• Journal of Computing Science and Engineering
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• v.8 no.2
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• pp.94-106
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• 2014

Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique that is based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect statistics about the appearance order of attributes from the query logs, to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics around the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, a traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective, and has good performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.1
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• pp.163-171
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• 1996

For the effective analysis of two dimensional plane problems, Treffiz finite elements and cavity elements have been proposed. These element matrix equaitons were formulated on the basis of hybrid variational principle and Treffiz function sets derived consitstently from the complex theoy of plane elasticity. In order to suggest the accuracy chatacteristics of the proposed Treffiz elements typical plane problems were analyzed and these results were compared with ones obtained by using the conveintional displacement type elements. The accuracy of the proposed elements is less sensitive to the element size and shape than the conventional displacement type elements. These elements, being able to be formed with multi-nodes, give the convenient modeling of an analytic domain. The cavity elements give the comparatively exact values of stress concentration factors of stress intensity factors and can be effectively used for the analysis of mechanical stuctures containing various cavities.

• Applied Science and Convergence Technology
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• v.23 no.6
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• pp.376-386
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• 2014

Zinc oxysulfide nanocrystals (ZnOS NCs) were synthesized by forming ZnS phase on a ZnO matrix. ZnO nanocrystals (NCs) with a diameter of 10 nm were synthesized by forced hydrolysis in an organic solvent. As-synthesized ZnO NCs aggregated with each other due to the high surface energy. As acetic acid (AA) was added into the milky suspension of the aggregated ZnO NCs, transparent solution of well dispersed ZnO NCs formed. Finally ZnOS NCs were formed by adding thioacetic acid (TAA) to the transparent solution. The effect of recrystallization on the structural, optical and electrical properties of the ZnOS NCs were studied. The results of UV-vis absorption confirmed the band gap tunability caused by increasing the curing temperature of ZnOS thin films. This may have originated from the larger effective size due to the recrystallization of zinc sulfide (ZnS). From XRD result we identified that ZnOS thin films have a zinc blende crystal structure of ZnS without wurtzite ZnO structure. This is probably due to the small amount of ZnO phases. These assertions were verified through EDS of FE-SEM, XPS and EDS mapping of HR-TEM results; we clearly proved that ZnOS were comprised of ZnS and ZnO phases.

• ETRI Journal
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• v.38 no.1
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• pp.174-184
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• 2016

Information identification with image data by means of low-level visual features has evolved as a challenging research domain. Conventional text-based mapping of image data has been gradually replaced by content-based techniques of image identification. Feature extraction from image content plays a crucial role in facilitating content-based detection processes. In this paper, the authors have proposed four different techniques for multiview feature extraction from images. The efficiency of extracted feature vectors for content-based image classification and retrieval is evaluated by means of fusion-based and data standardization-based techniques. It is observed that the latter surpasses the former. The proposed methods outclass state-of-the-art techniques for content-based image identification and show an average increase in precision of 17.71% and 22.78% for classification and retrieval, respectively. Three public datasets - Wang; Oliva and Torralba (OT-Scene); and Corel - are used for verification purposes. The research findings are statistically validated by conducting a paired t-test.

• Computational Structural Engineering
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• v.7 no.4
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• pp.85-101
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• 1994

A family of variational principles governing the dynamics of laminated plate has been derived using a variationally consistent shear deformable discrete laminated plate theory with particular reference to finite element procedures. The theoretical basis for the derivation is Sandhu's generalized procedure for the variational formulation of linear coupled boundary value problem. As the bilinear mapping to write the operator matrix of the field equations in self-adjoint form, convolution product was employed. Boundary conditions, initial conditions and probable internal discontinuity were explicitly included in the governing functionals. Some interesting extensions and specializations of the general variational principle were presented, which can provide many different finite element formulations for the problem.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.376-376
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• 2010

이차이온질량분석기(SIMS)는 수 kV의 에너지를 갖는 일차이온($O_2^+$, $Cs^+$)을 시료표면에 충돌시켜 표면에서 떨어져 나온 이온의 질량 및 개수를 분석하는 장비이다. SIMS는 성분원소의 깊이분포도 측정, 질량분석, Image mapping등 다양한 분석을 할 수 있다. 특히 극미량 분석이나 깊이분포도 분석에서 가장 뛰어난 성능을 가지고 있어 아직까지 많이 사용하고 있다. 하지만 SIMS는 이온빔을 이용한 스퍼터링(Sputtering) 방법으로 분석을 하므로 파괴적이며 매질효과가 심하다. 또한 Matrix 물질의 함량이나 물질 자체가 변한다면 Sputtering rate도 그에 따라 변하게 된다. 이러한 현상에 의해 Sputtering rate는 다른 물질이 섞여 있는 경우 Sputtering rate이 빠른 물질이 먼저 Sputtering이 되는 Preferential Sputtering 현상이 나타나기 때문에 계면에서 깊이분해능에 좋지 않은 영향을 주게 된다. 본 연구에서는 SIMS로 Si(100) 기판 위에 약 100nm 두께로 Fe가 증착된 시료를 분석하였다. 이차이온으로 $O_2^+$이온을 사용하였으며 이온의 입사각을 변화시켜 각 조건에서 생기는 Fe 표면의 Topograph을 SEM으로 관찰하였으며, Topograph와 SIMS깊이분해능의 관계을 이해하고 $O_2^+$ 이온의 입사각 변화에 따른 Fe 표면의 Topograph의 형태와 산화도를 이해하고자 한다.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.32 no.4
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• pp.341-348
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• 2019

This paper reports the microstructure and electrochemical properties of Si-Al-Fe ternary amorphous alloys prepared by rapid solidification as an anode for lithium secondary batteries. The microstructure was analyzed using XRD and HR-TEM with EDS mapping. In accordance with DSC analysis, annealing was performed to crystallize the active nano-Si in the amorphous alloy. Thus, nano-Si forms (~80 nm) embedded in the matrix alloy, such as $Fe_2Al_3Si_3$, $FeSi_2$, and $Fe_{0.42}Si_{2.67}$, were successfully synthesized. The electrode based on the Si-Al-Fe ternary alloy delivered an initial discharge capacity of approximately $700mAh^{g-1}$, and exhibited a high Coulombic efficiency of 99.0~99.6% from the $2^{nd}$ to $70^{th}$ cycles.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.169-176
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• 2021

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

• Communications for Statistical Applications and Methods
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• v.29 no.1
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• pp.65-83
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• 2022

Although various statistical methods have been developed to map time-dependent genetic factors, most identified genetic variants can explain only a small portion of the estimated genetic variation in longitudinal traits. Gene-gene and gene-time/environment interactions are known to be important putative sources of the missing heritability. However, mapping epistatic gene-gene interactions is extremely difficult due to the very large parameter spaces for models containing such interactions. In this paper, we develop a Gaussian process (GP) based nonparametric Bayesian variable selection method for longitudinal data. It maps multiple genetic markers without restricting to pairwise interactions. Rather than modeling each main and interaction term explicitly, the GP model measures the importance of each marker, regardless of whether it is mostly due to a main effect or some interaction effect(s), via an unspecified function. To improve the flexibility of the GP model, we propose a novel grid-based method for the within-subject dependence structure. The proposed method can accurately approximate complex covariance structures. The dimension of the covariance matrix depends only on the number of fixed grid points although each subject may have different numbers of measurements at different time points. The deviance information criterion (DIC) and the Bayesian predictive information criterion (BPIC) are proposed for selecting an optimal number of grid points. To efficiently draw posterior samples, we combine a hybrid Monte Carlo method with a partially collapsed Gibbs (PCG) sampler. We apply the proposed GP model to a mouse dataset on age-related body weight.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.3
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• pp.143-149
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• 2022

This excerpt delineates a quantitative measure of relationship between a research title and its respective abstract extracted from different journal articles documented through a Korean Citation Index (KCI) database published through various journals. In this paper, we propose a machine learning-based similarity metric that does not assume normality on dataset, realizes the imbalanced dataset problem, and zero-variance problem that affects most of the rule-based algorithms. The advantage of using this algorithm is that, it eliminates the limitations experienced by Pearson correlation coefficient (r) and additionally, it solves imbalanced dataset problem. A total of 107 journal articles collected from the database were used to develop a corpus with authors, year of publication, title, and an abstract per each. Based on the experimental results, the proposed algorithm achieved high correlation coefficient values compared to others which are cosine similarity, euclidean, and pearson correlation coefficients by scoring a maximum correlation of 1, whereas others had obtained non-a-number value to some experiments. With these results, we found that an effective title must have high correlation coefficient with the respective abstract.