2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)最新文献

Evaluation and Comparison of DCT Approximations on FPGA for Hardware Reduction 基于FPGA的硬件约简DCT逼近方法的评估与比较

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258670

R. V. Escobar, A. M. Patiño, I. M. Moreno, M. G. Ramirez, E. R. Archundia, J. Gnecchi

引用次数: 1

Design of a Hardware in the Loop scheme in DC-DC converters using LabVIEW FPGA 基于LabVIEW FPGA的DC-DC变换器硬件在环方案设计

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258691

M. H. Cervantes, M. G. Ramirez, A. Téllez-Anguiano, F. M. Cardenas, J. C. Gómez, A. M. Patiño

引用次数: 0

Study of Light Degradation in High Power LEDs as a Function of the Feeding Waveform 大功率led的光衰减随馈电波形的变化规律研究

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258706

M. Juárez, A. Vázquez, R. Santillan, Adolfo R. López, G. Vázquez, J. M. Sosa

引用次数: 0

A Robust Control Strategy for Voltage Regulation in Electrical Distribution Networks by Means of Power Electronic Converters 基于电力电子变流器的配电网稳压鲁棒控制策略

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258689

Serafin Ramos-Paz, F. Ornelas‐Tellez, J. Rico-Melgoza, Antonio Ramos-Yépez

引用次数: 0

Control and Grid Connection of Fuel Cell Power System 燃料电池发电系统的控制与并网

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258729

G. Suárez-Velázquez, G. Mejia-Ruiz, P. García-Vite

引用次数: 3

Fault Magnitude Estimation Based on an Extended Residual Generator 基于扩展残差发生器的故障幅度估计

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258746

A. Lizarraga, O. Begovich, Antonio Ramírez

引用次数: 0

Embedded System for Inattention Detection in Driving Task 驾驶任务中注意力检测的嵌入式系统

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258738

Angelicue Castro, D. Vargas, Manuel Matuz Cruz

引用次数: 1

Detection and Feature Extraction of Single Power Quality Disturbances Based on Discrete Wavelet Transform, Energy Distribution and RMS Extraction Methods 基于离散小波变换、能量分布和均方根提取方法的单次电能质量扰动检测与特征提取

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258676

Eilen García-Rodríguez, E. Reyes-Archundia, J. Gutiérrez-Gnecchi, Arturo Méndez-Patiño, M. V. Chavez-Baez, Juan C. Olivares-Rojas

{"title":"Detection and Feature Extraction of Single Power Quality Disturbances Based on Discrete Wavelet Transform, Energy Distribution and RMS Extraction Methods","authors":"Eilen García-Rodríguez, E. Reyes-Archundia, J. Gutiérrez-Gnecchi, Arturo Méndez-Patiño, M. V. Chavez-Baez, Juan C. Olivares-Rojas","doi":"10.1109/ROPEC50909.2020.9258676","DOIUrl":"https://doi.org/10.1109/ROPEC50909.2020.9258676","url":null,"abstract":"Monitoring of power quality disturbances (PQD) in power systems is crucial in determining their causes and avoid equipment damages. In this work, a MatlabR algorithm was implemented to detect and extract the distinctive features of seven simple power quality disturbances (sag, swell, interruption, flicker, harmonics, oscillatory transient, and notch). Firstly, a database was generated with the seven types of disturbances designed from their mathematical models. The decomposition of the signals was subsequently performed using the Discrete Wavelet Transform (DWT) through the Multi-Resolution Analysis (MRA) with six levels of details. The sampling frequency was varied to identify the useful features that, with energy distribution and RMS extraction methods, serve as input to classifiers to distinguish disturbances. Three classifiers were considered to demonstrate the effectiveness of the algorithm to identify the useful features, Probabilistic Neural Network (PNN), k-nearest neighbors (k-nn) and Multilayer Feed Forward Neural Network (MLFF). With the proposed method, it was possible to detect the seven simple disturbances analyzed, maintaining a balance between simplicity, robustness, and efficiency, which will have an impact on guaranteeing a lower processing cost and can be used in real-time applications.","PeriodicalId":177447,"journal":{"name":"2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131551540","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

Evaluation of a Thermoelectric Generation system based on Differential-Power Processing architecture under non-uniform temperature conditions 非均匀温度条件下基于差分功率处理架构的热电发电系统评估

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258737

J. S. Artal-Sevil, C. Bernal-Ruíz, J. Beyza, V. M. Bravo

{"title":"Evaluation of a Thermoelectric Generation system based on Differential-Power Processing architecture under non-uniform temperature conditions","authors":"J. S. Artal-Sevil, C. Bernal-Ruíz, J. Beyza, V. M. Bravo","doi":"10.1109/ROPEC50909.2020.9258737","DOIUrl":"https://doi.org/10.1109/ROPEC50909.2020.9258737","url":null,"abstract":"This paper presents a Differential Power Processing (DPP) architecture applied to series-connected thermoelectric generators (TEG). Currently, thermoelectric technology is being considered as a promising power generation technology that can be used to recover waste heat energy. Thus, a thermoelectric generation system is studied under non-uniform temperature conditions in multiple TEG devices. The main objective is to allow each thermoelectric sub-module to reach its maximum power point more quickly. The purpose has been to improve the maximum power point tracking (MPPT) in each sub-module, thus it is possible to increase the efficiency with respect to the traditional method based on a global MPPT. Differential Power converters have been implemented in each TEG sub-module to provide an effective solution and mitigate the impact of the mismatch in the power obtained. The DPP architecture consists of a small micro-converter, at the submodular level, applied to the thermoelectric cell. The control algorithm is oriented to polarize each TEG device at its optimal point, which allows us an active balancing among the different TEG sub-modules regardless of the operating temperature. Matlab-Simulink has been the software used to develop the TEG module-array.","PeriodicalId":177447,"journal":{"name":"2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130955450","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: 4

A new approach for Pneumonia diagnosis using Convolutional Neural Networks 基于卷积神经网络的肺炎诊断新方法

2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2020-11-04 DOI: 10.1109/ROPEC50909.2020.9258749

Leonardo Alvarado, Pedro Illaisaca, Remigio Hurtado

{"title":"A new approach for Pneumonia diagnosis using Convolutional Neural Networks","authors":"Leonardo Alvarado, Pedro Illaisaca, Remigio Hurtado","doi":"10.1109/ROPEC50909.2020.9258749","DOIUrl":"https://doi.org/10.1109/ROPEC50909.2020.9258749","url":null,"abstract":"Convolutional Neural Networks (CNN) can be used as an efficient tool for detecting diseases between different types of medical imaging in a fast and reliable way, so that the article focuses on pneumonia disease, as yearly about 2.56 million people die in consequence of this illness. This paper illustrate the importance of data preprocessing as an effective approach for producing better results since raw data repercute in the training time, in consequence, it require more computational time to complete a determined machine learning problem. One of the main focal point is to introduce a novel and effective method to work with large amount of data and how it can be preprocessed for getting almost ideal results with a minimal lost of information due preprocessing. As the main result, the solution mentioned can help radiology and medical personnel to diagnose X-Ray images. Regarding the dataset, its name is Chest X-Ray Image Dataset, it's a public dataset of Kaggle and contains 5856 JPEG images organized in three directories. As future work, this model can be used to work with other types of Medical Images due to its adaptability.","PeriodicalId":177447,"journal":{"name":"2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132098724","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