The renewable resources based power generation is unpredictable since it highly depends on the conditions of climate. In India, after wind power, the second largest renewable based power generation is solar power. Therefore, forecasting for solar power generation is necessary since it depends on solar irradiance and temperature. In this paper, forecasting for solar power generation using machine learning has been done with and without using MPPT controller. The study has been done on Badabenakudi, Orissa, India. Machine learning based forecasting techniques has always been proved best than statistical based forecasting techniques. Different machine learning models have been applied on the data set taken. The result shows that Coarse Tree is the best model for solar power generating forecasting with MPPT controller having RMSE of 1.675 and Rational Quadratic Gaussian Process Regression (RQGPR) is the best model for solar power generation forecasting without MPPT controller having RMSE of 1.628.
{"title":"Machine Learning based Solar Power Generation Forecasting with and without MPPT Controller","authors":"Debottam Mukherjee, Samrat Chakraborty, Pabitra Kumar Guchhait, Joydeep Bhunia","doi":"10.1109/ICCE50343.2020.9290685","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290685","url":null,"abstract":"The renewable resources based power generation is unpredictable since it highly depends on the conditions of climate. In India, after wind power, the second largest renewable based power generation is solar power. Therefore, forecasting for solar power generation is necessary since it depends on solar irradiance and temperature. In this paper, forecasting for solar power generation using machine learning has been done with and without using MPPT controller. The study has been done on Badabenakudi, Orissa, India. Machine learning based forecasting techniques has always been proved best than statistical based forecasting techniques. Different machine learning models have been applied on the data set taken. The result shows that Coarse Tree is the best model for solar power generating forecasting with MPPT controller having RMSE of 1.675 and Rational Quadratic Gaussian Process Regression (RQGPR) is the best model for solar power generation forecasting without MPPT controller having RMSE of 1.628.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130913202","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}
Predictive Analytics is a bough of the advanced analytics which has been used to make predictions about unknown future events. It uses many techniques from Data Mining and Statistics Modelling to analyze the current data. In Statistics Modeling, Regression Analysis algorithms are some of the most popular processes used in Machine Learning Models. Random Forest is a supervised learning algorithm which uses Ensemble Learning method to take advantage of Bootstrap Aggregating. XGBoost is a scalable & accurate implementation of Gradient Boosting Machines (GBMs). It has been proved to push the limits of computing power. It is built & developed for the sole purpose of model performance and computational speed. Customers are the basis for growth of any type of business. In the study of sales and purchase, it is vital & crucial to be able to predict the amount of purchase or sales to increase benefit by catering from specific products to specific demographics. Our prediction analysis model can effectively help to improve the performance and increase the profit margin. Moreover, it can generalize the prediction of purchase or sales figures in any market which depends on the customers' past purchase pattern or behavior.
{"title":"An Efficient Predictive Analysis Model of Customer Purchase Behavior using Random Forest and XGBoost Algorithm","authors":"Subhatav Dhali, Monalisha Pati, Soumi Ghosh, Chandan Banerjee","doi":"10.1109/ICCE50343.2020.9290576","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290576","url":null,"abstract":"Predictive Analytics is a bough of the advanced analytics which has been used to make predictions about unknown future events. It uses many techniques from Data Mining and Statistics Modelling to analyze the current data. In Statistics Modeling, Regression Analysis algorithms are some of the most popular processes used in Machine Learning Models. Random Forest is a supervised learning algorithm which uses Ensemble Learning method to take advantage of Bootstrap Aggregating. XGBoost is a scalable & accurate implementation of Gradient Boosting Machines (GBMs). It has been proved to push the limits of computing power. It is built & developed for the sole purpose of model performance and computational speed. Customers are the basis for growth of any type of business. In the study of sales and purchase, it is vital & crucial to be able to predict the amount of purchase or sales to increase benefit by catering from specific products to specific demographics. Our prediction analysis model can effectively help to improve the performance and increase the profit margin. Moreover, it can generalize the prediction of purchase or sales figures in any market which depends on the customers' past purchase pattern or behavior.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127816517","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 : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290645
Sonali Basak, S. Sarkar, N. Das
The need for the enhancement of channel capacity of optical fiber, space division multiplexing (SDM) transmission fibers- such as the multicore fiber, the multimode fiber and the few-mode multicore fiber, etc., - have been researched for long-distance communication system. In this paper, a special type of homogeneous multicore fiber structure is introduced and the array of holes is placed between each core. The normalized propagation constant, mode field diameter (MFD) of LP01 mode of this structure are studied here. The empirical relation between normalized propagation constant and V number of LP01 mode of a single core single-mode fiber is compared with simulation results of LP01 mode of a multicore fiber. The mode field diameter of MCF is derived by noting the beam radius where the intensity drops to 1/e2 of the intensity on the beam axis. The theoretical prediction match well with COMSOL results. These results are useful for the investigation of the detailed characteristics of different types of MCF.
{"title":"A New Hole-walled Multi-core Fiber for Space Division Multiplexing for Improved Performance","authors":"Sonali Basak, S. Sarkar, N. Das","doi":"10.1109/ICCE50343.2020.9290645","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290645","url":null,"abstract":"The need for the enhancement of channel capacity of optical fiber, space division multiplexing (SDM) transmission fibers- such as the multicore fiber, the multimode fiber and the few-mode multicore fiber, etc., - have been researched for long-distance communication system. In this paper, a special type of homogeneous multicore fiber structure is introduced and the array of holes is placed between each core. The normalized propagation constant, mode field diameter (MFD) of LP01 mode of this structure are studied here. The empirical relation between normalized propagation constant and V number of LP01 mode of a single core single-mode fiber is compared with simulation results of LP01 mode of a multicore fiber. The mode field diameter of MCF is derived by noting the beam radius where the intensity drops to 1/e2 of the intensity on the beam axis. The theoretical prediction match well with COMSOL results. These results are useful for the investigation of the detailed characteristics of different types of MCF.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131322825","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 : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290646
S. Basu, Samiul Mamud
Removal of noise in ECG signal is an important aspect for the processing and analysis of signal. Since ECG is a low frequency signal, it can easily be corrupted by the external noise and artifacts. The main aim of the filtering is to eliminate the undesired frequency components while preserving the originality of the signal. Butterworth low pass filter is a fundamental type of IIR filter which is used widely in signal processing. The nature of the filtered output signal depends largely on the cut off frequency and order of the filter. This study aims to provide a comparative analysis of the cut off frequency and order of the filter on the ECG signal.
{"title":"Comparative Study on the Effect of Order and Cut off Frequency of Butterworth Low Pass Filter for Removal of Noise in ECG Signal","authors":"S. Basu, Samiul Mamud","doi":"10.1109/ICCE50343.2020.9290646","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290646","url":null,"abstract":"Removal of noise in ECG signal is an important aspect for the processing and analysis of signal. Since ECG is a low frequency signal, it can easily be corrupted by the external noise and artifacts. The main aim of the filtering is to eliminate the undesired frequency components while preserving the originality of the signal. Butterworth low pass filter is a fundamental type of IIR filter which is used widely in signal processing. The nature of the filtered output signal depends largely on the cut off frequency and order of the filter. This study aims to provide a comparative analysis of the cut off frequency and order of the filter on the ECG signal.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126336735","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 : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290650
Kaushik Sengupta, A. K. Mondal, D. Bose, Dilip K. Singh
Friction Stir Welding (FSW) is a solid-state joining process for welding similar and dissimilar materials with restriction to its use in some low melting point material. Electric Resistance Friction Stir Welding (ERFSW) is a new solid-state joining method for joining high strength materials. The paper presents the fundamental principle of ERFSW processes, impact of the variable parameters on weld quality, basic tool design along with its utilization, tool material and process parameters. The zones of the joint created by heating effect have also been discussed with its micro-structural study in brief. It has been demonstrated that ERFSW of high strength alloy is an emerging technology with numerous commercial applications which may have its great utilization in the field of space and an ignition prone area like mine.
{"title":"Fundamentals of Electric Resistance Friction Stir Welding of Metals: A Review","authors":"Kaushik Sengupta, A. K. Mondal, D. Bose, Dilip K. Singh","doi":"10.1109/ICCE50343.2020.9290650","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290650","url":null,"abstract":"Friction Stir Welding (FSW) is a solid-state joining process for welding similar and dissimilar materials with restriction to its use in some low melting point material. Electric Resistance Friction Stir Welding (ERFSW) is a new solid-state joining method for joining high strength materials. The paper presents the fundamental principle of ERFSW processes, impact of the variable parameters on weld quality, basic tool design along with its utilization, tool material and process parameters. The zones of the joint created by heating effect have also been discussed with its micro-structural study in brief. It has been demonstrated that ERFSW of high strength alloy is an emerging technology with numerous commercial applications which may have its great utilization in the field of space and an ignition prone area like mine.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126304451","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 : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290591
Ankita Saha, S. Chatterjee, A. Kundu
In this research paper, a study on Light Fidelity (LIFI) technology has been done for data transmission. Light Emitting Diode (LED) is used for data transmission in proposed technique using LIFI. A large number of data packets is transferred through light communication technology within less time period compared to existing techniques. Optical channel is used to transfer data between sender and receiver using specific data transmission protocol. In LIFI we have used an optical channel to encode an information into an optical signal. A receiver is there at the end to reproduces the message received from the optical signal.
{"title":"Analysis on Data Transmission using LIFI","authors":"Ankita Saha, S. Chatterjee, A. Kundu","doi":"10.1109/ICCE50343.2020.9290591","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290591","url":null,"abstract":"In this research paper, a study on Light Fidelity (LIFI) technology has been done for data transmission. Light Emitting Diode (LED) is used for data transmission in proposed technique using LIFI. A large number of data packets is transferred through light communication technology within less time period compared to existing techniques. Optical channel is used to transfer data between sender and receiver using specific data transmission protocol. In LIFI we have used an optical channel to encode an information into an optical signal. A receiver is there at the end to reproduces the message received from the optical signal.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131173341","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 : 2020-09-05DOI: 10.1109/icce50343.2020.9290673
{"title":"ICCE 2020 Copyright Page","authors":"","doi":"10.1109/icce50343.2020.9290673","DOIUrl":"https://doi.org/10.1109/icce50343.2020.9290673","url":null,"abstract":"","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134113919","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 : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290581
Mou De, A. Kundu, S. Guha
In cloud datacenter, job scheduling is used to manage real-time user data for improving response time of user query. Completion of tasks depends on datacenter availability, the capacity of virtual machines, network connection, and broker management policies. We propose a cloud-based architecture with a cloud service manager to select virtual machines from vmpool for optimizing tasks in the datacenter. The scheduling algorithm decides which virtual machine executes the task according to the nature and size of the task with minimal waiting time for execution.
{"title":"Advanced Cloud based Task Scheduling Architecture to Optimize Performance in Datacenter","authors":"Mou De, A. Kundu, S. Guha","doi":"10.1109/ICCE50343.2020.9290581","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290581","url":null,"abstract":"In cloud datacenter, job scheduling is used to manage real-time user data for improving response time of user query. Completion of tasks depends on datacenter availability, the capacity of virtual machines, network connection, and broker management policies. We propose a cloud-based architecture with a cloud service manager to select virtual machines from vmpool for optimizing tasks in the datacenter. The scheduling algorithm decides which virtual machine executes the task according to the nature and size of the task with minimal waiting time for execution.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115488284","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 : 2020-09-05DOI: 10.1109/ICCE50343.2020.9290626
Sanchita Gorai, Alekh Kumar, A. Kundu
The increase in pollution levels in water bodies has made water scarce for agricultural needs. Technology in irrigation not only overcomes this rising problem but also increases the efficiency of the farmers, thereby increasing the Gross Domestic Product of a country. This paper aims at using IoT in indoor irrigation methods to control the wastage of water and increase the efficiency of the soil. The proposed system reads the sensor data and handles water automatically (using relay motors and sprinklers) through a Control System board. A WiFi module is used for communication between sensors and base stations. Continuous detected information dealing with and showing on the server is cultivated using graphical UI. Remote checking of the field water system framework decreases human intercession and permits remote observing and controlling on the android phone. Distributed computing is an alluring answer for the huge measure of information created by the remote sensor arrangement. This paper proposes and assesses a cloud-based remote correspondence framework to screen and control a lot of sensors and actuators to evaluate the plants’ water needs. A light source powered by solar energy is used to manage the temperature of the soil according to season/crop needs.
{"title":"DeMux Controlled Sensor Based Smart Irrigation System","authors":"Sanchita Gorai, Alekh Kumar, A. Kundu","doi":"10.1109/ICCE50343.2020.9290626","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290626","url":null,"abstract":"The increase in pollution levels in water bodies has made water scarce for agricultural needs. Technology in irrigation not only overcomes this rising problem but also increases the efficiency of the farmers, thereby increasing the Gross Domestic Product of a country. This paper aims at using IoT in indoor irrigation methods to control the wastage of water and increase the efficiency of the soil. The proposed system reads the sensor data and handles water automatically (using relay motors and sprinklers) through a Control System board. A WiFi module is used for communication between sensors and base stations. Continuous detected information dealing with and showing on the server is cultivated using graphical UI. Remote checking of the field water system framework decreases human intercession and permits remote observing and controlling on the android phone. Distributed computing is an alluring answer for the huge measure of information created by the remote sensor arrangement. This paper proposes and assesses a cloud-based remote correspondence framework to screen and control a lot of sensors and actuators to evaluate the plants’ water needs. A light source powered by solar energy is used to manage the temperature of the soil according to season/crop needs.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124471106","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}
Permanent Magnet Synchronous (PMS) motor has huge applications in Electric Vehicles. Therefore, a correct prediction of both speed and torque is required for satisfactory result. A dataset is considered having real time data of ambient temperature, coolant temperature, direct axis and quadrature axis voltage and current, yoke temperature, rotor temperature and stator temperature for prediction of motor speed and torque. This dataset is collected from the test bench of University of Paderbon laboratory. Various machine learning models have been applied on the dataset. The result shows that Fine Tree is the best model for prediction of both speed and torque of the permanent magnet synchronous motor having lowest RMSE of 0.029224 and 0.052538 for prediction of speed and torque respectively.
{"title":"Application of Machine Learning for Speed and Torque Prediction of PMS Motor in Electric Vehicles","authors":"Debottam Mukherjee, Samrat Chakraborty, Pabitra Kumar Guchhait, Joydeep Bhunia","doi":"10.1109/ICCE50343.2020.9290632","DOIUrl":"https://doi.org/10.1109/ICCE50343.2020.9290632","url":null,"abstract":"Permanent Magnet Synchronous (PMS) motor has huge applications in Electric Vehicles. Therefore, a correct prediction of both speed and torque is required for satisfactory result. A dataset is considered having real time data of ambient temperature, coolant temperature, direct axis and quadrature axis voltage and current, yoke temperature, rotor temperature and stator temperature for prediction of motor speed and torque. This dataset is collected from the test bench of University of Paderbon laboratory. Various machine learning models have been applied on the dataset. The result shows that Fine Tree is the best model for prediction of both speed and torque of the permanent magnet synchronous motor having lowest RMSE of 0.029224 and 0.052538 for prediction of speed and torque respectively.","PeriodicalId":421963,"journal":{"name":"2020 IEEE 1st International Conference for Convergence in Engineering (ICCE)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122824458","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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