Base Mesh Construction using Global Parametrization

connected boundaries (e.g. redandgreen regions); thebasemesh (e) isgenerated usingthe metricdistortionontheparametrization(d). Introduction and Motivation Base mesh construction from a dense-polygon mesh is often used to reduce the complexity of geometryprocessingproblems. Inthebaseorcontrolmesh,eachface corresponds to a region on the original surface and is used to encode its geometry. This encoding can involve a different representatio

Processamento de imagens como metodologia auxiliar à análise de termogramas

Este artigo apresenta um estudo sobre processamento de imagens com o intuito de automatizar o método de análise de termogramas de pacientes com diagnóstico de câncer. O objetivo é desenvolver um modelo de segmentação semiautomática de imagens termográficas utilizando a linguagem computacional python.  É proposta uma rotina de segmentação, a partir de um algoritmo baseado em crescimento de regiões, capaz de agrupar pixels semelhantes para uma região de interesse (ROI) do termograma. Os testes foram realizados em vinte termogramas coletados de pacientes com neoplasia da mama e tireoide.  Como resultados verificou-se que o modelo proposto compreende a região tumoral com maior fidedignidade que o método de delimitação manual de ROI, assim as temperaturas médias e mínimas são mais elevadas (quando em comparação com o modelo manual), pois garante que pontos de temperatura fora da real extensão nodular não sejam incluídos na ROI. Quanto ao tempo operacional, o modelo proposto executa a tarefa de delimitação de ROI mais rapidamente do que modelo manual. Para trabalhos futuros, sugere-se o estudo estatístico de benignidade ou malignidade de nódulos, a partir da diferença térmica registrada na ROI dos termogramas em análise com a segmentação semiautomática.</jats:p

Structure-oriented filtering in unsupervised multiattribute seismic facies analysis

The growing use of automated methods in seismic interpretation highlights the importance of data treatment. We analyze the effect of structure-oriented filtering seismic data on machine learning techniques and apply the filter to the data before calculating seismic attributes. We apply this methodology to a migrated section of Buzios Field from the Brazilian presalt in Santos Basin. The analysis is restricted to the case of unsupervised methods, namely self-organized maps and generative topographic mapping. We use four different seismic attributes that are known to be good salt indicators. Two are based on texture, and two are based on coherence. The use of filtering can improve salt identification in clustering when more attributes are considered. </jats:p

Análise da Estrutura a Termo das Taxas de Juros

A Estrutura a Termo das Taxas de Juros (ETTJ) é um elemento essencial para formulação da política monetária. Ela é capaz de indicar as expectativas do mercado financeiro em relação as taxas de juros futuras. Nesse trabalho estudamos a formação da ETTJ com enfoque maior na matemática envolvida, pois na literatura esse assunto em geral é tratado apenas com foco na economia. Demonstramos as relações matemáticas entre as taxas de juros à vista, futuras e instantâneas. Estudamos também o modelo matemático de previsão da curva de juros proposta por Svensson. Esse modelo é de fácil aplicação pois necessita de poucos parâmetros para ajustar a curva de juros. Por esse motivo esse modelo tem sido amplamente usado em Bancos Centrais de diversos países inclusive pelo Banco Central do Brasil. Concluímos com uma aplicação do modelo de Svensson utilizando os preços dos títulos prefixados do Tesouro Direto

A Semantically Annotated 15-Class Ground Truth Dataset for Substation Equipment to Train Semantic Segmentation Models

The lack of annotated semantic segmentation datasets for electrical substations in the literature poses a significant problem for machine learning tasks; before training a model, a dataset is needed. This paper presents a new dataset of electric substations with 1660 images annotated with 15 classes, including insulators, disconnect switches, transformers and other equipment commonly found in substation environments. The images were captured using a combination of human, fixed and AGV-mounted cameras at different times of the day, providing a diverse set of training and testing data for algorithm development. In total, 50,705 annotations were created by a team of experienced annotators, using a standardized process to ensure accuracy across the dataset. The resulting dataset provides a valuable resource for researchers and practitioners working in the fields of substation automation, substation monitoring and computer vision. Its availability has the potential to advance the state of the art in this important area

Comparison of Traditional Image Segmentation Methods Applied to Thermograms of Power Substation Equipment

The variation in the thermal state of electrical energy substation equipment is normally associated with natural wear or equipment failure. This can be detected by infrared thermography, but technically it demands a long time to analyze these images. Computational analysis can allow an automated, more agile, and more efficient analysis to detect overheated regions in thermographic images. Therefore, it is necessary to segment the region of interest in the images; however, the results may diverge depending on the technique used. Thus, this article presents the improvement of four different techniques implemented in Python and applied in a substation under real operating conditions for a period of eleven months. The performance of the four methods was compared using eight statistical performance measures, and the efficiency was measured by the runtime. The segmentation results showed that the methods based on a threshold (Otsu and Histogram-Based Threshold) were fast, with processing times of 0.11 to 0.24 s, but caused excessive segmentation, presenting the lowest accuracy (0.160 and 0.444) and precision (0.004 and 0.049, respectively). The clustering-based methods (Cluster K-means and Fuzzy C-means) showed similar results to each other but were more accurate (0.936 to 1.000), more precise (0.965 to 1.000), and slower, with 2.55 and 38.8 s, respectively, compared to the threshold methods. The Fuzzy C-means method obtained the highest values of specificity, accuracy, and precision among the methods under analysis, followed by the Cluster K-means method

Impacts of Atmospheric and Load Conditions on the Power Substation Equipment Temperature Model

Infrared thermography is a predictive maintenance tool used in substations to identify a disturbance in electrical equipment that could lead to poor operation and potential failure in the future. According to Joule&rsquo;s law, the temperature of electrical equipment is proportional to the current flowing through it. Other external factors, such as solar incidence, air humidity, wind speed, and air temperature, can interfere with its operating temperatures. Based on this premise, this article aims to analyze the influence of atmospheric and load conditions on the operational cycle of thermography-monitored equipment in order to describe the operating temperature of the object using only external data and to show the impacts of external influences on the final temperature reached by the object. Five multivariate time series regression models were developed to describe the maximum equipment temperature. The final model achieved the best fit between the measured and model temperature based on the Akaike information criterion (AIC) metric, where all external variables were used to compose the model. The proposed model shows the impacts of each external factor on equipment temperature and could be used to create a predictive maintenance strategy for power substations to avoid failure