Low cost non-intrusive home energy monitoring

This thesis presents a low cost non-intrusive home energy monitor built on top of Non-Intrusive Load Monitoring (NILM) concepts and techniques. NILM solutions are already considered low cost alternatives to the big majority of existing commercial energy monitors but the goal here is to make its cost even lower by using a mini netbook as a whole in one solution. The mini netbook is installed in the homes main circuit breaker and computes power consumption by reading current and voltage from the built-in sound card. At the same time, feedback to the users is provided using the 11’’ LCD screen as well as other built-in I/O modules. Our meter is also capable of detecting changes in power and tries to find out which appliance lead to that change and it is being used as part of an eco-feedback platform that was build to study the long terms of energy eco-feedback in individuals. In this thesis the steps that were taken to come up with such a system are presented, from the basics of AC power measurements to the implementation of an event detector and classifier that was used to disaggregate the power load. In the last chapter results from some validation tests that have been performed are presented in order to validate the experiment. It is believed that such a system will not only be important as an energy monitor, but also as an open system than can be easily changed to accommodate and test new or existing nonintrusive load monitoring techniques.

Implementação de classificador de tarefas mentais baseado em EEG

BARBOSA, André F. ; SOUZA, Bryan C. ; PEREIRA JUNIOR, Antônio ; MEDEIROS, Adelardo A. D.de, . Implementação de Classificador de Tarefas Mentais Baseado em EEG. In: CONGRESSO BRASILEIRO DE REDES NEURAIS, 9., 2009, Ouro Preto, MG. Anais... Ouro Preto, MG, 2009

An investigation of Hebbian phase sequences as assembly graphs

Hebb proposed that synapses between neurons that fire synchronously are strengthened, forming cell assemblies and phase sequences. The former, on a shorter scale, are ensembles of synchronized cells that function transiently as a closed processing system; the latter, on a larger scale, correspond to the sequential activation of cell assemblies able to represent percepts and behaviors. Nowadays, the recording of large neuronal populations allows for the detection of multiple cell assemblies. Within Hebb's theory, the next logical step is the analysis of phase sequences. Here we detected phase sequences as consecutive assembly activation patterns, and then analyzed their graph attributes in relation to behavior. We investigated action potentials recorded from the adult rat hippocampus and neocortex before, during and after novel object exploration (experimental periods). Within assembly graphs, each assembly corresponded to a node, and each edge corresponded to the temporal sequence of consecutive node activations. The sum of all assembly activations was proportional to firing rates, but the activity of individual assemblies was not. Assembly repertoire was stable across experimental periods, suggesting that novel experience does not create new assemblies in the adult rat. Assembly graph attributes, on the other hand, varied significantly across behavioral states and experimental periods, and were separable enough to correctly classify experimental periods (Naïve Bayes classifier; maximum AUROCs ranging from 0.55 to 0.99) and behavioral states (waking, slow wave sleep, and rapid eye movement sleep; maximum AUROCs ranging from 0.64 to 0.98). Our findings agree with Hebb's view that assemblies correspond to primitive building blocks of representation, nearly unchanged in the adult, while phase sequences are labile across behavioral states and change after novel experience. The results are compatible with a role for phase sequences in behavior and cognition.

Relação entre características morfológicas e produção de leite em vacas da raça Gir

O objetivo deste trabalho foi determinar parâmetros genéticos relacionados a características morfológicas e suas correlações genéticas com a produção de leite, em vacas da raça Gir. Utilizaram-se 3.805 registros provenientes de 2.142 vacas. O modelo utilizado na análise de características morfológicas continha os efeitos fixos de rebanho, ano e estação de classificação, estádio da lactação e idade da vaca à classificação, além da identificação do classificador. Quanto à produção de leite, foram incluídos no modelo os efeitos fixos de rebanho, ano e estação de parição e idade da vaca ao parto. Os parâmetros genéticos foram obtidos por meio do aplicativo REMLF90. As estimativas de herdabilidade variaram de 0,09 a 0,54. A variabilidade genética aditiva da maioria das características é suficiente para que ganhos genéticos anuais significativos possam ser alcançados com o processo de seleção. As correlações genéticas entre as características morfológicas variaram de baixas a altas e, entre elas e a produção de leite, de baixas a moderadas. Altas correlações genéticas entre algumas características morfológicas implicam a possibilidade de exclusão de algumas delas do programa de melhoramento genético da raça Gir, no Brasil. As correlações genéticas entre produção de leite e algumas características morfológicas indicam que estas podem ser utilizadas na formação de índices de seleção.

Classificação de imagens de ambientes coralinos: uma abordagem empregando uma combinação de classificadores e máquina de vetor de suporte

The use of the maps obtained from remote sensing orbital images submitted to digital processing became fundamental to optimize conservation and monitoring actions of the coral reefs. However, the accuracy reached in the mapping of submerged areas is limited by variation of the water column that degrades the signal received by the orbital sensor and introduces errors in the final result of the classification. The limited capacity of the traditional methods based on conventional statistical techniques to solve the problems related to the inter-classes took the search of alternative strategies in the area of the Computational Intelligence. In this work an ensemble classifiers was built based on the combination of Support Vector Machines and Minimum Distance Classifier with the objective of classifying remotely sensed images of coral reefs ecosystem. The system is composed by three stages, through which the progressive refinement of the classification process happens. The patterns that received an ambiguous classification in a certain stage of the process were revalued in the subsequent stage. The prediction non ambiguous for all the data happened through the reduction or elimination of the false positive. The images were classified into five bottom-types: deep water; under-water corals; inter-tidal corals; algal and sandy bottom. The highest overall accuracy (89%) was obtained from SVM with polynomial kernel. The accuracy of the classified image was compared through the use of error matrix to the results obtained by the application of other classification methods based on a single classifier (neural network and the k-means algorithm). In the final, the comparison of results achieved demonstrated the potential of the ensemble classifiers as a tool of classification of images from submerged areas subject to the noise caused by atmospheric effects and the water column

Análise e classificação de imagens de lesões da pele por atributos de cor, forma e textura utilizando máquina de vetor de suporte

The skin cancer is the most common of all cancers and the increase of its incidence must, in part, caused by the behavior of the people in relation to the exposition to the sun. In Brazil, the non-melanoma skin cancer is the most incident in the majority of the regions. The dermatoscopy and videodermatoscopy are the main types of examinations for the diagnosis of dermatological illnesses of the skin. The field that involves the use of computational tools to help or follow medical diagnosis in dermatological injuries is seen as very recent. Some methods had been proposed for automatic classification of pathology of the skin using images. The present work has the objective to present a new intelligent methodology for analysis and classification of skin cancer images, based on the techniques of digital processing of images for extraction of color characteristics, forms and texture, using Wavelet Packet Transform (WPT) and learning techniques called Support Vector Machine (SVM). The Wavelet Packet Transform is applied for extraction of texture characteristics in the images. The WPT consists of a set of base functions that represents the image in different bands of frequency, each one with distinct resolutions corresponding to each scale. Moreover, the characteristics of color of the injury are also computed that are dependants of a visual context, influenced for the existing colors in its surround, and the attributes of form through the Fourier describers. The Support Vector Machine is used for the classification task, which is based on the minimization principles of the structural risk, coming from the statistical learning theory. The SVM has the objective to construct optimum hyperplanes that represent the separation between classes. The generated hyperplane is determined by a subset of the classes, called support vectors. For the used database in this work, the results had revealed a good performance getting a global rightness of 92,73% for melanoma, and 86% for non-melanoma and benign injuries. The extracted describers and the SVM classifier became a method capable to recognize and to classify the analyzed skin injuries

Desenvolvimento de um sistema de automação para uma planta de inertização de resíduos por plasma

The traditional processes for treatment of hazardous waste are questionable for it generates other wastes that adversely affect people s health. As an attempt to minimize these problems, it was developed a system for treatment of hazardous waste by thermal plasma, a more appropriate technology since it produces high temperatures, preventing the formation of toxic pollutants to human beings. The present work brings out a solution of automation for this plant. The system has local and remote monitoring resources to ensure the operators security as well as the process itself. A special attention was given to the control of the main reactor temperature of the plant as it is the place where the main processing occurs and because it presents a complex mathematical model. To this, it was employed cascaded controls based on Fuzzy logic. A process computer, with a particular man-machine interface (MMI), provides information and controls of the plant to the operator, including by Internet. A compact PLC module is in charge of the central element of management automation and plant control which receives information from sensors, and sends it to the MMI

Detecção e isolamento de falhas em sistemas dinâmicos baseados em redes neurais

This master dissertation presents the development of a fault detection and isolation system based in neural network. The system is composed of two parts: an identification subsystem and a classification subsystem. Both of the subsystems use neural network techniques with multilayer perceptron training algorithm. Two approaches for identifica-tion stage were analyzed. The fault classifier uses only residue signals from the identification subsystem. To validate the proposal we have done simulation and real experiments in a level system with two water reservoirs. Several faults were generated above this plant and the proposed fault detection system presented very acceptable behavior. In the end of this work we highlight the main difficulties found in real tests that do not exist when it works only with simulation environments

Estudo do acoplamento entre superfícies seletivas de frequência assimétricas em estruturas de multicamadas

This work presents the development of new microwaves structures, filters and high gain antenna, through the cascading of frequency selective surfaces, which uses fractals Dürer and Minkowski patches as elements, addition of an element obtained from the combination of the other two simple the cross dipole and the square spiral. Frequency selective surfaces (FSS) includes a large area of Telecommunications and have been widely used due to its low cost, low weight and ability to integrate with others microwaves circuits. They re especially important in several applications, such as airplane, antennas systems, radomes, rockets, missiles, etc. FSS applications in high frequency ranges have been investigated, as well as applications of cascading structures or multi-layer, and active FSS. In this work, we present results for simulated and measured transmission characteristics of cascaded structures (multilayer), aiming to investigate the behavior of the operation in terms of bandwidth, one of the major problems presented by frequency selective surfaces. Comparisons are made with simulated results, obtained using commercial software such as Ansoft DesignerTM v3 and measured results in the laboratory. Finally, some suggestions are presented for future works on this subject

Máquina de vetores-suporte intervalar

The Support Vector Machines (SVM) has attracted increasing attention in machine learning area, particularly on classification and patterns recognition. However, in some cases it is not easy to determinate accurately the class which given pattern belongs. This thesis involves the construction of a intervalar pattern classifier using SVM in association with intervalar theory, in order to model the separation of a pattern set between distinct classes with precision, aiming to obtain an optimized separation capable to treat imprecisions contained in the initial data and generated during the computational processing. The SVM is a linear machine. In order to allow it to solve real-world problems (usually nonlinear problems), it is necessary to treat the pattern set, know as input set, transforming from nonlinear nature to linear problem. The kernel machines are responsible to do this mapping. To create the intervalar extension of SVM, both for linear and nonlinear problems, it was necessary define intervalar kernel and the Mercer s theorem (which caracterize a kernel function) to intervalar function

Análise de antenas de microfita em rampa com múltiplas camadas dielétricas

The present work deals with the ana1ysis of microstrip patch antennas printed on tapered dielectric substrates. We investigate the influence ofthe substrate height variations on the properties of configurations such as microstrip patch antennas, microstrip patch antennas with overlay and suspendeô microstrip patch antennas. The dielectric substrates can be isotropic or anisotropic ones. This accurate analysis is based on the full-wave formulation. It is carried out initially for the determination of the impedance matrix, through the use of the spectral¬domain immitance approach. We use a model based on a segmentation of the considered line into uniform microstrip line subsections. Normalized phase constants and characteristic impedances are obtained by means of the Galerkin numerical technique. Then, the cascaded combination of the uniform microstrip subsections are analyzed through an interactive procedure. Numerical results are presented for the input reflection coefficient, voltage standing wave ratio, resonant frequency, and radiation pattems ofthe E_plane and H-plane diagrams. It is found that the variations in the substrate height profile produce a great influence on the bandwidth of microstrip antennas. This procedure gives bandwidth improvements without altering considerably the resonant frequency. Furthermore, the tapered microstrip antenna can be used as a lightweight altemative for bandwidth control and to eXtend the use of microstiip antenna technology to a wider variety of applications. Finally, suggestions for the continuity of this work are presented

Aplicação de sistemas multi-classificadores no diagnóstico de falhas em motores de indução trifásicos

Equipment maintenance is the major cost factor in industrial plants, it is very important the development of fault predict techniques. Three-phase induction motors are key electrical equipments used in industrial applications mainly because presents low cost and large robustness, however, it isn t protected from other fault types such as shorted winding and broken bars. Several acquisition ways, processing and signal analysis are applied to improve its diagnosis. More efficient techniques use current sensors and its signature analysis. In this dissertation, starting of these sensors, it is to make signal analysis through Park s vector that provides a good visualization capability. Faults data acquisition is an arduous task; in this way, it is developed a methodology for data base construction. Park s transformer is applied into stationary reference for machine modeling of the machine s differential equations solution. Faults detection needs a detailed analysis of variables and its influences that becomes the diagnosis more complex. The tasks of pattern recognition allow that systems are automatically generated, based in patterns and data concepts, in the majority cases undetectable for specialists, helping decision tasks. Classifiers algorithms with diverse learning paradigms: k-Neighborhood, Neural Networks, Decision Trees and Naïves Bayes are used to patterns recognition of machines faults. Multi-classifier systems are used to improve classification errors. It inspected the algorithms homogeneous: Bagging and Boosting and heterogeneous: Vote, Stacking and Stacking C. Results present the effectiveness of constructed model to faults modeling, such as the possibility of using multi-classifiers algorithm on faults classification

Sistema inteligente para detecção de vazamentos em dutos de petróleo usando transformada Wavelet e redes neurais

This work consists in the use of techniques of signals processing and artificial neural networks to identify leaks in pipes with multiphase flow. In the traditional methods of leak detection exists a great difficulty to mount a profile, that is adjusted to the found in real conditions of the oil transport. These difficult conditions go since the unevenly soil that cause columns or vacuum throughout pipelines until the presence of multiphases like water, gas and oil; plus other components as sand, which use to produce discontinuous flow off and diverse variations. To attenuate these difficulties, the transform wavelet was used to map the signal pressure in different resolution plan allowing the extraction of descriptors that identify leaks patterns and with then to provide training for the neural network to learning of how to classify this pattern and report whenever this characterize leaks. During the tests were used transient and regime signals and pipelines with punctures with size variations from ½' to 1' of diameter to simulate leaks and between Upanema and Estreito B, of the UN-RNCE of the Petrobras, where it was possible to detect leaks. The results show that the proposed descriptors considered, based in statistical methods applied in domain transform, are sufficient to identify leaks patterns and make it possible to train the neural classifier to indicate the occurrence of pipeline leaks

Análise de onda completa de superfície seletiva em freqüência do tipo anteparo duplo

This work presents a theoretical analysis and numerical and experimental results of the scattering characteristics of frequency selective surfaces, using elements of type patch perfectly conductor. The structures are composed of two frequency selective surfaces on isotropic dielectric substrates cascaded, separated by a layer of air. The analysis is performed using the method of equivalent transmission line in combination with the Galerkin method, to determine the transmission and reflection characteristics of the structures analyzed. Specifically, the analysis uses the impedance method, which models the structure by an equivalent circuit, and applies the theory of transmission lines to determine the dyadic Green's function for the cascade structure. This function relates the incident field and surface current densities. These fields are determined algebraically by means of potential incidents and the imposition of the continuity of the fields in the dielectric interfaces. The Galerkin method is applied to the numerical determination of the unknown weight coefficients and hence the unknown densities of surface currents, which are expanded in terms of known basis functions multiplied by these weight coefficients. From the determination of these functions, it becomes possible to obtain numerical scattered fields at the top and bottom of the structures and characteristics of transmission and reflection of these structures. At work, we present numerical and experimental results for the characteristics of transmission and reflection. Comparisons were made with other results presented in literature, and it was observed a good agreement in the cases presented suggestions continuity of the work are presented

Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated