IEEE Latin America Transactions最新文献_第5页

Redefining Human-Machine Collaboration: Industry 5.0 to Improve Safety and Efficiency 重新定义人机协作：工业5.0提高安全性和效率

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-07-08 DOI: 10.1109/TLA.2025.11072501

Francisco Antonio Lloret Abrisqueta;Antonio Guerrero González;Roberto Zapata Martinez

{"title":"Redefining Human-Machine Collaboration: Industry 5.0 to Improve Safety and Efficiency","authors":"Francisco Antonio Lloret Abrisqueta;Antonio Guerrero González;Roberto Zapata Martinez","doi":"10.1109/TLA.2025.11072501","DOIUrl":"https://doi.org/10.1109/TLA.2025.11072501","url":null,"abstract":"This study presents an innovative implementation of Industry 5.0 principles in a window production line, integrating advanced robotics and artificial intelligence technologies to improve operational efficiency and worker well-being. A robotic cell was designed to automate the handling of heavy components in the final production stage, resulting in a 35% reduction in cycle times and a significant decrease in ergonomic risks. Additionally, an interactive voice assistant based on generative AI was implemented, allowing operators to access system data and technical information in real-time through cognitive interaction. The results show a substantial improvement in job satisfaction, with a 278% increase in the perception of occupational health. This approach not only optimizes productivity but also redefines workers' roles, aligning with the human-centered vision of Industry 5.0. The study demonstrates how the integration of advanced technologies can create safer, more efficient, and adaptable work environments in modern manufacturing.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 8","pages":"729-735"},"PeriodicalIF":1.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072501","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepRetinaNet: An Automated AI-Based Framework for Retinal Disease Diagnosis DeepRetinaNet：一个基于人工智能的视网膜疾病自动诊断框架

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-07-08 DOI: 10.1109/TLA.2025.11072496

Akshya Kumar Sahoo;Priyadarsan Parida;Manoj Kumar Panda;Chittaranjan Nayak;N. Mohankumar

{"title":"DeepRetinaNet: An Automated AI-Based Framework for Retinal Disease Diagnosis","authors":"Akshya Kumar Sahoo;Priyadarsan Parida;Manoj Kumar Panda;Chittaranjan Nayak;N. Mohankumar","doi":"10.1109/TLA.2025.11072496","DOIUrl":"https://doi.org/10.1109/TLA.2025.11072496","url":null,"abstract":"Automated retinal disease diagnosis leveraging cutting-edge computer vision methodologies supports clinicians in the early identification of pathological conditions. This investigation delivers a novel framework, DeepRetinaNet for automating retinal disease diagnosis. The developed DeepRetinaNet model has two stages of novelties, including vessel extraction followed by disease identification. In the vessel extraction stage, the green channel, known for its heightened sensitivity to retinal vascular structures, is extracted from the source images. Subsequently, the vessel extraction network: RetiSegNet, processes these green channel images to extract retinal vessels, generating binary vessel maps. During the fusion phase, the original fundus images are combined with the extracted vessel maps to produce fused representations, encapsulating enriched spatial details from both sources. In the identification stage, these fused images are utilized to train the proposed classification framework: STDeepNet, which incorporates Modified Identity (MI), Modified Convolution (MCONV) blocks, and Long Short-Term Memory (LSTM) layers to effectively identify the diseases. The efficacy of the developed technique is corroborated using visual illustration and objective analysis. Also, the efficiency of the designed framework is verified on six benchmark datasets. The proposed framework demonstrates superior performance compared to 49 state-of-the-art methods, achieving notable accuracy in retinal disease diagnosis.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 8","pages":"718-728"},"PeriodicalIF":1.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072496","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

De-Occlusion Face Model based on Deep Occlusor Segmentation and Deep Inpainting Models 基于深度咬合器分割和深度图像修复模型的去遮挡人脸模型

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-07-08 DOI: 10.1109/TLA.2025.11072503

Miguel Gutierrez;Mario Chacon-Murguia;Juan Ramirez-Quintana

{"title":"De-Occlusion Face Model based on Deep Occlusor Segmentation and Deep Inpainting Models","authors":"Miguel Gutierrez;Mario Chacon-Murguia;Juan Ramirez-Quintana","doi":"10.1109/TLA.2025.11072503","DOIUrl":"https://doi.org/10.1109/TLA.2025.11072503","url":null,"abstract":"Image inpainting is a computer vision task that reconstructs missing image regions. Given its potential for various applications, it is an area of great interest. Despite advances in this field thanks to deep models such as autoencoders and generative adversarial networks, fundamental challenges persist, such as the causal interpretation of information loss and the risk of overfitting and lack of diversity in the features obtained with autoencoders. In this context, this article presents an innovative deep network model to solve occluded face inpainting. The model focuses on attributing the loss of information to the occlusion. The proposed model consists of two deep models: one for segmenting the object occluding the face, called SOCLNET, and another for reconstructing the face, IFACENET. SOCLNET is an improvement of the DeepLabv3 network by adding self-attention mechanisms. IFACENET is based on an autoencoder with an ensemble learning approach in the encoder to improve the diversity of the extracted features. SOCLNET was evaluated to demonstrate that the segmentation of occluding objects works adequately, even on out-of-distribution images. Its performance metrics were Pixel Accuracy = 0.93 and IoU = 0.788. The IFACENET model was compared against other state-of-the-art models using the Celeb-HQ database. The quantitative results of IFACENET show an average performance of SSIM = 0.95, PSNR = 26.813, and L1 = 0.261 with different mask values, being competitive with the state of the art. Additionally, qualitative results of IFACENET are shown to demonstrate the visual outcomes of face inpainting. Based on those results, it can be concluded that the proposed model effectively solves the reconstruction of occluded faces, opening new perspectives in the research of image reconstruction.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 8","pages":"662-674"},"PeriodicalIF":1.3,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072503","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144581452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward a new dataset: Mexican Traffic Signs ReWaIn-MTS for detection using deep learning 迈向新数据集：墨西哥交通标志rewin - mts，用于深度学习检测

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-06-20 DOI: 10.1109/TLA.2025.11045643

Daniela Bolaños-Flores;Tania Aglae Ramirez-delreal;Hamurabi Gamboa-Rosales;Guadalupe O. Gutierrez-Esparza

{"title":"Toward a new dataset: Mexican Traffic Signs ReWaIn-MTS for detection using deep learning","authors":"Daniela Bolaños-Flores;Tania Aglae Ramirez-delreal;Hamurabi Gamboa-Rosales;Guadalupe O. Gutierrez-Esparza","doi":"10.1109/TLA.2025.11045643","DOIUrl":"https://doi.org/10.1109/TLA.2025.11045643","url":null,"abstract":"A variety of factors along the road can endanger the safety of drivers or pedestrians and lead to high-impact accidents while driving, which is why traffic signs are essential elements that provide information about the condition of the road during the trip. Traffic sign detection and classification is a research area in computer vision. Its applications are mainly in autonomous conduction or assistance driving. Convolutional neural networks (CNNs) have outstanding detection results compared to conventional methods. In this work, we employed machine learning techniques based on CNNs to categorize and detect Mexican traffic signs. A dataset focused on traffic signs was outlined for the Mexican territory within the main urban roads in eight different cities. The dataset contains 2,283 road elements divided into 37 classes for training and validation of algorithms; a novel methodology is proposed to apply data augmentation and obtain better performance in classification and detection models. The mean Average Precision (mAP) metric compares the performance in state-of-the-art detection methods, particularly YOLOv5, YOLOv8, and the Transformer DETR, obtaining better results with trained models incorporating data augmentation.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 7","pages":"584-591"},"PeriodicalIF":1.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045643","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local Volt-Var Control Applied in an Islanded Microgrid Using Supervised Learning Techniques 基于监督学习技术的孤岛微电网局部伏无控制

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-06-20 DOI: 10.1109/TLA.2025.11045645

Diego Dias Domingues;Sérgio José Melo Almeida;Eduardo Antonio César Costa

{"title":"Local Volt-Var Control Applied in an Islanded Microgrid Using Supervised Learning Techniques","authors":"Diego Dias Domingues;Sérgio José Melo Almeida;Eduardo Antonio César Costa","doi":"10.1109/TLA.2025.11045645","DOIUrl":"https://doi.org/10.1109/TLA.2025.11045645","url":null,"abstract":"The electrical sector pursuit of technical and ecological alternatives makes it possible to integrate and cooperatively optimize dispersed energy resources, enhancing the stability, dependability, and resilience of contemporary energy systems. Microgrids and artificial intelligence are two ideas that could be included into contemporary power grids in an effort to lower costs and pollution emissions. This work proposes a new energy control and management strategy based on smart devices in this context. It explores machine-learning techniques for implementing supervised learning algorithms to perform automatic volt-var control adjustments and mitigate voltage fluctuations at the point of common coupling using smart inverters. The techniques explored and compared in this study include multilayer perceptron, SVM, and random forest. The results were consistent, with average accuracies above 90%, indicating the relevance of the analyzed models for this application. Thus, this research seeks to improve power quality in islanded microgrids with high penetration of distributed generation and explore the potential of artificial intelligence in decision-making processes.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 7","pages":"600-608"},"PeriodicalIF":1.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045645","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rapid Prototyping of FPGA Controlled Common Ground Single-Phase Transformerless Five-Level Inverter using Xilinx System Generator 基于Xilinx System Generator的FPGA控制共地单相无变压器五电平逆变器的快速原型设计

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-06-20 DOI: 10.1109/TLA.2025.11045647

Ravali Palakurthi;Kirubakaran Annamalai

{"title":"Rapid Prototyping of FPGA Controlled Common Ground Single-Phase Transformerless Five-Level Inverter using Xilinx System Generator","authors":"Ravali Palakurthi;Kirubakaran Annamalai","doi":"10.1109/TLA.2025.11045647","DOIUrl":"https://doi.org/10.1109/TLA.2025.11045647","url":null,"abstract":"This paper presents a Field Programmable Gate Array (FPGA) implementation for rapid prototyping of a new single-phase transformerless five-level inverter for PV applications. The inverter features a reduced device count, a common ground that eliminates the leakage current issue, and 100% DC utilization. It is capable of supplying both real and reactive power. A simple proportional-resonant (PR) controller is developed and uses a level-shifted pulse width modulation scheme to generate the firing pulses. Grid synchronization is achieved using a robust arc-tangent method-based phase-locked loop (PLL) strategy. To evaluate the open-loop performance, an experimental prototype is developed, and its responses are presented. Moreover, a hardware-in-the-loop (HIL) co-simulation is performed for grid interface to achieve real-time constraints on an Atlys Spartan 6 FPGA using Xilinx System Generator in the MATLAB/Simulink environment, and the results are reported. Finally, a detailed comparison of various five-level inverter topologies is presented to highlight the merits of the proposed topology.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"23 7","pages":"609-618"},"PeriodicalIF":1.3,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045647","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PAPR Reduction Technique for Mobile Communication Systems Using Neural Networks 基于神经网络的移动通信系统PAPR降低技术

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-06-20 DOI: 10.1109/TLA.2025.11045671

Bianca S. de C. da Silva;Pedro H. C. de Souza;Luciano L. Mendes

引用次数: 0

Design and Validation of an ABS and TCS Control Strategy Applied in an Automotive Simulator Using Model-Based Design Methodology 基于模型的汽车模拟器ABS与TCS控制策略设计与验证

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-06-20 DOI: 10.1109/TLA.2025.11045641

Igor Sales Bezerra Souza;Lucas Torres;André Murilo;Rafael Rodrigues da Silva

引用次数: 0

Table of Contents July 2025 目录2025年7月

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-06-20 DOI: 10.1109/TLA.2025.11045668

引用次数: 0

Multi-Objective Optimization for Safety in the Grounding of AC Substations 交流变电站接地安全的多目标优化

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2025-06-20 DOI: 10.1109/TLA.2025.11045670

Diego Arias-Cazco;Luis Cruz-Panchi

引用次数: 0