Controle on-line por atributos para o número de não-conformidades no item inspecionado com base em uma sequência de inspeção

This paper proposes a procedure to control on-line processes for attributes, using an Shewhart control chart with two control limits (warning limit and control limit) and will be based on a sequence of inspection (h). The inspection procedure is based on Taguchi et al. (1989), in which to inspect the item, if the number of non-conformities is higher than an upper control limit, the process needs to be stopped and some adjustment is required; and, if the last inspection h, from all items inspected present a number of non-conformities between the control limit and warning limit. The items inspected will suffer destructive inspection, being discarded after inspection. Properties of an ergodic Markov chain are used to get the expression of average cost per item and the aim was the determination of four optimized parameters: the sampling interval of the inspections (m); the constant W to draw the warning limit (W); the constant C to draw the control limit (C), where W £ C, and the length of sequence of inspections (h). Numerical examples illustrate the proposed procedure

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

Otimização em comitês de classificadores: uma abordagem baseada em filtro para seleção de subconjuntos de atributos

Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionality without loss of performance. With the development of research in ensemble of classifiers and the verification that this type of model has better performance than the individual models, if the base classifiers are diverse, comes a new field of application to the research of feature selection. In this new field, it is desired to find diverse subsets of features for the construction of base classifiers for the ensemble systems. This work proposes an approach that maximizes the diversity of the ensembles by selecting subsets of features using a model independent of the learning algorithm and with low computational cost. This is done using bio-inspired metaheuristics with evaluation filter-based criteria

Uma Análise de métodos de distriubuição de atributos em comitês de classificadores

The objective of the researches in artificial intelligence is to qualify the computer to execute functions that are performed by humans using knowledge and reasoning. This work was developed in the area of machine learning, that it s the study branch of artificial intelligence, being related to the project and development of algorithms and techniques capable to allow the computational learning. The objective of this work is analyzing a feature selection method for ensemble systems. The proposed method is inserted into the filter approach of feature selection method, it s using the variance and Spearman correlation to rank the feature and using the reward and punishment strategies to measure the feature importance for the identification of the classes. For each ensemble, several different configuration were used, which varied from hybrid (homogeneous) to non-hybrid (heterogeneous) structures of ensemble. They were submitted to five combining methods (voting, sum, sum weight, multiLayer Perceptron and naïve Bayes) which were applied in six distinct database (real and artificial). The classifiers applied during the experiments were k- nearest neighbor, multiLayer Perceptron, naïve Bayes and decision tree. Finally, the performance of ensemble was analyzed comparatively, using none feature selection method, using a filter approach (original) feature selection method and the proposed method. To do this comparison, a statistical test was applied, which demonstrate that there was a significant improvement in the precision of the ensembles

Seleção de atributos em comitês de classificadores utilizando algoritmos genéticos

Classifier ensembles are systems composed of a set of individual classifiers and a combination module, which is responsible for providing the final output of the system. In the design of these systems, diversity is considered as one of the main aspects to be taken into account since there is no gain in combining identical classification methods. The ideal situation is a set of individual classifiers with uncorrelated errors. In other words, the individual classifiers should be diverse among themselves. One way of increasing diversity is to provide different datasets (patterns and/or attributes) for the individual classifiers. The diversity is increased because the individual classifiers will perform the same task (classification of the same input patterns) but they will be built using different subsets of patterns and/or attributes. The majority of the papers using feature selection for ensembles address the homogenous structures of ensemble, i.e., ensembles composed only of the same type of classifiers. In this investigation, two approaches of genetic algorithms (single and multi-objective) will be used to guide the distribution of the features among the classifiers in the context of homogenous and heterogeneous ensembles. The experiments will be divided into two phases that use a filter approach of feature selection guided by genetic algorithm

Competitividade de destinos turísticos e o imperativo sustentável: avaliação de dimensões e atributos condicionantes no Pólo Costa das Dunas, RN, Brasil

Being available as a tourist destination is a necessary condition but not enough for the expansion and success of tourism activity. To be successful, tourism requires investment, inputs, appropriate planning and management, like any other economic activity. A fundamental goal of the destination management is to understand how the competitiveness of a tourist destination can be improved and sustained. Competitive position of tourism can be measured and assessed by various models. Evaluating the indicators of competitiveness of a tourist destination involves a multivariate analysis, ranging from issues directly related to tourism activity itself to the indirect factors. These are elements that are interrelated and that together will point out the competitive condition of this destination. From the definition and characterization of competitiveness, sustainability and management in the context of tourist destinations, understood as the main concepts of this study, we present the main theoretical and methodological models of assessment of competitiveness of tourist destinations in the literature and represent the state of the issue in the scientific treatment of the subject. These models, designed by researchers from several countries and applied in different tourist destinations, are confronted about their structure, indicators considered and localities in which they were applied. The aim of this study was to know and evaluate the condition of tourist competitiveness of the destination Pólo Costa das Dunas, from the constraints attributes of superior performance of the evaluation model of tourist competitiveness of destinations Competenible, suggested by Mazaro, and that suit the requirements of international market aware of the strength and importance of sustainability. The condition of competitiveness of tourist destination in Rio Grande do Norte Pólo Costa das Dunas was moderate. The competitive strengths and weaknesses of the destination Pólo Costa das Dunas revealed through the dozens of sustainable attributes of the model Competenible showed guidelines and initiatives that can be taken to guide strategic decisions related to their planning and management. Thus, this study should serve as support for strategic planning and long-term management of the sector and as a crucial tool for making decisions related to public policies, sectoral investments, monitor processes, strategic planning, direction and control of the local and regional tourism development of destinations

Interpretação sísmica e análise de atributos - área do levantamento sísmico 3D de siririzinho (Bacia Sergipe-Alagoas, NE do Brasil)

The main objective of the present thesis was the seismic interpretation and seismic attribute analysis of the 3D seismic data from the Siririzinho high, located in the Sergipe Sub-basin (southern portion of Sergipe-Alagoas Basin). This study has enabled a better understanding of the stratigraphy and structure that the Siririzinho high experienced during its development. In a first analysis, we used two types of filters: the dip-steered median filter, was used to remove random noise and increase the lateral continuity of reflections, and fault-enhancement filter was applied to enhance the reflection discontinuities. After this filtering step similarity and curvature attributes were applied in order to identify and enhance the distribution of faults and fractures. The use of attributes and filtering greatly contributed to the identification and enhancement of continuity of faults. Besides the application of typical attributes (similarity and curvature) neural network and fingerprint techniques were also used, which generate meta-attributes, also aiming to highlight the faults; however, the results were not satisfactory. In a subsequent step, well log and seismic data analysis were performed, which allowed the understanding of the distribution and arrangement of sequences that occur in the Siririzinho high, as well as an understanding of how these units are affected by main structures in the region. The Siririzinho high comprises an elongated structure elongated in the NS direction, capped by four seismo-sequences (informally named, from bottom to top, the sequences I to IV, plus the top of the basement). It was possible to recognize the main NS-oriented faults, which especially affect the sequences I and II, and faults oriented NE-SW, that reach the younger sequences, III and IV. Finally, with the interpretation of seismic horizons corresponding to each of these sequences, it was possible to define a better understanding of geometry, deposition and structural relations in the area.