• Proceedings of the Korean Operations and Management Science Society Conference
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• 2000.10a
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• pp.103-106
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• 2000

As the industrial environment becomes more competitive, supply chain management (SCM) has become recognized as a major strategy in the business world. Some of current researches are categorized into review papers, deterministic models, stochastic models, simulation models and discussed in this paper. A hybrid approach combining analytic model and simulation model and the simulation optimization method are proposed as future research areas with other analytical subjects.

• Proceedings of the KSME Conference
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• 2001.06a
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• pp.939-944
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• 2001

Genetic algorithm is modelled on natural evolution and simulated annealing is based on the simulation of thermal annealing. Both genetic algorithm and simulated annealing are stochastic method. So they can find global optimum values. For compare efficiency of SA and GA's, some function value was maximized. In the result, that was a little better than GA's.

• Journal of the Korea Computer Graphics Society
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• v.7 no.1
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• pp.37-49
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• 2001

We propose an optimization of mutual information for multiresolution image registration to represent useful information as integrated form obtaining from complementary information of multi modality images. The method applies mutual information as cost function to measure the statistical dependency or information redundancy between the image intensities of corresponding pixels in both images, which is assumed to be maximal if the images are geometrically aligned. As experimental results we validate visual inspection for accuracy, changning initial condition and addictive noise for robustness. Since our method uses the native image rather than prior feature extraction, few user interaction is required to perform the registration. In addition it leads to robust density estimation and convergence as applying non-parametric density estimation and stochastic multiresolution optimization.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.6
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• pp.1-14
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• 2007

Optimal design method of nonlinear damping system for seismic response control of adjacent structures is studied in this paper. The objective functions of the optimal design are defined by structural response and total amount of the dampers. In order to obtain a solution minimizing two mutually conflicting objective functions simultaneously, multi-objective optimization technique based on genetic algorithm is adopted. In addition, stochastic linearization method is embedded into the multi-objective framework to efficiently estimate the seismic responses of the adjacent structures interconnected by nonlinear hysteretic dampers without performing nonlinear time-history analyses. As a numerical example to demonstrate the effectiveness of the proposed technique, 20-story and 10-story buildings are considered and MR dampers of which hysteretic behaviors vary with the magnitude of the input voltage are considered as nonlinear hysteretic damper interconnecting two adjacent buildings. The proposed approach can provide the optimal number and capacities of the MR dampers, which turned out to be more economical than the uniform distribution system while maintaining similar control performance. The proposed damper system is verified to show more stable performance in terms of the pounding probability between two adjacent buildings. The applicability of the proposed method to the design problem for optimally placing semi-active control system is examined as well.

• Water Engineering Research
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• v.1 no.1
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• pp.63-74
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• 2000

This paper presents a two- phase search scheme for optimal pipe expansion of expansion of existing water distribution systems. In pipe network problems, link flows affect the total cost of the system because the link flows are not uniquely determined for various pipe diameters. The two-phase search scheme based on stochastic optimization scheme is suggested to determine the optimal link flows which make the optimal design of existing pipe network. A sample pipe network is employed to test the proposed method. Once the best tree network is obtained, the link flows are perturbed to find a near global optimum over the whole feasible region. It should be noted that in the perturbation stage the loop flows obtained form the sample existing network are employed as the initial loop flows of the proposed method. It has been also found that the relationship of cost-hydraulic gradient for pipe expansion of existing network affects the total cost of the sample network. The results show that the proposed method can yield a lower cost design than the conventional design method and that the proposed method can be efficiently used to design the pipe expansion of existing water distribution systems.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1322-1327
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• 2003

It is important to determine supporting locations for structural stability of a structure under non-uniform load in space interfered by other parts. In this case, There are many local optima with discontinuous design space. Therefore, The traditional optimization methods based on derivative are not suitable. Whereas, Genetic algorithm(GA) based on stochastic search technique is a very robust and general method. This paper has been presented to determine supporting locations of the vertical supports for reducing stress of the KSTAR(Korea super Superconducting Tokamak Advanced Research) IVCC(In-vessel control coil) under non-uniform electromagnetic load and space interfered by other parts using genetic algorithm. For this study, we develop a program combining finite element analysis with a genetic algorithm to perform structural analysis of IVCC. In addition, this paper presents a technique to perform optimization with FEM when design variables are trapped in an incongruent design space.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.3
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• pp.277-288
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• 2014

This article reviews various state estimation methods for nonlinear systems, particularly with a perspective of a process control engineer. Nonlinear state estimation methods can be classified into the following two categories: stochastic approaches and deterministic approaches. The current review compares the Bayesian approach, which is mainly a stochastic approach, and the MHE (Moving Horizon Estimation) approach, which is mainly a deterministic approach. Though both methods are reviewed, emphasis is given to the latter as it is particularly well-suited to highly nonlinear systems with slow sampling rates, which are common in chemical process applications. Recent developments in underlying theories and supporting numerical algorithms for MHE are reviewed. Thanks to these developments, applications to large-scale and complex chemical processes are beginning to show up but they are still limited at this point owing to the high numerical complexity of the method.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• Nuclear Engineering and Technology
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• v.51 no.3
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Journal of Digital Convergence
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• v.17 no.9
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• pp.127-133
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• 2019

A callable product is one of service derivatives using options to generate demand and reduce risk. This paper compares two booking admission controls for callable products, the online and the batch admission controls. To this end, the paper computes the optimal booking policy by using the backward dynamic programming and the stochastic optimization method. Intuitively, the provider should outperform under the batch control by utilizing demand information. The contribution of the paper is to show that the two controls are equivalent in terms of the booking strategy and the expected profit, which enables the provider to keep its current control method. The paper develops the closed-form solutions for the three fare classes. The future work is to extend the result to the model with complicated fare structures.