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Full-Text Articles in Engineering
Performance Analysis For Genetic Algorithms., Hermrean Wong
Performance Analysis For Genetic Algorithms., Hermrean Wong
Dissertations
Genetic algorithms have been shown effective for solving complex optimization problems such as job scheduling, machine learning, pattern recognition, and assembly planning. Due to the random process involved in genetic algorithms, the analysis of performance characteristics of genetic algorithms is a challenging research topic. Studied in this dissertation are methods to analyze convergence of genetic algorithms and to investigate whether modifications made to genetic algorithms, such as varying the operator rates during the iterative process, improve their performance. Both statistical analysis, which is used for investigation of different modifications to the genetic algorithm, and probability analysis, which is used to …
A Fuzzy Based Load Model For Power System Direct Load Control, K. Bhattacharyya, Mariesa Crow
A Fuzzy Based Load Model For Power System Direct Load Control, K. Bhattacharyya, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
Demand side management programs are strategies designed to alter the shape of the load curve. In order to successfully implement such a strategy, customer acceptance of the program is vital. It is thus desirable to design a model for direct load control which may accommodate customer preferences. This paper presents a methodology for optimizing both customer satisfaction and utility unit commitment savings, based on a fuzzy load model for the direct load control of appliances.
Optimization Of Design Parameters For Spines Of Various Geometries, Rong-Hua Yeh
Optimization Of Design Parameters For Spines Of Various Geometries, Rong-Hua Yeh
Journal of Marine Science and Technology
This work presents a systematic study on the optimum base diameter, length, heat dissipation, temperature profile, and efficiency of spines by employing fin parameter versus fin efficiency method. The temperature dependent heat transfer coefficient is assumed to be a power-law type. Several common fin geometries including cylindrical, convex parabolic, conical, and concave parabolic profiles are investigated respectively. It is found that the optimum dimensions of spines are functions of fin volume, heat transfer coefficient at fin base, and thermal conductivity. To facilitate the thermal design of heat transfer components, simple mathematical expressions as well as design charts are presented.
Microwave Detection Optimization Of Disbond In Layered Dielectrics With Varying Thickness, Stoyan I. Ganchev, Nasser N. Qaddoumi, Emarit Ranu, R. Zoughi
Microwave Detection Optimization Of Disbond In Layered Dielectrics With Varying Thickness, Stoyan I. Ganchev, Nasser N. Qaddoumi, Emarit Ranu, R. Zoughi
Electrical and Computer Engineering Faculty Research & Creative Works
The detection sensitivity optimization of air disbond in layered dielectric composites, using an open-ended rectangular waveguide, is studied both theoretically and experimentally. The sensitivity of the disbond detection is strongly influenced by the proper choice of parameters such as the operating frequency and the layered composite geometry (conductor backed or terminated by an infinite half-space of air). The capability of optimizing the measurement system parameters to detect and estimate the thickness of a disbonded layer independent of some changes in the thickness of the dielectric coating is also demonstrated. The impact of the parameters influencing detection optimization is theoretically investigated …
Applying Neural Networks To Find The Minimum-Cost Coverage Of A Boolean Function, Pong P. Chu
Applying Neural Networks To Find The Minimum-Cost Coverage Of A Boolean Function, Pong P. Chu
Electrical and Computer Engineering Faculty Publications
To find a minimal expression of a boolean function includes a step to select the minimum cost cover from a set of implicants. Since the selection process is an NP-complete problem, to find an optimal solution is impractical for large input data size. Neural network approach is used to solve this problem. We first formalize the problem, and then define an ''energy function'' and map it to a modified Hopfield network, which will automatically search for the minima. Simulation of simple examples shows the proposed neural network can obtain good solutions most of the time.