2020 IEEE India Council International Subsections Conference (INDISCON)最新文献_第4页

Coordinated Frequency Based Demand Side Management Scheme with Active Power Curtailment of Solar PV in a Battery Hybrid Stand-Alone Microgrid 电池混合独立微电网中太阳能光伏有功弃电的基于频率的需求侧协调管理方案

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00028

Sourav Chakraborty, P. Arvind, Souvik Bera, Deepak Kumar

{"title":"Coordinated Frequency Based Demand Side Management Scheme with Active Power Curtailment of Solar PV in a Battery Hybrid Stand-Alone Microgrid","authors":"Sourav Chakraborty, P. Arvind, Souvik Bera, Deepak Kumar","doi":"10.1109/INDISCON50162.2020.00028","DOIUrl":"https://doi.org/10.1109/INDISCON50162.2020.00028","url":null,"abstract":"Maintaining frequency in a stand-alone micro-grid is a more challenging task due to its less inertia. The intermittent nature of renewable energy sources also may lead to unbalance in the network with sudden load changes. With micro-grids operating as subsets of the larger regional electric power grid, they ensure a stable and reliable functioning. To perpetuate its resiliency, an optimal control strategy becomes necessary. This paper presents an efficient, coordinated frequency control solution in a stand-alone hybrid micro-grid to maintain the microgrid frequency adhering to IEEE Std. 1547. A significant portion of this paper comprises two-stage integration of solar photovoltaic with maximum power point tracking (MPPT) having a PV power curtailment algorithm to curtail the excess power during surplus availability of irradiance and a Battery Energy Storage System to deliver uninterrupted power. The efficacy of the control algorithm is demonstrated through the implementation of a 5-bus standalone micro-grid and validated the results considering different case studies on a Matlab/Simulink environment.","PeriodicalId":371571,"journal":{"name":"2020 IEEE India Council International Subsections Conference (INDISCON)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130334282","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}

引用次数: 0

Flame Temperature Prediction Using Machine Learning Model 利用机器学习模型预测火焰温度

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00042

Goutam Agrawal, Rutuparnna Mishra, Anshit Ransingh, S. Chakravarty

{"title":"Flame Temperature Prediction Using Machine Learning Model","authors":"Goutam Agrawal, Rutuparnna Mishra, Anshit Ransingh, S. Chakravarty","doi":"10.1109/INDISCON50162.2020.00042","DOIUrl":"https://doi.org/10.1109/INDISCON50162.2020.00042","url":null,"abstract":"The intensity or amount of heat present in any material, substance, or object is known as temperature. The process of measuring temperature is a tiresome and complex task from any visible heat source. The process of measuring temperature is known as thermometry. It plays a vital role in various industrial and manufacturing processes. There are several devices or gadgets present which are used for measuring temperature like a thermistor, Resistance Temperature Detector (RTD), infrared thermometer, thermocouples, pyrometers, etc. Every temperature measuring instrument has its demerits. While measuring temperature in some devices, one must be very alert because it is a necessity to check that the temperature of the material or substance should be less than or equal to the instrument temperature. In some instruments, the high temperature reduces productivity, and the efficiency of the sensors present in it. Some devices face the drawback of difference in temperature because in such types of devices there is a threshold temperature. If the temperature exceeds the threshold temperature in such a case, the measured temperature will differ with the temperature of the system. Under such circumstances, it will deviate from the original heat transfer property. To overcome all these drawbacks a machine learning model is proposed to detect approx. temperature using the color-temperature correlation approach. In this proposed system, histogram backprojection is used for pre-processing of the input image to derive the color of the flame. To predict the temperature, Support Vector Machine (SVM) and Artificial Neural Network (ANN) have been used and compared. Simulation results show that Support Vector Machine outperforms Artificial Neural Network.","PeriodicalId":371571,"journal":{"name":"2020 IEEE India Council International Subsections Conference (INDISCON)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132023847","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}

引用次数: 2

Detection of Near Duplicates over Graph Datasets Using Pruning 利用剪枝技术检测图数据集上的近重复项

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00068

P. Naveena, P. S. Rao

{"title":"Detection of Near Duplicates over Graph Datasets Using Pruning","authors":"P. Naveena, P. S. Rao","doi":"10.1109/INDISCON50162.2020.00068","DOIUrl":"https://doi.org/10.1109/INDISCON50162.2020.00068","url":null,"abstract":"Graphs are widely used formalism to model data in various domains such as natural language processing, chemoinformatics, computer vision, information retrieval and software engineering. Finding similar graphs is essential for many applications in these domains. Graph isomorphism finds exact duplicate graphs. However, it fails to quantify similarity and it's computationally expensive. To overcome both these bottlenecks, a number of graph similarity measures have been proposed. Graph Similarity Self-Join (GSSJ) is the problem of finding all pairs of graphs that have similarity score above a predefined threshold. For a graph dataset with n graphs, Naive solution involves similarity score computation for all (n/2) pairs of graphs. This problem is both compute and data intensive. Existing algorithms for this problem support only graph edit distance as the similarity measure. Overarching goal of this research is to develop algorithms for graph similarity self-join that support multiple graph similarity measures. Major contribution of this research will be better indexing mechanisms for graphs and tight bounds on graph similarity.","PeriodicalId":371571,"journal":{"name":"2020 IEEE India Council International Subsections Conference (INDISCON)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132536417","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}

引用次数: 0

Probabilistic Forecasting of Daily PV Generation Using Quantile Regression Method 基于分位数回归法的光伏日发电量概率预测

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00060

D. S. Tripathy, B. Prusty, D. Jena

{"title":"Probabilistic Forecasting of Daily PV Generation Using Quantile Regression Method","authors":"D. S. Tripathy, B. Prusty, D. Jena","doi":"10.1109/INDISCON50162.2020.00060","DOIUrl":"https://doi.org/10.1109/INDISCON50162.2020.00060","url":null,"abstract":"Probabilistic PV generation forecasting plays a significant role in the uncertainty management of power systems with higher penetration of PV generation. PV generation forecasting is more challenging due to the stochastic nature of weather conditions. Various outmoded probability models have been espoused for PV generation uncertainty; the most popular ones rely on specific parametric density functions to fit forecasting error. However, PV generation uncertainty has varying probability distribution patterns, and a parametric distribution for forecast error may not always be applicable at different time instants and places. Non-parametric approaches, e.g., quantile regression, on the other hand, estimate the predictive densities directly from the data without any constraints on the distribution shape. On this note, the benefit of the association of a few potential and sensible regressors set with the intricate PV generation pattern is envisioned for effective probabilistic forecasting. The regressors for the proposed quantile regression model are chosen based on the physics of the underlying phenomenon. The effectiveness of the proposed probabilistic forecasting is tested using real-world multi-time instant PV generation data collected from the USA. Out-of-sample quantile forecasts are generated for the PV generation, which is found to be accurate with a minimum deviation of estimated quantiles from the theoretical quantiles. Probability densities are found from these estimated quantiles, and their goodness-of-fit is tested using the famous KS test.","PeriodicalId":371571,"journal":{"name":"2020 IEEE India Council International Subsections Conference (INDISCON)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115276101","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}

引用次数: 1

A Double Tuning Fork Shaped Printed Antenna for Satellite Applications 用于卫星的双音叉形印刷天线

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00019

Harikrishna Paik, K. Sarojini

引用次数: 0

Design of DSP Controlled Passive Cell Balancing Network based Battery Management System for EV Application 基于DSP控制无源电池平衡网络的电动汽车电池管理系统设计

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00029

Sanket Dalvi, S. Thale

{"title":"Design of DSP Controlled Passive Cell Balancing Network based Battery Management System for EV Application","authors":"Sanket Dalvi, S. Thale","doi":"10.1109/INDISCON50162.2020.00029","DOIUrl":"https://doi.org/10.1109/INDISCON50162.2020.00029","url":null,"abstract":"Growing response for Electric Vehicles (EV) across the world is an implication of techno-economical efforts targeted to mitigate the challenges related to fossil fuels. Energy storage powering EVs is a very critical component. A battery pack used as energy storage in EVs uses many battery cells connected in series and parallel. These battery cells need close monitoring and management system during its operation in EVs. Such a system referred to as ‘Battery Management System’ (BMS) ensures a safe operating envelope while increasing battery power delivery capabilities and improving lifetime. The cell voltage balancing along with the State of Charge (SOC) and State of Health (SOH) monitoring are some of the critical functions of BMS. For EVs to become the best techno-commercial alternative for gasoline-based vehicles, BMS and battery packs will play a very crucial role. This paper highlights the state of the art of BMS and illustrates the passive cell balancing network design for Lithium-Iron-Phosphate (LiFePO4) batteries based on Digital Signal Processor (DSP) TMS320F28379D controller. Some key simulation and hardware results are presented to demonstrate the SOC estimation using the Coulombs counting method and battery cell balancing mechanism.","PeriodicalId":371571,"journal":{"name":"2020 IEEE India Council International Subsections Conference (INDISCON)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114834302","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}

引用次数: 6

A Review of Machine Learning Methodologies for Dental Disease Detection 牙病检测的机器学习方法综述

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00025

Gautam Chitnis, Vidhi Bhanushali, A. Ranade, Tejasvini Khadase, Vaishnavi Pelagade, Jitendra Chavan

{"title":"A Review of Machine Learning Methodologies for Dental Disease Detection","authors":"Gautam Chitnis, Vidhi Bhanushali, A. Ranade, Tejasvini Khadase, Vaishnavi Pelagade, Jitendra Chavan","doi":"10.1109/INDISCON50162.2020.00025","DOIUrl":"https://doi.org/10.1109/INDISCON50162.2020.00025","url":null,"abstract":"Dental diseases have become commonplace in today‘s fast paced world. Currently, most medical practitioners rely on manual analysis of a patient's oral cavity for initial diagnosis. Later, they rely on manual analysis of radiographs or x-rays for advanced diagnosis. To reduce this effort, systems are proposed for disease detection techniques working with radiographs or x-rays, which are only accessible to dental practitioners. Other techniques that work on raw, visible light based images of oral cavity have been trained on miniscule datasets with a narrow list of diseases that can be detected. There have been efforts in recent times to repurpose the general use machine learning algorithms such as CNNs for the particular task of disease detection and classification in medical imaging. The field of dentistry can benefit greatly by focusing more research on visible light images, allowing practitioners to offload the initial review of a patient to machines, giving them more bandwidth to work with cases that require more of their attention. This review intends to provide a comprehensive survey of currently proposed machine learning based dental disease detection systems along with suggestions towards what can be improved in the future to provide a better insight to researchers working in this domain.","PeriodicalId":371571,"journal":{"name":"2020 IEEE India Council International Subsections Conference (INDISCON)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130637507","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}

引用次数: 5

Modelling and Control of a Three Phase PVGT System 三相PVGT系统的建模与控制

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00031

R. W. Kotla, S. Yarlagadda

引用次数: 2

Convolutional Neural Network Based Smart Door Lock System 基于卷积神经网络的智能门锁系统

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00041

Rutupamna Mishra, Anshit Ransingh, M. Behera, S. Chakravarty

{"title":"Convolutional Neural Network Based Smart Door Lock System","authors":"Rutupamna Mishra, Anshit Ransingh, M. Behera, S. Chakravarty","doi":"10.1109/INDISCON50162.2020.00041","DOIUrl":"https://doi.org/10.1109/INDISCON50162.2020.00041","url":null,"abstract":"The worth of having liberty from any type of hazard, threat, risk, and any other vulnerabilities is known as security. It is the bane of modern-day society. Without security, all will act in a disadvantageous position both psychologically and mentally. A wide variety of locks are used but the most secure one in recent times is the biometric lock system. Moreover, by witnessing the recent situation it is quite evident that the biometric system can become a place of transmission of dangerous viruses as it is seen in the recent COVID-19 pandemic. Due to this reason, the government has banned all biometric systems and in some places, contactless security systems like a lock system using unique identification features like face, retina, and many more are encouraged. To overcome all these difficulties a contactless remote sensing locking system has a far-reaching advantage. In this context, a locking system is designed by collaborating IoT and machine learning techniques. So in this project, a system has been developed which can grant access by simply capturing your face snap. Every human has his/ her facial identification which is unique. Here the extracted feature is used as a passkey and is matched with the database. This project is a part of IoT and Machine Learning. This is developed on a Raspberry Pi and a Pi camera. The system is trained using the Convolutional Neural Network (CNN) approach to recognize the face of the authorized personnel only and to report the unauthorized in case of trespassing.","PeriodicalId":371571,"journal":{"name":"2020 IEEE India Council International Subsections Conference (INDISCON)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114610406","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}

引用次数: 2

Predicting Diabetes Using Outlier Detection and Multilayer Perceptron with Optimal Stochastic Gradient Descent 基于最优随机梯度下降的离群值检测和多层感知器预测糖尿病

2020 IEEE India Council International Subsections Conference (INDISCON) Pub Date : 2020-10-01 DOI: 10.1109/INDISCON50162.2020.00023

S. Ranjeeth, Venkata Ajay Krishna Kandimalla, Gangadhar Reddy D

引用次数: 4