Gathering enough reliable information for the moment state of metal structures is needed to measure the rest life time and to assure usage without failures for devices in thermo-electric power plants. This information can be obtained by microstructure analysis of metal specimens by using plastic replicas and structural analysis. During the inservice process (high pressure and temperature) the structural and the phase composition of the steel are changed bringing different damages which can lead to complete breakdown. It is appropriate to develop methods for automated classification of steel structures aiming high recognition accuracy, reliability and lack of any subjective evaluation. The goal of the represented research is to propose a new method for automated classification of heat resistant steel structures aiming higher accuracy in comparison to other existing methods. The proposed method is based on recognition of microscope images for representative steel structures having different aging stage. In the preprocessing stage the histograms of the images are extracted and a set of reduced numbers of the cover curve values are given to the input neurons of a MLP type neural network.The achieved 100% accuracy is a good preposition to implement the method for automated calculation of the remaining capacity of the steel avoiding the subjective evaluation factor and implementing it in a real time working system Index Terms -automated classification, texture analysis, MLP neural networks, supervised learning
A model for image and data pre-processing and communication between a dedicated PC and a PLC with Neural Network (NN) application is proposed in this paper. The proposed model defines guidelines for creating a multithreaded application for receiving real-time data from several digital cameras, parallel image pre-processing based on predefined user algorithms, calculation of input data vector for NN and sending the input vector to the PLC NN application. The model was developed and verified in the laboratory "Intelligent
This article deals with an approach for demand modeling when designing size ranges of technical products. The proposed modeling approach is a stage of the size ranges optimization problem, and by it information can be obtained regarding the requested sizes and their respective quantities (demand). The approach is comprised of four main stagesmarket segmentation and choice of potential customers, collecting demand data, description and processing of the collected data, and determination of a demand model. The approach is applied for the demand modeling of a particular product -pneumatic modules for linear motion. In the analysis of the market research data the procedures of principle component analysis, factor analysis and regression by principal components are used. The study is carried out in the environment of STATGRAPHICS and SPSS.
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