2020 International Conference on Computational Performance Evaluation (ComPE)最新文献_第10页

An Ensemble Kalman Filter based Explicit Nonlinear Model Predictive Control Design for Two Degree Freedom of Helicopter Model 基于集成卡尔曼滤波的二自由度直升机显式非线性模型预测控制设计

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200043

Lakshmi Dutta, Dushmanta Kumar Das

引用次数: 0

Ameliorated Anti-Spoofing Application for PCs with Users’ Liveness Detection Using Blink Count 基于眨眼计数的用户活跃度检测的改进防欺骗应用

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200166

Arpita Nema

引用次数: 2

Electricity Demand Prediction using Data Driven Forecasting Scheme: ARIMA and SARIMA for Real-Time Load Data of Assam 使用数据驱动预测方案的电力需求预测:ARIMA和SARIMA用于阿萨姆邦的实时负荷数据

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200031

Kakoli Goswami, A. B. Kandali

{"title":"Electricity Demand Prediction using Data Driven Forecasting Scheme: ARIMA and SARIMA for Real-Time Load Data of Assam","authors":"Kakoli Goswami, A. B. Kandali","doi":"10.1109/ComPE49325.2020.9200031","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200031","url":null,"abstract":"Aim of forecasting electrical load focuses in predicting satisfactorily and accurately the demand that might increase or decrease in the future. A large number of engineering applications count on accurate and reliable prediction models for electrical load demand. A precise forecasting of load helps in planning the capacity and operations of power companies to reliably supply energy to the consumers. In this study electrical load (L) in Assam is predicted using a data driven forecasting scheme. The study is carried out using daily 24 hourly L data obtained from SLDC, Kahilipara, Assam. The study focuses mainly on two types of regression model: ARIMA and SARIMA and also provides a performance evaluation of the models. The input data has been split into two groups of training and testing data to build the forecasting model. The correctness of the forecasting models has been assessed using the different error matrices. The final results indicated that the SARIMA model that considers the seasonality of load data provided better prediction with minimum error. MATLABR2016a was used during the entire analysis.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"48 2","pages":"570-574"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72599894","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: 20

Assessment of Technical Parameters of Renewable Energy System : An Overview 可再生能源系统技术参数评估综述

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200123

Alankrita, S. Srivastava, S. Gupta, A. Pandey

{"title":"Assessment of Technical Parameters of Renewable Energy System : An Overview","authors":"Alankrita, S. Srivastava, S. Gupta, A. Pandey","doi":"10.1109/ComPE49325.2020.9200123","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200123","url":null,"abstract":"This paper discusses different components of hybrid renewable energy system on basis of technical parameters, sizing issues, power converter architecture and challenges faced by each of them. Since optimal operating point of whole hybrid system is required, it is necessary that not only each component operate at its own optimal operating point, but it should also complement operating point of other components. Main challenge with PV system is associated with partial shading and local maximum power point, which affects maximum power extraction from system. In case of Wind system, sizing of turbines, power fluctuation which can affect grid frequency is major issue. Battery energy storage system suffers from battery aging issues. Technical limitations of individual components can somewhat be addressed by complimentary advantage of other component and power flow fluctuation can addressed by suitable design of energy storage system. Cost of energy storage system be reduced too if extra power can be injected to the grid as in the case of grid connected system. Some hybrid system provides possibility of better resource utilization potential, while other can provide better dynamic and/or steady state performance depending on requirements. Hybrid system can also address shortcoming of one energy resource by providing complimentary parameter which can make up for such shortcoming. Factors which determine what hybrid system to choose for depends purely on requirements.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"39 1","pages":"107-112"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72673784","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

Wireless Stethoscope with Bluetooth Technology 无线听诊器与蓝牙技术

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200163

Janhvi Malwade, Suaad Sayyed, Jamima Nasir, Yashada Parab, Geetha Narayanan, Shishir Gupta

引用次数: 2

A Lightweight CNN Architecture for Land Classification on Satellite Images 用于卫星图像土地分类的轻量级CNN架构

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200100

Gourab Patowary, Meenakshi Agarwalla, S. Agarwal, M. Sarma

{"title":"A Lightweight CNN Architecture for Land Classification on Satellite Images","authors":"Gourab Patowary, Meenakshi Agarwalla, S. Agarwal, M. Sarma","doi":"10.1109/ComPE49325.2020.9200100","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200100","url":null,"abstract":"Land cover classification using satellite images is an important tool in the study of terrestrial resources. Satellite based information is presently available as huge sets of high resolution images from a large number of satellites like Sentinel, Landsat-8, etc. Land cover classification from these images is a difficult task because of very large sized data and high variation types. Deep Neural Networks can play a vital role in this regard and can perform classification on these large sized data. Related works in this field have used lighter models and included a large number of handcrafted parameters which requires domain knowledge on the subject. It is realised that most models are too shallow for such a complicated image. In this paper, a deeper Convolutional Neural Network (CNN) model without any satellite image specific parameters is proposed. On SAT4 and SAT6 images, our 13-layered network has achieved better accuracy upto 99.84% and 99.47% which is state-of-the-art. It is still called lightweight model because most models in Artificial Intelligence(AI)-CNN are much deeper and larger than ours.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"82 1","pages":"362-366"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76188117","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

Braille Book Reader using Raspberry Pi 盲文阅读器使用树莓派

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200110

Abhishek Sharma, S. Devi, J. K. Verma

引用次数: 3

Effect of Cooling Systems on the Energy Efficiency of Data Centers: Machine Learning Optimisation 冷却系统对数据中心能源效率的影响:机器学习优化

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200088

Rajendra Kumar, S. Khatri, Mario José Diván

{"title":"Effect of Cooling Systems on the Energy Efficiency of Data Centers: Machine Learning Optimisation","authors":"Rajendra Kumar, S. Khatri, Mario José Diván","doi":"10.1109/ComPE49325.2020.9200088","DOIUrl":"https://doi.org/10.1109/ComPE49325.2020.9200088","url":null,"abstract":"The number of data centers is increasing rapidly since more people are now using cloud services for data storage and management. Due to this, the total power consumption of the data centers is also increasing. The energy efficiency of the data centers is not very high due to a variety of reasons like heat loss by equipment and power factor issues. This paper attempts to review the existing work around 2015 to 2019 and understand the issues faced by the data centers. The energy usage by the data centers and the effect of the temperatures are reviewed along with the methods of optimisation through Machine Learning (ML) algorithms. Some of the factors affecting the energy consumption of the data centers are the airflow, heat loss, ambient temperature, among others. The gap in the existing research is obtained by identifying the various factors that affect the cooling of the data centers. The effect of the cooling parameters is optimised at different locations of the data centers as per the requirement. Reinforced learning techniques have been seen to be efficient in terms of optimisation. A combination of Support Vector Machine (SVM) and Ant Colony Optimisation (ACO) is suggested as a future scope of this study.","PeriodicalId":6804,"journal":{"name":"2020 International Conference on Computational Performance Evaluation (ComPE)","volume":"43 1","pages":"596-600"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77568882","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

Design of Microstrip Tunable Bandpass Filter using Ferroelectric Thin Film Varactor 铁电薄膜变容管微带可调谐带通滤波器的设计

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200050

Santosh Kumar, C. Yadav, Barasha Mali

引用次数: 1

An Image Processing Approach for Grading of Mangoes based on Maturity 基于成熟度分级的芒果图像处理方法

2020 International Conference on Computational Performance Evaluation (ComPE) Pub Date : 2020-07-01 DOI: 10.1109/ComPE49325.2020.9200114

Md. Baig Mohammad, Lakshmi Narayana Thalluri, Renuka Devireddy, Priyanka Ch., Rajiya Sulthana

引用次数: 2