2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)最新文献_第5页

Current THD Analysis of Model Predictive Control based Grid-Connected PV Inverter 基于模型预测控制的并网光伏逆变器THD现状分析

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679406

A. Podder, M. Habibullah, N. K. Roy

引用次数: 6

An Expert Multi-Modal Person Authentication System Based on Feature Level Fusion of Iris and Retina Recognition 基于虹膜和视网膜识别特征级融合的专家多模态身份认证系统

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679153

Antu Saha, J. Saha, Barshon Sen

引用次数: 7

Measuring Vulnerabilities of Bangladeshi Websites 衡量孟加拉国网站的漏洞

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679426

Mohd. Moniruzzaman, F. Chowdhury, M. Ferdous

引用次数: 4

A Comparative Analysis of General, Sieve-of-Eratosthenes and Rabin-Miller Approach for Prime Number Generation 素数生成的一般、Sieve-of-Eratosthenes和Rabin-Miller方法的比较分析

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679358

Arnab Kanti Tarafder, Tamal Chakroborty

引用次数: 1

A Framework for Analyzing Real-Time Tweets to Detect Terrorist Activities 一个分析实时推文以检测恐怖活动的框架

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679430

M. Abrar, M. Arefin, Md. Sabir Hossain

引用次数: 10

CPG-Based Industrial Manipulators on AVR System for PTP Applications 基于cpg的工业机械手在AVR系统中的应用

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679138

A. Alam, H. Islam, Mohammad Tausiful Islam, S. Rashid, Md Jonayed Hossen, Md Sultan Mahmud

引用次数: 1

A New Nine Level Inverter with Low TSV and Fewer Numbers of Components for Renewable Energy Systems 用于可再生能源系统的低TSV、少组件的新型九电平逆变器

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679224

Md. Fazla Elahi, Md. Aminul Islam

{"title":"A New Nine Level Inverter with Low TSV and Fewer Numbers of Components for Renewable Energy Systems","authors":"Md. Fazla Elahi, Md. Aminul Islam","doi":"10.1109/ECACE.2019.8679224","DOIUrl":"https://doi.org/10.1109/ECACE.2019.8679224","url":null,"abstract":"A new nine-level inverter (NLI) topology is proposed in this paper which generates nine voltage levels in the output with fewer numbers of components. This NLI is equipped with ten semiconductor switches, four capacitors and one dc voltage source. The nine voltage levels are produced through different plausible combinations of the dc source and capacitors by using the power semiconductor switches. Here, in any mode, maximum two switches conduct at a time. This reduces the conduction losses and improves the efficiency of the system. Another important feature of this topology is the low total standing voltage (TSV) of the power semiconductor switches. To reduce the switching loss of the power switches, gate signals are generated following a proposed modulation strategy. To validate the NLI, a number of simulations are performed in Simulink. A comparison of the proposed inverter with the existing topologies shows that the number of required components and de sources are significantly lower. Therefore the proposed NLI structure can be used in renewable energy systems to make the power generation process more efficient.","PeriodicalId":226060,"journal":{"name":"2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114634887","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

Handwritten Isolated Bangla Compound Character Recognition 手写体孤立孟加拉复合字识别

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679258

Asfi Fardous, Shyla Afroge

引用次数: 16

Model Predictive Control Framework Design for Frequency Regulation of PREYs Participating in Interconnected Smart Grid 参与互联智能电网的猎物频率调节模型预测控制框架设计

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679201

Mizanur Rahman, S. Sarker, S. Das, M. F. Ali

引用次数: 1

A Machine Learning Approach for Heart Rate Estimation from PPG Signal using Random Forest Regression Algorithm 基于随机森林回归算法的PPG信号心率估计的机器学习方法

2019 International Conference on Electrical, Computer and Communication Engineering (ECCE) Pub Date : 2019-02-01 DOI: 10.1109/ECACE.2019.8679356

Shikder Shafiul Bashar, Md. Sazal Miah, A. Z. Karim, Md. Abdullah Al Mahmud, Zahid Hasan

{"title":"A Machine Learning Approach for Heart Rate Estimation from PPG Signal using Random Forest Regression Algorithm","authors":"Shikder Shafiul Bashar, Md. Sazal Miah, A. Z. Karim, Md. Abdullah Al Mahmud, Zahid Hasan","doi":"10.1109/ECACE.2019.8679356","DOIUrl":"https://doi.org/10.1109/ECACE.2019.8679356","url":null,"abstract":"In this paper, a new method is proposed to estimate the heart rate (HR) from wearable devices. A promising feature in today's world of HR monitoring is Photoplethysmography (PPG). However, during physical exercise HR estimation accuracy is seriously affected by noise and motion artifacts (MA). To reduce the effect of MA and estimate HR changes there are many conventional algorithms. Here, a new approach to estimate HR which is called multi-model machine learning approach (MMMLA) is shown. In this proposed algorithm, it firstly trains and tests the model for the different feature and different data set. Then it separates noisy and non-noisy data by K-means clustering. This lets the machine learn from noisy and non-noisy data. Then the Random Forest Regression algorithm is used to fit data and predict HR from test data. Here, feature engineering is also done, in other words, a different set of the feature is chosen and check their behavior with our proposed model and the error rate for every set of the feature was calculated. The mean absolute error and root mean square (RMS) error of HR was calculated. The lowest mean absolute error found in this research was 1.11 beats per minute (BPM). This result shows the capability of proposed machine learning-empowered system in HR estimation from PPG signal.","PeriodicalId":226060,"journal":{"name":"2019 International Conference on Electrical, Computer and Communication Engineering (ECCE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123991552","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: 18