Pub Date : 2024-10-25DOI: 10.1109/TLA.2024.10735445
Meisam Mahdavi;Augustine Awaafo;Francisco Jurado
There is an overreliance of the energy sector of many developed and developing countries on fossil fuels to satisfy their growing energy needs. Paraguay and Morocco are noted to derive the greater share of their energy from fossil fuel imports. However, the high import bills and carbon emissions, as well as the depleting nature of fossil resources have compelled these countries to seek sustainable power sources. Bioenergy from agricultural residues is an example of such sources due to the high agricultural production in Paraguay and Morocco. Therefore, in this study, the potential of electric energy generation from the biomass of three different varieties of Bertoni namely; Gawi, SugHigh3, and Pop in rural regions of Morocco has been analyzed. The analysis showed that the capacity of the electricity generation from stevia biomass for the different regions considered in the study ranged from 421.2 to 16865 W/ha, while the leaf yield and HHV variation for the different varieties ranged between 2.15 t/ha and 7.86 t/ha, and 21.24 MJ/kg and 27.83 MJ/kg, respectively. By considering a 1.66-kW biogas generator with operating hours of 8761 per year and LHV of 26.436 MJ/kg, as well as gasification ratio of 0.7 and 63.1% carbon content for HOMER simulation, a total capacity of 6.64 MW is suggested for installation in Tazuta. The findings indicate that Bertonis dry leaves are excellent biomass resources for energy production in rural regions of Berkane, Larache, Marrakech, Rabat, and Sefrou and they can give us good lessons for rural electrification of Paraguay.
{"title":"A Sustainable Rural Electrification of Morocco Using Stevia Biomass Power Generation: Lessons for Paraguay","authors":"Meisam Mahdavi;Augustine Awaafo;Francisco Jurado","doi":"10.1109/TLA.2024.10735445","DOIUrl":"https://doi.org/10.1109/TLA.2024.10735445","url":null,"abstract":"There is an overreliance of the energy sector of many developed and developing countries on fossil fuels to satisfy their growing energy needs. Paraguay and Morocco are noted to derive the greater share of their energy from fossil fuel imports. However, the high import bills and carbon emissions, as well as the depleting nature of fossil resources have compelled these countries to seek sustainable power sources. Bioenergy from agricultural residues is an example of such sources due to the high agricultural production in Paraguay and Morocco. Therefore, in this study, the potential of electric energy generation from the biomass of three different varieties of Bertoni namely; Gawi, SugHigh3, and Pop in rural regions of Morocco has been analyzed. The analysis showed that the capacity of the electricity generation from stevia biomass for the different regions considered in the study ranged from 421.2 to 16865 W/ha, while the leaf yield and HHV variation for the different varieties ranged between 2.15 t/ha and 7.86 t/ha, and 21.24 MJ/kg and 27.83 MJ/kg, respectively. By considering a 1.66-kW biogas generator with operating hours of 8761 per year and LHV of 26.436 MJ/kg, as well as gasification ratio of 0.7 and 63.1% carbon content for HOMER simulation, a total capacity of 6.64 MW is suggested for installation in Tazuta. The findings indicate that Bertonis dry leaves are excellent biomass resources for energy production in rural regions of Berkane, Larache, Marrakech, Rabat, and Sefrou and they can give us good lessons for rural electrification of Paraguay.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 11","pages":"911-919"},"PeriodicalIF":1.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10735445","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142517913","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}
This paper introduces a new topology for five-phase OEWIM using Finite Control Set Model Predictive Torque Control (FCS-MPTC). This topology shows the enhancement in the steady-state performance by reducing flux and torque ripples and minimizing the percentage of total harmonic distortion (%THD) in stator current. The FCS-MPTC scheme proposed here employs a shared DC link for both inverters, ensuring zero common mode current, thereby eliminating the need for a large isolation transformer. This topology generates Synthetic voltage vectors (SVV) which result from the vector summation of the individual inverter virtual voltage vectors. Common Mode Voltage (CMV) across the motor windings is nullified using this topology. Another notable aspect of FCS-MPTC is its ability to suppress high harmonic currents through the windings by reducing the average voltage in the non-torque-producing plane (x-y plane). Experimental validation compares the effectiveness of FCS-MPTC against traditional Three-Level Direct Torque Control (TL-DTC) and Five-Level Direct Torque Control (FL-DTC) methodologies
{"title":"Minimization of Flux and Torque Ripples of FPOEW Induction Motor with FCS-MPTC using Synthetic Voltage Vectors","authors":"Naresh Rayavarapu;Swati Devabhaktuni;Venkata Subba Reddy Chagam Reddy","doi":"10.1109/TLA.2024.10735446","DOIUrl":"https://doi.org/10.1109/TLA.2024.10735446","url":null,"abstract":"This paper introduces a new topology for five-phase OEWIM using Finite Control Set Model Predictive Torque Control (FCS-MPTC). This topology shows the enhancement in the steady-state performance by reducing flux and torque ripples and minimizing the percentage of total harmonic distortion (%THD) in stator current. The FCS-MPTC scheme proposed here employs a shared DC link for both inverters, ensuring zero common mode current, thereby eliminating the need for a large isolation transformer. This topology generates Synthetic voltage vectors (SVV) which result from the vector summation of the individual inverter virtual voltage vectors. Common Mode Voltage (CMV) across the motor windings is nullified using this topology. Another notable aspect of FCS-MPTC is its ability to suppress high harmonic currents through the windings by reducing the average voltage in the non-torque-producing plane (x-y plane). Experimental validation compares the effectiveness of FCS-MPTC against traditional Three-Level Direct Torque Control (TL-DTC) and Five-Level Direct Torque Control (FL-DTC) methodologies","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 11","pages":"933-944"},"PeriodicalIF":1.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10735446","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518129","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}
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.
{"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":"22 11","pages":"899-910"},"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}
Pub Date : 2024-10-07DOI: 10.1109/TLA.2024.10706025
María Laura Montoro;Maria Nadia Pantano;Cecilia Fernandez;Maria Fabiana Sardella;Gustavo Scaglia
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.
{"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":"22 10","pages":"829-834"},"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}
In this paper, a reliability analysis to determinate the most preponderant negative effect between the amplitude and intensity of Extreme Operating Gust (EOG) in back-to-back (BTB) power converter connected to PMSG-based wind turbine is proposed. For this, a 42 factorial design is proposed to analyze the impact of amplitude and intensity of the EOG gust on the electrical variables measured at the BTB power converter such as the current, voltage and active power. Note that for this analysis the La Ventosa wind database allocated in Oaxaca, Mexico is considered. The simulation of this system was developed using the capabilities of the PSIM software. Finally, the results of the reliability analysis are presented, determining the factor with the greatest impact on the reliability of the BTB power converter.
{"title":"Impact of the Extreme Operating Gusts on Power Converter Connected to PMSG-based Wind Turbine for Reliability Analysis","authors":"Gregorio Martínez Reyes;Emmanuel Hernández Mayoral;Efraín Dueñas Reyes;Reynaldo Iracheta Cortez;José Rafael Dorrego Portela","doi":"10.1109/TLA.2024.10705994","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705994","url":null,"abstract":"In this paper, a reliability analysis to determinate the most preponderant negative effect between the amplitude and intensity of Extreme Operating Gust (EOG) in back-to-back (BTB) power converter connected to PMSG-based wind turbine is proposed. For this, a 42 factorial design is proposed to analyze the impact of amplitude and intensity of the EOG gust on the electrical variables measured at the BTB power converter such as the current, voltage and active power. Note that for this analysis the La Ventosa wind database allocated in Oaxaca, Mexico is considered. The simulation of this system was developed using the capabilities of the PSIM software. Finally, the results of the reliability analysis are presented, determining the factor with the greatest impact on the reliability of the BTB power converter.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 10","pages":"854-863"},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705994","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376533","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}
Pub Date : 2024-10-04DOI: 10.1109/TLA.2024.10705972
Jose Yael Lopez Hernandez;Enrique Gonzalez;Raul Pena;Antonio Carlos Bento;Sergio Camacho-Leon
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.
{"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":"22 10","pages":"842-853"},"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}
Pub Date : 2024-10-04DOI: 10.1109/TLA.2024.10705971
Ricardo C. Camara de M. Santos;Mateus Coelho;Ricardo Oliveira
Real-time object detection in images is one of the most important areas in computer vision and finds applications in several fields, such as security systems, protection, independent vehicles, and robotics. Many of these applications need to use edge hardware platforms, and it is vital to know the performance of the object detector on these hardware platforms before developing the system. Therefore, in this work, we executed performance benchmark tests of the YOLOv7-tiny model for real-time object detection using a camera and three embedded hardware platforms: Raspberry Pi 4B, Jetson Nano, and Jetson Xavier NX. We tested and analyzed the NVIDIA platforms and their different power modes. The Raspberry Pi 4B achieved an average of 0.9 FPS. The Jetson Xavier NX achieved 30 FPS, the maximum possible FPS rate, in three power modes. In the tests, it was possible to notice that the maximum CPU clock of the Jetson Xavier NX impacts the FPS rate more than the GPU clock itself. The Jetson Nano achieved 7.4 and 5.2 FPS in its two power consumption modes.
{"title":"Real-time Object Detection Performance Analysis Using YOLOv7 on Edge Devices","authors":"Ricardo C. Camara de M. Santos;Mateus Coelho;Ricardo Oliveira","doi":"10.1109/TLA.2024.10705971","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705971","url":null,"abstract":"Real-time object detection in images is one of the most important areas in computer vision and finds applications in several fields, such as security systems, protection, independent vehicles, and robotics. Many of these applications need to use edge hardware platforms, and it is vital to know the performance of the object detector on these hardware platforms before developing the system. Therefore, in this work, we executed performance benchmark tests of the YOLOv7-tiny model for real-time object detection using a camera and three embedded hardware platforms: Raspberry Pi 4B, Jetson Nano, and Jetson Xavier NX. We tested and analyzed the NVIDIA platforms and their different power modes. The Raspberry Pi 4B achieved an average of 0.9 FPS. The Jetson Xavier NX achieved 30 FPS, the maximum possible FPS rate, in three power modes. In the tests, it was possible to notice that the maximum CPU clock of the Jetson Xavier NX impacts the FPS rate more than the GPU clock itself. The Jetson Nano achieved 7.4 and 5.2 FPS in its two power consumption modes.","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 10","pages":"799-805"},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705971","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376789","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}
Pub Date : 2024-10-04DOI: 10.1109/TLA.2024.10705973
Nestor Gonzalez-Cabrera;Daniel Ernesto Hernandez Reyes;Vicente Torres García
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.
{"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":"22 10","pages":"864-870"},"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}
Pub Date : 2024-10-04DOI: 10.1109/TLA.2024.10705968
{"title":"Table of Contents October 2024","authors":"","doi":"10.1109/TLA.2024.10705968","DOIUrl":"https://doi.org/10.1109/TLA.2024.10705968","url":null,"abstract":"","PeriodicalId":55024,"journal":{"name":"IEEE Latin America Transactions","volume":"22 10","pages":"798-798"},"PeriodicalIF":1.3,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376986","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}
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.
{"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":"22 10","pages":"806-820"},"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}
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