• Journal of Information Technology Services
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• 제10권3호
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• pp.95-112
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• 2011

This paper proposes a new registry model for systematically referencing standards in e-Business field. We have many systems that provide standards and additional information. However, there are several problems such as inefficient standard information, dependency on a standard type, high standard information acquisition cost, no relations between standard information, and so on. In this paper, a new registry model and its prototype implementation are described. The proposed model is defined based on ISO/IEC 11179-Metadata registries, which is one of the international standards for interoperability between data. The proposed model provides an integrated- systematic standard information support, and also considers technology stack and business processes for e-Business systems. This paper develops a prototype for the proposed model and implementation result. Finally, to show the contribution of our proposal, this paper shows the comparative evaluation between previous systems and our proposal with various comparative items.

• Proceedings of the KIEE Conference
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• 대한전기학회 2008년도 추계학술대회 논문집 전력기술부문
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• pp.77-79
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• 2008

The power expansion planning is large and capital intensive capacity planning. In the past, the expansion planning was established with the proper supply reliability in order to minimize social cost. However, the planning not used cost minimizing objective function in the power markets with many market participants. This paper proposed the power expansion planning model using multi-criteria decision rule. This model used multi objective function considering not only cost minimizing but also GENCO's intension. This paper compared proposed model with WASP model in order to verify the result of proposed model.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제58권3호
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• pp.462-466
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• 2009

The power expansion planning is large and capital intensive capacity planning. In the past, the expansion planning was established with the proper supply reliability in order to minimize social cost. However, the planning can't use cost minimizing objective function in the power markets with many market participants. This paper proposed the power expansion planning model using multi-criteria decision rule. This model used multi objective function considering not only cost minimizing but also GENCO's intension. This paper compared proposed model with WASP model in order to verify the result of proposed model.

• The Journal of Korea Robotics Society
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• 제4권1호
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• Journal of the Korean Mathematical Society
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• 제55권3호
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• pp.719-734
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• 2018

An image restoration problem with Poisson noise arises in many applications of medical imaging, astronomy, and microscopy. To overcome ill-posedness, Total Variation (TV) model is commonly used owing to edge preserving property. Since staircase artifacts are observed in restored smooth regions, higher-order TV regularization is introduced. However, sharpness of edges in the image is also attenuated. To compromise benefits of TV and higher-order TV, the weighted sum of the non-convex TV and non-convex higher order TV is used as a regularizer in the proposed variational model. The proposed model is non-convex and non-smooth, and so it is very challenging to solve the model. We propose an iterative reweighted algorithm with the proximal linearized alternating direction method of multipliers to solve the proposed model and study convergence properties of the algorithm.

• Journal of Engineering Education Research
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• 제23권4호
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• pp.28-36
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• 2020

Capstone design education is essential in the engineering design process according to the certification standards of ABEEK. Capstone design process should be properly trained in undergraduate courses in order to increase the design ability of systems, components and processes within realistic constraints. In this study, a modified design model as a capstone design education model was proposed to reduce the separation between the design process at industrial sites and the design process at university education. The modified design model based on 6-sigma methodology is composed of 6 design steps such as define, measure, analyse, design, verify, and report. Each step has appropriated design contents and tools, and is configured to generate design results. The proposed design model was directly applied to the capstone design class for automotive engineering in Kunsan National University, and it was confirmed that the proposed DMADVR methodology was a very useful design education model to enhance the design ability, teamwork ability and communication skills required by ABEEK.

• Computers and Concrete
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• 제18권6호
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• pp.1213-1234
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• 2016

This paper describes a deformation-based strut-and-tie model for the flexural members at post-yield state. Boundary deformation conditions by flexural post-yield response are chosen in terms of the flexural bar strains as the main factor influenced on the shear strength. The main purpose of the proposed model is to predict the shear capacities of the flexural members associated with the given flexural deformation conditions. To verify the proposed strut-and-tie model, the estimated shear strengths depending on the flexural deformation are compared with the experimental results. The experimental data are in good agreement with the values obtained by the proposed model.

• Journal of Computing Science and Engineering
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• 제8권2호
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• pp.65-77
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• 2014

To address the problem of multi-target retrieval (MTR) of remote sensing images, this study proposes a new object-level feature representation model. The model provides an enhanced application image representation that improves the efficiency of MTR. Generating the model in our scheme includes processes, such as object-oriented image segmentation, feature parameter calculation, and symbolic image database construction. The proposed model uses the spatial representation method of the extended nine-direction lower-triangular (9DLT) matrix to combine spatial relationships among objects, and organizes the image features according to MPEG-7 standards. A similarity metric method is proposed that improves the precision of similarity retrieval. Our method provides a trade-off strategy that supports flexible matching on the target features, or the spatial relationship between the query target and the image database. We implement this retrieval framework on a dataset of remote sensing images. Experimental results show that the proposed model achieves competitive and high-retrieval precision.

• Journal of Information Technology Services
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• 제15권1호
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• pp.153-166
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• 2016

Ensemble classification is an approach that combines individually trained classifiers in order to improve prediction accuracy over individual classifiers. Ensemble techniques have been shown to be very effective in improving the generalization ability of the classifier. But base classifiers need to be as accurate and diverse as possible in order to enhance the generalization abilities of an ensemble model. Bagging is one of the most popular ensemble methods. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. In this study we proposed a new bagging variant ensemble model, Randomized Bagging (RBagging) for improving the standard bagging ensemble model. The proposed model was applied to the bankruptcy prediction problem using a real data set and the results were compared with those of the other models. The experimental results showed that the proposed model outperformed the standard bagging model.

• Speech Sciences
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• 제13권2호
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• pp.75-84
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• 2006

Various methods have been proposed to overcome the problem of speech recognition in the noisy conditions. Among them, the model compensation methods like the parallel model combination (PMC) and Jacobian adaptation (JA) have been found to perform efficiently. The JA is quite effective when we have hidden Markov models (HMMs) already trained in a similar condition as the target environment. In a previous work, we have proposed an improved method for the JA to make it more robust against the changing environments in recognition. In this paper, we further improved its performance by compensating the delta-mean vectors and covariance matrices of the HMM and investigated its feasibility in the multi-model structure for the noisy speech recognition. From the experimental results, we could find that the proposed improved the robustness of the JA and the multi-model approach could be a viable solution in the noisy speech recognition.