Preliminary studies: Comparing LSTM and BLSTM Deep Neural Networks for Power Consumption Prediction

Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast. The Data Sets (DSs) were selected for their different contexts and scales, aiming the assessment of the models' robustness. Four DSs were used, related to the power consumption of: (a) a household in France; (b) a university building in Santar\'em, Brazil; (c) the T\'etouan city zones, in Morocco; and (c) the Singapore aggregated electric demand. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. The Friedman's test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating improves significantly the LSTM performance concerning different scales of electric power consumption.Comment: 38 pages, in English, 13 figures and 13 table

Assessment of neural networks training strategies for histomorphometric analysis of synchrotron radiation medical images

Abstract Micro-computed tomography (μCT) obtained by synchrotron radiation (SR) enables magnified images with a high space resolution that might be used as a non-invasive and non-destructive technique for the quantitative analysis of medical images, in particular the histomorphometry (HMM) of bony mass. In the preprocessing of such images, conventional operations such as binarization and morphological filtering are used before calculating the stereological parameters related, for example, to the trabecular bone microarchitecture. However, there is no standardization of methods for HMM based on μCT images, especially the ones obtained with SR X-ray. Notwithstanding the several uses of artificial neural networks (ANNs) in medical imaging, their application to the HMM of SR-μCT medical images is still incipient, despite the potential of both techniques. The contribution of this paper is the assessment and comparison of well-known training algorithms as well as the proposal of training strategies (combinations of training algorithms, sub-image kernel and symmetry information) for feed-forward ANNs in the task of bone pixels recognition in SR-μCT medical images. For a quantitative comparison, the results of a cross validation and a statistical analysis of the results for 36 training strategies are presented. The ANNs demonstrated both very low mean square errors in the validation, and good quality segmentation of the image of interest for application to HMM in SR-μCT medical images

Analysis of U-Net Neural Network Training Parameters for Tomographic Images Segmentation

Image segmentation is one of the main resources in computer vision. Nowadays, this procedure can be made with high precision using Deep Learning, and this fact is important to applications of several research areas including medical image analysis. Image segmentation is currently applied to find tumors, bone defects and other elements that are crucial to achieve accurate diagnoses. The objective of the present work is to verify the influence of parameters variation on U-Net, a Deep Convolutional Neural Network with Deep Learning for biomedical image segmentation. The dataset was obtained from Kaggle website (www.kaggle.com) and contains 267 volumes of lung computed tomography scans, which are composed of the 2D images and their respective masks (ground truth). The dataset was subdivided in 80% of the volumes for training and 20% for testing. The results were evaluated using the Dice Similarity Coefficient as metric and the value 84% was the mean obtained for the testing set, applying the best parameters considered

Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images

Analysis of U-Net Neural Network Training Parameters for Tomographic Images Segmentation

Image segmentation is one of the main resources in computer vision. Nowadays, this procedure can be made with high precision using Deep Learning, and this fact is important to applications of several research areas including medical image analysis. Image segmentation is currently applied to find tumors, bone defects and other elements that are crucial to achieve accurate diagnoses. The objective of the present work is to verify the influence of parameters variation on U-Net, a Deep Convolutional Neural Network with Deep Learning for biomedical image segmentation. The dataset was obtained from Kaggle website (www.kaggle.com) and contains 267 volumes of lung computed tomography scans, which are composed of the 2D images and their respective masks (ground truth). The dataset was subdivided in 80% of the volumes for training and 20% for testing. The results were evaluated using the Dice Similarity Coefficient as metric and the value 84% was the mean obtained for the testing set, applying the best parameters considered

Segmentation of Lung Tomographic Images Using U-Net Deep Neural Networks

Deep Neural Networks (DNNs) are among the best methods of Artificial Intelligence, especially in computer vision, where convolutional neural networks play an important role. There are numerous architectures of DNNs, but for image processing, U-Net offers great performance in digital processing tasks such as segmentation of organs, tumors, and cells for supporting medical diagnoses. In the present work, an assessment of U-Net models is proposed, for the segmentation of computed tomography of the lung, aiming at comparing networks with different parameters. In this study, the models scored 96% Dice Similarity Coefficient on average, corroborating the high accuracy of the U-Net for segmentation of tomographic images

U-NET aplicada a segmentação de ossos em microtomografias computadorizadas obtidas por radiação síncrotron para análises histomorfométricas

Actually, artificial intelligence (AI) participates increasingly in the elaboration of biomedical diagnoses. Clinical applications have used deep learning (DP) methods in the segmentation process, helping in the early treatment of diseases. Based on this principle, this work proposes, via Deep Neural Network (DNN), U-Net, to segment images of rat tibia, the main idea was to use AI architectures added to the image quantification technique, bone histomorphometry. To obtain the images, it was used the non-destructive technique of Computerized Microtomography obtained by X-rays from Synchrotron Radiation (µTC-RS). The initial objective was to enable models to eliminate marrow and other artifacts, leaving only bone; the final objective was to contribute to the state of the art in the use of PA-based methods in contrast to traditional segmentation methods, seeking to apply them to biomedical images. In this study, the developed models resulted in an average of approximately 90% for the Sørensen-Dice coefficient metric, demonstrating a high replicability rate.Atualmente, a inteligência artificial (IA) participa cada vez mais na elaboração de diagnósticos biomédicos. Aplicações clínicas têm utilizado de métodos de aprendizagem profunda (AP) no processo de segmentação, auxiliando no tratamento antecipado de doenças. Partindo desse pressuposto, este trabalho propõe, via Rede Neural Profunda (RNP), U-Net, segmentar imagens de tíbia de rato, tendo como ideia central utilizar arquiteturas de IA somada a técnica de quantificação de imagem, histomorfometria óssea. Para obtenção das imagens foi utilizado a técnica não destrutiva de Microtomografia Computadorizada obtida por raio-x oriundos de Radiação Síncrotron (µTC-RS). O objetivo inicial foi capacitar modelos para eliminar medula e outros artefatos, permanecendo somente osso; tendo como objetivo final buscar contribuir com o estado da arte no que dita o uso de métodos baseados em AP em contrapartida com métodos tradicionais de segmentação, na busca de aplicá-las em imagens biomédicas. Nesse estudo, os modelos desenvolvidos resultaram em uma média aproximada de 90% para a métrica do coeficiente do Sørensen-Dice, demonstrando uma alta taxa de replicabilidade

Comparing Long Short-Term Memory (LSTM) and bidirectional LSTM deep neural networks for power consumption prediction

Electric consumption prediction methods are investigated for many reasons, such as decision-making related to energy efficiency as well as for anticipating demand and the dynamics of the energy market. The objective of the present work is to compare two Deep Learning models, namely the Long Short-Term Memory (LSTM) model, and the Bi-directional LSTM (BLSTM) for univariate electric consumption Time Series (TS) short-term forecast model. The Data Sets (DSs) were selected for their different contexts and scales, with the goal of assessing the robustness of the models. Four DSs were used, related to the power consumption of: (a) a household in France; (b) a university building in Santarém, Brazil; (c) the Tétouan city zones, in Morocco; and (d) the aggregated electric demand of Singapore. The metrics RMSE, MAE, MAPE and R2 were calculated in a TS cross-validation scheme. Friedman’s test was applied to normalized RMSE (NRMSE) results, showing that BLSTM outperforms LSTM with statistically significant difference (p = 0.0455), corroborating the fact that bidirectional weight updating significantly improves the LSTM performance with respect to different scales of electric power consumption. The present work provides statistical evidence supporting the conclusion that BLSTM outperforms LSTM models according to the tests performed, based on a complete methodology for TS prediction, and also establishes a baseline for future investigation of electric consumption TS prediction

Vulnerabilidade Ambiental ao Derramamento de Óleo em Santarém (PA)

Derramamentos de óleo podem impactar o ambiente natural e antrópico. O objetivo do presente trabalho trata do mapeamento da vulnerabilidade ambiental ao derramamento de óleo, provenientes dos transportes fluviais, comercias e turísticos na orla da cidade de Santarém (estado do Pará). Para isso, foram utilizados como base mapas temáticos de: uso/ocupação do solo e cobertura vegetal, declividade, solo, geologia e de inundação, aplicando análise e modelagem em (SIG), através da álgebra de mapas, em categorias hierárquicas de acordo com classes de vulnerabilidade para cada variável mapeada, com valores de “1” (menos vulnerável) a “5” (mais vulnerável). A análise do mapa de vulnerabilidade ambiental, demonstrou regiões de alta e muito alta vulnerabilidade localizadas na área urbana onde ocorrem inundações. A predominância da classe de média vulnerabilidade, correspondente a 45% dos resultados devido principalmente à interação da classe de uso do solo com a unidade litológica de sedimentos referentes ao Depósitos Aluvionares e a Formação Alter do Chão. Nesta classe está inserido o Porto Organizado de Santarém com atividades intensas de transporte de cargas e pessoas. Assim, os resultados produzidos servem de instrumento auxiliar ao planejamento ambiental para as regiões mais vulneráveis ao derrame de óleo na orla de Santarém – PA.Oil spills can affect the natural and anthropic environment. The aim of the present work is the mapping of the environmental vulnerability to oil spills derived from fluvial, commercial and tourist transports. The study area is located on the border of Santarém – PA, Brazil, at the confluence of Amazonas and Tapajós rivers. To do so, thematic maps of land use/occupation and vegetation cover, slope, soil, geology, and flood were used as a base. They were rasterized and later reclassified with the ArcMap 10.3 software in hierarchical categories according to classes of vulnerability for each variable mapped, using values from "1" (less vulnerable) to "5" (more vulnerable), adapted from the established methodology. Based on the analysis of the environmental vulnerability map, it was possible to observe in the study area that very high and high vulnerability occurs at the extreme east, central region and near the mouth of the Irurá stream. Such an area is an urban area where floods occur. The predominance of the medium vulnerability classes in the region, corresponding to 45% of the studied area, is due mainly to the interaction of the land-use class with the lithological unit of sediments referring to Alluvial Reservoirs and Alter do Chão Formation. In this class is located the Organized Port of Santarém with intense activities of transport of loads and people.Derramamentos de óleo podem impactar o ambiente natural e antrópico. O objetivo do presente trabalho trata do mapeamento da vulnerabilidade ambiental ao derramamento de óleo, provenientes dos transportes fluviais, comercias e turísticos na orla da cidade de Santarém (estado do Pará). Para isso, foram utilizados como base mapas temáticos de: uso/ocupação do solo e cobertura vegetal, declividade, solo, geologia e de inundação, aplicando análise e modelagem em (SIG), através da álgebra de mapas, em categorias hierárquicas de acordo com classes de vulnerabilidade para cada variável mapeada, com valores de “1” (menos vulnerável) a “5” (mais vulnerável). A análise do mapa de vulnerabilidade ambiental, demonstrou regiões de alta e muito alta vulnerabilidade localizadas na área urbana onde ocorrem inundações. A predominância da classe de média vulnerabilidade, correspondente a 45% dos resultados devido principalmente à interação da classe de uso do solo com a unidade litológica de sedimentos referentes ao Depósitos Aluvionares e a Formação Alter do Chão. Nesta classe está inserido o Porto Organizado de Santarém com atividades intensas de transporte de cargas e pessoas. Assim, os resultados produzidos servem de instrumento auxiliar ao planejamento ambiental para as regiões mais vulneráveis ao derrame de óleo na orla de Santarém – PA