Intelligent alarm processing

This paper presents the analysis that have been carried out in the alarm system of the DCRanger EMS. The intention of this study is to present the problem of alarm processing in electric energy control centers, its various aspects and operational difficulties due to operator needs. Some tests are produced in order to identify the desirable features an alarm system should possess in order to be of effective help in the operative duty. © 2006 IEEE.

A educação e a teoria da complexidade na formação de professores: problemas e desafios

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Retificador entrelaçado com elevado fator de potência para operação de um veículo trólebus alimentado por redes em CA Ou CC

This paper presents the development and the main results for an interleaved boost rectifier operating as a special input power stage for a trolleybus type vehicle, allowing its feeding by alternate current (AC) or direct current (DC) distribution power systems. When feeding with two wires (single phase) alternate current distribution system, the converter accomplish active power factor correction, providing a relatively sinusoidal current with low total harmonic distortion (THD) and fully complying with IEC 61000-3-4 standards. In addition, a management control system promotes the required automatic operation changes for the proposed rectifier when the vehicle is changing from the DC distribution power system to the AC distribution power system and vice-versa, keeping its original electrical DC system characteristics for the adjustable speed driver sub-system. The main experimental results for a prototype rated at 150kW are presented, considering its application for a trolleybus with DC adjustable speed driver, demonstrating the proposed converter benefits and the possibility of AC feeding system for trolleybus type vehicle.

Manejo de minimelancia cultivada em fibra da casca de coco, sob ambiente protegido

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

An Intelligent System for Petroleum Well Drilling Cutting Analysis

Cutting analysis is a important and crucial task task to detect and prevent problems during the petroleum well drilling process. Several studies have been developed for drilling inspection, but none of them takes care about analysing the generated cutting at the vibrating shale shakers. Here we proposed a system to analyse the cutting's concentration at the vibrating shale shakers, which can indicate problems during the petroleum well drilling process, such that the collapse of the well borehole walls. Cutting's images are acquired and sent to the data analysis module, which has as the main goal to extract features and to classify frames according to one of three previously classes of cutting's volume. A collection of supervised classifiers were applied in order to allow comparisons about their accuracy and efficiency. We used the Optimum-Path Forest (OPF), Artificial Neural Network using Multi layer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC) for this task. The first one outperformed all the remaining classifiers. Recall that we are also the first to introduce the OPF classifier in this field of knowledge. Very good results show the robustness of the proposed system, which can be also integrated with other commonly system (Mud-Logging) in order to improve the last one's efficiency.

A novel intelligent system to nitrogen content prediction in plants using indirect chlorophyll measurements

The accurate identification of the nitrogen content in plants is extremely important since it involves economic aspects and environmental impacts, Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification in plants involves a lot of non-linear parameters and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought SPAD index using artificial neural networks (ANN). The network acts as identifier of relationships among, crop varieties, fertilizer treatments, type of leaf and nitrogen content in the plants (target). So, nitrogen content can be generalized and estimated and from an input parameter set. This approach can form the basis for development of an accurate real time system to predict nitrogen content in plants.

Effects of exercise training on the cardiovascular system: Pharmacological approaches

Physical exercise promotes beneficial health effects by preventing or reducing the deleterious effects of pathological conditions, such as arterial hypertension, coronary artery disease, atherosclerosis, diabetes mellitus, osteoporosis, Parkinson's disease, and Alzheimer disease. Human movement studies are becoming an emerging science in the epidemiological area and public health. A great number of studies have shown that exercise training, in general, reduces sympathetic activity and/or increases parasympathetic tonus either in human or laboratory animals. Alterations in autonomic nervous system have been correlated with reduction in heart rate (resting bradycardia) and blood pressure, either in normotensive or hypertensive subjects. However, the underlying mechanisms by which physical exercise produce bradycardia and reduces blood pressure has not been fully understood. Pharmacological studies have particularly contributed to the comprehension of the role of receptor and transduction signaling pathways on the heart and blood vessels in response to exercise training. This review summarizes and examines the data from studies using animal models and human to determine the effect of exercise training on the cardiovascular system. (c) 2007 Elsevier B.V. All rights reserved.

Effects of physical training on the immune system in diabetic rats

Aims: This study aims to investigate the influence of physical training on the immune system of diabetic rats. Materials and Methods: Adult male Wistar rats were distributed into Sedentary Control (SC), Trained Control (TC), Sedentary Diabetic (SD) and Trained Diabetic (TD) groups were used. Diabetes was induced by alloxan (32 mg/bw-i.v.). Training protocol consisted of swimming, at 32 18C, one hour/day, five days/week, supporting an overload equivalent to 5 of the body weight, during four weeks. At the end of the experiment the rats were sacrificed by decapitation and blood samples were collected for glucose, insulin, albumin, hematocrit determinations, total and differential leukocyte counting. Additionally, liver samples for glycogen analyses were obtained. Results: The results were analyzed by one way at a significance level of 5. Diabetes reduced blood insulin, liver glycogen stores and increased blood glucose and neutrophil count. Physical training restored glycemia, liver glycogen levels, neutrophils and lymphocytes count in diabetic rats. Conclusions: In summary, physical training was able to improve metabolic and immunological aspects in the experimental diabetic rats.

An intelligent system for pavement management

This paper describes a method for the evaluation of pavement condition through artificial neural networks using the MLP backpropagation technique. Two of the most used procedures for detecting the pavement conditions were applied: the overall severity index and the irregularity index. Tests with the model demonstrated that the simulation with the neural network gives better results than the procedures recommended by the highway officials. This network may also be applied for the construction of a graphic computer environment.

An intelligent system to real time rainfall prediction using radar data

This work presents a new approach for rainfall measurements making use of weather radar data for real time application to the radar systems operated by institute of Meteorological Research (IPMET) - UNESP - Bauru - SP-Brazil. Several real time adjustment techniques has been presented being most of them based on surface rain-gauge network. However, some of these methods do not regard the effect of the integration area, time integration and distance rainfall-radar. In this paper, artificial neural networks have been applied for generate a radar reflectivity-rain relationships which regard all effects described above. To evaluate prediction procedure, cross validation was performed using data from IPMET weather Doppler radar and rain-gauge network under the radar umbrella. The preliminary results were acceptable for rainfalls prediction. The small errors observed result from the spatial density and the time resolution of the rain-gauges networks used to calibrate the radar.

An intelligent system to detect drilling problems through drilled cuttings return analysis

Cuttings return analysis is an important tool to detect and prevent problems during the petroleum well drilling process. Several measurements and tools have been developed for drilling problems detection, including mud logging, PWD and downhole torque information. Cuttings flow meters were developed in the past to provide information regarding cuttings return at the shale shakers. Their use, however, significantly impact the operation including rig space issues, interferences in geological analysis besides, additional personel required. This article proposes a non intrusive system to analyze the cuttings concentration at the shale shakers, which can indicate problems during drilling process, such as landslide, the collapse of the well borehole walls. Cuttings images are acquired by a high definition camera installed above the shakers and sent to a computer coupled with a data analysis system which aims the quantification and closure of a cuttings material balance in the well surface system domain. No additional people at the rigsite are required to operate the system. Modern Artificial intelligence techniques are used for pattern recognition and data analysis. Techniques include the Optimum-Path Forest (OPF), Artificial Neural Network using Multilayer Perceptrons (ANN-MLP), Support Vector Machines (SVM) and a Bayesian Classifier (BC). Field test results conducted on offshore floating vessels are presented. Results show the robustness of the proposed system, which can be also integrated with other data to improve the efficiency of drilling problems detection. Copyright 2010, IADC/SPE Drilling Conference and Exhibition.

Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.

Role of Training and Detraining on Inflammatory and Metabolic Profile in Infarcted Rats: Influences of Cardiovascular Autonomic Nervous System

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