• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• Journal of the Korean Physical Society
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• v.73 no.11
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• pp.1644-1649
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• 2018

Aiming at infrared thermal images with different buried depth defects, we study a variety of image segmentation algorithms based on the threshold to develop global search ability and the ability to find the defect area accurately. Firstly, the iterative thresholding method, the maximum entropy method, the minimum error method, the Ostu method and the minimum skewness method are applied to image segmentation of the same infrared thermal image. The study shows that the maximum entropy method and the minimum error method have strong global search capability and can simultaneously extract defects at different depths. However none of these five methods can accurately calculate the defect area at different depths. In order to solve this problem, we put forward a strategy of "divide and conquer". The infrared thermal image is divided into several local thermal maps, with each map containing only one defect, and the defect area is calculated after local image processing of the different buried defects one by one. The results show that, under the "divide and conquer" strategy, the iterative threshold method and the Ostu method have the advantage of high precision and can accurately extract the area of different defects at different depths, with an error of less than 5%.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.43-46
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• 2000

In this paper, we propose a fast adaptive diamond search algorithm(FADS) for block matching motion estimation. Fast motion estimation algorithms reduce the computational complexity by using the UESA (Unimodal Error Search Assumption) that the matching error monotonically increases as the search moves away from the global minimum error. Recently many fast BMAs(Block Matching Algorithms) make use of the fact that the global minimum points in real world video sequences are centered at the position of zero motion. But these BMAs, especially in large motion, are easily trapped into the local minima and result in poor matching accuracy. So, we propose a new motion estimation algorithm using the spatial correlation among the adjacent blocks. We change the origin of search window according to the spatially adjacent motion vectors and their MAE(Mean Absolute Error). The computer simulation shows that the proposed algorithm has almost the same computational complexity with UCBDS(Unrestricted Center-Biased Diamond Search)〔1〕, but enhance PSNR. Moreover, the proposed algorithm gives almost the same PSNR as that of FS(Full Search), even for the large motion case, with half the computational load.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.7
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• pp.758-768
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• 2005

The ball screw actuated by the electric motor is widely used as an essential actuator for driving the mechanical system by virtue of accuracy and force transmission capability. In this paper, a design of the global sliding mode control is presented to drive the ball screw actuator along the minimum time trajectory, In the proposed control scheme, if the ranges of parametric uncertainties and torque limits of the system are specified, the arrival time of the load along the minimum time trajectory can be estimated. Also, the arriving time at the reference input and the maximum acceleration are expressed in a closed form solution. Conversely, the capacity of a ball screw actuator including the motor can be easily designed if the external load and its transportation time are specified. The superior performance of the proposed control scheme and analysis is validated by the computer simulation and experiments comparing with other sliding mode controllers.

• Journal of the Korean Operations Research and Management Science Society
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• v.39 no.4
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• pp.137-152
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• 2014

In this paper, I suggest several techniques to estimate covariance matrix and compare the performance of the global minimum variance portfolio (GMVP) in terms of out of sample mean standard deviation and return. As a result, the return differences among the GMVPs are insignificant. The mean standard deviation of the GMVP using historical covariance is sensitive to the estimation window and the number of assets in the portfolio. Among the model covariance, the GMVP using constant systematic risk ratio model or using short sale restriction shows the best performance. The performance difference between the GMVPs using historical covariance and model covariance becomes insignificant as the historical covariance is estimated with longer estimation window. Lastly, the implied volatilities from ELW prices do not lead to superior performance to the historical variance.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.8
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• pp.54-63
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• 1993

This paper proposes a method for improving the performances of the neural network for optimization by an optimal estimation of initial states. The optimal initial state that leads to the global minimum is estimated by using the stochastic approximation. And then the update rule of Hopfield model, which is the high speed deterministic algorithm using the steepest descent rule, is applied to speed up the optimization. The proposed method has been applied to the tavelling salesman problems and an optimal task partition problems to evaluate the performances. The simulation results show that the convergence speed of the proposed method is higher than conventinal Hopfield model. Abe's method and Boltzmann machine with random initial neuron output setting, and the convergence rate to the global minimum is guaranteed with probability of 1. The proposed method gives better result as the problem size increases where it is more difficult for the randomized initial setting to give a good convergence.

• Journal of the Korean Statistical Society
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• v.7 no.1
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• pp.27-43
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• 1978

This paper deals with the problem of obtaining a minimum cost acceptance sampling plan for destructive testing. The cost model is constructed under the assumption that the sampling procedure takes the following form; 1) lots rejected on the first sample are acreened with a non-destructive testing, 2) the screening is assumed to be imperfect, and therefore, after the screening, a second sample is taken to determine whether to accept the lot of to scrap it. The usual sampling procedures for destructive testing can be regarded as special cases of the above one. Utilizing Hald's Bayesian approach, procedures for finding the global optimal sampling plans are given. However, when the lot size is large, the global plan is very different to obtain even with the aid of an electronic computer. Therefore a method of finding suboptimal plan is suggested. An example with uniform prior is also given.

• Interaction and multiscale mechanics
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• v.5 no.3
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• pp.211-228
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• 2012

This paper reports the development of a generalized inverse analysis formulation for the parameter estimation of four-parameter Burger model. The analysis is carried out by formulating the problem as a mathematical programming formulation in terms of identification of the design vector, the objective function and the design constraints. Thereafter, the formulated constrained nonlinear multivariable problem is solved with the aid of fmincon: an in-built constrained optimization solver module available in MatLab. In order to gain experience, a synthetic case-study is considered wherein key issues such as the determination and setting up of variable bounds, global optimality of the solution and minimum number of data-points required for prediction of parameters is addressed. The results reveal that the developed technique is quite efficient in predicting the model parameters. The best result is obtained when the design variables are subjected to a lower bound without any upper bound. Global optimality of the solution is achieved using the developed technique. A minimum of 4-5 randomly selected data-points are required to achieve the optimal solution. The above technique has also been adopted for real-time settlement of four oil refineries with encouraging results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.10 s.241
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• pp.1399-1406
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• 2005

Recently authors have proposed a new method to estimate failure strength of a pipe with local wall thinning subject to either internal pressure or global bending. The proposed method was based on the equivalent stress averaged over the minimum ligament in the locally wall thinned region, and the simple scheme to estimate the equivalent stress in the minimum ligament was proposed, based on the reference stress concept. This paper extends the new method to combined internal pressure and global bending. The proposed method is validated against FE results for various geometries of local wall thinning under combined loading. The effect of internal pressure is also investigated in the present study. Comparison of maximum moments, predicted according to the proposed method, with published full-scale pipe test data fur locally wall-thinned pipes under combined internal pressure and global bending, shows good agreement.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.10 s.241
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• pp.1369-1376
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• 2005

In structural design, the design variables are frequently selected from certain discrete values. Various optimization algorithms have been developed fDr discrete design. It is well known that many function evaluations are needed in such optimization. Recently, sequential algorithm with orthogonal arrays (SOA), which is a search algorithm for a local minimum in a discrete space, has been developed. It considerably reduces the number of function evaluations. However, it only finds a local minimum and the final solution depends on the initial values of the design variables. A new algorithm is proposed to adopt a genetic algorithm (GA) in SOA. The GA can find a solution in a global sense. The solution from the GA is used as the initial design of SOA. A sequential usage of the GA and SOA is carried out in an iterative manner until the convergence criteria are satisfied. The performance of the algorithm is evaluated by various examples.