• Transactions of the Korean Society of Mechanical Engineers
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• v.17 no.6
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• pp.1389-1403
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• 1993

A procedure has be developed to optimize the product's tipping position in designing a die face for manufacturing automobile outer panels. Two design requirements are considered in optimizing the tipping position. One is to satisfy that all the points on the product should have a uniform distribution of drawing depths. The other is to guarantee that the user-specified area on the product should first contact with the binder wrap. The problem to satisfy the design requirements described above is analogous to the flatness calculation problem in the area of metrology if some constraints can be imposed. Thus the problem can be solved by the simulated annealing method, which is one of the optimization methods. The developed procedure was tested with the real die face design problem and the usefulness was verified by the diagram of the drawing depth.

• Journal of Sensor Science and Technology
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• v.6 no.2
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• pp.145-154
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• 1997

Based on a new search strategy using circuit simulation and simulated annealing with local search, a technique for design automation of high-performance operational amplifiers is proposed. For arbitrary circuit topology and performance specifications, through discrete optimization of a cost function with discrete design variables the design of operational amplifiers is performed. A special-purpose circuit simulator and some heuristics are used to reduce the design time. Through the design of a low-power high-speed fully differential CMOS operational amplifier usable in smart sensors and 10-b 25-MS/s pipelined A/D converters, it has been demonstrated that a design tool developed using the proposed technique can be used for designing high-performance operational amplifiers with less design knowledge and less design effort.

• The Transactions of the Korea Information Processing Society
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• v.2 no.3
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• pp.417-424
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• 1995

This paper describes MFT(Mean Field Theory) neural network with continuous with continuous variables is applied to quantification analysis problem. A quantification analysis problem, one of the important problems in statistics, is NP complete and arises in the optimal location of objects in the design space according to the given similarities only. This paper presents a MFT neural network with continuous variables for the quantification problem. Starting with reformulation of the quantification problem to the penalty problem, this paper propose a "one-variable stochastic simulated annealing(one-variable SSA)" based on the mean field approximation. This makes it possible to evaluate of the spin average faster than real value calculating in the MFT neural network with continuous variables. Consequently, some experimental results show the feasibility of this approach to overcome the difficulties to evaluate the spin average value expressed by the integral in such models.ch models.