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In this paper a newly developed fuzzy supervisory control system for the injection molding process is presented. The system performs automatic tuning of the machine operating points and reduces the human effort for a complete optimization of the machine settings. The experimental results obtained from the application of the proposed fuzzy control architecture in a real industrial environment were encouraging
Keywords. Injection Molding Process, incremental fuzzy supervisory control, self-tuning systems, knowledge-based systems
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Cancer chemotherapy with application of one drug is studied. The negative and inhibiting effect of the tumor on normal cells is taken into account. Under certain hypotheses, we determine the optimal regimen that minimizes the tumor burden at the end of a fixed period of therapy while maintaining certain normal cell populations above prescribed levels. More precisely, it is demonstrated that the optimal strategy corresponds to injection of the drug at the maximal rate.
Keywords. Optimal chemotherapy regimen, in uence of tumors on normal cells
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This paper describes a novel approach to map electric fields using artificial neural networks. The networks acts as an identifier of structural features of the high voltage substations design so that output parameters can be estimated and generalised from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical fields intensity and critical voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of the bus bars, and wave forms. A comparative analysis with the finite element method is also provided to illustrate this new methodology.
Key Words. Artificial neural networks, High voltage, Electric fields, Substations, Atmospheric impulses.
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| Long range forecasts of hourly loads spanning 52 weeks (1 year) not only facilitates preparation of capital repair schedules of generating units for preventive maintenance in an integrated system, but may also obviate the need for medium range forecasts in certain situations. The varying nature of power system data having multiple periodicity of 24 hours/168 hours (1 day) /(1 week) makes it suitable for the application of Digital Image Processing technique. An attempt has been made to represent the data in the form of an image replacing the time variables by space variables. Thus the inter pixel gap of the image represents the sampling time of 1 hour along the horizontal axis and 24 hours along the vertical axis. Transforming the image by 2-D Ordered Walsh Transform (OWT) and modeling the OWTs of successive years via an Arti cial Neural Network (ANN), forecasts are made by taking inverse of the forecast OWTs from the ANN. The dynamics of the process is captured by an input output relation for the ANN model, the parameters of which can be obtained by training the ANN with a given large set of sample input/output data (OWTs).
Keywords: Long Range Forecast, Arti cial Neural Network, Ordered Walsh Transform
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In this paper several algorithms for computing the specially proposed Fuzzy Model Network Systems (FMNS) are presented and analyzed. FMNS are complex structures consisting of a number of interrelated simple fuzzy model units and linear junction units (modules). The main computation problem in FMNS is to calculate a part or the entire set of the unmeasured variables in the system with a predetermined structure and known set of measured variables. The computational algorithms presented in this paper include: 1) non-iterative computation of a feedforward type of FMNS; 2) iterative inverse calculation of one-dimensional fuzzy model units and 3) iterative calculation of closed loop (cyclic type) FMNS by use of a specially proposed fuzzy iteration block. All the proposed algorithms are explained and illustrated on numerical examples with comments about their practical application to industrial systems and plants.
Key Words. Network structures, fuzzy models, inverse fuzzy models, iterative algorithms
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The aim of this work is the modeling of the process of cooling of a metal plate in the presence of thermal reflecting panels on the intermediate table of a hot strip mill. A mathematical model of the process of cooling has been developed in order to study the temperature behavior in the metal under different initial and boundary conditions. The problem of reconstruction of temperature field in a metal plate is investigated, as well as methods for its decision. Also the problems of data reconciliation and fault measurement diagnosis are discussed in this paper. The soft computing method is implemented in a combination with the mathematical model of the cooling process of the metal. The obtained results show that this method is a suitable and powerful tool, capable to eliminate the initial uncertainties in temperature measurement and to estimate the temperature profile inside the strip.
Key words. Modeling, heat retaining panel system, data reconciliation, soft computing method
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