IEEE Latin America Transactions最新文献_第2页

D-AI2-M: Ethanol Production Forecasting in Brazil Using Data-Centric Artificial Intelligence Methodology D-AI2-M：利用以数据为中心的人工智能方法预测巴西乙醇产量

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-25 DOI: 10.1109/TLA.2024.10735449

Antonio Mello;Lucas Giusti;Tarsila Tavares;Fernando Alexandrino;Gustavo Guedes;Jorge Soares;Rafael Barbastefano;Fabio Porto;Diego Carvalho;Eduardo Ogasawara

{"title":"D-AI2-M: Ethanol Production Forecasting in Brazil Using Data-Centric Artificial Intelligence Methodology","authors":"Antonio Mello;Lucas Giusti;Tarsila Tavares;Fernando Alexandrino;Gustavo Guedes;Jorge Soares;Rafael Barbastefano;Fabio Porto;Diego Carvalho;Eduardo Ogasawara","doi":"10.1109/TLA.2024.10735449","DOIUrl":"https://doi.org/10.1109/TLA.2024.10735449","url":null,"abstract":"Ethanol serves as one of Brazils primary biofuels. The country produces two main types of ethanol: i) hydrous ethanol, directly utilized as vehicle fuel, and ii) anhydrous ethanol, presently integrated at a rate of 27% into regular gasoline. In 2023, data from the National Agency of Petroleum, Natural Gas, and Biofuels (ANP) indicated that the total volume of ethanol sold in Brazil (hydrous and anhydrous) was just over 28 million cubic meters (m3), which corresponded to almost 22% of the total volume of liquid fuels sold in the country. These numbers illustrate the importance of this biofuel in Brazil. Just six states account for approximately 90% of Brazilian ethanol production. The logistical challenge arises from production seasonality and the necessity to transport ethanol from production sites to distribution and resale networks. Commonly, such prediction is supported using econometric models, such as ARIMA. Considering the recent advances in Artificial Intelligence, this challenge prompts the research question: Can we enhance monthly hydrous and anhydrous ethanol production prediction for the primary Brazilian-producing states using Artificial Intelligence Models (AIM) How should data be prepared for such an approach This study aims to contribute to logistical planning by employing D-AI2-M - a Data-Centric Artificial Intelligence (DAI) methodology - to aid in selecting AIM for ethanol production time series in the principal Brazilian-producing states. Our quantitative experimental evaluation demonstrates the superior forecasting performance of D-AI2-M in two approaches: i) Local: where different D-AI2-M outperform the benchmark models depending on the specific time series, and ii) Global: where a single D-AI2-M achieves the best mean performance across the complete set of evaluated time series.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10735449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518016","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

Mathematical Modeling for Pectin Extraction in Melon waste 甜瓜废料中果胶提取的数学建模

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-07 DOI: 10.1109/TLA.2024.10706025

María Laura Montoro;Maria Nadia Pantano;Cecilia Fernandez;Maria Fabiana Sardella;Gustavo Scaglia

{"title":"Mathematical Modeling for Pectin Extraction in Melon waste","authors":"María Laura Montoro;Maria Nadia Pantano;Cecilia Fernandez;Maria Fabiana Sardella;Gustavo Scaglia","doi":"10.1109/TLA.2024.10706025","DOIUrl":"https://doi.org/10.1109/TLA.2024.10706025","url":null,"abstract":"This article proposes a novel model for the extraction of pectin in melon peels and seeds. The methodology is based on the extraction of pectin in an acid medium for 180 minutes at 70, 80, and 90C, evaluating the performance of the product at each temperature. The kinetics of pectin extraction from melon peels and seeds, regardless of the working temperature, presents three stages: rapid release, then a plateau, followed by smooth growth until reaching the maximum amount of the product extracted. This process can be assimilated as a sequence of subprocesses, each with its own delay time and constants time. Based on the experimental results, each stage or period is mathematically modeled as a second-order linear with delay time. This dynamic model takes into account the work matrix, as well as the extraction mechanism used. The deviation of the model concerning to the experimental data is minimal, compared to the empirical and mechanistic models found in the literature for pectin extraction. The latter are based on oversimplified assumptions, leading to significant disparities between experimentally obtained and mathematically simulated results.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142397507","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

Impact of the Extreme Operating Gusts on Power Converter Connected to PMSG-based Wind Turbine for Reliability Analysis 极端运行阵风对与基于 PMSG 的风力涡轮机相连的电力转换器的影响可靠性分析

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI: 10.1109/TLA.2024.10705994

Gregorio Martínez Reyes;Emmanuel Hernández Mayoral;Efraín Dueñas Reyes;Reynaldo Iracheta Cortez;José Rafael Dorrego Portela

引用次数: 0

Implementation of an adaptive data logging algorithm in low-cost IoT nodes for supply chain transport monitoring 在用于供应链运输监控的低成本物联网节点中实施自适应数据记录算法

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI: 10.1109/TLA.2024.10705972

Jose Yael Lopez Hernandez;Enrique Gonzalez;Raul Pena;Antonio Carlos Bento;Sergio Camacho-Leon

{"title":"Implementation of an adaptive data logging algorithm in low-cost IoT nodes for supply chain transport monitoring","authors":"Jose Yael Lopez Hernandez;Enrique Gonzalez;Raul Pena;Antonio Carlos Bento;Sergio Camacho-Leon","doi":"10.1109/TLA.2024.10705972","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705972","url":null,"abstract":"In IoT-based supply chain transportation, low rates for data loss, minimizing time to destination, and optimizing energy consumption are paramount. These factors can be influenced by variable parameters, data volume, logging procedures, positioning complexities, and communication hiccups during transit. This study introduces an adaptive data logging algorithm for a cost-effective IoT node, addressing these challenges. This innovation enables real-time data acquisition and remote display via a web interface. Experimental tests demonstrate the prototype's reliability in both controlled indoor and dynamic outdoor environments, particularly in environmental and GPS data collection. Results reveal 5.24% data loss indoors and 23.24% via the web interface. Outdoors, data loss peaks at 55.34%, increasing to 82.76% with the web interface. However, the obtained information is adequate for prototype validation. The algorithm reduces data by 74%, leading to lower data processing and power transmission needs. Moreover, determining the distance from GPS coordinates is essential for predicting travel times and monitoring vehicle velocity to maximize efficiency. The results from this prototype are expected to enhance the development of advanced models, thus enriching future scientific research initiatives that aim to incorporate IoT technology into transportation systems.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705972","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142377148","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

Real-time Object Detection Performance Analysis Using YOLOv7 on Edge Devices 在边缘设备上使用 YOLOv7 进行实时物体检测性能分析

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI: 10.1109/TLA.2024.10705971

Ricardo C. Camara de M. Santos;Mateus Coelho;Ricardo Oliveira

引用次数: 0

Transmission Network Expansion Planning Considering Uncertainty in Demand with Global and Nodal Approach 考虑需求不确定性的输电网络扩建规划与全局和节点方法

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI: 10.1109/TLA.2024.10705973

Nestor Gonzalez-Cabrera;Daniel Ernesto Hernandez Reyes;Vicente Torres García

{"title":"Transmission Network Expansion Planning Considering Uncertainty in Demand with Global and Nodal Approach","authors":"Nestor Gonzalez-Cabrera;Daniel Ernesto Hernandez Reyes;Vicente Torres García","doi":"10.1109/TLA.2024.10705973","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705973","url":null,"abstract":"Transmission expansion planning aims to establish when and where to install new infrastructure such as transmission lines, cables, generators and transformers in the electrical power system. The planning must be motivated mainly to satisfy the increase in demand, consequently increase the reliability of the system and provide non-discriminatory access for generators and consumers to the electrical grid. In this sense, this work aims to propose a methodology to handle demand uncertainty by reducing scenarios through the K-means clustering algorithm, which is used to construct representative demand curves that allow using a static model of stochastic linear optimization with less computational effort, which seeks to minimize the investment and operating costs of the electrical system, meeting the total demand of the system. The global demand and nodal demand approach of the system is compared, observing the behaviour of investment and operating costs, as well as their advantages. The results demonstrate that the formulation can be estimate the number of scenarios through mathematical metrics and the global demand approach has the advantage of only needing data on the behavior of the total demand of the system.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376970","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

Table of Contents October 2024 目录 2024 年 10 月

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI: 10.1109/TLA.2024.10705968

引用次数: 0

Addressing Class Imbalance in Healthcare Data: Machine Learning Solutions for Age-Related Macular Degeneration and Preeclampsia 解决医疗保健数据中的类别失衡问题：针对年龄相关性黄斑变性和先兆子痫的机器学习解决方案

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI: 10.1109/TLA.2024.10705995

Antonieta Martinez-Velasco;Lourdes Martínez -Villaseñor;Luis Miralles-Pechuán

{"title":"Addressing Class Imbalance in Healthcare Data: Machine Learning Solutions for Age-Related Macular Degeneration and Preeclampsia","authors":"Antonieta Martinez-Velasco;Lourdes Martínez -Villaseñor;Luis Miralles-Pechuán","doi":"10.1109/TLA.2024.10705995","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705995","url":null,"abstract":"The use of machine learning in healthcare has transformed the way diseases are diagnosed and treatments are optimized. However, medical databases often lack balanced data due to challenges in data collection caused by privacy regulations. Certain health conditions are under represented, which hampers machine learning performance. To address this problem, a hybrid approach has been proposed that combines the Synthetic Minority Oversampling Technique (SMOTE) with under sampling and uses two specific techniques tailored for imbalanced datasets. Comparative evaluations were conducted using various thresholds to reduce one class and employingBalanced Accuracy to mitigate bias toward the majority class, with popular machine learning methods. The results showed that Balanced Bagging and Balanced Random Forest consistently outperformed other methods, performing the best with an average ranking of 1.42 and 3.58 out of 32 configurations in the two datasets, respectively. Tree-based approaches such as Random Forest and Gradient Boosting demonstrated similar effectiveness, emphasizing the power of aggregating predictions from multiple trees to reduce bias. Notably, under sampling andSMOTE proved advantageous for non-tree-based models likeKNN, SVM, and Logistic Regression showcasing their usefulness across different algorithms. This study provides a robust solution for handling imbalanced datasets in healthcare, which could potentially optimize healthcare interventions and improve patient outcomes and care.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376985","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

Deep Learning Based Hybrid Beamforming for mmWave Dual-Functional Radar-Communication 基于深度学习的毫米波双功能雷达-通信混合波束成形

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI: 10.1109/TLA.2024.10705967

Xiaoyou Yu;Tianchu Li;Ziyun Tian;Miao Yu

引用次数: 0

IoT-based Environmental Monitoring and Prediction of Banana Moisture Content in a Solar Greenhouse Dryer 基于物联网的环境监测以及太阳能温室干燥机中香蕉水分含量的预测

IF 1.3 4区工程技术

IEEE Latin America Transactions Pub Date : 2024-10-04 DOI: 10.1109/TLA.2024.10705969

Baltazar López-Velasco;Agustin Ruiz-Garcia;José Guillermo Cebada-Reyes;Carlos Alberto Villaseñor-Perea

{"title":"IoT-based Environmental Monitoring and Prediction of Banana Moisture Content in a Solar Greenhouse Dryer","authors":"Baltazar López-Velasco;Agustin Ruiz-Garcia;José Guillermo Cebada-Reyes;Carlos Alberto Villaseñor-Perea","doi":"10.1109/TLA.2024.10705969","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705969","url":null,"abstract":"Modernizing the drying process will reduce agricultural product waste and environmental pollution. The aim of this study was to design a monitoring system based on the internet of things (IoT), temperature and relative humidity for a solar dryer. This system consists of a data collection module that gathers data regarding temperature (Ta), external relative humidity (RH) and on/off time of the solar dryer exhaust fans; a communication module that transmits Ta and RH information via LoRa and Wifi to ThingSpeak for monitoring on a mobile device; and a power module providing electrical power for system operation by solar energy. The operation of the IoT monitoring system was evaluated in three drying experiments of Dominican bananas (Musa paradisiaca var sapientum), in which system performance was satisfactory, allowing the user to visualize graphically in a web and mobile interface the behavior of Ta and RH inside the dryer. The data measured by the system were used to predict banana moisture content with an autoregressive model with exogenous variables (ARX) identified online. The mathematical model found predicted the behavior of moisture content with a good goodness of fit, with values of R2 = 0.99, MSE = 1.2910-5 and MAE = -5.0310-6. The solar dryer allowed reducing the moisture content of bananas up to 19.84 % wet basis (w.b.) in a period of 4 days and by 20.03% w.b. for 5 days in the presence of rainfall.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705969","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376618","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