Pub Date : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760899
Kunal Agarwal, S. Vadhera
With the advent of 21st century, all countries of the world are striving to meet their needs from renewable energy and leave as low carbon footprint as possible; depletion of fossil fuels and climate change being the root reasons. India has the intent of achieving half of its energy needs by renewables by the year 2030 and as of 31st March, 2021, the wind capacity of India was found to be thirty-nine GW. Producing energy from wind is one of the cleanest and environment friendly ways of producing electricity as it is omnipresent. This paper focuses on estimating the unpredictable wind speeds at one of the windiest sites in India - Mahabaleshwar taking eight meteorological parameters as input for a period of twenty-seven months (from IMD) with the help of neural network tool in MATLAB using Levenberg-Marquardt method under Nonlinear Autoregressive with External Input consisting of more than two thousand datapoints. The model predicts the wind speed with agreeable regression and mean square error values. Accurate prediction of wind speed helps in locating wind farm sites, predicting power output from wind farms, scheduling maintenance of wind turbines and preparation against catastrophic wind speeds.
{"title":"Short-term Wind Speed Prediction using ANN","authors":"Kunal Agarwal, S. Vadhera","doi":"10.1109/ICSCDS53736.2022.9760899","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760899","url":null,"abstract":"With the advent of 21st century, all countries of the world are striving to meet their needs from renewable energy and leave as low carbon footprint as possible; depletion of fossil fuels and climate change being the root reasons. India has the intent of achieving half of its energy needs by renewables by the year 2030 and as of 31st March, 2021, the wind capacity of India was found to be thirty-nine GW. Producing energy from wind is one of the cleanest and environment friendly ways of producing electricity as it is omnipresent. This paper focuses on estimating the unpredictable wind speeds at one of the windiest sites in India - Mahabaleshwar taking eight meteorological parameters as input for a period of twenty-seven months (from IMD) with the help of neural network tool in MATLAB using Levenberg-Marquardt method under Nonlinear Autoregressive with External Input consisting of more than two thousand datapoints. The model predicts the wind speed with agreeable regression and mean square error values. Accurate prediction of wind speed helps in locating wind farm sites, predicting power output from wind farms, scheduling maintenance of wind turbines and preparation against catastrophic wind speeds.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"8 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116781818","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760813
T. Kavitha, S. Deepika, K. Nattaraj, P. Shanthini, M. Puranaraja
The agriculture plays an important role in the growth of every country's economy. In India, Agriculture is one of the most important occupations and a large amount of food is produced by the farmers. The climate and other environmental changes, uneven rainfall has become a major problem in the agriculture field. Machine learning and Deep learning approaches now-a-days play a major role in giving better solution for this problem. Crop type prediction involves predicting the type of crop before cultivation based on the historically available data such as weather, climatic conditions, soil and previous crop yield. Our work focuses on giving a solution to the farmers to decide on the suitable crop to cultivate. The publicly available crop dataset is used for training and testing our model. Crop Prediction is done using Random Forest (RF) machine learning algorithm. The proposed work also recommends the fertilizer to use for the increasing the crop production by using the soil type and the type of crop. The system predicts the plant diseases using ResNet architecture to avoid the spread of crop diseases.
{"title":"Smart System for Crop and Diseases Prediction using Random Forest and Resnet Architecture","authors":"T. Kavitha, S. Deepika, K. Nattaraj, P. Shanthini, M. Puranaraja","doi":"10.1109/ICSCDS53736.2022.9760813","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760813","url":null,"abstract":"The agriculture plays an important role in the growth of every country's economy. In India, Agriculture is one of the most important occupations and a large amount of food is produced by the farmers. The climate and other environmental changes, uneven rainfall has become a major problem in the agriculture field. Machine learning and Deep learning approaches now-a-days play a major role in giving better solution for this problem. Crop type prediction involves predicting the type of crop before cultivation based on the historically available data such as weather, climatic conditions, soil and previous crop yield. Our work focuses on giving a solution to the farmers to decide on the suitable crop to cultivate. The publicly available crop dataset is used for training and testing our model. Crop Prediction is done using Random Forest (RF) machine learning algorithm. The proposed work also recommends the fertilizer to use for the increasing the crop production by using the soil type and the type of crop. The system predicts the plant diseases using ResNet architecture to avoid the spread of crop diseases.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124824895","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760753
R. Ravi, G. Devaraj, J. M. Esther, R. Kabilan, Zahariya Gabriel, U. Muthuraman
Because of the mobility and scalability given by wireless networks, many applications have been made possible. MANET is a very important application in wireless networks. A fixed network infrastructure is not required for MANET. The node which is present in it can act both as transmitter as well receiver. The ability of MANET nodes to self-configure makes such as uses in military emergency application. MANET, is also used as vulnerable to malevolent attackers. New Enhanced Adaptive ACKnowledgment (NEAACK), is also a new technique which specifically intended to MANETs, is used in this research. NEAACK is used to find forge acknowledgement attacks as well as to detect misbehaving nodes. The integrity, authentication, and non-repudiation of NEAACK are all ensured by the Digital Signature Algorithm. The Routing Overhead will be decreased and Ratio of packet delivery will get increased.
{"title":"Malicious Finding and Validation Scheme Using New Enhanced Adaptive Ack","authors":"R. Ravi, G. Devaraj, J. M. Esther, R. Kabilan, Zahariya Gabriel, U. Muthuraman","doi":"10.1109/ICSCDS53736.2022.9760753","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760753","url":null,"abstract":"Because of the mobility and scalability given by wireless networks, many applications have been made possible. MANET is a very important application in wireless networks. A fixed network infrastructure is not required for MANET. The node which is present in it can act both as transmitter as well receiver. The ability of MANET nodes to self-configure makes such as uses in military emergency application. MANET, is also used as vulnerable to malevolent attackers. New Enhanced Adaptive ACKnowledgment (NEAACK), is also a new technique which specifically intended to MANETs, is used in this research. NEAACK is used to find forge acknowledgement attacks as well as to detect misbehaving nodes. The integrity, authentication, and non-repudiation of NEAACK are all ensured by the Digital Signature Algorithm. The Routing Overhead will be decreased and Ratio of packet delivery will get increased.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"371 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123406186","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760788
Li Yang, Feng Si Ruo, You Yi
Based on user big data, this paper studies the intelligent network system for the recycling and recycling of construction waste. First, by using the building area estimation method and gray prediction model to estimate and predict the production of construction waste in China, combined with the current status of construction waste recycling at home and abroad, it is found China's construction waste recycling has problems such as weak environmental protection awareness, insufficient government support, imperfect laws and regulations, and imperfect management policies. Through the combination of theory and practice, put forward the road to develop the construction waste recycling industry, clarify the participants in the operation of the construction waste recycling industry, build a construction waste recycling industry operation model, and increase the recycling efficiency by 7.1%.
{"title":"The Construction Waste Recycling and Recycling Intelligent Network System based on User Big Data","authors":"Li Yang, Feng Si Ruo, You Yi","doi":"10.1109/ICSCDS53736.2022.9760788","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760788","url":null,"abstract":"Based on user big data, this paper studies the intelligent network system for the recycling and recycling of construction waste. First, by using the building area estimation method and gray prediction model to estimate and predict the production of construction waste in China, combined with the current status of construction waste recycling at home and abroad, it is found China's construction waste recycling has problems such as weak environmental protection awareness, insufficient government support, imperfect laws and regulations, and imperfect management policies. Through the combination of theory and practice, put forward the road to develop the construction waste recycling industry, clarify the participants in the operation of the construction waste recycling industry, build a construction waste recycling industry operation model, and increase the recycling efficiency by 7.1%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123668222","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760819
Sasirekha P, Navinkumar T M, Anton Amala Praveen A, Bharani Prakash T, Swapna P, M. Vinothkumar
The solar radiation estimation is very important for developing and design of solar energy production system in generation of non-renewable energy. But the data set of Global solar radiation is not easily obtainable in all places of India due to some technical issues and cost in measurement technologies. Consequently it is important to forecasting the solar radiation prediction using some techniques by input parameters namely Time, Radiation, Temperature, Pressure, Humidity, Wind Direction, Speed, Time Sun rise and Time sun set. In this paper the author focused on analyzing the solar radiation prediction using Random Forest technique. This analysis gives more clear knowledge in prediction performance using machine learning algorithms.
{"title":"Comparative Analysis of Prediction on Solar Radiation in Energy Generation System using Random Forest and Decision Tree","authors":"Sasirekha P, Navinkumar T M, Anton Amala Praveen A, Bharani Prakash T, Swapna P, M. Vinothkumar","doi":"10.1109/ICSCDS53736.2022.9760819","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760819","url":null,"abstract":"The solar radiation estimation is very important for developing and design of solar energy production system in generation of non-renewable energy. But the data set of Global solar radiation is not easily obtainable in all places of India due to some technical issues and cost in measurement technologies. Consequently it is important to forecasting the solar radiation prediction using some techniques by input parameters namely Time, Radiation, Temperature, Pressure, Humidity, Wind Direction, Speed, Time Sun rise and Time sun set. In this paper the author focused on analyzing the solar radiation prediction using Random Forest technique. This analysis gives more clear knowledge in prediction performance using machine learning algorithms.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122557565","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760787
N. Subraja, D. Venkatasekhar
The object detection in satellite imagery is a primary and elaborate one receiving lot of interest in latest years and performs an essential function for wide range of applications. After the massive fulfillment of Deep learning techniques in computer imaginative and prescient discipline, they're presently being studied in the context of satellite imagery for unique functions like object identification, object tracking, object classification, semantic segmentation of aerial/satellite images. Although diverse assessment research associated with object detection from satellite/aerial imagery are carried out, this observation provides an assessment of the latest development in the discipline of object detection from satellite imagery with the use of deep learning. This paper elaborates the detection of roads, buildings, solar panels and vehicles using Modified U-Net Convolutional networks and achieves more accuracy compared to the previous ones.
{"title":"Satellite Image Segmentation using Modified U-Net Convolutional Networks","authors":"N. Subraja, D. Venkatasekhar","doi":"10.1109/ICSCDS53736.2022.9760787","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760787","url":null,"abstract":"The object detection in satellite imagery is a primary and elaborate one receiving lot of interest in latest years and performs an essential function for wide range of applications. After the massive fulfillment of Deep learning techniques in computer imaginative and prescient discipline, they're presently being studied in the context of satellite imagery for unique functions like object identification, object tracking, object classification, semantic segmentation of aerial/satellite images. Although diverse assessment research associated with object detection from satellite/aerial imagery are carried out, this observation provides an assessment of the latest development in the discipline of object detection from satellite imagery with the use of deep learning. This paper elaborates the detection of roads, buildings, solar panels and vehicles using Modified U-Net Convolutional networks and achieves more accuracy compared to the previous ones.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122708137","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9761010
Xiaomei Yang, Wenqiang Guo, Sixiu Wang
Based on the analysis of the status quo of archive social media information at home and abroad, chain analysis under the background of discussing big data is not only a strategic step in the construction of archives informatization, but also an inevitable choice for its effective construction and utilization. The first is to learn from the international standards of the document management system and the open archive information system, and to design the social media information network archive structure at the top level, so that the efficiency of the archive information organization in the standard integration process is increased by 6.3%; the second is the heterogeneous source of a variety of archive social media information Data is used as the source of analysis to conduct in-depth excavation of various contents, and the integration mechanism of constructing new archives resources from the integration of objects, methods and forms of use has increased by 7.8%.
{"title":"Research on Social Media Information Network Archiving in the Context of Big Data and Chain Analysis","authors":"Xiaomei Yang, Wenqiang Guo, Sixiu Wang","doi":"10.1109/ICSCDS53736.2022.9761010","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9761010","url":null,"abstract":"Based on the analysis of the status quo of archive social media information at home and abroad, chain analysis under the background of discussing big data is not only a strategic step in the construction of archives informatization, but also an inevitable choice for its effective construction and utilization. The first is to learn from the international standards of the document management system and the open archive information system, and to design the social media information network archive structure at the top level, so that the efficiency of the archive information organization in the standard integration process is increased by 6.3%; the second is the heterogeneous source of a variety of archive social media information Data is used as the source of analysis to conduct in-depth excavation of various contents, and the integration mechanism of constructing new archives resources from the integration of objects, methods and forms of use has increased by 7.8%.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122856301","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760957
Jing Li
Construction of the college students' course management information system based on data center and parallel model is discussed in this research. First, this research work continues to refine and generate more detailed classes according to the relationship between the internal components of each subsystem. For example, the bus class can be divided into: internal, local, system, external and other bus classes according to its layout range attributes. Then, this research work considers the parallel model, wherein the site management mainly includes basic functions such as site addition, deletion, replacement and site attribute management. The addition of the site is completed by the system administrator. When generating, just select the corresponding template as needed, and then set the site parameters, such as site name, folder name, etc., the system can automatically generate the course website according to the template. Furthermore, the data center optimization model is designed to make the model efficient.
{"title":"Construction of College Students' Course Management Information System Based on Data Center and Parallel Model","authors":"Jing Li","doi":"10.1109/ICSCDS53736.2022.9760957","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760957","url":null,"abstract":"Construction of the college students' course management information system based on data center and parallel model is discussed in this research. First, this research work continues to refine and generate more detailed classes according to the relationship between the internal components of each subsystem. For example, the bus class can be divided into: internal, local, system, external and other bus classes according to its layout range attributes. Then, this research work considers the parallel model, wherein the site management mainly includes basic functions such as site addition, deletion, replacement and site attribute management. The addition of the site is completed by the system administrator. When generating, just select the corresponding template as needed, and then set the site parameters, such as site name, folder name, etc., the system can automatically generate the course website according to the template. Furthermore, the data center optimization model is designed to make the model efficient.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114151807","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760827
Zhaoliang Liu
In order to achieve the goal of low-energy operation of HVAC in large stadiums, a building central air-conditioning energy-saving monitoring network based on physical sensor network technology has been developed, which can realize real-time monitoring and control of central air-conditioning systems in large public buildings or building groups. The network nodes form a self-organizing wireless sensor network according to the IEEE802.15.4/ZigBee protocol, using wireless temperature, humidity and other sensors to provide temperature and humidity in the building environment, water supply and return water temperature on the secondary side of the power center plate heat exchanger, etc. Perform real-time acquisition. The on-site operating parameters are transmitted to the upper computer of the monitoring center through the GPRS network, which realizes the energy-saving management and optimization control of the entire system operation by the upper computer.
{"title":"Low-Energy-Consumption Operation Debugging Method of Large-Scale Gymnasium HVAC System Based on Physical Sensor Network","authors":"Zhaoliang Liu","doi":"10.1109/ICSCDS53736.2022.9760827","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760827","url":null,"abstract":"In order to achieve the goal of low-energy operation of HVAC in large stadiums, a building central air-conditioning energy-saving monitoring network based on physical sensor network technology has been developed, which can realize real-time monitoring and control of central air-conditioning systems in large public buildings or building groups. The network nodes form a self-organizing wireless sensor network according to the IEEE802.15.4/ZigBee protocol, using wireless temperature, humidity and other sensors to provide temperature and humidity in the building environment, water supply and return water temperature on the secondary side of the power center plate heat exchanger, etc. Perform real-time acquisition. The on-site operating parameters are transmitted to the upper computer of the monitoring center through the GPRS network, which realizes the energy-saving management and optimization control of the entire system operation by the upper computer.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114189123","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 : 2022-04-07DOI: 10.1109/ICSCDS53736.2022.9760863
R. Durga, G. Haasya, D. Durga, S. Nayak
In this paper a Frequency Reconfigurable antenna with a U-slot is designed with a high gain of 9.2dB and can be reconfigured from 1.61-1.68GHz, the analysis of reconfigurability aspect and the behaviour of lumped components is studied using Machine Learning. Reconfigurable antennas are those which are capable of changing the resonant frequency based on the switching circuits used. These switching circuits use an additional load such as PIN Diodes, MEMS Switches etc., We considered PIN Diode since it is easy to design and is economical for our analysis in HFS S. The effect of each lumped component (R, L &C) is individually studied and it is observed that R value has a correlation coefficient of 0.961 with return loss, and correlation coefficient of 0.090 is obtained for frequency. R value is said to have significant effect on reconfigurability aspect of the antenna.
本文设计了一种具有 U 型槽的频率可重构天线，其增益高达 9.2dB，可在 1.61-1.68GHz 频率范围内进行重构，并使用机器学习方法对可重构性方面的分析和块状元件的行为进行了研究。可重构天线是指能够根据所使用的开关电路改变谐振频率的天线。这些开关电路使用 PIN 二极管、MEMS 开关等附加负载，我们考虑使用 PIN 二极管，因为它易于设计，在 HFS S 中进行分析也很经济。可以说，R 值对天线的可重构性有重大影响。
{"title":"Design, Implementation and Machine Learning Analysis of Frequency Reconfigurable Microstrip Antenna for Defense Applications","authors":"R. Durga, G. Haasya, D. Durga, S. Nayak","doi":"10.1109/ICSCDS53736.2022.9760863","DOIUrl":"https://doi.org/10.1109/ICSCDS53736.2022.9760863","url":null,"abstract":"In this paper a Frequency Reconfigurable antenna with a U-slot is designed with a high gain of 9.2dB and can be reconfigured from 1.61-1.68GHz, the analysis of reconfigurability aspect and the behaviour of lumped components is studied using Machine Learning. Reconfigurable antennas are those which are capable of changing the resonant frequency based on the switching circuits used. These switching circuits use an additional load such as PIN Diodes, MEMS Switches etc., We considered PIN Diode since it is easy to design and is economical for our analysis in HFS S. The effect of each lumped component (R, L &C) is individually studied and it is observed that R value has a correlation coefficient of 0.961 with return loss, and correlation coefficient of 0.090 is obtained for frequency. R value is said to have significant effect on reconfigurability aspect of the antenna.","PeriodicalId":433549,"journal":{"name":"2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129743921","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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