• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.6 s.237
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• pp.842-851
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• 2005

In this paper, a new method for constrained optimization of noisy functions is proposed. In approximate optimization using response surface methods, if constraints have severe noise, the approximate feasible region defined by approximate constraints is apt to include some of the infeasible region defined by actual constraints. This can cause the approximate optimum to converge into the infeasible region. In the proposed method, the approximate optimization is performed with the approximate constraints shifted by their deviations, which are calculated using a diagonal quadratic response surface method. This can prevent the approximate optimum from converging into the infeasible region. To fit the objective and constraints into diagonal quadratic models, we select the center and 4 additional points along each axis of design variables as experimental points. The deviation of each function is calculated using the differences between the real and approximate function values at the experimental points. A sequential approximate optimization technique based on the trust region algorithm is adopted to manage approximate models. The proposed approach is validated by solving some design problems. The results of the problems show the effectiveness of the proposed method.

• Geomechanics and Engineering
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• v.18 no.3
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• pp.225-234
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• 2019

A numerical stepwise approach for cavity expansion problem in strain-softening rock or soil mass is investigated, which is compatible with Mohr-Coulomb and generalized Hoek-Brown failure criteria. Based on finite difference method, plastic region is divided into a finite number of concentric rings whose thicknesses are determined internally to satisfy the equilibrium and compatibility equations, the material parameters of the rock or soil mass are assumed to be the same in each ring. For the strain-softening behavior, the strength parameters are assumed to be a linear function of deviatoric plastic strain (${\gamma}p^*$) for each ring. Increments of stress and strain for each ring are calculated with the finite difference method. Assumptions of large-strain for soil mass and small-strain for rock mass are adopted, respectively. A new numerical stepwise approach for limited pressure and plastic radius are obtained. Comparisons are conducted to validate the correctness of the proposed approach with Vesic's solution (1972). The results show that the perfectly elasto-plastic model may underestimate the displacement and stresses in cavity expansion than strain-softening coefficient considered. The results of limit expansion pressure based on the generalised H-B failure criterion are less than those obtained based on the M-C failure criterion.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.421-433
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• 2011

The importance of acquisition of road information has recently been increased with a rapid growth of spatial-related services such as urban information system and location based service. This paper proposes an automatic road extraction method using object-based approach which was issued alternative of pixel-based method recently. Firstly, the spatial objects were created by MSRS(Modified Seeded Region Growing) method, and then the key road objects were extracted by using properties of objects such as their shape feature information and adjacency. The omitted road objects were also traced considering spatial correlation between extracted road and their neighboring objects. In the end, the final road region was extracted by connecting discontinuous road sections and improving road surfaces through their geometric properties. To assess the proposed method, quantitative analysis was carried out. From the experiments, the proposed method generally showed high road detection accuracy and had a great potential for the road extraction from high resolution satellite images.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.661-664
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• 1998

Since the region segmentation at high resolution contains most of viable semantic object contours in an image, the bottom-up approach for image segmentation is appropriate for the application such as MPEG-4 which needs to preserve semantic object contours. However, the conventioal region merging methods, that follow the region segmentation, have poor performance in keeping low-contrast semantic object contours. In this paper, we propose an image segmentation algorithm based on classified region merging. The algorithm pre-segments an image with a large number of small regions, and also classifies it into several classes having similar gradient characteristics. Then regions only in the same class are merged according to the boundary weakness or statisticsal similarity. The simulation result shows that the proposed image segmentation preserves semantic object contours very well even with a small number of regions.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.443-455
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• 1999

A simulation-based approach to estimating the probability of an arbitrary region under a multivariate normal distribution is developed. In specific, the probability is expressed as the ratio of the unrestricted and the restricted multivariate normal density functions, where the restriction is given by the region whose probability is of interest. The density function of the restricted distribution is then estimated by using a sample generated from the Gibbs sampling algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.990-999
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• 2007

In this paper, we propose the region-based image retrieval method using JSEG which is a method for unsupervised segmentation of color-texture regions. JSEG is an algorithm that discretizes an image by color classification, makes the J-image by applying a region to window mask, and then segments the image by using a region growing and merging. The segmented image from JSEG is given to a user as the query image, and a user can select a few segmented regions as the query region. After finding the MBR of regions selected by user query and generating the multiple window masks based on the center point of MBR, we extract the feature vectors from selected regions. We use the accumulated histogram as the global descriptor for performance comparison of extracted feature vectors in each method. Our approach fast and accurately supplies the relevant images for the given query, as the feature vectors extracted from specific regions and global regions are simultaneously applied to image retrieval. Experimental evidence suggests that our algorithm outperforms the recent image-based methods for image indexing and retrieval.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.3
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• pp.141-149
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• 2019

The paper proposes a knowledge innovation performance model by the dynamic data envelopment analysis with slacks-based measure approach for evaluating the effectiveness of 30 regional knowledge innovation activities in China from 2010 to 2016. In recent years, China has paid more attention to knowledge innovation activities, as central and local governments have pushed on with their innovation projects by lots of investment whatever the difficulties may be. Decision-maker is usually interested in judge its knowledge innovation performance relative to target benchmark by exploring whether one provincial administration region performs better among others and/or if the growth of economy will be benefited greatly by the knowledge innovation activities. To acquire the managerial insight about this issue from a comprehensively designed performance evaluation model, knowledge innovation activity is conceptualized as an intertemporal production process. Invention patent and regional gross product are imposed on desirable outputs, highlighting the need for knowledge economy. The empirical result shows that knowledge innovation has a positive effect on economic development. At the same time, decision-maker should be interest in the economic effect of patents' type and quality. The government should then encourage new technical applications with greater commercial value from a market-oriented perspective, in order to benefit the most from the innovation process in the short-run.

• International Journal of Aeronautical and Space Sciences
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• v.4 no.1
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• pp.34-44
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• 2003

An advanced aeroelastic analysis using a computational structural dynamics (CSD), finite element method (FEM) and computational fluid dynamics (CFD) is presented in this Paper. A general aeroelastic analysis system is originally developed and applied to realistic design problems in the transonic flow region, where strong shock wave interactions exist. The present computational approach is based on the modal-based coupled nonlinear analysis with the matched-point concept and adopts the high-speed parallel processing technique on the low-cost network based PC-clustered machines. It can give very accurate and useful engineering data on the structural dynamic design of advanced flight vehicles. For the nonlinear unsteady aerodynamics in high transonic flow region, Euler equations using the unstructured grid system have been applied to easily consider complex configurations. It is typically shown that the advanced numerical approach can give very realistic and practical results for design engineers and safe flight tests. One can find that the present study conducts a virtual flutter flight test which are usually very dangerous in reality.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.4 s.316
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• pp.93-100
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• 2007

In this paper, a novel approach based on wavelet transform is proposed to process the scraped character which is represented on digital image. The basis idea is that the scraped character is described by its textured neighborhood, and it is decomposed into multiresolution features at different levels with its background region. The image is first decomposed into sub bands by applying Daubechies wavelets. Character features are extracted from the low frequency sub-bands by partition, FCM clustering and area-based region process. High frequency ones are activated by applying local energy density over a moving mask. Features are synthesized in order to reconstruct the original image state through inverse wavelet transform Background region is eliminated and character is extracted. The experimental results demonstrate the effectiveness of the proposed method.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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• pp.114-125
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• 2003

This paper proposes multivariate process capability indices (PCIs) for skewed populations using $T^2$rand modified process region approaches. The proposed methods are based on the multivariate version of a weighted standard deviation method which adjusts the variance-covariance matrix of quality characteristics and approximates the probability density function using several multivariate Journal distributions with the adjusted variance-covariance matrix. Performance of the proposed PCIs is investigated using Monte Carlo simulation, and finite sample properties of the estimators are studied by means of relative bias and mean square error.