Pub Date : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170288
M. Chaudhari, Suchindra Uday Walavalkar, J. Hajgude, Rahul Ishwar Nailwal, Keshav Parpyani
The purpose of this study is to illustrate the technologies used as well as the repercussions and problems brought about by the rising competition and expansion of the Indian educational system. We aim to create a uniform framework for universities to use in developing nations like India that will help students by using technologies like blockchain. Our study is being laid out with the goal of appreciating the use of unconventional methods where students are most responsible for increasing student engagement in learning and creating a more welcoming and competitive atmosphere.
{"title":"An Innovative Approach Towards Enhancing The Indian Higher Education Systems Using Blockchain.","authors":"M. Chaudhari, Suchindra Uday Walavalkar, J. Hajgude, Rahul Ishwar Nailwal, Keshav Parpyani","doi":"10.1109/IConSCEPT57958.2023.10170288","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170288","url":null,"abstract":"The purpose of this study is to illustrate the technologies used as well as the repercussions and problems brought about by the rising competition and expansion of the Indian educational system. We aim to create a uniform framework for universities to use in developing nations like India that will help students by using technologies like blockchain. Our study is being laid out with the goal of appreciating the use of unconventional methods where students are most responsible for increasing student engagement in learning and creating a more welcoming and competitive atmosphere.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131161194","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170200
V. Ramya, R. Marimuthu
This paper proposes a single-phase AC-DC-DC converter circuit for charging and discharging batteries and powering loads. The battery is added to the proposed system to reduce the energy consumption caused by the primary AC input voltage. The article implies an AC-DC-DC system with a single-stage, three-port, full-bridge converter. Like a conventional single-phase inverter with H-bridge topology, the ac input is single-phase and operates on two legs. Consequently, each leg serves as both an inverter and a buck-boost converter. Furthermore, the converter only employs four switches and diodes to regulate the flow of electricity between the three ports. A thorough topological analysis and simulation results validate the proposed converter system’s benefits
{"title":"Three Port Full Bridge PFC Converter for Hybrid AC/DC/DC System with Fuzzy Logic Control","authors":"V. Ramya, R. Marimuthu","doi":"10.1109/IConSCEPT57958.2023.10170200","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170200","url":null,"abstract":"This paper proposes a single-phase AC-DC-DC converter circuit for charging and discharging batteries and powering loads. The battery is added to the proposed system to reduce the energy consumption caused by the primary AC input voltage. The article implies an AC-DC-DC system with a single-stage, three-port, full-bridge converter. Like a conventional single-phase inverter with H-bridge topology, the ac input is single-phase and operates on two legs. Consequently, each leg serves as both an inverter and a buck-boost converter. Furthermore, the converter only employs four switches and diodes to regulate the flow of electricity between the three ports. A thorough topological analysis and simulation results validate the proposed converter system’s benefits","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114019790","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170191
Subha Danushika Fernando, S. Yasakethu, P.W.M.G.N. Wanasinghe, H. M. K. K. M. B. Herath
Due to their cutting-edge features and capacity to improve the whole camping experience, smart tents sometimes referred to as intelligent or high-tech tents, are becoming more and more significant in the camping and outdoor business. Modern technology included in these tents, such as built-in sensors, Wi-Fi connectivity, and automation systems, allow users to control numerous aspects of the tent from their smartphones. Due to their high-tech nature, smart tents are pricey. It is evident that tropical countries like Sri Lanka cannot effectively utilize the available smart tents for camping. Additionally, there is a need to inexpensively transform a conventional tent into a smart camping tent. In order to address these issues, this research aimed at developing a smart camping tent that can adapt to its dynamic environment. The system was developed by aiding fuzzy-P controlling mechanisms and IoT (Internet of Things) technologies. The experiment results suggested that the smart tent worked at 82.5% accuracy. The fuzzy system showed 81.5% accuracy while the P controller showed 85.0% accuracy.
{"title":"IoT-Enabled Smart Camping Tent for Dynamic Environment","authors":"Subha Danushika Fernando, S. Yasakethu, P.W.M.G.N. Wanasinghe, H. M. K. K. M. B. Herath","doi":"10.1109/IConSCEPT57958.2023.10170191","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170191","url":null,"abstract":"Due to their cutting-edge features and capacity to improve the whole camping experience, smart tents sometimes referred to as intelligent or high-tech tents, are becoming more and more significant in the camping and outdoor business. Modern technology included in these tents, such as built-in sensors, Wi-Fi connectivity, and automation systems, allow users to control numerous aspects of the tent from their smartphones. Due to their high-tech nature, smart tents are pricey. It is evident that tropical countries like Sri Lanka cannot effectively utilize the available smart tents for camping. Additionally, there is a need to inexpensively transform a conventional tent into a smart camping tent. In order to address these issues, this research aimed at developing a smart camping tent that can adapt to its dynamic environment. The system was developed by aiding fuzzy-P controlling mechanisms and IoT (Internet of Things) technologies. The experiment results suggested that the smart tent worked at 82.5% accuracy. The fuzzy system showed 81.5% accuracy while the P controller showed 85.0% accuracy.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127886794","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170123
Koteswaramma Dodda, G. Muneeswari
Early detection of cancer improves survival chances. Some cancers, such as pancreatic cancer, are hard to identify or detect earlier, and the stages progress aggressively. This review discusses the recent advancements of biomarkers for the early detection of pancreatic cancer. Genomic, protein, blood, and urine biomarkers of pancreatic cancer, as well as corresponding biosensors for diagnosis of pancreatic cancer, have been evaluated, each of these instances show that new biosensors are emerging as an incredibly prominent substitute to defined processes. In order to predict the overall survival of patients with pancreatic ductal adenocarcinoma cancer (PDAC) this review discusses the state-of-the-art machine learning (ML) techniques utilized and a panel of biomarkers for early cancer diagnosis. Recent studies emphasize the significance of machine learning algorithms like support vector machines (SVM), decision tree (DT), naive bayes like algorithms confusing and enormous volumes of data. The phases of the disease and the chance of survival do not significantly correlate. In clinical practice, ML techniques need to undergo the proper level of validation. Pathologists can better manage patients when they have knowledge of the patient’s condition, the surgical procedure to be performed, individualized therapy, the best use of available resources and medications to prescribe due to accurate predictions.
{"title":"Biomarkers for Early Detection of Pancreatic Cancer: A Review","authors":"Koteswaramma Dodda, G. Muneeswari","doi":"10.1109/IConSCEPT57958.2023.10170123","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170123","url":null,"abstract":"Early detection of cancer improves survival chances. Some cancers, such as pancreatic cancer, are hard to identify or detect earlier, and the stages progress aggressively. This review discusses the recent advancements of biomarkers for the early detection of pancreatic cancer. Genomic, protein, blood, and urine biomarkers of pancreatic cancer, as well as corresponding biosensors for diagnosis of pancreatic cancer, have been evaluated, each of these instances show that new biosensors are emerging as an incredibly prominent substitute to defined processes. In order to predict the overall survival of patients with pancreatic ductal adenocarcinoma cancer (PDAC) this review discusses the state-of-the-art machine learning (ML) techniques utilized and a panel of biomarkers for early cancer diagnosis. Recent studies emphasize the significance of machine learning algorithms like support vector machines (SVM), decision tree (DT), naive bayes like algorithms confusing and enormous volumes of data. The phases of the disease and the chance of survival do not significantly correlate. In clinical practice, ML techniques need to undergo the proper level of validation. Pathologists can better manage patients when they have knowledge of the patient’s condition, the surgical procedure to be performed, individualized therapy, the best use of available resources and medications to prescribe due to accurate predictions.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121196378","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10169904
N. Poornima, D. Abilash, M. Theodaniel
In the Stock market, the volatility of leading MNCs’(Multi-National Corporations) shares is a major matter of concern and comes under the limelight nowadays. Unlike the 1920s sudden surge and dot-com crash, the contemporary world has never seen such a biggest bull or bear particularly in the past few decades. The stock market is majorly influenced by the credibility opinion of the general public on the firm. In the 21st century, the emergence of research LLC (Limited Liability Company) which gains profit from short selling of the shares by manipulating the share of a certain firm by exposing the legality of trespassing norms has made the researchers include a current public sentiment on the firm since short selling is a matter of one day. The first and foremost impact of such exposure would be instantly taken to Twitter, a credible social media. In order to infer the associativity of sentiment analysis on the stock market analysis we have taken time-series data of a recently exposed firm which faces the biggest bear in the market from Yahoo Finance for the timeline of 07-02-22 to 03-02-2023 and the Twitter data for the same timeline had been accessed by is a scraper for Social Networking Services (SNS). The extracted tweet data with almost 1000 tweets each day has been analyzed by Meta’s roBERTa, an NLP(Natural Language Processing)-based framework for sentiment analysis. It is used to predict whether the market will be bearish or bullish on the day. Then the sentiment flag attribute and the market data attribute have been used to build a 3-layered Long Short Term Memory (LSTM), an ANN(Artificial Neural Network) where the data will be predicted for the same day’s stock movement. The results show that the sentiment reflects on the stock’s movement and the accuracy of the proposed work is about 96.14%.
{"title":"Improvising the Stock Prediction by Integrating with roBERTa and LSTM","authors":"N. Poornima, D. Abilash, M. Theodaniel","doi":"10.1109/IConSCEPT57958.2023.10169904","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10169904","url":null,"abstract":"In the Stock market, the volatility of leading MNCs’(Multi-National Corporations) shares is a major matter of concern and comes under the limelight nowadays. Unlike the 1920s sudden surge and dot-com crash, the contemporary world has never seen such a biggest bull or bear particularly in the past few decades. The stock market is majorly influenced by the credibility opinion of the general public on the firm. In the 21st century, the emergence of research LLC (Limited Liability Company) which gains profit from short selling of the shares by manipulating the share of a certain firm by exposing the legality of trespassing norms has made the researchers include a current public sentiment on the firm since short selling is a matter of one day. The first and foremost impact of such exposure would be instantly taken to Twitter, a credible social media. In order to infer the associativity of sentiment analysis on the stock market analysis we have taken time-series data of a recently exposed firm which faces the biggest bear in the market from Yahoo Finance for the timeline of 07-02-22 to 03-02-2023 and the Twitter data for the same timeline had been accessed by is a scraper for Social Networking Services (SNS). The extracted tweet data with almost 1000 tweets each day has been analyzed by Meta’s roBERTa, an NLP(Natural Language Processing)-based framework for sentiment analysis. It is used to predict whether the market will be bearish or bullish on the day. Then the sentiment flag attribute and the market data attribute have been used to build a 3-layered Long Short Term Memory (LSTM), an ANN(Artificial Neural Network) where the data will be predicted for the same day’s stock movement. The results show that the sentiment reflects on the stock’s movement and the accuracy of the proposed work is about 96.14%.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127039024","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10169951
N. Sankar, V. Vaideeswaran, J. S. Kumar, M. Rajan Singaravel
This paper proposes an off-board charger for electric vehicles (EV) that can charge multiple EVs with grid power in “grid-to-vehicle” (G2V) mode and in “vehicle-to-vehicle” (V2V) mode. In addition, the proposed charger can feed power to the grid in “vehicle-to-grid” (V2G) mode. In the G2V and V2V combined modes, both grid power and another EV’s power are used simultaneously to charge another EV. By using this mode, the power fed from the grid can be reduced. A three-phase pulse width modulation (PWM) rectifier is used as the front-end converter that maintains a constant DC link voltage and unity power factor (UPF) at the grid side. In accordance with the IEEE 519 standard, the total harmonic distortion (THD) of grid current in V2G, G2V, and combined G2V and V2V modes is maintained at less than 5%. To maintain a constant charging and discharging current for EVs, a half-bridge bidirectional DC/DC converter is employed. The simulation of all four modes is validated using PSIM Professional.
{"title":"Grid Connected Off-Board EV Charger with V2G / G2V and V2V Capability","authors":"N. Sankar, V. Vaideeswaran, J. S. Kumar, M. Rajan Singaravel","doi":"10.1109/IConSCEPT57958.2023.10169951","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10169951","url":null,"abstract":"This paper proposes an off-board charger for electric vehicles (EV) that can charge multiple EVs with grid power in “grid-to-vehicle” (G2V) mode and in “vehicle-to-vehicle” (V2V) mode. In addition, the proposed charger can feed power to the grid in “vehicle-to-grid” (V2G) mode. In the G2V and V2V combined modes, both grid power and another EV’s power are used simultaneously to charge another EV. By using this mode, the power fed from the grid can be reduced. A three-phase pulse width modulation (PWM) rectifier is used as the front-end converter that maintains a constant DC link voltage and unity power factor (UPF) at the grid side. In accordance with the IEEE 519 standard, the total harmonic distortion (THD) of grid current in V2G, G2V, and combined G2V and V2V modes is maintained at less than 5%. To maintain a constant charging and discharging current for EVs, a half-bridge bidirectional DC/DC converter is employed. The simulation of all four modes is validated using PSIM Professional.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126957104","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170449
Indra Kumar Chaudhry, Chaudhary Pratap Singh, Ravi Nandan Ray
An energy-efficient and optimized voltage CMOS voltage level shifter is proposed in this paper. There are several uses for the CMOS Voltage level shifters (LS) in power supply architecture. The voltage level shifter’s main function is to change the voltage level from low to high and vice versa. The proposed voltage LS circuit design uses a select signal (Vin) voltage switch logic, which accepts an input signal between 0.3 Volt and 0.6 Volt and produces an output signal with peak-to-peak voltage ranging from 1.2Volt to 0.6 Volt. The proposed LS circuit design is validated in ASAP7 7nm Fin-Fet technology, it outperforms a recently Wilson current mirror level shifter with Zero threshold voltage architecture in terms of latency and power dissipation by 42.76% and 39.6%, respectively. Power consumption and propagation delay are both significantly minimized by the proposed design topology.
{"title":"A Voltage Level Shifter Design for High Performance Application in Near Threshold Voltage Regime","authors":"Indra Kumar Chaudhry, Chaudhary Pratap Singh, Ravi Nandan Ray","doi":"10.1109/IConSCEPT57958.2023.10170449","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170449","url":null,"abstract":"An energy-efficient and optimized voltage CMOS voltage level shifter is proposed in this paper. There are several uses for the CMOS Voltage level shifters (LS) in power supply architecture. The voltage level shifter’s main function is to change the voltage level from low to high and vice versa. The proposed voltage LS circuit design uses a select signal (Vin) voltage switch logic, which accepts an input signal between 0.3 Volt and 0.6 Volt and produces an output signal with peak-to-peak voltage ranging from 1.2Volt to 0.6 Volt. The proposed LS circuit design is validated in ASAP7 7nm Fin-Fet technology, it outperforms a recently Wilson current mirror level shifter with Zero threshold voltage architecture in terms of latency and power dissipation by 42.76% and 39.6%, respectively. Power consumption and propagation delay are both significantly minimized by the proposed design topology.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115982990","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170027
Somya Srivastav, Kalpna Guleria, Shagun Sharma
A software that examines network traffic and searches for inconsistencies is known as an Intrusion Detection System (IDS). Network changes that seem to be abnormal or unexpected could be evidence of fraud at any phase, from the beginning of an attempt through the end of an intrusion. Data sharing is required to be safe since it primarily relies on the internet. Encryption processes and verification are unsuitable for internet security, and firewalls are unable to recognize fragmented fake transmissions. Additionally, attackers frequently update their strategy, tools, techniques, and tactics, which can have bad consequences like productivity losses, financial harm, data loss, etc. Therefore, it is essential to set up a trustworthy IDS, which is an extremely difficult task. In this work, the accuracy of an IDS system is forecasted by using a variety of supervised Machine Learning (ML) algorithms, including Decision tree (DT), Random Forest (RT), K-Nearest Neighbor (KNN), and Logistic Regression (LR) models. For the analysis, the dataset is collected from Kaggle, and the method that produces the highest accuracy is recommended for making future forecasts of intrusion. Furthermore, the outcomes have resulted in accuracy, execution speed, precision, F-measure, and recall. Additionally, the random forest performed best with the highest accuracy of 98.65% which can be recommended for the enhanced dataset to be implemented for better results for an IDS.
{"title":"Machine Learning Based Predictive Model for Intrusion Detection","authors":"Somya Srivastav, Kalpna Guleria, Shagun Sharma","doi":"10.1109/IConSCEPT57958.2023.10170027","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170027","url":null,"abstract":"A software that examines network traffic and searches for inconsistencies is known as an Intrusion Detection System (IDS). Network changes that seem to be abnormal or unexpected could be evidence of fraud at any phase, from the beginning of an attempt through the end of an intrusion. Data sharing is required to be safe since it primarily relies on the internet. Encryption processes and verification are unsuitable for internet security, and firewalls are unable to recognize fragmented fake transmissions. Additionally, attackers frequently update their strategy, tools, techniques, and tactics, which can have bad consequences like productivity losses, financial harm, data loss, etc. Therefore, it is essential to set up a trustworthy IDS, which is an extremely difficult task. In this work, the accuracy of an IDS system is forecasted by using a variety of supervised Machine Learning (ML) algorithms, including Decision tree (DT), Random Forest (RT), K-Nearest Neighbor (KNN), and Logistic Regression (LR) models. For the analysis, the dataset is collected from Kaggle, and the method that produces the highest accuracy is recommended for making future forecasts of intrusion. Furthermore, the outcomes have resulted in accuracy, execution speed, precision, F-measure, and recall. Additionally, the random forest performed best with the highest accuracy of 98.65% which can be recommended for the enhanced dataset to be implemented for better results for an IDS.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114459569","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170303
G. Sreeramulu Mahesh, M. Chandrashekar, A. Muni Sankar, S. Mohan, S. Chandra Sekhar, P. S. Ranjit
Most of the IT industries with microgrids are facing the impacts of harmonics on lighting loads, UPS, Elevators and Air Handling Units with Variable Frequency Drives, thus reduces the equipment performance and its life cycle. Many passive filters like single tuned filter, double tuned filter or combination of both single and double tuned filters et are implemented practically in the industries. It requires a careful tuning, otherwise, the resonance problems will arouse in the entire microgrid and leads to instability between the generation and loads, thus the design of an optimized passive filter is still a challenging problem for mitigating the harmonics. The Cuckoo Search Algorithm (CSA) is proposed in this paper to design the passive harmonic filter to reduce the Total Harmonic Distortion and size of the filter, which provides better tuning than the manual tuning. The advantages of this algorithm are: simple and provides less latency in processing in the iterations. This CSA is implemented on real time data from an IT industry with Fluke analyzer and the filter design is carried out using MATLAB simulation software and desired waveforms are presented.
{"title":"An Optimized Filter Design with Cuckoo Search Algorithm for Industrial Microgrids","authors":"G. Sreeramulu Mahesh, M. Chandrashekar, A. Muni Sankar, S. Mohan, S. Chandra Sekhar, P. S. Ranjit","doi":"10.1109/IConSCEPT57958.2023.10170303","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170303","url":null,"abstract":"Most of the IT industries with microgrids are facing the impacts of harmonics on lighting loads, UPS, Elevators and Air Handling Units with Variable Frequency Drives, thus reduces the equipment performance and its life cycle. Many passive filters like single tuned filter, double tuned filter or combination of both single and double tuned filters et are implemented practically in the industries. It requires a careful tuning, otherwise, the resonance problems will arouse in the entire microgrid and leads to instability between the generation and loads, thus the design of an optimized passive filter is still a challenging problem for mitigating the harmonics. The Cuckoo Search Algorithm (CSA) is proposed in this paper to design the passive harmonic filter to reduce the Total Harmonic Distortion and size of the filter, which provides better tuning than the manual tuning. The advantages of this algorithm are: simple and provides less latency in processing in the iterations. This CSA is implemented on real time data from an IT industry with Fluke analyzer and the filter design is carried out using MATLAB simulation software and desired waveforms are presented.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114834294","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 : 2023-05-25DOI: 10.1109/IConSCEPT57958.2023.10170396
Sagar Deep Deb, R. Kashyap, A. Abhishek, R. Lavanya, Pushp Paritosh, R. K. Jha
Numerous studies have focused on enhancing the effectiveness of identifying leaf diseases through image classification. However, it is essential to develop a classification system with fewer parameters to enable it to operate efficiently on mobile devices. As a result, A lot of research works are going on to make the neural network computationally light so that we can utilise these networks on a mobile device as it cannot afford a GPU to run in background because of the space and memory limitations of a portable device. In this study, we propose a deep learningbased approach for tomato leaf disease detection using a series of convolutional and depthwise convolutional layers. The proposed model contains only 17,209 trainable parameters. The model was able to achieve high accuracy of 92.10 % on tomato crop from a publicly available PlantVillage dataset while utilizing a smaller number of parameters.
{"title":"Tomato leaf disease detection using series of Convolutional and Depthwise Convolutional Layers","authors":"Sagar Deep Deb, R. Kashyap, A. Abhishek, R. Lavanya, Pushp Paritosh, R. K. Jha","doi":"10.1109/IConSCEPT57958.2023.10170396","DOIUrl":"https://doi.org/10.1109/IConSCEPT57958.2023.10170396","url":null,"abstract":"Numerous studies have focused on enhancing the effectiveness of identifying leaf diseases through image classification. However, it is essential to develop a classification system with fewer parameters to enable it to operate efficiently on mobile devices. As a result, A lot of research works are going on to make the neural network computationally light so that we can utilise these networks on a mobile device as it cannot afford a GPU to run in background because of the space and memory limitations of a portable device. In this study, we propose a deep learningbased approach for tomato leaf disease detection using a series of convolutional and depthwise convolutional layers. The proposed model contains only 17,209 trainable parameters. The model was able to achieve high accuracy of 92.10 % on tomato crop from a publicly available PlantVillage dataset while utilizing a smaller number of parameters.","PeriodicalId":240167,"journal":{"name":"2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128555876","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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