• Journal of the Korea Computer Graphics Society
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• v.23 no.5
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• pp.19-27
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• 2017

In this paper, we propose an automatic segmentation method of the mandible using shape-constrained information in cranio-maxillo-facial CBCT images. The proposed method consists of the following two steps. First, the mandible segmentation based on the global shape information is performed through the statistical shape model generated using the MDCT images. Second, improvement of mandible segmentation is performed considering the local shape information and intensity characteristics of the mandible. To evaluate the performance of the proposed method, the proposed method was evaluated qualitatively and quantitatively based on the results of manual segmentation by expert. Experimental results show that the Dice Similarity Coefficient of the proposed method was 95.64% and 90.97%, respectively, in the mandible body region including the narrow region of large curvature and the condyle region with large positional variance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1058-1063
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• 2020

This paper presents a new insulation resistance calculation technique to prevent electric shock and fire accidents due to the dielectric breakdown in the primary insulation section of the IT ground system. The solar power generation market is growing rapidly due to the recent expansion of renewable energy and energy storage systems, but as the insulation is destroyed and fire accidents frequently occur, a device for monitoring the insulation resistance state is indispensable to the IT grounding method. Compared to the conventional algorithm that use a method of multiplying a time constant to a fixed coefficient, the proposed insulation resistance calculation method has a fast response time and high accuracy over a wide insulation resistance range by applying a different coefficient according to the values of the insulation impedance. The proposed dynamic time constant based insulation resistance calculation technique reduces the response time by up to 39.29 seconds and improves the error rate by 20.11%, compared to the conventional method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.659-675
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• 2006

Recently, significant progress has been made in performance-based engineering methods that rely mainly on nonlinear static seismic analysis procedures. The Capacity Spectrum Method (CSM) and the Displacement Coefficient Method (DCM) are the representative nonlinear static seismic analysis procedures. In order to evaluate the applicability of the procedures to the seismic evaluation and design process of new and existing structures, the accuracy of both CSM and DCM should be evaluated in advance. The accuracy of seismic responses by the nonlinear static procedures is evaluated in comparison with the shaking table test results for the structural wall specimen subjected to the far field and near field earthquakes. Also conducted are comparative studies where the shaking table test results are compared with those from nonlinear dynamic analysis procedures, i.e., Single-Degree-of-Freedom (SDOF), equivalent SDOF and Multi-Degree-of-Freedom (MDOF) systems.

• Structural Engineering and Mechanics
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• v.90 no.6
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• pp.577-590
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• 2024

Considering the Column-Supported Group Silos (CSGSs) often arranged by rows in practical applications, earthquake responses will be affected by group effect. Since group effect presenting uncertainties, establishing the analytic model and evaluating characteristics of CSGSs seems necessary. This study aimed at providing a simplified method to evaluate seismic performances of the CSGSs. Firstly, the CSGSs with different storage granule heights are used as numerical examples to derive the base shear formula for three-particle dynamic analytical model. Then, the base shear distribution coefficient is defined as the group effect index. The simplified calculation method of the group silos based on the distribution coefficients is proposed. Finally, based on the empty, half, and full granular storage conditions, the empirical design parameters for the group silos system are given by combining finite element simulation with shaking table test. The group effect of storage granule heights of group silos on its frequency and base shear are studied by comparative analysis between group silos and independent single silo. The results show that the frequency of CSGSs decreases with the increasing weight of the stored granule. The connection between the column top and silo bottom plate is vulnerable, and structural measures should be strengthened to improve its damage resistance. In case of different storage granule heights, distribution coefficients are effective to reconstruction the group effect. The complex calculations of seismic response for CSGSs can be avoided by adopting the empirical distribution coefficients obtained in this study. The proposed method provides a theoretical reference for evaluation on the seismic performances of the CSGSs.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.5
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• pp.297-311
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• 2024

In this study, a combined seakeeping performance evaluation method has been developed for the design purpose of the light aircraft carrier CVX of Korean Navy. A frequency domain analysis method was developed for evaluation of safe operating envelope up to sea state 6, while a time domain analysis method was developed for survival condition of sea state 7 and higher. The frequency-domain solver AdFLOW-Navy was developed by adding empirical formula of roll damping and fin-stabilizer to the existing AdFLOW by KRISO, which was based on the three-dimensional higher order boundary element method (HOBEM). For the estimation of the roll damping coefficient, a two-dimensional cross-section was automatically extracted from the three-dimensional panel, and the roll damping coefficient was analyzed for the two-dimensional cross-section. As for the time domain analysis method, KIMAPS-Navy was developed by improving and expanding the KIMAPS series developed by KRISO which is based on the impulse response function by utilizing the hydrodynamic coefficients obtained from the AdFLOW-Navy. In addition, a weakly nonlinear analysis approach was applied to analyze highly nonlinear motion under heavy sea states. Finally numeraical analysis results were compared with model tests, which showed practical usefulness of the present combined seakeeping analysis approach.

• Journal of KIISE:Software and Applications
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• v.32 no.11
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• pp.1111-1120
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• 2005

In collaborative filtering systems most users do not rate preferences; so User-Item matrix shows great sparsity because it has missing values for items not rated by users. Generally, the systems predict the preferences of an active user based on the preferences of a group of users. However, default voting methods predict all missing values for all users in User-Item matrix. One of the most common methods predicting default voting values tried two different approaches using the average rating for a user or using the average rating for an item. However, there is a problem that they did not consider the characteristics of items, users, and the distribution of data set. We replace the missing values in the User-Item matrix by the default noting method using user coefficient of variance. We select the threshold of user coefficient of variance by using equations automatically and determine when to shift between the user averages and item averages according to the threshold. However, there are not always regular relations between the averages and the thresholds of user coefficient of variances in datasets. It is caused that the distribution information of user coefficient of variances in datasets affects the threshold of user coefficient of variance as well as their average. We decide the threshold of user coefficient of valiance by combining them. We evaluate our method on MovieLens dataset of user ratings for movies and show that it outperforms previously default voting methods.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.5
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• pp.31-36
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• 2011

In this paper, Mel-Frequency Cepstral Coefficient (MFCC), Decorrelated Filter Bank (DFB), Octave-based Spectral Contrast (OSC), Zero-Crossing Rate (ZCR), and Spectral Contract/Roll-Off are combined as a set of multiple feature-vectors for the music genre classification system based on the Support Vector Machine (SVM) classifier. In the conventional system, feature vectors for the entire genre classes are normalized for the SVM model training and classification. However, in this paper, selected feature vectors that are compared based on the One-Against-One (OAO) SVM classifier are only used for normalization. Using OSC as a single feature-vector and the multiple feature-vectors, we obtain the genre classification rates of 60.8% and 77.4%, respectively, with the conventional normalization method. Using the proposed normalization method, we obtain the increased classification rates by 8.2% and 3.3% for OSC and the multiple feature-vectors, respectively.

• Wind and Structures
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• v.26 no.4
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• pp.191-204
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• 2018

This article presents a study of the largest-ever (height = 220 m) cooling tower using the large eddy simulation (LES) method. Information about fluid fields around the tower and 3D aerodynamic time history in full construction process were obtained, and the wind pressure distribution along the entire tower predicted by the developed model was compared with standard curves and measured curves to validate the effectiveness of the simulating method. Based on that, average wind pressure distribution and characteristics of fluid fields in the construction process of ultra-large cooling tower were investigated. The characteristics of fluid fields in full construction process and their working principles were investigated based on wind speeds and vorticities under different construction conditions. Then, time domain characteristics of ultra-large cooling towers in full construction process, including fluctuating wind loads, extreme wind loads, lift and drag coefficients, and relationship of measuring points, were studied and fitting formula of extreme wind load as a function of height was developed based on the nonlinear least square method. Additionally, the frequency domain characteristics of wind loads on the constructing tower, including wind pressure power spectrum at typical measuring points, lift and drag power spectrum, circumferential correlations between typical measuring points, and vertical correlations of lift coefficient and drag coefficient, were analyzed. The results revealed that the random characteristics of fluctuating wind loads, as well as corresponding extreme wind pressure and power spectra curves, varied significantly and in real time with the height of the constructing tower. This study provides references for design of wind loads during construction period of ultra-large cooling towers.

• The KIPS Transactions:PartB
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• v.13B no.6 s.109
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• pp.625-632
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• 2006

Collaborative filtering is a technology that aims at teaming predictive models of user preferences. Collaborative filtering systems have succeeded in Ecommerce market but they have shortcomings of high dimensionality and sparsity. In this paper we propose the nearest neighbor collaborative filtering method using non-negative matrix factorization(NNMF). We replace the missing values in the user-item matrix by using the user variance coefficient method as preprocessing for matrix decomposition and apply non-negative factorization to the matrix. The positive decomposition method using the non-negative decomposition represents users as semantic vectors and classifies the users into groups based on semantic relations. We compute the similarity between users by using vector similarity and selects the nearest neighbors based on the similarity. We predict the missing values of items that didn't rate by a new user based on the values that the nearest neighbors rated items.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.