999 resultados para Classificação Funcional


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The objective of this work is to draw attention to the importance of use of techniques of loss prevention in small retail organization, analyzing and creating a classification model related to the use of these in companies. This work identifies the fragilities and virtues of companies and classifies them relating the use of techniques of loss prevention. The used methodology is based in a revision of the available literature on measurements and techniques of loss prevention, analyzing the processes that techniques needed to be adopted to reduce losses, approaching the "pillars" of loss prevention, the cycle life of products in retail and cycles of continues improvement in business. Based on the objectives of this work and on the light of researched techniques, was defined the case study, developed from a questionnaire application and the researcher's observation on a net of 16 small supermarkets. From those studies a model of classification of companies was created. The practical implications of this work are useful to point mistakes in retail administration that can become losses, reducing the profitability of companies or even making them impracticable. The academic contribution of this study is a proposal of an unpublished model of classification for small supermarkets based on the use of techniques of loss prevention. As a result of the research, 14 companies were classified as Companies with Minimum Use of Loss Prevention Techniques - CMULPT, and 02 companies were classified as Companies with Deficient Use of Loss Prevention Techniques - CDULPT. The result of the research concludes that on average the group was classified as being Companies with Minimum Use of Techniques of Prevention of Losses EUMTPP, and that the companies should adopt a program of loss prevention focusing in the identification and quantification of losses and in a implantation of a culture of loss prevention

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Este trabalho tem como objetivo estudar os sistemas de Classificações existentes para a garantia da gestão da qualidade no setor hoteleiro, tendo como foco principal a Matriz de Classificação para os Meios de Hospedagem da EMBRATUR e a ISO 9000, observando os benefícios que esses sistemas e/ou processos de gestão poderão vir a proporcionar para o setor hoteleiro no que se refere à qualidade de seus serviços. Para a obtenção dessas informações foi realizada uma análise comparativa dos sistemas de gestão da qualidade através de pesquisas bibliográficas e de questionários enviados para empreendimentos hoteleiros certificados e classificados, onde os principais resultados fornecidos pela pesquisa foram trabalhados de forma a apresentar, de maneira clara, a superioridade de um sistema em relação ao outro

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This thesis treats of a avaliation of a laboral gimnastics program of a electrical energy enterprise from Rio Grande do Norte. The leading aim of this work is to analyse the laboral gimnastics program with emphasis on functional performing and personal changes of staff of different categories. The work shows two divisions: at first, it was carried out the bibliographic study approaching the concepts and historic about the ergonomy and the main focus of study, the laboral gimnastics , performing factors and lifestyle.The second time , it was carried out the field study where was used a questionnaire to a 160 staff population which participate of laboral gymnastics program; then the data were analysed through three statistics analyses: multivaried__ clusters, qui-quadrado and multiple linear regression. The results aim to the confirmation that the laboral gymnastics program developed two years ago on enterprise proposes to the white-collar and operational workers an improving on their functional performing and also, to the changes in some aspects of lifestyle, like the food, leisure and stress level. In conclusion, it was checked that the investiment in life quality programs offer benefits both to the staff and the enterprise which will account with good services and satisfaction of customer. Therefore, it was concluded that the laboral gymnastics while a ergonomic tool and a physical activity, it is an investiment which needs more and more to be strengthened and inserted by many segments of society

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A utilização de dois critérios de seleção na pré-desmama, ganho médio diário do nascimento à desmama (GMD) e dias para ganhar 160 kg do nascimento à desmama (D160), foi estudada, analisando-se informações de 16.592 animais, provenientes do controle de desenvolvimento ponderal da Associação Brasileira dos Criadores de Zebu, nascidos no período de 1978 a 1994. Foram incluídos no modelo o efeito fixo de grupo de contemporâneos e os efeitos aleatórios genético aditivo de animal e materno, de ambiente permanente materno e o erro. A covariância entre os efeitos direto e materno foi considerada igual a zero. As estimativas dos componentes de variância e herdabilidade foram obtidas pelo método da máxima verossimilhança restrita e os valores genéticos preditos (VGs), por modelos animais uni-característica. As estimativas de herdabilidade foram: 0,12; 0,05; 0,10 e 0,05 para GMD (efeito direto), GMD (efeito materno), D160 (efeito direto) e D160 (efeito materno), respectivamente. Foram estimadas a correlação genética entre GMD e D160 (efeito direto e materno) e a correlação de classificação (Spearman) entre os valores genéticos para as categorias de touros, vacas e bezerros. As estimativas de correlação genética entre GMD e D160 foram 0,86 e 0,88, para o efeito direto e materno, respectivamente. As estimativas de correlação de ;rank;, também foram altas, entretanto, nenhuma foi igual a um, resultando em alterações na classificação dos animais. A relação entre as médias aritmética (A) e harmônica (H) e o desvio-padrão (S) do GMD ajustado para efeitos ambientais e maternos (GMDc) foi verificada utilizando-se um modelo restrito, sem intercepto, mediante as regressões linear e quadrática do S do GMDc sobre a diferença entre a média aritmética e média harmônica (A-H). Os resultados evidenciaram que, semelhantemente a H, o critério D160 apresentou a propriedade de discriminar touros com progênie mais uniforme.

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The purpose of this paper is to identify the utilization of two models of orthotics used in the conservative treatment of rizartrose in patients/consumers that were referred by the medical clinic to the LAI (Laboratory of Integrated Accessibility). It describes this group s perception related to the functional aspects of the two models, evaluates the value of the product attributed by the patient/consumer and describes the importance attributed to each model. The research s objective consists in identifying which model this is preferred from the point of view by the patients that use either the dorsal or ventral models used in the conservative treatment of rizartrose . This work is then based on the exploratory research through the investigation of the bibliography available and in the research field. The research field used the case study methodology to investigate the phenomenon in the real context. The data were collected using a patient questionnaire during two times after the use of the dorsal orthotic and after the use of the ventral orthotic. The questionnaire was used to identify the relationship between the patients/consumers and the two different types of orthotics. A general descriptive analysis was used in order to verify the patient s abilities executing certain activities daily living using the dorsal and ventral orthotics, the product s functional aspects and its value from the patient s/consumer s point of view. The results show that the preference for the dorsal or ventral models is determined by the individual needs of each person. The results also show that other variables, such as the use of the orthotic on the dominant or non-dominant hand, play a role in the preferred model by the patient/consumer and need to be further investigated

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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

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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

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Post dispatch analysis of signals obtained from digital disturbances registers provide important information to identify and classify disturbances in systems, looking for a more efficient management of the supply. In order to enhance the task of identifying and classifying the disturbances - providing an automatic assessment - techniques of digital signal processing can be helpful. The Wavelet Transform has become a very efficient tool for the analysis of voltage or current signals, obtained immediately after disturbance s occurrences in the network. This work presents a methodology based on the Discrete Wavelet Transform to implement this process. It uses a comparison between distribution curves of signals energy, with and without disturbance. This is done for different resolution levels of its decomposition in order to obtain descriptors that permit its classification, using artificial neural networks

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The precision and the fast identification of abnormalities of bottom hole are essential to prevent damage and increase production in the oil industry. This work presents a study about a new automatic approach to the detection and the classification of operation mode in the Sucker-rod Pumping through dynamometric cards of bottom hole. The main idea is the recognition of the well production status through the image processing of the bottom s hole dynamometric card (Boundary Descriptors) and statistics and similarity mathematics tools, like Fourier Descriptor, Principal Components Analysis (PCA) and Euclidean Distance. In order to validate the proposal, the Sucker-Rod Pumping system real data are used

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This work proposes the specification of a new function block according to Foundation Fieldbus standards. The new block implements an artificial neural network, which may be useful in process control applications. The specification includes the definition of a main algorithm, that implements a neural network, as well as the description of some accessory functions, which provide safety characteristics to the block operation. Besides, it also describes the block attributes emphasizing its parameters, which constitute the block interfaces. Some experimental results, obtained from an artificial neural network implementation using actual standard functional blocks on a laboratorial FF network, are also shown, in order to demonstrate the possibility and also the convenience of integrating a neural network to Fieldbus devices

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O presente trabalho teve como objetivo avaliar a qualidade pós-colheita de jabuticabas submetidas a diferentes temperaturas de armazenamento refrigerado (AR). Após a colheita, os frutos fisiologicamente maduros foram acondicionados em bandejas de poliestireno expandido (EPS), revestidas por filme plástico de polietileno de baixa densidade (PEBD) e armazenados sob refrigeração a 0; 3; 6; 9 e 12 ± 1ºC e U.R. 87 ± 2%, sendo avaliados a cada 5 dias. Os frutos foram avaliados quanto à perda de massa, atividade respiratória, pH, acidez titulável, sólidos solúveis, ácido ascórbico, textura, pectina total e solúvel, atividade da enzima polifenoloxidase (PFO), compostos fenólicos e atividade antioxidante. Para frutos refrigerados a 9 e 12ºC, o pico respiratório atrasou em relação aos demais tratamentos, além de apresentarem as menores taxas respiratórias. O teor de sólidos solúveis aumentou com o tempo de armazenamento para todas as temperaturas, contudo, em 9 e 12ºC, esse aumento foi em menor proporção. A firmeza e o teor de ácido ascórbico também foram superiores nos frutos armazenados a 9 e 12ºC, enquanto os contéudos de pectina solúvel foram menores. Observou-se a diminuição da atividade da enzima PFO ao longo dos 30 dias do AR, independentemente da temperatura utilizada; entretanto, os menores valores foram encontrados nos frutos mantidos a 9 e 12ºC. Os frutos armazenados a 12ºC apresentaram os maiores conteúdos de compostos fenólicos totais e a maior atividade antioxidante ao final do experimento. Nesse sentido, a temperatura de 12ºC foi a mais efetiva na manutenção da qualidade pós-colheita das jabuticabas.

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This work proposes a new methodology to verify those analog circuits, providing an automated tools to help the verifiers to have a more truthful result. This work presents the development of new methodology for analog circuits verification. The main goal is to provide a more automated verification process to certify analog circuits functional behavior. The proposed methodology is based on the golden model technique. A verification environment based on this methodology was built and results of a study case based on the validation of an operational amplifier design are offered as a confirmation of its effectiveness. The results had shown that the verification process was more truthful because of the automation provided by the tool developed

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The Brain-Computer Interfaces (BCI) have as main purpose to establish a communication path with the central nervous system (CNS) independently from the standard pathway (nervous, muscles), aiming to control a device. The main objective of the current research is to develop an off-line BCI that separates the different EEG patterns resulting from strictly mental tasks performed by an experimental subject, comparing the effectiveness of different signal-preprocessing approaches. We also tested different classification approaches: all versus all, one versus one and a hierarchic classification approach. No preprocessing techniques were found able to improve the system performance. Furthermore, the hierarchic approach proved to be capable to produce results above the expected by literature

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Reinforcement learning is a machine learning technique that, although finding a large number of applications, maybe is yet to reach its full potential. One of the inadequately tested possibilities is the use of reinforcement learning in combination with other methods for the solution of pattern classification problems. It is well documented in the literature the problems that support vector machine ensembles face in terms of generalization capacity. Algorithms such as Adaboost do not deal appropriately with the imbalances that arise in those situations. Several alternatives have been proposed, with varying degrees of success. This dissertation presents a new approach to building committees of support vector machines. The presented algorithm combines Adaboost algorithm with a layer of reinforcement learning to adjust committee parameters in order to avoid that imbalances on the committee components affect the generalization performance of the final hypothesis. Comparisons were made with ensembles using and not using the reinforcement learning layer, testing benchmark data sets widely known in area of pattern classification

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Modern wireless systems employ adaptive techniques to provide high throughput while observing desired coverage, Quality of Service (QoS) and capacity. An alternative to further enhance data rate is to apply cognitive radio concepts, where a system is able to exploit unused spectrum on existing licensed bands by sensing the spectrum and opportunistically access unused portions. Techniques like Automatic Modulation Classification (AMC) could help or be vital for such scenarios. Usually, AMC implementations rely on some form of signal pre-processing, which may introduce a high computational cost or make assumptions about the received signal which may not hold (e.g. Gaussianity of noise). This work proposes a new method to perform AMC which uses a similarity measure from the Information Theoretic Learning (ITL) framework, known as correntropy coefficient. It is capable of extracting similarity measurements over a pair of random processes using higher order statistics, yielding in better similarity estimations than by using e.g. correlation coefficient. Experiments carried out by means of computer simulation show that the technique proposed in this paper presents a high rate success in classification of digital modulation, even in the presence of additive white gaussian noise (AWGN)