People use various social media platforms like twitter to report the disaster and disaster related information during crisis situations such as natural and man-made disasters. Detecting these disaster events can improve situational awareness for general public, response agencies and aid agencies. Many works has done for disaster event detection which uses traditional machine learning techniques but in recent years deep neural network models have demonstrated better results for many problems over traditional machine learning models. It is advantageous of using deep learning models because the model has the capacity to capture more than one layers of information. Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are three such deep neural networks. CNN can recognize local features in a multidimensional field and LSTM and GRU network can learn sequential data as it has the ability of remembering previously read data. In this paper, we use two hybrid deep neural network models named CNN-LSTM and CNN-GRU, which is developed by combining both CNN, LSTM, and CNN, GRU networks for doing event detection during disaster situation on Twitter data. We provide the detailed explanation of both models and perform comparison against Support Vector Machine (SVM), CNN, and LSTM models. Our result shows that both models can successfully identify the presence of disaster related events accurately from twitter data. We found that CNN-LSTM model is the best model achieving significant improvement of 18.74% than regular SVM model.
{"title":"Detecting Disaster Related Tweets Using Hybrid Deep Neural Network Models","authors":"Nayan Ranjan Paul, Manmohan Sahoo, Sukanta Kumar Hati, Tapaswini Sahoo","doi":"10.1109/ICATME50232.2021.9732732","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732732","url":null,"abstract":"People use various social media platforms like twitter to report the disaster and disaster related information during crisis situations such as natural and man-made disasters. Detecting these disaster events can improve situational awareness for general public, response agencies and aid agencies. Many works has done for disaster event detection which uses traditional machine learning techniques but in recent years deep neural network models have demonstrated better results for many problems over traditional machine learning models. It is advantageous of using deep learning models because the model has the capacity to capture more than one layers of information. Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) are three such deep neural networks. CNN can recognize local features in a multidimensional field and LSTM and GRU network can learn sequential data as it has the ability of remembering previously read data. In this paper, we use two hybrid deep neural network models named CNN-LSTM and CNN-GRU, which is developed by combining both CNN, LSTM, and CNN, GRU networks for doing event detection during disaster situation on Twitter data. We provide the detailed explanation of both models and perform comparison against Support Vector Machine (SVM), CNN, and LSTM models. Our result shows that both models can successfully identify the presence of disaster related events accurately from twitter data. We found that CNN-LSTM model is the best model achieving significant improvement of 18.74% than regular SVM model.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"21 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130699844","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732727
Debottam Mukherjee, Samrat Chakraborty
Modern day grids with its inherent operating characteristics are susceptible to faults. Grid operators must detect as well as classify the current system conditions like normal or faulty from the current raw sets of measurement data available at SCADA (supervisory control and data acquisition system). With the rapid deployment of micro PMUs, faults are detected from the measurements in real time, but their classification in real time still possess a challenging task. This paper focus on a diligent comparison between several deep learning and machine learning methodologies for classifying faults (L-G, LL-G, LLL-G) in real time. In real life scenarios L-G fault being most frequent and LLL-G being rare, an imbalanced dataset is generally developed for supervised learning approach leading to a biased classifier. To mitigate this issue this paper proposes SMOTE based oversampling over the imbalanced dataset. The dataset used in this work is derived from the Drexel University's Reconfigurable Distribution Automation and Control (RDAC) software/hardware laboratory.
{"title":"Classification of Faults in Power System with Probabilistic Neural Networks: An Imbalanced Learning Approach","authors":"Debottam Mukherjee, Samrat Chakraborty","doi":"10.1109/ICATME50232.2021.9732727","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732727","url":null,"abstract":"Modern day grids with its inherent operating characteristics are susceptible to faults. Grid operators must detect as well as classify the current system conditions like normal or faulty from the current raw sets of measurement data available at SCADA (supervisory control and data acquisition system). With the rapid deployment of micro PMUs, faults are detected from the measurements in real time, but their classification in real time still possess a challenging task. This paper focus on a diligent comparison between several deep learning and machine learning methodologies for classifying faults (L-G, LL-G, LLL-G) in real time. In real life scenarios L-G fault being most frequent and LLL-G being rare, an imbalanced dataset is generally developed for supervised learning approach leading to a biased classifier. To mitigate this issue this paper proposes SMOTE based oversampling over the imbalanced dataset. The dataset used in this work is derived from the Drexel University's Reconfigurable Distribution Automation and Control (RDAC) software/hardware laboratory.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"73 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130972449","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732746
Chinmayee Panda, U. Bhanja
The effect of Subcarrier intensity modulation with QPSK is discussed in this paper. QPSK is chosen as the best modulation scheme among BPSK, OOK and PSK. This work simulates PSK-SIM, BPSK-SIM and QPSK-SIM applied to FSO system at spectral efficiency (SE) of 4bps/HZ and the analysis is performed in weak and strong turbulence conditions. The investigation shows that QPSK-SIM-FSO system shows BER of the order of 10−8as compared to PSK and BPSK-SIM-FSO system in different turbulence conditions.
{"title":"QPSK-Subcarrier Intensity modulated FSO System","authors":"Chinmayee Panda, U. Bhanja","doi":"10.1109/ICATME50232.2021.9732746","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732746","url":null,"abstract":"The effect of Subcarrier intensity modulation with QPSK is discussed in this paper. QPSK is chosen as the best modulation scheme among BPSK, OOK and PSK. This work simulates PSK-SIM, BPSK-SIM and QPSK-SIM applied to FSO system at spectral efficiency (SE) of 4bps/HZ and the analysis is performed in weak and strong turbulence conditions. The investigation shows that QPSK-SIM-FSO system shows BER of the order of 10−8as compared to PSK and BPSK-SIM-FSO system in different turbulence conditions.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125559228","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732764
Zeba Malik, Anika Saxena, Kaptan Singh
There are many applications where it is required to smartly monitor the environmental parameters. Therefore in this paper a smart IOT based environmental data monitoring system Transceiver is designed. The Humidity of the air is responsible for the various chemical and biological properties of the environment. Therefore humidity and temperature data sensors are used for environmental air monitoring. A secure key based AES encryption standards is used at the transmitting end. The cipher data is uploaded over IOT cloud. At the receiver end a smart decryption algorithm is designed. The NodeMCU based ESP8266 Wi-Fi router is designed at the transceiver end. Paper present the secure environmental monitoring system, which is applicable in air conditioning, ventilation and weather monitoring. The data is stored over ThingSpeak cloud server.
{"title":"Designing a Secure IOT data Encryption algorithm for Smart Environmental Monitoring System","authors":"Zeba Malik, Anika Saxena, Kaptan Singh","doi":"10.1109/ICATME50232.2021.9732764","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732764","url":null,"abstract":"There are many applications where it is required to smartly monitor the environmental parameters. Therefore in this paper a smart IOT based environmental data monitoring system Transceiver is designed. The Humidity of the air is responsible for the various chemical and biological properties of the environment. Therefore humidity and temperature data sensors are used for environmental air monitoring. A secure key based AES encryption standards is used at the transmitting end. The cipher data is uploaded over IOT cloud. At the receiver end a smart decryption algorithm is designed. The NodeMCU based ESP8266 Wi-Fi router is designed at the transceiver end. Paper present the secure environmental monitoring system, which is applicable in air conditioning, ventilation and weather monitoring. The data is stored over ThingSpeak cloud server.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"345 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120875915","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732734
R. K. Pradhan, Sudhanshu Ranjan Dwibedi, Prakash Kumar Mahapatra, Braja Kishore Barik
An autonomous drone assisted imagery system for animal chasing on national highway, along with accident detection and automated emergency service measure and road quality detection for repairing by image processing is the innovative objective of this paper. A dedicated optical network has been proposed to provide high-speed data communication to cameras and drones, which later can also be utilized for various public related mass information services to government and private sectors. The autonomous transport and traffic control system can also track rampant illegal vehicle parking on highways, detect over speeding and overloaded goods vehicle for safety and load violation. In future, on road Wi-Fi service can be provided for commercial and urgent services by incorporating WAP units at specific points. Earlier drone-based systems have been used for survey and surveillance, but this will be a unique concept to facilitate various needs and requirements solely based on highways related problems and common occurring events, which need to be controlled on regular basis.
{"title":"An Autonomous Drone and Optical Network Based Animal Chaser and Multi Purpose Highway Traffic Monitoring System","authors":"R. K. Pradhan, Sudhanshu Ranjan Dwibedi, Prakash Kumar Mahapatra, Braja Kishore Barik","doi":"10.1109/ICATME50232.2021.9732734","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732734","url":null,"abstract":"An autonomous drone assisted imagery system for animal chasing on national highway, along with accident detection and automated emergency service measure and road quality detection for repairing by image processing is the innovative objective of this paper. A dedicated optical network has been proposed to provide high-speed data communication to cameras and drones, which later can also be utilized for various public related mass information services to government and private sectors. The autonomous transport and traffic control system can also track rampant illegal vehicle parking on highways, detect over speeding and overloaded goods vehicle for safety and load violation. In future, on road Wi-Fi service can be provided for commercial and urgent services by incorporating WAP units at specific points. Earlier drone-based systems have been used for survey and surveillance, but this will be a unique concept to facilitate various needs and requirements solely based on highways related problems and common occurring events, which need to be controlled on regular basis.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120963110","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732731
H. Singh, Vaibhav Jha
This paper present the literature survey of different optimization mechanism of location determination and capacity determination for distributed generation unit in distributed network. As know that the better location and capacity of any distributed generation unit play a vital role in this present power system scenario or modern era of power system so that many researchers recently developed several mechanism for determination of capacity and location of distributed generation unit. An exact computation of capacity and location reduces the power losses and stable the voltage profile so this kind of computation very needful for the distributed generation unit. The researchers have found several issue and problem when they have made such computation of location and capacity of distributed generation unit. The main issue of exact computation for DG's position, location and capacity is limitation of distributed system when it has been made for real time scenario. There are several initiatives taken by the Governments in term of subsidies and make sure the improvement in distributed network, increases the utilization of distributed generation unit for both purposes commercial and domestic. This paper discusses the comparative performances of optimization algorithms and their contribution toward this field.
{"title":"Optimization Methods of Location and Capacity Determination for Distributed Generation in Distributed Network: A Review","authors":"H. Singh, Vaibhav Jha","doi":"10.1109/ICATME50232.2021.9732731","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732731","url":null,"abstract":"This paper present the literature survey of different optimization mechanism of location determination and capacity determination for distributed generation unit in distributed network. As know that the better location and capacity of any distributed generation unit play a vital role in this present power system scenario or modern era of power system so that many researchers recently developed several mechanism for determination of capacity and location of distributed generation unit. An exact computation of capacity and location reduces the power losses and stable the voltage profile so this kind of computation very needful for the distributed generation unit. The researchers have found several issue and problem when they have made such computation of location and capacity of distributed generation unit. The main issue of exact computation for DG's position, location and capacity is limitation of distributed system when it has been made for real time scenario. There are several initiatives taken by the Governments in term of subsidies and make sure the improvement in distributed network, increases the utilization of distributed generation unit for both purposes commercial and domestic. This paper discusses the comparative performances of optimization algorithms and their contribution toward this field.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036425","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732758
Manik, V. Niranjan, R. Saxena
In this work, an interface circuit for readout integrated circuit (ROIC) has been proposed employing source follower per detector (SFD) unit cell and current mirror integration (CMI) in order to have high performance with less circuit complexity. In terms of complexity, SFD unit cell is a good choice because it has three only transistors and less complexity. SFD is a simple interface topology for high density and low power ROICs. The SFD unit cell has disadvantage of instable detector bias voltage that makes it non-linear and increases the noise factor at the output side. The interfaced approach has been proposed here to combine the two basic unit cells. To reduce the noise factor at the output side here SFD is interfaced with the current mirror integration unit cell. It is shown here that the noise performance improves by about 98.93% with respect to the conventional SFD unit cell. The proposed circuit shows 18.18% degradation in power after simulation. Cadence 180 nm technologies have been used for the simulations/calculations.
{"title":"Noise and Power efficient source follower per detector unit cell for readout integrated circuits of photodiode sensor arrays","authors":"Manik, V. Niranjan, R. Saxena","doi":"10.1109/ICATME50232.2021.9732758","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732758","url":null,"abstract":"In this work, an interface circuit for readout integrated circuit (ROIC) has been proposed employing source follower per detector (SFD) unit cell and current mirror integration (CMI) in order to have high performance with less circuit complexity. In terms of complexity, SFD unit cell is a good choice because it has three only transistors and less complexity. SFD is a simple interface topology for high density and low power ROICs. The SFD unit cell has disadvantage of instable detector bias voltage that makes it non-linear and increases the noise factor at the output side. The interfaced approach has been proposed here to combine the two basic unit cells. To reduce the noise factor at the output side here SFD is interfaced with the current mirror integration unit cell. It is shown here that the noise performance improves by about 98.93% with respect to the conventional SFD unit cell. The proposed circuit shows 18.18% degradation in power after simulation. Cadence 180 nm technologies have been used for the simulations/calculations.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123748964","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732710
Roopam Mandloi, Khushboo Sawant, Swapnil Vyas
Clustering means that identical items are held together. Document clustering is an application of clustering, which refers to having related text records together. Document clustering plays a crucial role in the creation of search engines, where a document category is expected to be listed in the minimum answer period as a result of the query. Document clustering is critical in terms of the final objective of tracking membership, rundown, collection of subjects and data retrieval in a competent manner. First the designation is related to the upgrading of data recovery procedures. Recently, clustering techniques have been related in the regions, which involve browsing the gathered knowledge or ordering the findings of the web indices to address the query posed by the clients. This paper elaborates on the idea of document cum text clustering. This paper would include a survey of recent work in the area of text clustering. This paper would also include a critical analysis of current text clustering techniques.
{"title":"Literature Survey of Various Techniques of Documents Clustering","authors":"Roopam Mandloi, Khushboo Sawant, Swapnil Vyas","doi":"10.1109/ICATME50232.2021.9732710","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732710","url":null,"abstract":"Clustering means that identical items are held together. Document clustering is an application of clustering, which refers to having related text records together. Document clustering plays a crucial role in the creation of search engines, where a document category is expected to be listed in the minimum answer period as a result of the query. Document clustering is critical in terms of the final objective of tracking membership, rundown, collection of subjects and data retrieval in a competent manner. First the designation is related to the upgrading of data recovery procedures. Recently, clustering techniques have been related in the regions, which involve browsing the gathered knowledge or ordering the findings of the web indices to address the query posed by the clients. This paper elaborates on the idea of document cum text clustering. This paper would include a survey of recent work in the area of text clustering. This paper would also include a critical analysis of current text clustering techniques.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132741284","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732722
M. Pal, S. Agrawal
With the advancement of technology, the way of social connectivity is also changing. In this modern era most of the people are connected to each other through digital medium of communications. There are various platforms which facilitates social connectivity. The platforms like twitter, Facebook, WhatsApp, and google meet, zoom app etc. provides facilities like tweets, chats, video chats, video conferencing, personal conversation, group conversation etc. As the popularity of this social media platforms are increasing, threats to the privacy, online frauds, spamming activities and vulnerability is also increasing on this platforms. These platforms are becoming prime targets for attackers. There are various spam and spammer detection techniques that are developed for securing social media users from such attackers. A review of recent spam and spammer detection techniques is provided in this paper.
{"title":"Review of Various types of Spam and Spammer Detection Techniques","authors":"M. Pal, S. Agrawal","doi":"10.1109/ICATME50232.2021.9732722","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732722","url":null,"abstract":"With the advancement of technology, the way of social connectivity is also changing. In this modern era most of the people are connected to each other through digital medium of communications. There are various platforms which facilitates social connectivity. The platforms like twitter, Facebook, WhatsApp, and google meet, zoom app etc. provides facilities like tweets, chats, video chats, video conferencing, personal conversation, group conversation etc. As the popularity of this social media platforms are increasing, threats to the privacy, online frauds, spamming activities and vulnerability is also increasing on this platforms. These platforms are becoming prime targets for attackers. There are various spam and spammer detection techniques that are developed for securing social media users from such attackers. A review of recent spam and spammer detection techniques is provided in this paper.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134273561","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 : 2021-01-08DOI: 10.1109/ICATME50232.2021.9732756
R. Ahmad, Mahesh Parwani, A. A. Mansoori, Roshni Atulkar, P. K. Purohit
In the present study, we compare the observed values of foF2 (ionospheric f2 layer critical frequency) and ionospheric empirical modal IRI-2016 predictions. To accomplish this study we used the period of January to December 2016 which was the low solar activity period (declining phase of solar cycle 24th). For this study, we have selected a low latitude station SANYA, CHINA (18.34° N 109.42° E), a mid-latitude station ATHENS, GREECE (38.0° N 23.5°E), and a high latitude station YAKUTSK, RUSSIA (62.00° N 129.60° E). We examined monthly as well as seasonal variability of the ionosphere in comparison with IRI-2016 over all the three latitudes. Our analysis found that the monthly values of observed foF2 were maximum during March, May, and June for the low, mid, and high latitudes, respectively, while minimum values were observed during December at all three latitudes. For seasonal analysis, we found that the highest values of foF2 were recorded during the winter season while the lowest values were recorded in the equinox seasons of all three stations. When correlating between observed and model data, we found that the model is most suitable for mid-latitude, and its coefficient of correlation is 0.78.
{"title":"A comparative study of observed Ionospheric critical frequency (using ionosonde) and the IRI-2016 model","authors":"R. Ahmad, Mahesh Parwani, A. A. Mansoori, Roshni Atulkar, P. K. Purohit","doi":"10.1109/ICATME50232.2021.9732756","DOIUrl":"https://doi.org/10.1109/ICATME50232.2021.9732756","url":null,"abstract":"In the present study, we compare the observed values of foF2 (ionospheric f2 layer critical frequency) and ionospheric empirical modal IRI-2016 predictions. To accomplish this study we used the period of January to December 2016 which was the low solar activity period (declining phase of solar cycle 24th). For this study, we have selected a low latitude station SANYA, CHINA (18.34° N 109.42° E), a mid-latitude station ATHENS, GREECE (38.0° N 23.5°E), and a high latitude station YAKUTSK, RUSSIA (62.00° N 129.60° E). We examined monthly as well as seasonal variability of the ionosphere in comparison with IRI-2016 over all the three latitudes. Our analysis found that the monthly values of observed foF2 were maximum during March, May, and June for the low, mid, and high latitudes, respectively, while minimum values were observed during December at all three latitudes. For seasonal analysis, we found that the highest values of foF2 were recorded during the winter season while the lowest values were recorded in the equinox seasons of all three stations. When correlating between observed and model data, we found that the model is most suitable for mid-latitude, and its coefficient of correlation is 0.78.","PeriodicalId":414180,"journal":{"name":"2021 International Conference on Advances in Technology, Management & Education (ICATME)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115701889","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：