Organizational modeling with a semantic wiki: formalization of content and automatic diagram generation

A key to maintain Enterprises competitiveness is the ability to describe, standardize, and adapt the way it reacts to certain types of business events, and how it interacts with suppliers, partners, competitors, and customers. In this context the field of organization modeling has emerged with the aim to create models that help to create a state of self-awareness in the organization. This project's context is the use of Semantic Web in the Organizational modeling area. The Semantic Web technology advantages can be used to improve the way of modeling organizations. This was accomplished using a Semantic wiki to model organizations. Our research and implementation had two main purposes: formalization of textual content in semantic wiki pages; and automatic generation of diagrams from organization data stored in the semantic wiki pages.

DEMO models based automatic workflow process generation

Nowadays, more than half of the computer development projects fail to meet the final users' expectations. One of the main causes is insufficient knowledge about the organization of the enterprise to be supported by the respective information system. The DEMO methodology (Design and Engineering Methodology for Organizations) has been proved as a well-defined method to specify, through models and diagrams, the essence of any organization at a high level of abstraction. However, this methodology is platform implementation independent, lacking the possibility of saving and propagating possible changes from the organization models to the implemented software, in a runtime environment. The Universal Enterprise Adaptive Object Model (UEAOM) is a conceptual schema being used as a basis for a wiki system, to allow the modeling of any organization, independent of its implementation, as well as the previously mentioned change propagation in a runtime environment. Based on DEMO and UEAOM, this project aims to develop efficient and standardized methods, to enable an automatic conversion of DEMO Ontological Models, based on UEAOM specification into BPMN (Business Process Model and Notation) models of processes, using clear semantics, without ambiguities, in order to facilitate the creation of processes, almost ready for being executed on workflow systems that support BPMN.

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

Compressão Seletiva de Imagens Coloridas com Detecção Automática de Regiões de Interesse

There has been an increasing tendency on the use of selective image compression, since several applications make use of digital images and the loss of information in certain regions is not allowed in some cases. However, there are applications in which these images are captured and stored automatically making it impossible to the user to select the regions of interest to be compressed in a lossless manner. A possible solution for this matter would be the automatic selection of these regions, a very difficult problem to solve in general cases. Nevertheless, it is possible to use intelligent techniques to detect these regions in specific cases. This work proposes a selective color image compression method in which regions of interest, previously chosen, are compressed in a lossless manner. This method uses the wavelet transform to decorrelate the pixels of the image, competitive neural network to make a vectorial quantization, mathematical morphology, and Huffman adaptive coding. There are two options for automatic detection in addition to the manual one: a method of texture segmentation, in which the highest frequency texture is selected to be the region of interest, and a new face detection method where the region of the face will be lossless compressed. The results show that both can be successfully used with the compression method, giving the map of the region of interest as an input

Aquatic weed automatic classification using machine learning techniques

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Extração automática de contornos de telhados usando dados de varredura a laser e campos randômicos de Markov

This paper proposes a methodology for automatic extraction of building roof contours from a Digital Elevation Model (DEM), which is generated through the regularization of an available laser point cloud. The methodology is based on two steps. First, in order to detect high objects (buildings, trees etc.), the DEM is segmented through a recursive splitting technique and a Bayesian merging technique. The recursive splitting technique uses the quadtree structure for subdividing the DEM into homogeneous regions. In order to minimize the fragmentation, which is commonly observed in the results of the recursive splitting segmentation, a region merging technique based on the Bayesian framework is applied to the previously segmented data. The high object polygons are extracted by using vectorization and polygonization techniques. Second, the building roof contours are identified among all high objects extracted previously. Taking into account some roof properties and some feature measurements (e. g., area, rectangularity, and angles between principal axes of the roofs), an energy function was developed based on the Markov Random Field (MRF) model. The solution of this function is a polygon set corresponding to building roof contours and is found by using a minimization technique, like the Simulated Annealing (SA) algorithm. Experiments carried out with laser scanning DEM's showed that the methodology works properly, as it delivered roof contours with approximately 90% shape accuracy and no false positive was verified.

IFTrace: Video segmentation of deformable objects using the Image Foresting Transform

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic detection of thermal damage in grinding process by artificial neural network

Esse trabalho tem por objetivo o desenvolvimento de um sistema inteligente para detecção da queima no processo de retificação tangencial plana através da utilização de uma rede neural perceptron multi camadas, treinada para generalizar o processo e, conseqüentemente, obter o limiar de queima. em geral, a ocorrência da queima no processo de retificação pode ser detectada pelos parâmetros DPO e FKS. Porém esses parâmetros não são eficientes nas condições de usinagem usadas nesse trabalho. Os sinais de emissão acústica e potência elétrica do motor de acionamento do rebolo são variáveis de entrada e a variável de saída é a ocorrência da queima. No trabalho experimental, foram empregados um tipo de aço (ABNT 1045 temperado) e um tipo de rebolo denominado TARGA, modelo ART 3TG80.3 NVHB.

Power performance evaluation of an electric home fan with triac-based automatic speed control system

In order to provide a low cost system of thermal comfort, a common model of home fan, 40 cm diameter size, had its manual four-button control system replaced by an automatic speed control. The new control system has a temperature sensor feeding a microcontroller that, by using an optic coupling, DIAC or TRIAC-based circuit, varies the RMS value of the fan motor input voltage and its speed, according to the room temperature. Over a wide range of velocity, the fan net power and the motor fan input power were measured working under both control system. The temperature of the motor stator and the voltage waveforms were observed too. Measured values analysis showed that the TRIAC-based control system makes the fan motor work at a very low power factor and efficiency values. The worst case is at low velocity range where the higher fan motor stator temperatures were registered. The poor power factor and efficiency and the harmonics signals inserted in the motor input voltage wave by the TRIAC commutation procedure are correlated.

Automatic dimming multi-lamp fluorescent management system with active input power factor corrector stage

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Stability analysis of power systems described with detailed models by automatic method

The power system stability analysis is approached taking into explicit account the dynamic performance of generators internal voltages and control devices. The proposed method is not a direct method in the usual sense since conclusion for stability or instability is not exclusively based on energy function considerations but it is automatic since the conclusion is achieved without an analyst intervention. The stability test accounts for the nonconservative nature of the system with control devices such as the automatic voltage regulator (AVR) and automatic generation control (AGC) in contrast with the well-known direct methods. An energy function is derived for the system with machines forth-order model, AVR and AGC and it is used to start the analysis procedure and to point out criticalities. The conclusive analysis itself is made by means of a method based on the definition of a region surrounding the equilibrium point where the system net torque is equilibrium restorative. This region is named positive synchronization region (PSR). Since the definition of the PSR boundaries have no dependence on modelling approximation, the PSR test conduces to reliable results. (C) 2008 Elsevier Ltd. All rights reserved.

Sistema de agentes poligínicos para esteganálise de imagens digitais

In this work, we propose a multi agent system for digital image steganalysis, based on the poliginic bees model. Such approach aims to solve the problem of automatic steganalysis for digital media, with a case study on digital images. The system architecture was designed not only to detect if a file is suspicious of covering a hidden message, as well to extract the hidden message or information regarding it. Several experiments were performed whose results confirm a substantial enhancement (from 67% to 82% success rate) by using the multi-agent approach, fact not observed in traditional systems. An ongoing application using the technique is the detection of anomalies in digital data produced by sensors that capture brain emissions in little animals. The detection of such anomalies can be used to prove theories and evidences of imagery completion during sleep provided by the brain in visual cortex areas

Productive performance of Nile tilapia (Oreochromis niloticus) fed at different frequencies and periods with automatic dispenser

Avaliou-se o desempenho de tilápias-do-nilo (Oreochromis niloticus) produzidas em tanque-rede, providas de dispensadores automáticos de ração, alimentadas em diferentes frequências - uma vez por hora e a cada duas horas - e períodos - durante o dia, à noite ou ambos. Dezoito tanques-rede de 1.0m³ foram colocados em um tanque de 2000m² com dois metros de profundidade e renovação de água de 5%. Cento e setenta tilápias, com peso inicial de 16.0±4.9g foram distribuídas em cada tanque-rede de 1m³ e a taxa alimentar foi ajustada a cada 21 dias junto com as biometrias. As medidas foram coletadas de março a julho (outono e inverno). Observou-se diferença significativa para peso final (P<0.05) entre os tratamentos. O aumento da frequência alimentar melhorou o desempenho produtivo de tilápias-do-nilo produzidas em tanque-rede e permitiu melhor manejo alimentar. A melhor conversão alimentar para alta frequência, 24 vezes dia-1, pode resultar em uma economia de até 360kg de ração por tonelada de peixe produzido, melhorando a sustentabilidade econômica para produção de tilápia e sugerindo menor poluição ambiental.

Recursive hierarchic segmentation analysis of bone mineral density changes on digital panoramic images

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)