Pub Date : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847677
Ravi Nandan Ray, M. M. Tripathi, Chaudhary Indra Kumar
This paper presents an energy efficient voltage CMOS voltage level shifter. Voltage level shifter is used for multi-supply design applications. The main purpose of voltage level shifter is to convert the voltage level from one level to another. We verified our voltage level shifter in ASAP7 7nm Fin-Fet technology. The proposed voltage level shifter is based on differential cascade voltage switch logic, which takes an input voltage in the range of 0.25V to 0.6V and provides an output of 0.7V. Our voltage level shifter improves propagation delay and power dissipation with 48% and 43%, respectively, with recently reported Wilson current mirror voltage level shifter with Zero-Vth design. The proposed design technique comes up with significantly lower power consumption and drastically reduced propagation delay over a wide range of temperatures (-25 to 25 degree Celsius), as compared to existing technologies.
{"title":"High Performance Energy Efficient CMOS Voltage Level Shifter Design","authors":"Ravi Nandan Ray, M. M. Tripathi, Chaudhary Indra Kumar","doi":"10.1109/CONIT55038.2022.9847677","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847677","url":null,"abstract":"This paper presents an energy efficient voltage CMOS voltage level shifter. Voltage level shifter is used for multi-supply design applications. The main purpose of voltage level shifter is to convert the voltage level from one level to another. We verified our voltage level shifter in ASAP7 7nm Fin-Fet technology. The proposed voltage level shifter is based on differential cascade voltage switch logic, which takes an input voltage in the range of 0.25V to 0.6V and provides an output of 0.7V. Our voltage level shifter improves propagation delay and power dissipation with 48% and 43%, respectively, with recently reported Wilson current mirror voltage level shifter with Zero-Vth design. The proposed design technique comes up with significantly lower power consumption and drastically reduced propagation delay over a wide range of temperatures (-25 to 25 degree Celsius), as compared to existing technologies.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114959286","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848385
Darshana N. Tambe, L. Ragha
Software is the heart of over 99% of all modern-day devices which include smartphones, personal computers, internet of things (IoT) networks, etc. This software is built by a team of engineers which divide the final product into multiple smaller components and these components are integrated together to build the final software, due to which inherent interfacing vulnerabilities & bugs are injected into it. Multiple bugs are also injected into the system due to inexperience or mistakes made by software engineers & programmers. To identify these mistakes, a wide variety of bug prediction & tracing models are proposed by researchers, which assist programmers to predict & track these bugs. But these models have large variations in terms of accuracy, precision, recall, delay, computational complexity, cost of deployment and other performance metrics, due to which it is ambiguous for software designers to identify best bug tracing method(s) for their application deployments. To reduce this ambiguity, a discussion about design of different bug tracing & prediction models and their statistical comparison is done in this paper. This comparison includes evaluation of accuracy, precision, recall, computational complexity and scalability under different scenarios. Based on this comparison, in this paper experiments were performed on five publically available datasets from NASA MDP repository using different algorithms i.e. DRF, LSVM, LR, RF, and kNN. From the results it was observed that kNN algorithm outperforms average 98.8% accuracy on these five datasets and hence kNN were considered to be the most significant with its selected features. In the future, this performance can be improved via use of CNN & LSTM based models, which can utilize the base kNN layer, and estimate highly dense features for efficient classification performance.
{"title":"Analysis of Software Bug Prediction and Tracing Models from a Statistical Perspective Using Machine Learning","authors":"Darshana N. Tambe, L. Ragha","doi":"10.1109/CONIT55038.2022.9848385","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848385","url":null,"abstract":"Software is the heart of over 99% of all modern-day devices which include smartphones, personal computers, internet of things (IoT) networks, etc. This software is built by a team of engineers which divide the final product into multiple smaller components and these components are integrated together to build the final software, due to which inherent interfacing vulnerabilities & bugs are injected into it. Multiple bugs are also injected into the system due to inexperience or mistakes made by software engineers & programmers. To identify these mistakes, a wide variety of bug prediction & tracing models are proposed by researchers, which assist programmers to predict & track these bugs. But these models have large variations in terms of accuracy, precision, recall, delay, computational complexity, cost of deployment and other performance metrics, due to which it is ambiguous for software designers to identify best bug tracing method(s) for their application deployments. To reduce this ambiguity, a discussion about design of different bug tracing & prediction models and their statistical comparison is done in this paper. This comparison includes evaluation of accuracy, precision, recall, computational complexity and scalability under different scenarios. Based on this comparison, in this paper experiments were performed on five publically available datasets from NASA MDP repository using different algorithms i.e. DRF, LSVM, LR, RF, and kNN. From the results it was observed that kNN algorithm outperforms average 98.8% accuracy on these five datasets and hence kNN were considered to be the most significant with its selected features. In the future, this performance can be improved via use of CNN & LSTM based models, which can utilize the base kNN layer, and estimate highly dense features for efficient classification performance.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114079192","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847701
Akash Trivedi, P. S. Chatterjee
It is extremely difficult to care for animals' health in remote areas, particularly in India. It is too difficult to care for cattle not only in remote areas, but also on large farms with a large number of them. As a result, this paper's primary goal is to develop a health monitoring system capable of routinely monitoring dairy cow health. The monitoring system's goal is to detect various diseases based on behavioural changes and symptoms. We installed different sensors on the cow's body as well as in various locations around the farm to record the dairy cows' behavioural changes. Those sensory readings are sent to the cloud. CARE, our proposed algorithm, will classify possible diseases based on recorded cow behaviour. The proposed algorithm detects cow diseases with high accuracy. This framework was created as part of the smart health monitoring system.
{"title":"CARE: IoT enabled Cow Health Monitoring System","authors":"Akash Trivedi, P. S. Chatterjee","doi":"10.1109/CONIT55038.2022.9847701","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847701","url":null,"abstract":"It is extremely difficult to care for animals' health in remote areas, particularly in India. It is too difficult to care for cattle not only in remote areas, but also on large farms with a large number of them. As a result, this paper's primary goal is to develop a health monitoring system capable of routinely monitoring dairy cow health. The monitoring system's goal is to detect various diseases based on behavioural changes and symptoms. We installed different sensors on the cow's body as well as in various locations around the farm to record the dairy cows' behavioural changes. Those sensory readings are sent to the cloud. CARE, our proposed algorithm, will classify possible diseases based on recorded cow behaviour. The proposed algorithm detects cow diseases with high accuracy. This framework was created as part of the smart health monitoring system.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130102833","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847720
Karan Bhat, Vaibhavi Ghumare, Siddhesh Khadake, H. Gadade
Lexical simplification means the process of providing alternatives to the complex words in the sentence with texts that are much more simpler to understand, while also preserving the context and grammar of the original text to make the whole sentence more easier to understand. All of the recent work involving lexical simplification relies on unsupervised tasks to learn simpler alternatives of complex words. But the drawback of most of these researches has been the fact that they provide simpler words without taking the context of the complex word in the sentence in account. In this paper, we are proposing a lexical simplifier which is based on contextual learnings from the sentence. We have applied the pre-trained representation model, BERT. It is a very powerful tool which can make use of the wider context of the sentence in both forward and backward direction. We have also taken the word frequency indicator from the Subtlex list, to produce results that will be more correct both semantically and grammatically. We have also added a web extension for the simplification of the text on the webpage, which takes the input from the user, processes the text on the server end, and gives the result in return after computation is over.
{"title":"Web Extension for Lexical Simplification of Text","authors":"Karan Bhat, Vaibhavi Ghumare, Siddhesh Khadake, H. Gadade","doi":"10.1109/CONIT55038.2022.9847720","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847720","url":null,"abstract":"Lexical simplification means the process of providing alternatives to the complex words in the sentence with texts that are much more simpler to understand, while also preserving the context and grammar of the original text to make the whole sentence more easier to understand. All of the recent work involving lexical simplification relies on unsupervised tasks to learn simpler alternatives of complex words. But the drawback of most of these researches has been the fact that they provide simpler words without taking the context of the complex word in the sentence in account. In this paper, we are proposing a lexical simplifier which is based on contextual learnings from the sentence. We have applied the pre-trained representation model, BERT. It is a very powerful tool which can make use of the wider context of the sentence in both forward and backward direction. We have also taken the word frequency indicator from the Subtlex list, to produce results that will be more correct both semantically and grammatically. We have also added a web extension for the simplification of the text on the webpage, which takes the input from the user, processes the text on the server end, and gives the result in return after computation is over.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124524666","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847769
Sravankumar Jogunuri, F. T. Josh
Renewable energy integration to the conventional power grid is a challenge and requires an accurate forecasting of power output from the renewable energy sources for ensuring the reliability and grid stability. Many forecasting techniques for different time horizons were developed using different machine learning techniques. In the recent past mostly forecasting techniques based on artificial neural networks were developed. But, looking at the environmental parameters like insolation, temperature, sky clearness index and cloud cover etc., and its variable behavior makes the forecasting more complex. To address., complex and non-linearity issues in many applications, deep neural networks were proved effective and hence an attempt made in this paper forecasting power from solar photovoltaic plant for very short-term durations through deep neural networks model and compared the same with ANN model with only one hidden layer and found significant improved accuracy in deep neural networks.
{"title":"Deep Neural Network based Forecasting of Short-Term Solar Photovoltaic Power output","authors":"Sravankumar Jogunuri, F. T. Josh","doi":"10.1109/CONIT55038.2022.9847769","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847769","url":null,"abstract":"Renewable energy integration to the conventional power grid is a challenge and requires an accurate forecasting of power output from the renewable energy sources for ensuring the reliability and grid stability. Many forecasting techniques for different time horizons were developed using different machine learning techniques. In the recent past mostly forecasting techniques based on artificial neural networks were developed. But, looking at the environmental parameters like insolation, temperature, sky clearness index and cloud cover etc., and its variable behavior makes the forecasting more complex. To address., complex and non-linearity issues in many applications, deep neural networks were proved effective and hence an attempt made in this paper forecasting power from solar photovoltaic plant for very short-term durations through deep neural networks model and compared the same with ANN model with only one hidden layer and found significant improved accuracy in deep neural networks.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"02 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129894543","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848104
S. Anand, Neha Manjunath
With the help of IoT (Internet of Things) devices, the world is becoming more connected. To accomplish this, a vast amount of data must be safely stored and accessed, yet IoT devices have limited memory and computing time. As a result, a huge storage room with secure space for storage is required. SDN (Software-Defined Networking) is a revolutionary network technology that incorporates a new paradigm of unsecured apps and Internet-of- Things (IoT) services. Enemies hoping to upset the activity of an IoT framework can use the malevolent bundle change assault (MP A), a basic however powerful assault that has recently been found in loT in light of remote sensor organizations. We offer a strategy for securing and dependably conveying information within the sight of dynamic aggressors to oppose MP As that takes advantage of SDN's programmability and flexibility. Our method ensures that loT devices are aware of any changes. The suggested solution's effectiveness and performance were assessed in a series of extensive tests using a prototype implementation. The findings show that even if malicious forwarding devices only modify a small percentage of the data, they may be reliably and promptly identified and circumvented. We examined the exhibition of our proposed framework utilizing OMNeT++ to recreate our whole situation and affirmed that the framework is secure and dependable in loT applications.
{"title":"A Reliable Software Defifined Networking based Framework for IoT Devices","authors":"S. Anand, Neha Manjunath","doi":"10.1109/CONIT55038.2022.9848104","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848104","url":null,"abstract":"With the help of IoT (Internet of Things) devices, the world is becoming more connected. To accomplish this, a vast amount of data must be safely stored and accessed, yet IoT devices have limited memory and computing time. As a result, a huge storage room with secure space for storage is required. SDN (Software-Defined Networking) is a revolutionary network technology that incorporates a new paradigm of unsecured apps and Internet-of- Things (IoT) services. Enemies hoping to upset the activity of an IoT framework can use the malevolent bundle change assault (MP A), a basic however powerful assault that has recently been found in loT in light of remote sensor organizations. We offer a strategy for securing and dependably conveying information within the sight of dynamic aggressors to oppose MP As that takes advantage of SDN's programmability and flexibility. Our method ensures that loT devices are aware of any changes. The suggested solution's effectiveness and performance were assessed in a series of extensive tests using a prototype implementation. The findings show that even if malicious forwarding devices only modify a small percentage of the data, they may be reliably and promptly identified and circumvented. We examined the exhibition of our proposed framework utilizing OMNeT++ to recreate our whole situation and affirmed that the framework is secure and dependable in loT applications.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"17 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126965318","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847813
M. Chinnaiah, Sanjay Dubey, N. Janardhan, V. Pathi, Nandan K, Anusha M
Agni is a vital component of the body's physiological function. Individuals' physical constitutions depended on summer, fall, winter, spring seasons, age and other factors all influence this. The Prakriti, which is concerned with physical and psychological development, determines the uniqueness of everyone. The Prakriti has an immediate effect on Vata, Pitta and Kapha. This paper proposes the pitta imbalance evaluation using machine learning algorithm. The proposed method provides novelty in analyzing pitta dosha with real time pitta datasets and machine learning algorithms. Vata-pitta prakriti impacts with lifestyle changes which have been evaluated in this proposed method. The Support Vector Machine (SVM) used for evaluation of pitta dosha. Authors taken 152 healthy persons for analyzing their tri-dosha, age group of 18–22 years, 72.4% boys and 27.6% girls.
{"title":"Analysis of Pitta Imbalance in young Indian adult using Machine Learning Algorithm","authors":"M. Chinnaiah, Sanjay Dubey, N. Janardhan, V. Pathi, Nandan K, Anusha M","doi":"10.1109/CONIT55038.2022.9847813","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847813","url":null,"abstract":"Agni is a vital component of the body's physiological function. Individuals' physical constitutions depended on summer, fall, winter, spring seasons, age and other factors all influence this. The Prakriti, which is concerned with physical and psychological development, determines the uniqueness of everyone. The Prakriti has an immediate effect on Vata, Pitta and Kapha. This paper proposes the pitta imbalance evaluation using machine learning algorithm. The proposed method provides novelty in analyzing pitta dosha with real time pitta datasets and machine learning algorithms. Vata-pitta prakriti impacts with lifestyle changes which have been evaluated in this proposed method. The Support Vector Machine (SVM) used for evaluation of pitta dosha. Authors taken 152 healthy persons for analyzing their tri-dosha, age group of 18–22 years, 72.4% boys and 27.6% girls.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123773374","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847972
Manaswita Datta, Bhawana Rudra
The process of selecting football team players will determines a team's performance. An effective team is made up of a successful group of individual talented players. In general, a football team player selection is a decision made by the club based on the best available information. Club managers and scouts travel to different countries to watch matches and hire the best talent that can help their club to perform better. But for the lower leagues, it becomes difficult to hire the same talents because of strict budget. Here we devise a method so that we can leverage the undervalued players to get selected by the clubs. Clearly the benefit will be in two fold. First, the smaller clubs can get better players at an affordable cost. Second, the bigger clubs can get same performance players at a lower price helping them in cost cutting. We employ novelty detection methods to find out the undervalued players from our data and investigate our method by using five machine learning models. For performance evaluation, the five machine learning models used are support vector machine, Random Forest, Decision Tree, Linear Regression and XGBoost. Here XGboost performed best both for 10 fold cross-validation and external testing with a RMSE of 0.0122 and 0.0107 respectively.
{"title":"An Intelligent Decision Support System for Bid Prediction of Undervalued Football Players","authors":"Manaswita Datta, Bhawana Rudra","doi":"10.1109/CONIT55038.2022.9847972","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847972","url":null,"abstract":"The process of selecting football team players will determines a team's performance. An effective team is made up of a successful group of individual talented players. In general, a football team player selection is a decision made by the club based on the best available information. Club managers and scouts travel to different countries to watch matches and hire the best talent that can help their club to perform better. But for the lower leagues, it becomes difficult to hire the same talents because of strict budget. Here we devise a method so that we can leverage the undervalued players to get selected by the clubs. Clearly the benefit will be in two fold. First, the smaller clubs can get better players at an affordable cost. Second, the bigger clubs can get same performance players at a lower price helping them in cost cutting. We employ novelty detection methods to find out the undervalued players from our data and investigate our method by using five machine learning models. For performance evaluation, the five machine learning models used are support vector machine, Random Forest, Decision Tree, Linear Regression and XGBoost. Here XGboost performed best both for 10 fold cross-validation and external testing with a RMSE of 0.0122 and 0.0107 respectively.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"10 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120922034","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9848266
S. Sanjay, S. Soorya, R. Vengatesh, K. C. S. H. Priya
COVID-19 has affected the livelihood of millions around the world. Pass-infection of the virus between the personnel is a large threat factor. During this pandemic, it's mandatory to wear a mask to prevent the spread of the COVID19. Biometrics and face detection are commonly used to track individual employees' attendance but face recognition methods are ineffective because wearing mask obscures a portion of the face. This biometric can be a medium for the transmission of viruses. The proposed system implements COVID preventive measures such as mask detection and monitors body temperature. In addition, the proposed system checks for authorized persons using RFID technology and employs fingerprint verification application via individual mobile phones for attendance purposes. The system predominantly inspects presence of face masks, then keeps track of body temperature and ultimately controls the automatic door associated with it using RFID technology and android app based fingerprint recognition to allow access to people with authorization.
{"title":"Security Access Control System Enhanced with Face Mask Detection and Temperature Monitoring for Pandemic Trauma","authors":"S. Sanjay, S. Soorya, R. Vengatesh, K. C. S. H. Priya","doi":"10.1109/CONIT55038.2022.9848266","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9848266","url":null,"abstract":"COVID-19 has affected the livelihood of millions around the world. Pass-infection of the virus between the personnel is a large threat factor. During this pandemic, it's mandatory to wear a mask to prevent the spread of the COVID19. Biometrics and face detection are commonly used to track individual employees' attendance but face recognition methods are ineffective because wearing mask obscures a portion of the face. This biometric can be a medium for the transmission of viruses. The proposed system implements COVID preventive measures such as mask detection and monitors body temperature. In addition, the proposed system checks for authorized persons using RFID technology and employs fingerprint verification application via individual mobile phones for attendance purposes. The system predominantly inspects presence of face masks, then keeps track of body temperature and ultimately controls the automatic door associated with it using RFID technology and android app based fingerprint recognition to allow access to people with authorization.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121041588","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 : 2022-06-24DOI: 10.1109/CONIT55038.2022.9847920
Padam Dhar Dwivedi, Ariiit Baral, S. Dutta
The Suspension insulator is an indispensable component in a power system network. With the increasing electricity demand, it's the utility's responsibility to provide reliable power to the consumer. Thus, condition monitoring of overhead insulators is necessary because contaminants present in the environment cause insulation flashover and affect the power system operation. In the current work, an 11kV porcelain disc insulator is used, artificially contaminated. After that, Detrended Fluctuation Analysis (DFA) and Harmonic Ratio method are applied to estimate the contamination level using surface leakage current data.
{"title":"Harmonic Ratio and Detrended Fluctuation Analysis Aided Reliable Estimation of contamination Level On Outdoor Suspension Insulators","authors":"Padam Dhar Dwivedi, Ariiit Baral, S. Dutta","doi":"10.1109/CONIT55038.2022.9847920","DOIUrl":"https://doi.org/10.1109/CONIT55038.2022.9847920","url":null,"abstract":"The Suspension insulator is an indispensable component in a power system network. With the increasing electricity demand, it's the utility's responsibility to provide reliable power to the consumer. Thus, condition monitoring of overhead insulators is necessary because contaminants present in the environment cause insulation flashover and affect the power system operation. In the current work, an 11kV porcelain disc insulator is used, artificially contaminated. After that, Detrended Fluctuation Analysis (DFA) and Harmonic Ratio method are applied to estimate the contamination level using surface leakage current data.","PeriodicalId":270445,"journal":{"name":"2022 2nd International Conference on Intelligent Technologies (CONIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122357216","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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