• Proceedings of the KIEE Conference
• /
• 1997.07b
• /
• pp.699-701
• /
• 1997

The simulated annealing(SA) algorithm is a stochastic strategy for search of the ground state and a powerful tool for optimization, based on the annealing process used for the crystallization in physical systems. It's main disadvantage is the long convergence time. Therefore, this paper proposes a stochastic algorithm combined with conventional deterministic optimization method to reduce the computation time, which is called SDS(Stochastic-Deterministic-Stochastic) method.

• Proceedings of the Acoustical Society of Korea Conference
• /
• autumn
• /
• pp.333-336
• /
• 2000

본 논문에서는 Monte-Carlo simulation과 deterministic simulation을 합성한 방법으로 특성허용 패턴을 만족하는 개별소자의 오차범위를 가중치에 따라 차별적으로 규정을 하였다. 일반적으로 사용되는 통계적인 방법은 불규칙한 특성을 갖는 랜덤오차를 정규분포를 갖는 랜덤변수로 모델링을 하여 허용 패턴으로부터 오차의 범위를 규정하는데, 이렇게 구해진 범위는 개별소자의 가중치의 영향을 고려하지 않고 일률적인 특성을 나타낸다는 단점이 있다. 이에 반해 deterministic simulation을 통해서 얻어진 오차의 범위는 가중치에 따라서 상대적인 범위를 결정할 수 있지만 해석 하고자하는 배열소자의 개수에 따라서 계산량이 지수승으로 증가하는 단점이 있어 10개 이상의 소자를 갖는 배열에는 적합하지 않다. 이러한 단점을 보완하기 위해서는 본 논문에서는 Monte-Carlo simulation과 deterministic simulation의 합성적 방법을 사용해서 배열소자의 증가에 따른 계산량의 증가를 줄이면서 각 가충치에 따라 상대적인 개별오차의 허용범위를 결정하였다. 그리고 이렇게 규정된 오차의 범위를 간단한 모의 실험을 통해서 검증하였다.

• Nuclear Engineering and Technology
• /
• v.45 no.2
• /
• pp.265-276
• /
• 2013

Pressure tubes made of Zr-2.5 wt% Nb alloy are important components consisting reactor coolant pressure boundary of a pressurized heavy water reactor, in which unanticipated through-wall cracks and rupture may occur due to a delayed hydride cracking (DHC). The Canadian Standards Association has provided deterministic and probabilistic structural integrity evaluation procedures to protect pressure tubes against DHC. However, intuitive understanding and subsequent assessment of flaw behaviors are still insufficient due to complex degradation mechanisms and diverse influential parameters of DHC compared with those of stress corrosion cracking and fatigue crack growth phenomena. In the present study, a deterministic flaw assessment program was developed and applied for systematic integrity assessment of the pressure tubes. Based on the examination results dealing with effects of flaw shapes, pressure tube dimensional changes, hydrogen concentrations of pressure tubes and plant operation scenarios, a simple and rough method for effective cooldown operation was proposed to minimize DHC risks. The developed deterministic assessment program for pressure tubes can be used to derive further technical bases for probabilistic damage frequency assessment.

• Investigative Magnetic Resonance Imaging
• /
• v.6 no.2
• /
• pp.137-146
• /
• 2002

Purpose : This paper introduces an improved classification approach which adopts a deterministic relaxation method and an agglomerative clustering technique for the classification of MRI using neural network. The proposed approach can solve the problems of convergency to local optima and computational burden caused by a large number of input patterns when a neural network is used for image classification. Materials and methods : Application of Hopfield neural network has been solving various optimization problems. However, major problem of mapping an image classification problem into a neural network is that network is opt to converge to local optima and its convergency toward the global solution with a standard stochastic relaxation spends much time. Therefore, to avoid local solutions and to achieve fast convergency toward a global optimization, we adopt MFA to a Hopfield network during the classification. MFA replaces the stochastic nature of simulated annealing method with a set of deterministic update rules that act on the average value of the variable. By minimizing averages, it is possible to converge to an equilibrium state considerably faster than standard simulated annealing method. Moreover, the proposed agglomerative clustering algorithm which determines the underlying clusters of the image provides initial input values of Hopfield neural network. Results : The proposed approach which uses agglomerative clustering and deterministic relaxation approach resolves the problem of local optimization and achieves fast convergency toward a global optimization when a neural network is used for MRI classification. Conclusion : In this paper, we introduce a new paradigm to classify MRI using clustering analysis and deterministic relaxation for neural network to improve the classification results.

• Journal of the Korea Furniture Society
• /
• v.20 no.4
• /
• pp.358-373
• /
• 2009

Probability based design(PBD)method has some advantages against current design methods. First, it can provide the quantitative values for the structural safety or capacity through the reliability index, $^{\beta}$. That presented the certainty on the corresponding structure for the designer or user, also that permitted the broad consideration in the safety of structures. In addition, it can give the quantitative lifetime of the related structure in the calculation process of target reliability index. Also, incidental economical efficiency can be expected because decrease of required structural material can be obtained by using the practical material data. Unlikely current deterministic structural design methods, main advantage is the reflection of real condition in the structural design process by application of the data with not small clear specimen but structural size material. Advanced countries, namely America, Canada, Europe, Australia and New Zealand already converted from allowable stress design(ASD) method to PBD method and used as a standard wooden structures code in the late 1980s and 1990s. Other domestic constructions standards such as the steel or concrete constructions accepted and used the PBD methods already. Accordingly, wooden structural design method also should be converted from deterministic ASD to probabilistic LRFD(Load and resistance factor design) in order to keep pace with worldwide demands for PBD. Hence, to suggest the reason of introduction the PBD in domestic wooden structural design and analysis, a brief example was used to show the different reliability index by using the different design methods. Definition, merits and demerits of deterministic ASD and probabilistic LRFD were followed. Also the three examples were presented to show the similarity and differences between ASD and LRFD. Finally, connection problems that might cause a disputation in wooden structural design and analysis were broadly examined.

• Structural Engineering and Mechanics
• /
• v.23 no.2
• /
• pp.195-207
• /
• 2006

An efficient and accurate algorithm is proposed to estimate cable safety factor of suspension bridges satisfying prescribed reliability levels. Uncertainties in the structure and load parameters are incorporated. The proposed algorithm integrates the concepts of the inverse reliability method and deterministic method for assessing cable safety factors of suspension bridges. The unique feature of the proposed method is that it offers a tool for cable safety assessment of suspension bridges, when the reliability level is specified as a target to be satisfied by the designer. After the accuracy and efficiency of the method are demonstrated through two numerical examples, the method is used to estimate cable safety factors of suspension bridges with span length ranging from 2000 to 5000 m. The results show that the deterministic method overestimates cable safety factor of suspension bridges because of neglecting the parameter uncertainty effects. The actual cable safety factor of suspension bridges should be estimated based on the proposed method.

• The Journal of Engineering Research
• /
• v.3 no.1
• /
• pp.65-73
• /
• 1998

One approach to improved image restoration methods has been the incorporation of additional source information via Gibbs priors that assume a source that is piecewise smooth. A natural Gibbs prior for expressing such constraints is an energy function defined on binary valued line processes as well as source intensities. However, the estimation of both continuous variables and binary variables is known to be a difficult problem. In this work, we consider the application of the deterministic annealing method. Unlike other methods, the deterministic annealing method offers a principled and efficient means of handling the problems associated with mixed continuous and binary variable objectives. The application of the deterministic annealing method results in a sequence of objective functions (defined only on the continuous variables) whose sequence of solutions approaches that of the original mixed variable objective function. The sequence is indexed by a control parameter (the temperature). The energy functions at high temperatures are smooth approximations of the energy functions at lower temperatures. Consequently, it is easier to minimize the energy functions at high temperatures and then track the minimum through the variation of the temperature. This is the essence of a continuation method. We show experimental results, which demonstrate the efficacy of the continuation method applied to a Bayesian restoration model.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.12 no.1
• /
• pp.56-61
• /
• 2016

After Japanese Fukushima nuclear power plant accident caused by the beyond design basis earthquake and tsunami, it has turned to be a major challenge for nuclear safety. IAEA, US NRC and EU have provided new safety design standards for beyond design basis event, Domestic regulatory bodies have also enacted guidances for licensees and applicants on additional methods related to beyond design basis events. This paper describes several evaluation methods for applying to nuclear power plants piping for beyond design basis earthquake. As a results, energy method based on the absorbed energy on nuclear power plant, deterministic method following design code and theory, experience method considering past earthquake data and information and probabilistic methods similar to probabilistic risk assessment were reviewed.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.27 no.7
• /
• pp.1234-1242
• /
• 2003

In this paper we present a simple and efficient robust optimal design formulation for MEMS structures and its application to a resonant-type micro probe. The basic idea is to use the gradient index (GI) to improve robustness of the objective and constraint functions. In the robust optimal design procedure, a deterministic optimization for performance of MEMS structures is followed by design sensitivity analysis with respect to uncertainties such as fabrication errors and change of operating conditions. During the process of deterministic optimization and sensitivity analysis, dominant performance and uncertain variables are identified to define GI. The GI is incorporated as a term of objective and constraint functions in the robust optimal design formulation to make both performance and robustness improved. While most previous approaches for robust optimal design require statistical information on design variations, the proposed GI based method needs no such information and therefore is cost-effective and easily applicable to early design stages. For the micro probe example, robust optimums are obtained to satisfy the targets for the measurement sensitivity and they are compared in terms of robustness and production yield with the deterministic optimums through the Monte Carlo simulation. This method, although shown for MEMS structures, may as well be easily applied to conventional mechanical structures where information on uncertainties is lacking but robustness is highly important.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.6 no.2
• /
• pp.52-56
• /
• 1996

In trainir~ga back-propagation neural network, the learning speed of the network is greatly affected by its learning rate. Most of off-line fashioned learning-rate selection methods, however, are empirical except for some deterministic methods. It is very tedious and difficult to find a good learning rate using the empirical methods. The deterministic methods cannot guarantee the quality of the quality of the learning rate. This paper proposes a new learning-rate selection method. Our off-line fashioned method selects a good learning rate through stochastically searching process using evolutionary programming. The simulation results show that the learning speed achieved by our method is superior to that of deterministic and empirical methods.