Pub Date : 2021-06-29DOI: 10.13052/JGEU0975-1416.923
Sharat Chandra Bhardwaj, A. Vidyarthi, B. Jassal, A. Shukla
Many atmospheric errors affect the positional accuracy of a satellite-based navigation device, such as troposphere, ionosphere, multipath, and so on, but the ionosphere is the most significant contributor to positional error. Since the ionosphere’s dynamics are highly complex, especially in low latitude and equatorial regions, a dual-frequency approach for calculating slant total electron content (STEC) for ionospheric delay estimation performs better in these conditions. However, the STEC is ambiguous and it cannot be used directly for ionospheric delay prediction, accurate positioning purposes, or ionospheric study. As a result, STEC estimation and pre-processing are required steps prior to any positioning application. There is very little literature available for STEC pre-processing in the NavIC system, necessitating an in-depth discussion. This paper focuses on how to extract navigational data from a raw binary file obtained from the Indian NavIC satellites, estimate and pre-process STEC, and build a database for STEC. It has been found that an hourly averaged STEC data is suitable for ionospheric studies and monthly mean value can be used for ionospheric behavioral research. Furthermore, the STEC is affected by diurnal solar activity, thus, the seven-month data analysis that includes summer and winter months has been used to study ionosphere action during the summer and winter months. It has been observed that STEC values are higher during the summer months than the winter months; some seasonal characteristics are also been found.
{"title":"Estimation and Assessment of Ionospheric Slant Total Electron Content (STEC) Using Dual-frequency NavIC Satellite System","authors":"Sharat Chandra Bhardwaj, A. Vidyarthi, B. Jassal, A. Shukla","doi":"10.13052/JGEU0975-1416.923","DOIUrl":"https://doi.org/10.13052/JGEU0975-1416.923","url":null,"abstract":"Many atmospheric errors affect the positional accuracy of a satellite-based navigation device, such as troposphere, ionosphere, multipath, and so on, but the ionosphere is the most significant contributor to positional error. Since the ionosphere’s dynamics are highly complex, especially in low latitude and equatorial regions, a dual-frequency approach for calculating slant total electron content (STEC) for ionospheric delay estimation performs better in these conditions. However, the STEC is ambiguous and it cannot be used directly for ionospheric delay prediction, accurate positioning purposes, or ionospheric study. As a result, STEC estimation and pre-processing are required steps prior to any positioning application. There is very little literature available for STEC pre-processing in the NavIC system, necessitating an in-depth discussion. This paper focuses on how to extract navigational data from a raw binary file obtained from the Indian NavIC satellites, estimate and pre-process STEC, and build a database for STEC. It has been found that an hourly averaged STEC data is suitable for ionospheric studies and monthly mean value can be used for ionospheric behavioral research. Furthermore, the STEC is affected by diurnal solar activity, thus, the seven-month data analysis that includes summer and winter months has been used to study ionosphere action during the summer and winter months. It has been observed that STEC values are higher during the summer months than the winter months; some seasonal characteristics are also been found.","PeriodicalId":142472,"journal":{"name":"Journal of Graphic Era University","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124125927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-29DOI: 10.13052/JGEU0975-1416.922
Jaspreet Kaur, P. Chattopadhyay, L. Singh, Kausik Chattopadhyay, N. Mishra
Various measures have been taken into account for the virus outbreak. But how much it successes to control outbreak to fights against COVID-19. Machine learning is used as a tool to study these complex impacts on various stages of the epidemic. While India is forced to open up the economy after an extended lockdown, the effect of lockdown, which is critical to decide the future course of action, is yet to be understood. The study suggests Support Vector Machine (SVM) and Polynomial Regression (PR) are better suited compared to Long Short-Term Memory (LSTM) in scenarios consisting of sparse and discrete events. The time-series memory of LSTM is outperformed by the contextual hyperplanes of SVM which classifies the data even more precisely. The study suggests while phase 1 of lockdown was effective, the rest of them were not. Had India continued with lockdown 1, it would have flattened the COVID-19 infection curve by mid of May 2020. With the current rate, India will hit the 8 million mark by 23 October 2020. The SVM model is further integrated with an SIR (Susceptible, Infected and Recovered) model of epidemiology, which suggests that 70% of India’s population is infected by this pandemic during this 8 month and the peak reached in October 2020 if vaccine not found. With increasing recovery rate increases the possibility of decreasing COVID-19 cases. According to the SVM model’s prediction, 90% of cases of COVID-19 will be end in February.
{"title":"Machine Learning Based Prediction and Impact Analysis of Various Lockdown Stages of COVID-19 Outbreak – A Case Study of India","authors":"Jaspreet Kaur, P. Chattopadhyay, L. Singh, Kausik Chattopadhyay, N. Mishra","doi":"10.13052/JGEU0975-1416.922","DOIUrl":"https://doi.org/10.13052/JGEU0975-1416.922","url":null,"abstract":"Various measures have been taken into account for the virus outbreak. But how much it successes to control outbreak to fights against COVID-19. Machine learning is used as a tool to study these complex impacts on various stages of the epidemic. While India is forced to open up the economy after an extended lockdown, the effect of lockdown, which is critical to decide the future course of action, is yet to be understood. The study suggests Support Vector Machine (SVM) and Polynomial Regression (PR) are better suited compared to Long Short-Term Memory (LSTM) in scenarios consisting of sparse and discrete events. The time-series memory of LSTM is outperformed by the contextual hyperplanes of SVM which classifies the data even more precisely. The study suggests while phase 1 of lockdown was effective, the rest of them were not. Had India continued with lockdown 1, it would have flattened the COVID-19 infection curve by mid of May 2020. With the current rate, India will hit the 8 million mark by 23 October 2020. The SVM model is further integrated with an SIR (Susceptible, Infected and Recovered) model of epidemiology, which suggests that 70% of India’s population is infected by this pandemic during this 8 month and the peak reached in October 2020 if vaccine not found. With increasing recovery rate increases the possibility of decreasing COVID-19 cases. According to the SVM model’s prediction, 90% of cases of COVID-19 will be end in February.","PeriodicalId":142472,"journal":{"name":"Journal of Graphic Era University","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126740401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-06-03DOI: 10.13052/JGEU0975-1416.921
F. Ahmad, Pushpendra Kumar, P. Patil
Quadcopter, a mechatronic device is now widely used due to its simple structure and vertical take-off and landing capability. It consists of four propellers which can lift and propelled the Quadcopter in 3-D space. The difference in angular speeds of propellers works as a steering system and responsible for attitude and altitude motion. Quadcopter self weight and pay load is carried by the propellers itself, and a bad design of the propeller may lead to crack. Quadcopter propellers are also subjected to thrust force and vibration during the flight. The main objective of this study is to find out the natural failure frequency of propeller. Two types of propellers have been designed in Creo 2.0 and analyzed in Ansys 16.2 for their vibration frequencies. Both the designs are analyzed with or without the thrust force under the vibration frequency. First six vibration modes have been calculated for both the propeller designs with Carbon Fiber Reinforced Polymer (CFRP) material. The obtained simulation results have been compared and analyzed to sustain the failure frequency.
{"title":"Analysis of a Pre-Stressed Quadcopter Propeller Using Finite element Approach","authors":"F. Ahmad, Pushpendra Kumar, P. Patil","doi":"10.13052/JGEU0975-1416.921","DOIUrl":"https://doi.org/10.13052/JGEU0975-1416.921","url":null,"abstract":"Quadcopter, a mechatronic device is now widely used due to its simple structure and vertical take-off and landing capability. It consists of four propellers which can lift and propelled the Quadcopter in 3-D space. The difference in angular speeds of propellers works as a steering system and responsible for attitude and altitude motion. Quadcopter self weight and pay load is carried by the propellers itself, and a bad design of the propeller may lead to crack. Quadcopter propellers are also subjected to thrust force and vibration during the flight. The main objective of this study is to find out the natural failure frequency of propeller. Two types of propellers have been designed in Creo 2.0 and analyzed in Ansys 16.2 for their vibration frequencies. Both the designs are analyzed with or without the thrust force under the vibration frequency. First six vibration modes have been calculated for both the propeller designs with Carbon Fiber Reinforced Polymer (CFRP) material. The obtained simulation results have been compared and analyzed to sustain the failure frequency.","PeriodicalId":142472,"journal":{"name":"Journal of Graphic Era University","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114900308","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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