• Journal of Korean Society of Steel Construction
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• v.16 no.1 s.68
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• pp.21-32
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• 2004

In this study, a continuous optimum design model with its application program for plane steel frame structures developed. In the model, the sequential unconstrained minimization technique (SUMT) transforming the nonlinear optimization problem with multidesign variables and constraints into an unconstrained minimization problem and the refined plastic hinge analysis method as one of the most effective second-order inelastic analysis methods for steel frame structures were implemented. The total weight of a steel frame structure was taken as the objective function, and the AISC-LRFD code requirements for the local and member buckling, flexural strength, shear strength, axial strength and size of the cross-sectional shapes of members were used for the derivation of constraint equations. To verify the appropriateness of the present model, the optimum designs of serveral plane steel frame structures subject to vertical and horizontal loads were conducted.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1530-1537
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• 2007

In this paper, we implemented a speech emotion recognition system that can distinguish human emotional states from recorded speech captured by a single microphone and classify them into four categories: neutrality, happiness, sadness and anger. In general, a speech recorded with a microphone contains background noises due to the speaker environment and the microphone characteristic, which can result in serious system performance degradation. In order to minimize the effect of these noises and to improve the system performance, a MA(Moving Average) filter with a relatively simple structure and low computational complexity was adopted. Then a SFS(Sequential Forward Selection) feature optimization method was implemented to further improve and stabilize the system performance. For speech emotion classification, a SVM pattern classifier is used. The experimental results indicate the emotional classification performance around 65% in the computer simulation and 62% on the ARM platform.

• The Korean Journal of Applied Statistics
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• v.7 no.1
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• pp.83-102
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• 1994

Some optimal designs and data analysis methods based on a Gaussian spatial linear model for computer simulation experiments are considered. For designs of computer experiments, Latin-hypercube designs and some optimal designs are combined. A two-stage computational (2-points exchange and Newton-type) algorithm for finding the optimal Latin-hypercube design is presented. The spatial prediction model which was discussed by Sacks, Welch, Mitchell and Wynn(1989) for computer experiments, is used for analysis of the simulated data. Moreover, a method of contructing sequential (optimal) Latin-hypercube designs is considered. An application of this approach to the quality improvement and optimization of the integrated circuit design via the main-effects plot and the sequential experimental strategy is presented.

• Water for future
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• v.29 no.6
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• pp.167-178
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• 1996

The simulation techniques of hydrologic data series have been developed for the purposes of the design of water resources system, the optimization of reservoir operation, and the design of flood control of reservoir, etx. While the stochastic models are usually used in most analysis of water resources fields for the generation of data sequences, the indexed sequential modeling (ISM) method based on generation of a series of overlapping short-term flow sequences directly from the historical record has been used for the data generation in western USA since the early of 1980's. It was reported that the reliable results by ISM were obtained in practical applications. In this study, we generate annual inflow series at a location of Hong Cheon Dam site by using ISM method and first order autoregressive model (AR(1)), and estimate the drought characteristics for the comparison aim between ISM and AR(1).

• Journal of the Computational Structural Engineering Institute of Korea
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• v.30 no.1
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• pp.87-94
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• 2017

Reliability analysis(RA) and Reliability-based design optimization(RBDO) require statistical modeling of input random variables, which is parametrically or nonparametrically determined based on experimental data. For the parametric method, goodness-of-fit (GOF) test and model selection method are widely used, and a sequential statistical modeling method combining the merits of the two methods has been recently proposed. Kernel density estimation(KDE) is often used as a nonparametric method, and it well describes a distribution function when the number of data is small or a density function has multimodal distribution. Although accurate statistical models are needed to obtain accurate RA and RBDO results, accurate statistical modeling is difficult when the number of data is small. In this study, the accuracy of two statistical modeling methods, SSM and KDE, were compared according to the number of data. Through numerical examples, the RA results using the input models modeled by two methods were compared, and appropriate modeling method was proposed according to the number of data.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.15 no.4
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• pp.675-691
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• 2002

The focus of this work is on the development of large-scale numerical optimization methods for optimal control of steady incompressible Navier-Stokes flows. The control is affected by the suction or injection of fluid on portions of the boundary, and the objective function of fluid on portions of the boundary, and the objective function represents the rate at which energy is dissipated in the fluid. We develop reduced Hessian sequential quadratic programming. Both quasi-Newton and Newton variants are developed and compared to the approach of eliminating the flow equations and variables, which is effectively the generalized reduced gradient method. Optimal control problems we solved for two-dimensional flow around a cylinder. The examples demonstrate at least an order-of-magnitude reduction in time taken, allowing the optimal solution of flow control problems in as little as half an hour on a desktop workstation.

• Korean Management Science Review
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• v.31 no.3
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• pp.113-130
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• 2014

Cooperative coevolutionary algorithm (CCEA) has proven to be a very powerful means of solving optimization problems through problem decomposition. CCEA implies the use of several populations, each population having the aim of finding a partial solution for a component of the considered problem. Populations evolve separately and they interact only when individuals are evaluated. Interactions are made to obtain complete solutions by combining partial solutions, or collaborators, from each of the populations. In this respect, we can think of various interaction modes. The goal of this research is to develop a CCEA for a supply chain network design (SCND) problem and identify which interaction mode gives the best performance for this problem. We present general design principle of CCEA for the SCND problem, which require several co-evolving populations. We classify these populations into two groups and classify the collaborator selection scheme into two types, the random-based one and the best fitness-based one. By combining both two groups of population and two types of collaborator selection schemes, we consider four possible interaction modes. We also consider two modes of updating populations, the sequential mode and the parallel mode. Therefore, by combining both four possible interaction modes and two modes of updating populations, we investigate seven possible solution algorithms. Experiments for each of these solution algorithms are conducted on a few test problems. The results show that the mode of the best fitness-based collaborator applied to both groups of populations combined with the sequential update mode outperforms the other modes for all the test problems.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• The Journal of Korean Society for Radiation Therapy
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• v.26 no.2
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• pp.207-216
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• 2014

Purpose : We present a method to reduce this gap and complete the treatment plan, to be made by the re-optimization is performed in the same conditions as the initial treatment plan different from Monaco treatment planning system. Materials and Methods : The optimization is carried in two steps when performing the inverse calculation for volumetric modulated radiation therapy or intensity modulated radiation therapy in Monaco treatment planning system. This study was the first plan with a complete optimization in two steps by performing all of the treatment plan, without changing the optimized condition from Step 1 to Step 2, a typical sequential optimization performed. At this time, the experiment was carried out with a pencil beam and Monte Carlo algorithm is applied In step 2. We compared initial plan and re-optimized plan with the same optimized conditions. And then evaluated the planning dose by measurement. When performing a re-optimization for the initial treatment plan, the second plan applied the step optimization. Results : When the common optimization again carried out in the same conditions in the initial treatment plan was completed, the result is not the same. From a comparison of the treatment planning system, similar to the dose-volume the histogram showed a similar trend, but exhibit different values that do not satisfy the conditions best optimized dose, dose homogeneity and dose limits. Also showed more than 20% different in comparison dosimetry. If different dose algorithms, this measure is not the same out. Conclusion : The process of performing a number of trial and error, and you get to the ultimate goal of treatment planning optimization process. If carried out to optimize the completion of the initial trust only the treatment plan, we could be made of another treatment plan. The similar treatment plan could not satisfy to optimization results. When you perform re-optimization process, you will need to apply the step optimized conditions, making sure the dose distribution through the optimization process.

• The Korean Journal of Nuclear Medicine Technology
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• v.27 no.2
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• pp.111-127
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• 2023

Purpose: Gallium-68 (⁶⁸Ga) is increasingly used in nuclear medicine imaging for various conditions such as lymphoma and neuroendocrine tumors by labeling tracers like Prostate Specific Membrane Antigen (PSMA) and DOTA-TOC. However, compared to Fluorine-18 (¹⁸F) used in conventional nuclear medicine imaging, ⁶⁸Ga has lower spatial resolution and relatively higher Signal to Background Ratio (SBR). Therefore, this study aimed to investigate the optimized parameters and reconstruction methods for PET/CT imaging using the ⁶⁸Ga radiotracer through model-based image evaluation. Materials and Methods: Based on clinical images of ⁶⁸Ga-PSMA PET/CT, a NEMA/IEC 2008 PET phantom model was prepared with a Hot vs Background (H/B) ratio of 10:1. Images were acquired for 9 minutes in list mode using DMIDR (GE, Milwaukee WI, USA). Subsequently, reconstructions were performed for 1 to 8 minutes using OS-EM (Ordered Subset Expectation Maximization) + TOF (Time of Flight) + Sharp IR (VPFX-S), and BSREM (Block Sequential Regularized Expectation Maximization) + TOF + Sharp IR (QCFX-S-400), followed by comparative evaluation. Based on the previous experimental results, images were reconstructed for BSREM + TOF + Sharp IR / 2 minutes (QCFX-S-2min) with varying β-strength values from 100 to 700. The image quality was evaluated using AMIDE (freeware, Ver.1.0.1) and Advanced Workstation (GE, USA). Results: Images reconstructed with QCFX-S-400 showed relatively higher values for SNR (Signal to Noise Ratio), CNR (Contrast to Noise Ratio), count, RC (Recovery Coefficient), and SUV (Standardized Uptake Value) compared to VPFX-S. SNR, CNR, and SUV exhibited the highest values at 2 minutes/bed acquisition time. RC showed the highest values for a 10 mm sphere at 2 minutes/bed acquisition time. For small spheres of 10 mm and 13 mm, an inverse relationship between β-strength increase and count was observed. SNR and CNR peaked at β-strength 400 and then decreased, while SUV and RC exhibited a normal distribution based on sphere size for β-strength values of 400 and above. Conclusion: Based on the experiments, PET/CT imaging using the ⁶⁸Ga radiotracer yielded the most favorable quantitative and qualitative results with a 2 minutes/bed acquisition time and BSREM reconstruction, particularly when applying β-strength 400. The application of BSREM can enhance accurate quantification and image quality in ⁶⁸Ga PET/CT imaging, and an optimization process tailored to each institution's imaging objectives appears necessary.