19 research outputs found

    Machine Learning

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    Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

    Robust computational intelligence techniques for visual information processing

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    The third part is exclusively dedicated to the super-resolution of Magnetic Resonance Images. In one of these works, an algorithm based on the random shifting technique is developed. Besides, we studied noise removal and resolution enhancement simultaneously. To end, the cost function of deep networks has been modified by different combinations of norms in order to improve their training. Finally, the general conclusions of the research are presented and discussed, as well as the possible future research lines that are able to make use of the results obtained in this Ph.D. thesis.This Ph.D. thesis is about image processing by computational intelligence techniques. Firstly, a general overview of this book is carried out, where the motivation, the hypothesis, the objectives, and the methodology employed are described. The use and analysis of different mathematical norms will be our goal. After that, state of the art focused on the applications of the image processing proposals is presented. In addition, the fundamentals of the image modalities, with particular attention to magnetic resonance, and the learning techniques used in this research, mainly based on neural networks, are summarized. To end up, the mathematical framework on which this work is based on, â‚š-norms, is defined. Three different parts associated with image processing techniques follow. The first non-introductory part of this book collects the developments which are about image segmentation. Two of them are applications for video surveillance tasks and try to model the background of a scenario using a specific camera. The other work is centered on the medical field, where the goal of segmenting diabetic wounds of a very heterogeneous dataset is addressed. The second part is focused on the optimization and implementation of new models for curve and surface fitting in two and three dimensions, respectively. The first work presents a parabola fitting algorithm based on the measurement of the distances of the interior and exterior points to the focus and the directrix. The second work changes to an ellipse shape, and it ensembles the information of multiple fitting methods. Last, the ellipsoid problem is addressed in a similar way to the parabola

    Forecasting: theory and practice

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    Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice

    Forecasting monthly airline passenger numbers with small datasets using feature engineering and a modified principal component analysis

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    In this study, a machine learning approach based on time series models, different feature engineering, feature extraction, and feature derivation is proposed to improve air passenger forecasting. Different types of datasets were created to extract new features from the core data. An experiment was undertaken with artificial neural networks to test the performance of neurons in the hidden layer, to optimise the dimensions of all layers and to obtain an optimal choice of connection weights – thus the nonlinear optimisation problem could be solved directly. A method of tuning deep learning models using H2O (which is a feature-rich, open source machine learning platform known for its R and Spark integration and its ease of use) is also proposed, where the trained network model is built from samples of selected features from the dataset in order to ensure diversity of the samples and to improve training. A successful application of deep learning requires setting numerous parameters in order to achieve greater model accuracy. The number of hidden layers and the number of neurons, are key parameters in each layer of such a network. Hyper-parameter, grid search, and random hyper-parameter approaches aid in setting these important parameters. Moreover, a new ensemble strategy is suggested that shows potential to optimise parameter settings and hence save more computational resources throughout the tuning process of the models. The main objective, besides improving the performance metric, is to obtain a distribution on some hold-out datasets that resemble the original distribution of the training data. Particular attention is focused on creating a modified version of Principal Component Analysis (PCA) using a different correlation matrix – obtained by a different correlation coefficient based on kinetic energy to derive new features. The data were collected from several airline datasets to build a deep prediction model for forecasting airline passenger numbers. Preliminary experiments show that fine-tuning provides an efficient approach for tuning the ultimate number of hidden layers and the number of neurons in each layer when compared with the grid search method. Similarly, the results show that the modified version of PCA is more effective in data dimension reduction, classes reparability, and classification accuracy than using traditional PCA.</div

    Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures

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    Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e. normal bearing and three different types of defected bearings on outer race, inner race and roller separately. Particle Swarm Optimization (PSO) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modelling based Continuous Wavelet Transform (IMCWT) model. Then, a three-dimensional feature space of the statistical parameters and a Nearest Neighbor (NN) classifier are respectively applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment

    Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

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    Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

    Vol. 15, No. 1 (Full Issue)

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    Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

    Get PDF
    Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems
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