Extraction of voltage harmonics using multi-layer perceptron neural network

This paper presents a harmonic extraction algorithm using artificial neural networks for Dynamic Voltage Restorers (DVRs). The suggested algorithm employs a feed forward Multi Layer Perceptron (MLP) Neural Network with error back propagation learning to effectively track and extract the 3rd and 5th voltage harmonics. For this purpose, two different MLP neural network structures are constructed and their performances compared. The effects of hidden layer, supervisors and learning rate are also presented. The proposed MLP Neural Network algorithm is trained and tested in MATLAB program environment. The results show that MLP neural network enable to extract each harmonic effectively.     

Fuzzy boundary layer tuning for sliding mode systems as applied to the control of a direct drive robot

Chattering in the control signal is a significant problem in sliding mode control (SMC). The boundary layer approach is one of the many modifications proposed in the literature to avoid the chattering. In this approach, instead of the discontinuous SMC, a continuous feedback control law is employed in a boundary layer around the sliding surface. The thickness of the boundary layer is an important design parameter. This paper proposes a fuzzy online tuning method to adjust the boundary layer thickness for the best system performance without chattering. The method features the measurement of the chattering in the control signal. The paper validates the performance of the algorithm by experiments on a direct drive robot with a range of different payloads.     

Dynamique des lasers à fibre dopée à I’erbium

The dynamics of the erbium doped fiber laser is analysed both experimentally and theoretically. This laser operates spontaneously in self-pulsing, in sinusoidal or in CW regime. Experiments have allowed to determine the control parameters : the cavity losses, the pumping rate and the ion pairs concentration. An antiphase dynamics is observed when the laser operates simultaneously at 1.55 µn and 1.536 µm. The self-pulsing behavior is attributed to a fast energy transfer which occurs between two neighboring ions (pair induced quenching). A simple model considering the active medium as a mixture of isolated ions and ions pairs is developped and leads to a good qualitative agreement with the experimental results.     

Genetic algorithm based approaches to solve the order batching problem and a case study in a distribution center

Journal of Intelligent Manufacturing - The order batching problem is a combinatorial optimization problem that arises in the warehouse order picking process. In the order batching problem, the aim...     

Investigation on replication of microfluidic channels by hot embossing

In this study, effects of embossing temperature, time, and force on production of a microfluidic device were investigated. Polymethyl methacrylate (PMMA) substrates were hot embossed by using a micromilled aluminum mold. The process parameters were altered to observe the variation of replication rate in width and depth as well as symmetry of the replicated microfluidic channels. Analysis of variance (ANOVA) on the experimental results indicated that embossing temperature was the most important process parameter, whereas embossing time and force have less impact. One distinguishing aspect of this study is that, the channels were observed to be skewed to either side of the channel depending on the location of the protrusions on the mold. The mechanism of the skewness was investigated by finite element analysis and discussed in detail. Results showed that the skewness depends on the flow characteristics of the material and could be reduced by increasing the embossing temperature. The best replication rates were obtained at parameter settings of 115°C, 10?kN, and 8?min for the molds with minimum 56?µm wide features of 120?µm depth. We also showed that the fabricated channels could be successfully sealed by solvent-assisted thermo-compressive bonding at 85°C under 5.5?kN force.     

A semantic backend for content management systems

The users of a content repository express the semantics they have in mind while defining the content items and their properties, and forming them into a particular hierarchy. However, this valuable semantics is not formally expressed, and hence cannot be used to discover meaningful relationships among the content items in an automated way. Although the need is apparent, there are several challenges in explicating this semantics in a fully automated way: first, it is difficult to distinguish between data and the metadata in the repository and secondly, not all the metadata defined, such as the file size or encoding type, contribute to the meaning. More importantly, for the developed solution to have practical value, it must address the constraints of the content management system (CMS) industry: CMS industry cannot change their repositories in production use and they need a generic solution not limited to a specific repository architecture. In this article, we address all these challenges through a set of tools developed which first semi-automatically explicate the content repository semantics to a knowledge-base and establish semantic bridges between this backend knowledge-base and the content repository. The repository content is dynamic; to be able to maintain the content repository semantics while new content is created, the changes in the repository semantics are reflected onto the knowledge-base through the semantic bridges. The tool set is complemented with a search engine that make use of the explicated semantics.     

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA–MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA–MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%.     

A data fusion framework with novel hybrid algorithm for multi-agent Decision Support System for Forest Fire

In this study Forest Fire Decision Support System (FOFDESS) which is a multi-agent Decision Support System for Forest Fire has been presented. Depending on the existing meteorological state and environmental observations, FOFDESS does the fire danger rating by predicting the forest fire and it can also approximate fire spread speed and quickly detect a started fire. Some data fusion algorithms such as Artificial Neural Network (ANN), Naive Bayes Classifier (NBC), Fuzzy Switching (FS) and image processing have been used for these operations in FOFDESS. These algorithms have been brought together by a designed data fusion framework and a novel hybrid algorithm called NABNEF (Naive Bayes Aided Neural-Fuzzy Algorithm) has been improved for fire danger rating in FOFDESS. In this state, FOFDESS is an integrated system which includes the dimensions of prediction, detection and management. As a result of the experiments, it was found out that FOFDESS helped determining the most accurate strategy for fire fighting by producing effective results.     

Decentralised controller design based on structured singular values

In this study, structured singular values are used in a different way from those commonly used in the robust control literature. It is shown that subject to conditions based on structured singular values, each local area controller can be designed independently. A MATLAB? program is developed to plot inverse structured singular values of multi input multi output (MIMO) system relative error matrix. This plot can be used to predict the stability of the global system with decentralised controller. Therefore decentralised controller design problem can be translated into an equivalent problem of decentralized controller design for a MIMO control system.     

A tool for multiobjective evolutionary algorithms

This paper introduces a software tool based on illustrative applications for the development, analysis and application of multiobjective evolutionary algorithms. The multiobjective evolutionary algorithms tool (MOEAT) written in C# using a variety of multiobjective evolutionary algorithms (MOEAs) offers a powerful environment for various kinds of optimization tasks. It has many useful features such as visualizing of the progress and the results of optimization in a dynamic or static mode, and decision variable settings. The performance measurements of well-known multiobjective evolutionary algorithms in MOEAT are done using benchmark problems. In addition, two case studies from engineering domain are presented.