Learning and Nonlinear Models最新文献_第2页

Transmission Line Fault Location Using MFCC and LS-SVR 基于MFCC和LS-SVR的输电线路故障定位

Learning and Nonlinear Models Pub Date : 2023-02-28 DOI: 10.21528/lnlm-vol21-no1-art8

Hermes Manoel Galvão Castelo Branco, James Blayne Oliveira Reis, Luan M. M. Pereira, Lucas da Costa Sá, R. A. L. Rabelo

{"title":"Transmission Line Fault Location Using MFCC and LS-SVR","authors":"Hermes Manoel Galvão Castelo Branco, James Blayne Oliveira Reis, Luan M. M. Pereira, Lucas da Costa Sá, R. A. L. Rabelo","doi":"10.21528/lnlm-vol21-no1-art8","DOIUrl":"https://doi.org/10.21528/lnlm-vol21-no1-art8","url":null,"abstract":"The location of Transmission Line (TL) Faults is a major problem in Electrical Power Systems (EPSs), since precisely identifying the point of occurrence of a fault in a TL it is possible to perform a faster restoration of the operation to the desired normal conditions. In this work we used a Least-Squares Support Vector Regression (LS-SVR) to locate faults in a TL with inputs provided by MFCC (Mel-Frequency Cepstral Coefficients) obtained from voltage signals during the fault. A modelled line based on parameters of a real line was used, with a total of 4008 fault situations being simulated on this Transmission Line. It is important to point out that MFCC are not used in applications involving EPS’s, and, according to the bibliographic research conducted by the team so far, no application of this feature extraction tool has been detected for the TL fault location problem. 3006 faults were used to train the model with cross-validation by the k-fold method, and 1002 faults were used for testing. The proposed methodology presented a good performance in the tests carried out, with a mean relative error of 0.000419 ±0.000640% when models are trained and tested with noiseless voltage signals. For models trained with voltage signals that present SNR ranging from 100 dB to 25 dB, the relative mean error ranged from 0.00334 ±0.00459%, in the first case, to 0.030580±0.043160% in the last.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"243 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130230516","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

Fuzzy Control of a High-Performance Boost Converter in Discontinuous Conduction Mode and its Application to a Photovoltaic Pumping System 断续导通模式下高性能升压变换器的模糊控制及其在光伏泵系统中的应用

Learning and Nonlinear Models Pub Date : 2023-02-28 DOI: 10.21528/lnlm-vol21-no1-art4

Igor Rocha de Sousa, Cláudio M. S. Medeiros, G. A. Barreto

引用次数: 0

A Hybrid Meta-Heuristic Approach for Optimal Meter Allocation in Electric Power Distribution Systems 配电系统电表优化配置的混合元启发式方法

Learning and Nonlinear Models Pub Date : 2023-02-28 DOI: 10.21528/lnlm-vol21-no1-art3

Thales Schuabb Almeida, Lucas Eduardo Silva Braga, L. W. Oliveira, E. J. Oliveira, J. C. S. Souza

引用次数: 0

Applying the Lifelong Machine Learning Paradigm in Tuberculosis Triage 终身机器学习范式在结核病分诊中的应用

Learning and Nonlinear Models Pub Date : 2022-12-31 DOI: 10.21528/lnlm-vol20-no2-art5

Regina Alves, Frederico Tavares, A. Trajman, J. Seixas

引用次数: 0

Application of Machine Learning Tools in the Evaluation of the Risk of Falls in the Elderly: An Integrative Review 机器学习工具在老年人跌倒风险评估中的应用:综合综述

Learning and Nonlinear Models Pub Date : 2022-12-31 DOI: 10.21528/lnlm-vol19-no2-art4

Daniele Alves Silva, Nayra Ferreira Lima Castelo Branco, H. M. C. Branco, G. A. Barreto

引用次数: 0

Tuning of Data Augmentation Hyperparameters to Covid-19 Detection in X-Ray Images with Deep Learning 基于深度学习的x射线图像Covid-19检测数据增强超参数调优

Learning and Nonlinear Models Pub Date : 2022-12-31 DOI: 10.21528/lnlm-vol20-no2-art1

Pedro Rici, Samara Oliveira Silva Santos, A. L. C. Ottoni

{"title":"Tuning of Data Augmentation Hyperparameters to Covid-19 Detection in X-Ray Images with Deep Learning","authors":"Pedro Rici, Samara Oliveira Silva Santos, A. L. C. Ottoni","doi":"10.21528/lnlm-vol20-no2-art1","DOIUrl":"https://doi.org/10.21528/lnlm-vol20-no2-art1","url":null,"abstract":"The Covid-19 pandemic has been declared in 2020 by the World Health Organization. One of the most relevant aspects of this respiratory disease is the fact that the infection caused by the new coronavirus has a high rate of spread. Thus, rapid and accurate diagnosis can contribute to reducing the transmission rate. In this aspect, in the literature, Deep Learning techniques are studied for application in the detection of this disease through X-ray images of the patient’s lung. However, one of the challenges in this area is the training of Convolutional Neural Network models with a database with few samples. One possibility is the generation of artificial images through Data Augmentation techniques. Thus, the objective of this work is to propose a careful methodology for the tuning of Data Augmentation hyperparameters for the classification of lung X-ray images in Covid-19 detection with Deep Learning. The proposed method consists of analyzing the accuracy of 36 Data Augmentation transformations applied to generate new images for training with balanced and unbalanced database. After the selection of hyperparameters, the classifier system achieved accuracies up to 100% on the testing stage, both for combinations and individual transformations with balanced database. Therefore, it is recommended to use a balanced database with the use of zoom, rotation, brightness in combination or individually, for Covid-19 versus Normal and Covid-19 versus Pneumonia classification.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134570360","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

Benign and Malign Breast Cancer Classification Using Support Vector Machines Optimized with Particle Swarm and Genetic Algorithms 基于粒子群和遗传算法优化的支持向量机的良恶性乳腺癌分类

Learning and Nonlinear Models Pub Date : 2022-12-31 DOI: 10.21528/lnlm-vol20-no2-art2

U. Contardi, P. Scalassara, Douglas Vieira Thomaz

{"title":"Benign and Malign Breast Cancer Classification Using Support Vector Machines Optimized with Particle Swarm and Genetic Algorithms","authors":"U. Contardi, P. Scalassara, Douglas Vieira Thomaz","doi":"10.21528/lnlm-vol20-no2-art2","DOIUrl":"https://doi.org/10.21528/lnlm-vol20-no2-art2","url":null,"abstract":"Breast cancer is a neoplastic disease that can be diagnosed either as benign or malign according to the growth-rate of the neoplastic lesion. Owing to the relevance of obtaining better detection tools, this work describes the development and optimization of support vector machines for the classification of the types of such cancer. Tests were performed using the breast cancer dataset of the University of Wisconsin Hospitals, USA, available at the Machine Learning Repository of the University of California Irvine. The radial basis function kernel was selected for the classifier and its hyperparameters were refined using two methods: particle swarm optimization and genetic algorithms. The results for the first method exhibited 97.71% accuracy, 96.30% sensitivity, and 98.65% of selectivity. On the other hand, using the second method, the accuracy was 95.78%, with sensitivity and selectivity of 96.73% and 95.25%, respectively. Therefore, there is an indication that these search algorithms are viable tools to optimize machine learning models for the purpose of breast cancer classification.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129184685","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

Intelligent Detection of Arrhythmia Episodes in Dialysis Patients 透析患者心律失常发作的智能检测

Learning and Nonlinear Models Pub Date : 2022-12-31 DOI: 10.21528/lnlm-vol20-no2-art3

Sergio Pinto Gomes Junior, J. Souza Filho, F. Henriques, M. Tcheou

引用次数: 0

A Tutorial on Fuzzy Time Series Forecasting Models: Recent Advances and Challenges 模糊时间序列预测模型教程:最新进展和挑战

Learning and Nonlinear Models Pub Date : 2022-12-31 DOI: 10.21528/lnlm-vol19-no2-art3

P. O. Lucas, Omid Orang, Petrônio C. L. Silva, E. M. Mendes, F. Guimarães

{"title":"A Tutorial on Fuzzy Time Series Forecasting Models: Recent Advances and Challenges","authors":"P. O. Lucas, Omid Orang, Petrônio C. L. Silva, E. M. Mendes, F. Guimarães","doi":"10.21528/lnlm-vol19-no2-art3","DOIUrl":"https://doi.org/10.21528/lnlm-vol19-no2-art3","url":null,"abstract":"Abstract: Time series forecasting is a powerful tool in planning and decision making, from traditional statistical models to soft computing and artificial intelligence approaches several methods have been developed to generate increasingly accurate forecasts. Fuzzy Time Series (FTS) methods have been introduced in the early 1990’s to handle data uncertainty and to undercome the statistical assumptions of linearity. Many studies have been reporting their good accuracy, simplicity, potential for interpretability and reduced computational complexity. This paper presents a tutorial for FTS methods. First, a review of the relevant literature is made, offering a foundation on the main concepts and FTS-based models for different time series and different types of forecasts. Then, the current challenges and possible solutions, are discussed alongside a timeline of the research developed in this area by the authors that aims at filling some of these gaps. Finally, a tutorial on the pyFTS library is presented. PyFTS is an open and free library coded in Python programming language that was developed by the MINDS Lab (Laboratory of Machine Intelligence and Data Science) and, also provides a set of transformation functions for pre-processing time series and a set of metrics and databases for benchmarking, in addition to implementing several FTS models in the literature.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116772700","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: 3

Unsupervised Class-Expert Learning for Supporting Covid-19 Triage Based on Computed Tomography Data 支持基于计算机断层扫描数据的Covid-19分类的无监督类专家学习

Learning and Nonlinear Models Pub Date : 2022-12-31 DOI: 10.21528/lnlm-vol20-no2-art6

Taís Aparecida Alvarenga, Luís Otávio Santos, D. Z. Rodríguez, D. Ferreira, B. Barbosa, J. Seixas

{"title":"Unsupervised Class-Expert Learning for Supporting Covid-19 Triage Based on Computed Tomography Data","authors":"Taís Aparecida Alvarenga, Luís Otávio Santos, D. Z. Rodríguez, D. Ferreira, B. Barbosa, J. Seixas","doi":"10.21528/lnlm-vol20-no2-art6","DOIUrl":"https://doi.org/10.21528/lnlm-vol20-no2-art6","url":null,"abstract":"Deep learning applications in medical imaging have been achieving promising results in the detection of diseases, among which clinical trials in terms of screening and diagnosis of patients with COVID-19 stand out. Computed Tomography (CT) images of the chest have been used by specialists for the diagnosis of COVID-19. However, due to the need of the moment and the possibility of using computational resources to help the medical team, it is observed in the literature several proposed works using supervised learning, however it lacks unsupervised methods for the screening and diagnosis of patients with COVID-19. In this work, the deep learning models Convolutional Neural Network (CNN) and Variational Autoencoders are used for feature extraction and later this information is used for binary and multiclass classification in unsupervised methods (k-means, Fuzzy C-Means and Self-Organizing Maps). For this purpose, a public database containing 4173 CT images (2168 CT slices from COVID-19, 758 slices from Healthy and 1247 slices from other lung diseases) was used. The results show that feature extraction via Variational Autoencoders has similar performance with state-of-the-art models in the literature for COVID-19, mainly for the binary classification with accuracies of 95.9%, 92.1% and 95.9% for k-means, Fuzzy C-Means and SOM, respectively, presenting competitive results in the literature. It also shows the importance of extracting features through convolutional networks to improve classification performance, resulting from the use of deep learning and its state of the art in the area of computer vision.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125991965","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