• Korean Management Science Review
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• v.22 no.1
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• pp.1-13
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• 2005

This paper proposed a quantitative evaluation method to measure the operational effectiveness of the local military manpower administrations. The proposed method compared the relative operational effectiveness level for 12 local military manpower administrations in Korea.. The method used the IDEA (imprecise Data Envelopment Analysis) model which Is able to measure relative operational effectiveness level, and also used two input variables (labor cost, operational cost) and three output variables (number of military applicants, number of civil application approved & processed, management accuracy level). Through the model output analysis, we presented the relative effectiveness scores, the reason for non-effectiveness, and the relationship between non-effective ness level and input/output variables for each local military manpower administration. We also presented a few recommendations how to improve the effectiveness level on particular local military manpower administration.

• International Journal of Naval Architecture and Ocean Engineering
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• v.9 no.6
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• pp.624-640
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• 2017

In this paper, the slamming coefficients on local members of a jacket structure under plunging breaker are studied based on numerical simulations. A 3D numerical model is used to investigate breaking wave forces on the local members of the jacket structure. A wide range of breaking wave conditions is considered in order to get generalized slamming coefficients on the jacket structure. In order to make quantitative comparison between CFD model and experimental data, Empirical Mode Decomposition (EMD) is employed for obtaining net breaking wave forces from the measured response, and the filtered results are compared with the computed results in order to confirm the accuracy of the numerical model. Based on the validated results, the slamming coefficients on the local members (front and back vertical members, front and back inclined members, and side inclined members) are estimated. The distribution of the slamming coefficients on local members is also discussed.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.1
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• pp.46-51
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• 2007

In this paper, we propose a new data clustering method using local probability and hypothesis theory. To cluster the test data set we analyze the local area of the test data set using local probability distribution and decide the candidate class of the data set using mean standard deviation and variance etc. To decide each class of the test data, statistical hypothesis theory is applied to the decided candidate class of the test data set. For evaluating, the proposed classification method is compared to the conventional fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm. The simulation results show more accuracy than results of fuzzy c-mean method, k-means algorithm and Discriminator analysis algorithm.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.58-66
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• 2007

This paper introduces the new generic dynamic neuro-fuzzy local modeling system (DNFLMS) that is based on a dynamic Takagi-Sugeno (TS) type fuzzy inference system for complex dynamic hydrological modeling tasks. The proposed DNFLMS applies a local generalization principle and an one-pass training procedure by using the evolving clustering method to create and update fuzzy local models dynamically and the extended Kalman filtering learning algorithm to optimize the parameters of the consequence part of fuzzy local models. The proposed DNFLMS is applied to develop the inference model to forecast the flow of Waikoropupu Springs, located in the Takaka Valley, South Island, New Zealand, and the influence of the operation of the 32 Megawatts Cobb hydropower station on springs flow. It is demonstrated that the proposed DNFLMS is superior in terms of model accuracy, model complexity, and computational efficiency when compared with a multi-layer perceptron trained with the back propagation learning algorithm and well-known adaptive neural-fuzzy inference system, both of which adopt global generalization.

• Coupled systems mechanics
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• v.6 no.4
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• pp.501-522
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• 2017

This paper introduces a numerical procedure to incorporate elasto-plastic local deformation effects in the dynamic analysis of beams. The appealing feature is that simple beam type finite elements can be used for the global model which needs not to be altered by the localized elasto-plastic deformations. An overlapping local sophisticated 2D membrane model replaces the internal forces of the beam elements in the predefined region where the localized deformations take place. An iterative coupling technique is used to perform this replacement. Comparisons with full membrane analysis are provided in order to illustrate the accuracy and efficiency of the method developed herein. In this study, the membrane formulation is able to capture the elasto-plastic material behaviour based on the von Misses yield criterion and the associated flow rule for plane stress. The Newmark time integration method is adopted for the step-by-step dynamic analysis.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.04a
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• pp.353-360
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• 2002

The objective of this study is to develop a technique that analyzes the global behavior of frame structures with local cracks. The technique is based on frame analysis and uses the stiffness matrix of cracked frame element. An algorithm proposed here analyzes a frame structure with local transverseedge cracks, considering the effects of crack length and location. Stress intensity factors are employed to calculate additional local compliance due to the cracks based on linear elastic fracture mechanics theory, and then this local compliance is utilized to derive the stiffness matrix of the cracked frame element. In order to verify the accuracy and reliability of the proposed approach, numerical results are compared with those of Finite Element Method for the cracked frame element, and the effects of single crack on the behavior of truss structure are also examined.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.106-109
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• 2009

Recently, image matching becomes important in Computer Aided Diagnosis (CAD) due to the huge amount of medical images. Specially, texture feature is useful in medical image matching. However, texture features such as co-occurrence matrices can't describe well the spatial distribution of gray levels of the neighborhood pixels. In this paper we propose a frequency domain-based texture feature extractor that describes the local spatial distribution for medical image retrieval. This method is based on 2D Local Discrete Fourier transform of local images. The features are extracted from local Fourier histograms that generated by four Fourier images. Experimental results using 40 classes Brodatz textures and 1 class of Emphysema CT images show that the average accuracy of retrieval is about 93%.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.16-22
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• 1999

In this paper, an efficient algorithm for estimating real edge locations to subpixel values is described. Digital images are acquired by projection into image plane and sampling process. However, most of real edge locations are lost in this process, which causes low measurement accuracy. For accurate measurement, we propose an algorithm which estimates the real boundary between two adjacent pixels in digital imagery, with subpixel accuracy. We first define 1D edge operator based on the moment invariant. To extend it to 2D data, the edge orientation of each pixel is estimated by the LSE(Least Squares Error)line/circle fitting of a set of pixels around edge boundary. Then, using the pixels along the line perpendicular to the estimated edge orientation the real boundary is calculated with subpixel accuracy. Experimental results using real images show that the proposed method is robust in local noise, while maintaining low measurement error.

• Korean Management Science Review
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• v.17 no.2
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• pp.39-54
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• 2000

This paper presents a causal forecasting model using guided genetic algorithm in continuous manufacturing process. The guide genetic algorithm(GGA) is an extended genetic algorithm(GA) using penalty function and population diversity index to increase forecasting accuracy. GGA adds to the canonical GA the concept of a penalty function to avoid selecting the unproductive chromosomes and to make a proper searching direction. Also, GGA modifies the current population using the similarity of chromosomes to avoid falling into the trap of local optimal solution. For investigation GGA performance, we used a set of real data that was collected in local glass melting processes, and experimental results show the proposed model results in the better forecasting accuracy than linear regression model and canonical GA.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.3
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• pp.794-814
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• 2012

We propose a novel method for improving Wi-Fi positioning accuracy while reducing the energy consumption of mobile devices. Our method presents three contributions. First, we jointly and intelligently select the optimal subset of access points for positioning via maximum mutual information criterion. Second, we further propose local discriminant embedding algorithm for nonlinear discriminative feature extraction, a process that cannot be effectively handled by existing linear techniques. Third, to reduce complexity and make input signal space more compact, we incorporate clustering analysis to localize the positioning model. Experiments in realistic environments demonstrate that the proposed method can lower energy consumption while achieving higher accuracy compared with previous methods. The improvement can be attributed to the capability of our method to extract the most discriminative features for positioning as well as require smaller computation cost and shorter sensing time.