Estimation of electrical machine speed using sensorless technology and neural networks

The use of sensorless technologies is an increasing tendency on industrial drivers for electrical machines. The estimation of electrical and mechanical parameters involved with the electrical machine control is used very frequently in order to avoid measurement of all variables related to this process. The cost reduction may also be considered in industrial drivers, besides the increasing robustness of the system, as an advantage of the use of sensorless technologies. This work proposes the use of a recurrent artificial neural network to estimate the speed of induction motor for sensorless control schemes using one single current sensor. Simulation and experimental results are presented to validate the proposed approach. ©2008 IEEE.

Video target tracking by using competitive neural networks

A target tracking algorithm able to identify the position and to pursuit moving targets in video digital sequences is proposed in this paper. The proposed approach aims to track moving targets inside the vision field of a digital camera. The position and trajectory of the target are identified by using a neural network presenting competitive learning technique. The winning neuron is trained to approximate to the target and, then, pursuit it. A digital camera provides a sequence of images and the algorithm process those frames in real time tracking the moving target. The algorithm is performed both with black and white and multi-colored images to simulate real world situations. Results show the effectiveness of the proposed algorithm, since the neurons tracked the moving targets even if there is no pre-processing image analysis. Single and multiple moving targets are followed in real time.

Cyclosporine-a and bone density around titanium implants: A histometric study in rabbits

Aim: Cyclosporine A (CsA) is an immunosuppressive agent commonly used to prevent organ transplantation rejection. It has been demonstrated that CsA may negatively affect osseointegration around dental implants. Therefore, the aim of this study was to evaluate the effect of CsA administration on bone density around titanium dental implants. Materials and Methods: Fourteen New Zealand rabbits were randomly divided into 2 groups with seven animals each. The test group (CsA) received daily subcutaneous injection of CsA (10mg/kg body weight) and the control group (CTL) received saline solution by the same route of administration. Three days after the beginning of immunosuppressive therapy, one machined dental implant (7.00 mm in lenght and 3.75 mm in diameter) was inserted bilaterally at the region of the tibial methaphysis. After 4 and 8 weeks the animals were sacrificed and the histometrical procedures were performed to analyse the bone density around the first four threads of the coronal part of the implant. Results: A significant increase in the bone density was observed from the 4- to the 8 week-period in the control group (37.41% + 14.85 versus 58.23% + 16.38 - p <0.01). In contrast, bone density consistently decreased in the test group overtime (46.31% + 17.38 versus 16.28 + 5.08 - p <0.05). In the 8-week period, there was a significant difference in bone density between the control and the test groups (58.23 + 16.38 eand16.28 + 5.08 - p= 0.001). Conclusion: Within the limits of this study, long-term CsA administration may reduce bone density around titanium dental implants during the osseointegration process.

Short-term multinodal load forecasting in distribution systems using general regression neural networks

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first individually forecast the local loads and the second forecast the global load and individually forecast the load participation factors to estimate the local loads. To design the forecasters it wasn't necessary the previous study of the local loads. Tests were made using a New Zealand distribution subsystem and the results obtained are compatible with the ones founded in the specialized literature. © 2011 IEEE.

Muscular strength, bone density and body composition of a woman with systemic lupus erythematosus submitted to a resistance training program: A case report

Introduction. Physical activity can provide long-term benefits for systemic lupus erythematosus (SLE). Objective. This study sought to demonstrate the effects of progressive resistance training on the muscular strength, bone mineral density (BMD) and body composition of pre-menopausal women with SLE undergoing glucocorticoid (GC) treatment. Materials and Methods. This is the case report of a 43-year-old African-South American premenopausal woman with non-extensive SLE and low bone density. A six-month program with three bimonthly cycles of 70%, 80%, and 90% intensity according to the 10 maximum-repetition test was used. Dual-energy X-ray absorptiometry (DXA) was used to measure the BMD, T-scores and body composition, and indirect fluorescence was used to measure the levels of antinuclear antibodies. Student's t-test was used. Results. Statistical improvement was noted in all strength exercises, including the 45° leg press (Δ%=+50%, p<0.001) and knee extension (Δ%=+15%, p=0.003) to maintain the BMD of the L2-L4 lumbar (Δ%=+0.031%; p=0.46) as well as the trochanter (Δ%=+0.037%; p=0.31) and BMI (Δ%=-0.8, p=0.54). Conclusion. In this case study, the presented methodology had a positive effect on strength and contributed to the maintenance of BMD and body composition in a woman with SLE undergoing GC treatment. © 2012 Revista Andaluza de Medicina del Deporte.

Fault location in underground systems using artificial neural networks and PSCAD/EMTDC

The need for high reliability and environmental concerns are making the underground networks the most appropriate choice of energy distribution. However, like any other system, underground distribution systems are not free of failures. In this context, this work presents an approach to study underground systems using computational tools by integrating the software PSCAD/EMTDC with artificial neural networks to assist fault location in power distribution systems. Targeted benefits include greater accuracy and reduced repair time. The results presented here shows the feasibility of the proposed approach. © 2012 IEEE.

Speed estimation for induction motor using neural networks method

This work presents an alternative approach based on neural network method in order to estimate speed of induction motors, using the measurement of primary variables such as voltage and current. Induction motors are very common in many sectors of the industry and assume an important role in the national energy policy. The nowadays methodologies, which are used in diagnosis, condition monitoring and dimensioning of these motors, are based on measure of the speed variable. However, the direct measure of this variable compromises the system control and starting circuit of an electric machinery, reducing its robustness and increasing the implementation costs. Simulation results and experimental data are presented to validate the proposed approach. © 2003-2012 IEEE.

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.

Neural networks models for wear patterns recognition of single-point dresser

Grinding is a workpiece finishing process for advanced products and surfaces. However, the constant friction between workpiece and grinding wheel causes the latter to lose its sharpness, thereby impairing the result of the grinding process. When this occurs, the dressing process is essential to sharpen the worn grains of the grinding wheel. The dressing conditions strongly influence the performance of the grinding operation; hence, monitoring them throughout the process can increase its efficiency. The purpose of this study was to classify the wear condition of a single-point dresser using intelligent systems whose inputs were obtained by digitally processing acoustic emission signals. Two multilayer perceptron (MLP) neural networks were compared for their classification ability, one using the root mean square (RMS) statistics and another the ratio of power (ROP) statistics as input. In this study, it was found that the harmonic content of the acoustic emission signal is influenced by the condition of the dresser, and that the condition of the tool under study can be classified by using the aforementioned statistics to feed a neural network. © IFAC.

Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route. (C) 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of the organizers of the 2013 International Conference on Computational Science

The bias in reversing the Box-Cox transformation in time series forecasting: An empirical study based on neural networks

The Box-Cox transformation is a technique mostly utilized to turn the probabilistic distribution of a time series data into approximately normal. And this helps statistical and neural models to perform more accurate forecastings. However, it introduces a bias when the reversion of the transformation is conducted with the predicted data. The statistical methods to perform a bias-free reversion require, necessarily, the assumption of Gaussianity of the transformed data distribution, which is a rare event in real-world time series. So, the aim of this study was to provide an effective method of removing the bias when the reversion of the Box-Cox transformation is executed. Thus, the developed method is based on a focused time lagged feedforward neural network, which does not require any assumption about the transformed data distribution. Therefore, to evaluate the performance of the proposed method, numerical simulations were conducted and the Mean Absolute Percentage Error, the Theil Inequality Index and the Signal-to-Noise ratio of 20-step-ahead forecasts of 40 time series were compared, and the results obtained indicate that the proposed reversion method is valid and justifies new studies. (C) 2014 Elsevier B.V. All rights reserved.

Tool Condition Monitoring of Single-Point Dresser Using Acoustic Emission and Neural Networks Models

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

Pruning methods to MLP neural networks considering proportional apparent error rate for classification problems with unbalanced data

This article deals with classification problems involving unequal probabilities in each class and discusses metrics to systems that use multilayer perceptrons neural networks (MLP) for the task of classifying new patterns. In addition we propose three new pruning methods that were compared to other seven existing methods in the literature for MLP networks. All pruning algorithms presented in this paper have been modified by the authors to do pruning of neurons, in order to produce fully connected MLP networks but being small in its intermediary layer. Experiments were carried out involving the E. coli unbalanced classification problem and ten pruning methods. The proposed methods had obtained good results, actually, better results than another pruning methods previously defined at the MLP neural network area. (C) 2014 Elsevier Ltd. All rights reserved.

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Data collection on bone density of mandibular alveolar bone on a Hounsfield scale of the dental apex of the cranial root of the first inferior molar in dogs using computed tomography at FMVZ-UNESP

The head is the most important and specialized region in the body because it contains a range of specialized organs and, because it has interconnections between specialized organs, there is a great overlap of images. Thus, computed tomography (CT) helps in diagnosing diseases in this region, such as oral conditions, as they provide millimetric slices or cuts and demonstrate the relationship between the various anatomical structures involved, in volume and depth. Within dentistry, CT helps in the identification of pathological processes such as infection, tumors, visualization of embedded teeth and bone bed. This study aimed to assess the density of the mandibular alveolar bone at a determined point to later predict how periodontal disease is involved in bone resorption. For this, we performed a blind retrospective study (n = 124) of the CT scan files of dog skulls at FMVZ-UNESP in order to determine the density of the jaw bone using a Hounsfield scale, in the region of the dental apex of the cranial root of the first molar tooth in dogs. The results obtained were evaluated using mean and standard deviation (27.28 +/- 9.53 HU) in order to predict the normal density of the mandibular alveolar bone in the studied region. Thus, this data analysis allows a more concise evaluation of bone resorption of mandibular alveolar bone and, therefore, provides an adequate surgical planning in cases of osteosynthesis given mainly by the presence of installed periodontal disease.