2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)最新文献_第10页

Methodology for Weapon Detection in Social Media Profiles using an Adaptation of YOLO-V5 and Natural Language Processing Techniques 基于YOLO-V5和自然语言处理技术的社交媒体档案武器检测方法

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018723

Alberto Baez-Velazquez, Aldo Hernandez-Suarez, G. Sánchez-Pérez, K. Toscano-Medina, J. Portillo-Portillo, J. Olivares-Mercado, H. Meana

{"title":"Methodology for Weapon Detection in Social Media Profiles using an Adaptation of YOLO-V5 and Natural Language Processing Techniques","authors":"Alberto Baez-Velazquez, Aldo Hernandez-Suarez, G. Sánchez-Pérez, K. Toscano-Medina, J. Portillo-Portillo, J. Olivares-Mercado, H. Meana","doi":"10.1109/ROPEC55836.2022.10018723","DOIUrl":"https://doi.org/10.1109/ROPEC55836.2022.10018723","url":null,"abstract":"Weapon identification has been a hot topic in the area of Object Recognition in recent years. However, its appli-cation has been virtually explored in social media. This work focuses on the detection of weapons in profiles that explicitly advocate their procession, both graphically and textually. This is a challenge, since access to a dataset is difficult; and once the samples are obtained, the dimensions and attributes of the images can vary significantly. In addition, the possession of a weapon does not imply that any offense or crime is being committed. To tackle these challenges, this manuscript presents a regularized adaptation of a Fast-Convolutional Neural Network (F-CNN) based on YOLO-V5, to merge and improve the results of the algorithm, along with a textual fingerprinting technique, to first corroborate if the intent of the post contains red flags of crime and violence. The results demonstrate that regularized adaptive models, mainly using Data Image Augmentation techniques, along with text classification, can provide better performance on unstructured data, such as those found in social media.","PeriodicalId":237392,"journal":{"name":"2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133700083","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

Design and development of an intent-based intelligent network using machine learning for QoS provisioning 设计和开发一个基于意图的智能网络，使用机器学习提供QoS

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018679

Marco Vinicio Barzallo Huanga, Remigio Ismael Hurtado Ortiz, Juan Daniel Amay Marca

{"title":"Design and development of an intent-based intelligent network using machine learning for QoS provisioning","authors":"Marco Vinicio Barzallo Huanga, Remigio Ismael Hurtado Ortiz, Juan Daniel Amay Marca","doi":"10.1109/ROPEC55836.2022.10018679","DOIUrl":"https://doi.org/10.1109/ROPEC55836.2022.10018679","url":null,"abstract":"The demand for bandwidth is currently a challenge for the use of the Internet that companies require, it is common to see daily the exorbitant amount of data that circulate through the network as files, calls, video calls, online shopping or subscription streaming services, this generates a bottleneck in network traffic therefore this makes maintenance and management difficult requiring time and human effort. To solve this problem it is proposed to use the architecture of differentiated services to prioritize a type of traffic and obtain Quality of Service (QoS), in addition the automation of activities will be used, that is, with artificial intelligence (AI) a neural network is developed to identify patterns and obtain predictions, machine learning (ML) that will predict when there will be events that alter the resources in the network. To demonstrate the effectiveness of the method, a proprietary dataset generated with data from the developed infrastructure is used, therefore the methods are evaluated with the quality metric Mean Absolute Error (MAE). At the end an Intention Based Network (IBN) will have been implemented, therefore this research intends to leave a base so that the proposed system can be improved or other methods can be developed to automate data centers.","PeriodicalId":237392,"journal":{"name":"2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133627776","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

Methodology for Characterization and Calibration of Linear Infusion Electromedical Devices 线性输注电子医疗设备的特性和校准方法学

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018580

J. Paredes, Alex Andrade, M. Loza, P. Rivadeneira, Alberto Sánchez

引用次数: 0

A Fault Diagnosis Comparative Approach for a Quadrotor UAV 一种四旋翼无人机故障诊断比较方法

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018633

J. J. Avilés-Espinoza, J. Anzurez-Marín, F. López‐Estrada

引用次数: 0

Minimization of energy storage in a solar farm by controlling the state of charge of a battery bank 通过控制电池组的充电状态来最小化太阳能发电厂的能量储存

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018652

M. Mendieta, L. I. Minchala, O. Probst

引用次数: 0

Grid-Forming Type Converter Decoupling and Resynchronization for an AC Microgrid 交流微电网成网型变流器解耦与再同步

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018617

A. J. T. G. De Raul, A. Maldonado, Perez R. Javier, A. Jose

引用次数: 1

Analysis of Scenarios for the Operation of a University Campus Microgrid using PSCAD/EMTDC 基于PSCAD/EMTDC的大学校园微电网运行方案分析

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018806

A. W. Serra, Anna D. Rocha, Patrícia S. Santos, S. L. Barcelos, O. Saavedra, R. G. Bento

引用次数: 0

DC line current in a compact MTDC system for DC short-circuit fault analysis 在紧凑型MTDC系统中对直流线路电流进行直流短路故障分析

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018680

Juan M. S. Fuentes-Burruel, E. Moreno-Goytia

引用次数: 0

Analysis and Design of Buck Converter $R^{2}P^{2}$ with Interleaved Function 交错函数降压变换器R^{2}P^{2}$的分析与设计

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018646

I. Reyes-Portillo, J. Morales-Saldaña, S. Méndez-Elizondo, A. Hernandez-Rodriguez, E. Palacios-Hernandez, C. A. Rivera-Romero

引用次数: 0

Data Analysis Architecture using Techniques of Machine Learning for the Prediction of the Quality of Blood Fonations against the Hepatitis C Virus 使用机器学习技术预测抗丙型肝炎病毒血液质量的数据分析体系结构

2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) Pub Date : 2022-11-09 DOI: 10.1109/ROPEC55836.2022.10018741

Paul Idrovo-Berrezueta, Denys Dutan-Sanchez, Remigio Hurtado-Ortiz, V. Robles-Bykbaev

{"title":"Data Analysis Architecture using Techniques of Machine Learning for the Prediction of the Quality of Blood Fonations against the Hepatitis C Virus","authors":"Paul Idrovo-Berrezueta, Denys Dutan-Sanchez, Remigio Hurtado-Ortiz, V. Robles-Bykbaev","doi":"10.1109/ROPEC55836.2022.10018741","DOIUrl":"https://doi.org/10.1109/ROPEC55836.2022.10018741","url":null,"abstract":"Nowadays the WHO (World health Organization) has difficulties improving the access to safe blood. The WHO have published that the problem with blood donations is that of the millions of blood donations that they receive one in four donations made from low-income countries do not test all the donated blood. This is a big problematic because a hospital cannot ensure a patient if the blood, he/she is receiving is safe. As a solution to this problematic, we have proposed the use a method based on CRISP-DM, where as a first procedure we apply a preparation to the data, then we prepared the dataset by cleaning the null variables, transforming the dataset by applying Hot Encoding, analysis the data with PCA (Principal Component Analysis) and using the 85% of variance, and using oversampling for the class that we have chosen. Once the dataset has been preprocessed we proceed to use the techniques of machine learning to help evaluate if a donor's blood is qualified or not for its use. We have applied a variety of machine learning techniques such as: RandomForest, KNN (K-Nearest-Neighbor), SVM (Support Vector Machine), and a neural network ANN (Artificial Neural Network). As a final step, we interpreted the results and got to a conclusion that the classifier that had the highest precision is the Random Forest classifier. For this this research we found a public dataset gathered by the university of Germany. This investigation has the objective to help improve the detection of hepatitis C in low-income countries and hopes to help improve the access to safe blood for patients who need them. In addition, we can apply this data analysis method for future investigations from which we encourage that tests be made with other techniques or models to analyze data.","PeriodicalId":237392,"journal":{"name":"2022 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)","volume":"1 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114028054","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