Learning and Nonlinear Models最新文献

Study and Analysis of Deep Learning Techniques for Solving Financial Problems 研究和分析解决金融问题的深度学习技术

Learning and Nonlinear Models Pub Date : 2024-01-19 DOI: 10.21528/lnlm-vol21-no2-art4

Wendell Avila, R. Salgado

引用次数: 0

An Active Learning Approach for Detecting Customer Induced Damages in Motherboards with Deep Neural Networks 利用深度神经网络检测主板中由客户引起的损坏的主动学习方法

Learning and Nonlinear Models Pub Date : 2024-01-19 DOI: 10.21528/lnlm-vol21-no2-art3

Lucas Cabral, Victor Farias, Lucas Sena, Iago Chaves, J. P. Gomes, João Pedro Santiago, Diego Sá, Javam Machado, João Paulo Madeiro

{"title":"An Active Learning Approach for Detecting Customer Induced Damages in Motherboards with Deep Neural Networks","authors":"Lucas Cabral, Victor Farias, Lucas Sena, Iago Chaves, J. P. Gomes, João Pedro Santiago, Diego Sá, Javam Machado, João Paulo Madeiro","doi":"10.21528/lnlm-vol21-no2-art3","DOIUrl":"https://doi.org/10.21528/lnlm-vol21-no2-art3","url":null,"abstract":"Identifying Customer Induced Damage (CID) is a key part in warranty programs of electronics manufacturers. CID is defined as any damage in the unit performed by an unauthorized person including the customer in a Printed Circuit Board (PCB). In such cases, damaged units are not covered by warranty. The inspection of CIDs is usually performed by humans which may be costly and error prone. Modern computer vision techniques for object detection using deep neural networks can automatically and accurately detect CIDs on PCBs. The training of such networks requires a large labeled dataset of image examples of CIDs. Daily, hardware factories and repair centers generate hundreds of unlabeled images. Labeling them manually is laborious and time-consuming. Therefore, it is crucial to label the minimum amount of images such that the trained neural network can achieve comparable accuracy as if it were trained with the whole dataset. To this end, we propose an active learning approach that selects the most informative images for the object detector. For that, our approach is based on the uncertainty of the object detector, i.e., it selects new images based on class probability distribution given by the object detector. Also, we tackle some challenges that are intrinsic to this problem: i) it is a multiclass object detection problem since there are many types of defects; ii) there is a class accuracy imbalance; iii) there is a focus on recall, e.g. false positives are less harmful than false negatives, and iv) there are many images with no object which should not be selected for labeling. We evaluate this approach by using it to iteratively sample data, train and evaluate a model, and compare it with randomly sampled data. The results show that our method consistently outperforms random sampling by an average margin of 21.6%, proving to be a viable alternative for reducing the labeling cost and increasing detection accuracy in this domain.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"65 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139613390","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

Analysis and Improvement of Machine Learning Models for Detecting Street Lighting Lamps 检测路灯的机器学习模型分析与改进

Learning and Nonlinear Models Pub Date : 2023-11-22 DOI: 10.21528/lnlm-vol21-no2-art2

Igor Caetano Silva, Ricardo Menezes Salgado, Igor Mattos Varejão, F. M. Varejão

{"title":"Analysis and Improvement of Machine Learning Models for Detecting Street Lighting Lamps","authors":"Igor Caetano Silva, Ricardo Menezes Salgado, Igor Mattos Varejão, F. M. Varejão","doi":"10.21528/lnlm-vol21-no2-art2","DOIUrl":"https://doi.org/10.21528/lnlm-vol21-no2-art2","url":null,"abstract":"The Brazilian public lighting network is maintained by city halls. To bill the energy provided to city halls, energy distribution companies should maintain an updated database of network poles, their lamp types, and wattages. However, it is common to encounter issues with misinformation, where the company is not notified about changes in the public lighting network by city halls and cannot update its database appropriately. To mitigate commercial losses, companies have resorted to sending teams for manual infrastructure checks, which is an expensive, time-consuming, and unreliable process. In this regard, this work aims to optimize the models proposed in the literature capable of accurately classifying the type and wattage of lamps on public lighting poles based on data collected from radiometric sensors and a professional camera. Data is processed using traditional machine learning and deep learning algorithms, along with more sophisticated validation techniques such as data transformation and hyperparameter optimization to achieve improved results. Based on this methodology, the results demonstrate that models employing more robust algorithms (Support Vector Machine, XGBoost, Random Forest, and Multilayer Perceptron) can attain a final average accuracy of 80-86%. This confirms the usefulness of this methodology as an alternative solution to address the issue of public lighting billing.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139247081","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

Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing 基于适应度共享遗传算法的局部规则解释方法

Learning and Nonlinear Models Pub Date : 2023-09-07 DOI: 10.21528/lnlm-vol21-no2-art1

Daniel A. Santos, J. A. Baranauskas, Renato Tinós

{"title":"Local Rule-Based Explanations Method Based on Genetic Algorithms with Fitness Sharing","authors":"Daniel A. Santos, J. A. Baranauskas, Renato Tinós","doi":"10.21528/lnlm-vol21-no2-art1","DOIUrl":"https://doi.org/10.21528/lnlm-vol21-no2-art1","url":null,"abstract":"The Local Rule Based Explanations method (LORE) explains decisions of black-box classifiers by using an interpretable model (Decision Tree – DT). The DT is trained with an artificial dataset generated by Genetic Algorithms (GAs). The primary objective of this approach is to replicate the decision boundaries of the black-box model in proximity to the instance under explanation. We show that the artificial examples generated by the GAs in LORE are not necessarily diverse. Consequently, we propose the integration of GAs with fitness sharing in LORE to generate a more diversified subset of artificial examples. The underlying motivation is to ensure that the local decision boundaries of the DT more closely resemble those of the black-box classifier. Experimental results with two classifiers (Multilayer Perceptron and Random Forests), and four classification problems, indicate that LORE with fitness sharing yields more diverse GA populations, consequently leading to improved local explanations. These findings underscore the effectiveness of incorporating fitness sharing into the LORE methodology for enhancing the explainability of black-box classifiers.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132820285","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

Uma Introdução Amigável às Redes Neurais para Grafos 图的神经网络的友好介绍

Learning and Nonlinear Models Pub Date : 2023-09-07 DOI: 10.21528/lnlm-vol19-no2-art5

Tiago Silva, Amauri Holanda Souza Junior, Diego Parente Paiva Mesquita

引用次数: 0

A Statistical Signal Processing Approach to Islanding Detection 孤岛检测的统计信号处理方法

Learning and Nonlinear Models Pub Date : 2023-02-28 DOI: 10.21528/lnlm-vol21-no1-art5

Robson Rosserrani Lima, A. Cerqueira, P. F. Ribeiro

{"title":"A Statistical Signal Processing Approach to Islanding Detection","authors":"Robson Rosserrani Lima, A. Cerqueira, P. F. Ribeiro","doi":"10.21528/lnlm-vol21-no1-art5","DOIUrl":"https://doi.org/10.21528/lnlm-vol21-no1-art5","url":null,"abstract":"The integration of distributed generation (DG) sources in the electric energy systems may bring new problems that need attention, one of these problems is the occurrence of unintentional islanding. Islanding is a condition in which part of the distribution network is disconnected from the system, and consumer units are still powered by one or more DGs, which can cause damage to equipment and pose risks to the safety of technicians. This paper shows an islanding detection method (IDM) in Power Systems with DG based on statistical signal processing. We used a MathWorks Simulink model of a grid-connected 250 kW photovoltaic (PV) array to simulate the behavior of the three-phase voltage signal in the point of common coupling (PCC) under the nominal operation, islanding condition, and fault condition using different load compositions. Principal Component Analysis (PCA) was used to extract the transitory events from the voltage signals, and then we used second-, third-, and fourth-order cumulants to generate features and the best ones were selected using the Fisher’s Discriminant Ratio (FDR). A Radial Basis Function Network (RBFN) makes the classification of the events. We found that, for this setup, we can achieve detection rates of 99% for both islanding condition detection and fault occurrence classification, no matter the power mismatch between the load and the DG.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"31 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":"124929454","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

Features Extraction and Selection with the Scattering Transform for Electrical Load Classification 基于散射变换的电力负荷分类特征提取与选择

Learning and Nonlinear Models Pub Date : 2023-02-28 DOI: 10.21528/lnlm-vol21-no1-art2

E. L. Aguiar, A. Lazzaretti, D. Pipa

{"title":"Features Extraction and Selection with the Scattering Transform for Electrical Load Classification","authors":"E. L. Aguiar, A. Lazzaretti, D. Pipa","doi":"10.21528/lnlm-vol21-no1-art2","DOIUrl":"https://doi.org/10.21528/lnlm-vol21-no1-art2","url":null,"abstract":"The Scattering Transform (ST) presents itself as an alternative approach to the classic methods that involve neural networks and deep learning techniques for the feature extraction and classification of electrical signals. Among its main advantages, one can emphasize that the coefficients of the ST are determined analytically and do not need to be learned, as typically performed in Convolutional Neural Networks (CNNs). Additionally, ST has time-shifting and small time-warping invariance, which reduces the need for precise temporal localization (detection) for subsequent classification. This paper originally proposes six feature extraction and selection methods applied to classification of Non-intrusive Load Monitoring (NILM) high-frequency signals. We visually analyze the separability among classes for the proposed Feature Extractors and validate the performance of the proposed methods varying several parameters for ST calculation, such as signal length, number of examples, and sampling frequency. The results outperform other state-of-the-art feature extraction techniques, reaching up to 100% of FScore for a publicly available dataset, demonstrating the feasibility and promising aspects of the ST for NILM problems.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"18 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":"134148819","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

Estimation of Kautz Poles in Wiener-Volterra Models Using Levenberg-Marquardt Algorithm 利用Levenberg-Marquardt算法估计Wiener-Volterra模型中的Kautz极点

Learning and Nonlinear Models Pub Date : 2023-02-28 DOI: 10.21528/lnlm-vol21-no1-art1

Higor de Souza Serafin, E. Oroski, A. Lazzaretti

引用次数: 0

Consumption Forecasting and Economic-Financial Evaluation of a Brazilian Company in the Free Market 自由市场下巴西一家公司的消费预测与经济财务评价

Learning and Nonlinear Models Pub Date : 2023-02-28 DOI: 10.21528/lnlm-vol21-no1-art6

Harold Dias Mello Junior, Karla Figueiredo, Marcos C. R. Seruffo, F. Costa, F. R. T. Moura, Fausto Marques Rodrigues Junior, Guilherme Baptista Bastos

{"title":"Consumption Forecasting and Economic-Financial Evaluation of a Brazilian Company in the Free Market","authors":"Harold Dias Mello Junior, Karla Figueiredo, Marcos C. R. Seruffo, F. Costa, F. R. T. Moura, Fausto Marques Rodrigues Junior, Guilherme Baptista Bastos","doi":"10.21528/lnlm-vol21-no1-art6","DOIUrl":"https://doi.org/10.21528/lnlm-vol21-no1-art6","url":null,"abstract":"The essential difference between the Free Contracting Environment (FCE) and the Regulated Contracting Environment (RCE) is the possibility of freely negotiating energy terms and prices with suppliers. Disconnected from the tariffs regulated by the government, in the FCE, consumers bear the costly difference between the contracted energy and that consumed. This cost can be reduced with accurate knowledge of the consumer profile, based on the analysis of historical data. In this article, a methodology is proposed to evaluate the migration of consumers to the FCE. In a case study, graphical statistical techniques help identify the profile of a consumer in the city of Rio de Janeiro, subgroup A4 and with green tariff modality, in the period from 2016 to 2019. Then, classical and artificial neural network-based methods are used for consumption forecasting twelve months ahead. In particular, Long and Short Term Memories (LSTM) networks performed better than Autoregressive Integrated Moving Average (ARIMA) models. At the end, it is demonstrated with economic and financial indicators, the right decision of this consumer to migrate to the FCE, prior to the analysis performed in this case study.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"4 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":"133043938","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

Development of a Low-Cost Relay Prototype for Real-Time Power Protection Functions 用于实时电源保护功能的低成本继电器样机的研制

Learning and Nonlinear Models Pub Date : 2023-02-28 DOI: 10.21528/lnlm-vol21-no1-art7

Pablo Rodrigues Lopes, Rui Bertho Junior

{"title":"Development of a Low-Cost Relay Prototype for Real-Time Power Protection Functions","authors":"Pablo Rodrigues Lopes, Rui Bertho Junior","doi":"10.21528/lnlm-vol21-no1-art7","DOIUrl":"https://doi.org/10.21528/lnlm-vol21-no1-art7","url":null,"abstract":"This paper presents a Raspberry Pi 3B+ based low-cost universal relay able to run power protection functions in real-time. The configurations necessary for this single-board computer to be able to provide real-time response are shown, as for latency tests to verify its response time. An experimental circuit was built to send three-phase fault signals from a Relay tester to the low-cost relay, in order to evaluate its response time to clear faults in comparison to a commercial relay. A neural networks algorithm was developed and executed in real-time by the proposed low-cost relay, which is able to differentiate three-phase faults from transient signals created from large load variations. The results show that the low-cost relay is capable of running simple and complex protection functions within a pre-defined runtime and acceptable precision, compared to a commercial protection relay. However, the sampling frequency the low-cost relay is able to handle is limited. The results have also shown that the low-cost relay meet the requirements for a soft real-time system, which is not ideal for practical power protection systems that require hard real-time systems.","PeriodicalId":386768,"journal":{"name":"Learning and Nonlinear Models","volume":"1 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":"125722724","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