionnaire survey through the google form to collect Bangladeshi student’s opinion on online class, build a corpus of 5005 data containing both Bangla and Romanized Bangla text. After data pre-processing and extracting the features, machine learning classifiers were deployed. Then performance measurement was done in terms of accuracy, precision, recall and F1 score. In the final evaluation, we achieved highest of 80% accuracy with SVM classifier, where the accuracy achieved by Logistic Regression, Random Forest and Multinomial Naïve Bayes classifier was 78%, 77% and 77% respectively. We tried to predictthe problems faced by students and suggested possible solutions about online class. The result showed that 27.9% student faced financial problem and 25.8% student faced unstable internet problem. 54.8% user suggested stable internet facility in low cost or free and 23.1% suggested financial assistance for online class as the possible solution of aforementioned problems.
{"title":"Analysis of Online Education System of Bangladesh during COVID-19 Pandemic Based on NLP and Machine Learning: Problem and Prospect","authors":"Subhra Palit, Shafayet Nur, Zulikha Khatun, Maqsudur Rahman, M. T. Ahmed","doi":"10.1109/ETI4.051663.2021.9619312","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619312","url":null,"abstract":"The Coronavirus Disease (COVID-19) pandemic has interrupted the education system throughout the world. Bangladesh is no unlike; all educational institutions are shut down across the country. The online teaching method is quite new especially for the developing countries like Bangladesh. Therefore, the main aim of this work is to mine student’s opinions about online class during this COVID-19 pandemic. To achieve this aim, this paper uses a questionnaire survey through the google form to collect Bangladeshi student’s opinion on online class, build a corpus of 5005 data containing both Bangla and Romanized Bangla text. After data pre-processing and extracting the features, machine learning classifiers were deployed. Then performance measurement was done in terms of accuracy, precision, recall and F1 score. In the final evaluation, we achieved highest of 80% accuracy with SVM classifier, where the accuracy achieved by Logistic Regression, Random Forest and Multinomial Naïve Bayes classifier was 78%, 77% and 77% respectively. We tried to predictthe problems faced by students and suggested possible solutions about online class. The result showed that 27.9% student faced financial problem and 25.8% student faced unstable internet problem. 54.8% user suggested stable internet facility in low cost or free and 23.1% suggested financial assistance for online class as the possible solution of aforementioned problems.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"78 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116308655","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 : 2021-05-19DOI: 10.1109/ETI4.051663.2021.9619280
B. Jyothi, P. Bhavana, B. Rao, M. K. Reddy
Currently the usage of power demand is increased by utility grids. In order to fulfilled the energy demand, tackle ecological concerns producing from the conservative energy sources, then researchers are moving towards the non- conservative (or) renewable energy resources integrated with DC micro grids to deliver power supply with improving system efficiency and especially cost reduction in distribution systems in a superior way. In non-conservative sources, solar energy is the cleanest and abundant green energy source available in nature. So, implementation of solar PV based DC micro grids technology is inexpensive, flexible, and energy-efficient to the end-users. But solar PV panels generate a low DC voltage. By using this low DC voltage as an input to the DC micro grids, definitely this grid does not serve any dc load properly. So, this problem can be erected with the help of DC-DC converters. The main motto of DC-DC converters is to properly produce the output voltage and ripple free output current to the dc load requirements. This paper gives the information regarding the various DC-DC converters applicable for solar PV based DC micro grids and at most, enlists the proposed MSC (Modified SEPIC Converter) is a best DC-DC converter for solar PV based DC micro grids based on the literature review discussed.
{"title":"A Review on Various DC-DC Converters for Photo Voltaic Based DC Micro Grids","authors":"B. Jyothi, P. Bhavana, B. Rao, M. K. Reddy","doi":"10.1109/ETI4.051663.2021.9619280","DOIUrl":"https://doi.org/10.1109/ETI4.051663.2021.9619280","url":null,"abstract":"Currently the usage of power demand is increased by utility grids. In order to fulfilled the energy demand, tackle ecological concerns producing from the conservative energy sources, then researchers are moving towards the non- conservative (or) renewable energy resources integrated with DC micro grids to deliver power supply with improving system efficiency and especially cost reduction in distribution systems in a superior way. In non-conservative sources, solar energy is the cleanest and abundant green energy source available in nature. So, implementation of solar PV based DC micro grids technology is inexpensive, flexible, and energy-efficient to the end-users. But solar PV panels generate a low DC voltage. By using this low DC voltage as an input to the DC micro grids, definitely this grid does not serve any dc load properly. So, this problem can be erected with the help of DC-DC converters. The main motto of DC-DC converters is to properly produce the output voltage and ripple free output current to the dc load requirements. This paper gives the information regarding the various DC-DC converters applicable for solar PV based DC micro grids and at most, enlists the proposed MSC (Modified SEPIC Converter) is a best DC-DC converter for solar PV based DC micro grids based on the literature review discussed.","PeriodicalId":129682,"journal":{"name":"2021 Emerging Trends in Industry 4.0 (ETI 4.0)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115772069","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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