• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.321-329
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• 2007

This paper reports on the application of network analysis approaches to investigate the characteristics of graph representations of Japanese word associations. Two semantic networks are constructed from two separate Japanese word association databases. The basic statistical features of the networks indicate that they have scale-free and small-world properties and that they exhibit hierarchical organization. A graph clustering method is also applied to the networks with the objective of generating hierarchical structures within the semantic networks. The method is shown to be an efficient tool for analyzing large-scale structures within corpora. As a utilization of the network clustering results, we briefly introduce two web-based applications: the first is a search system that highlights various possible relations between words according to association type, while the second is to present the hierarchical architecture of a semantic network. The systems realize dynamic representations of network structures based on the relationships between words and concepts.

• Asia pacific journal of information systems
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• v.16 no.3
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• pp.49-65
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• 2006

This study proposes a model for customer segmentation using the psychological characteristics of Internet banking customers. The model was developed through two phased clustering method, called SONN-HC by integrating self-organizing neural networks (SONN) and hierarchical clustering (HC) method. We applied the SONN-HC method to internet banking customer segmentation and performed an empirical analysis with 845 cases. The results of our empirical analysis show the psychological characteristics of Internet banking customers have significant differences among four clusters of the customers created by SONN-HC. From these results, we found that the psychological characteristics of Internet banking customers had an important role of planning a strategy for customer segmentation in a financial institution.

• Structural Engineering and Mechanics
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• v.38 no.4
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• pp.529-547
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• 2011

This paper presents the global-local finite cover method (GL-FCM) that is capable of analyzing structures involving local heterogeneities and propagating cracks. The suggested method is composed of two techniques. One of them is the FCM, which is one of the PU-based generalized finite element methods, for the analysis of local cohesive crack growth. The mechanical behavior evaluated in local heterogeneous structures by the FCM is transferred to the overall (global) structure by the so-called mortar method. The other is a method of mesh superposition for hierarchical modeling, which enables us to evaluate the average stiffness by the analysis of local heterogeneous structures not subjected to crack propagation. Several numerical experiments are conducted to validate the accuracy of the proposed method. The capability and applicability of the proposed method is demonstrated in an illustrative numerical example, in which we predict the mechanical deterioration of a reinforced concrete (RC) structure, whose local regions are subjected to propagating cracks induced by reinforcement corrosion.

• Journal of Fisheries and Marine Sciences Education
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• v.26 no.6
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• pp.1442-1450
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• 2014

The aim of this study is to develop a model for the alternative selection in port privatization using Brainstorming, the ISM and AHP methods. For this, 30 detailed attributing factors were identified by both previous studies and port users, Also, 13 attributing evaluation factors were identified by a group of port experts using the brainstorming method. These were made into a model of hierarchical structure with 3 levels, taking 1 goal factor, 5 evaluation factors and 7 alternative factors using the ISM method. The collected date of questionnaires through the AHP method were analyzed with a group of port experts for an empirical analysis. The result of the hierarchical level 2 shows that profitability is the most important factor, followed by public interest, management professionality, service quality and financial soundness. The analysis results of hierarchical level 3 shows that commercialization is the most important factor.

• Transactions of Materials Processing
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• v.16 no.2 s.92
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• pp.138-143
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• 2007

Superplastic forming/diffusion bonding (SPF/DB) processes were analyzed using a 3-D rigid visco-plastic finite element method. A constant-triangular element based on membrane approximation and an incremental theory of plasticity are employed for the formulation. The coulomb friction law is used for interface friction between tool and material. Pressure-time relationship for a given optimal strain rate is calculated by stress and pressure values at the previous iteration step. In order to improve the contact searching, hierarchical search algorithm has been applied and implemented into the code. Various geometries including sandwich panel and 3 sheet shape for 3-D SPF/DB model are analyzed using the developed program. The validity fer the analysis is verified by comparison between analysis and results in the literature.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1999.03b
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• pp.22-25
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• 1999

A dynamic explicit finite element code for simulating sheet forming processes has been developed The code utilises the discrete Kirchhoff shell element and contact force is treated by a conventional penalty method. In order to reduce the computational cost a new and robust contact searching algorithm has been developed and implemented into the code. in the method a hierarchical structure of tool segments called a tree structure is built for each tool at the initial stage of the analysis Tree is built in a way to divide a trunk to 8 sub-trunk 2 in each direction until the lowest level of the tree(leaf) contains exactly one segment of the tool. In order to have a well-balanced tree each box on each sub level contains one eighth of the segments. Then at each time step contact line from a node comes out of the surface of the tool. Simulation of various sheet forming processes were performed to verify the validity of the developed code with main focus on he usefulness of the developed contact searching algorithm.

• Structural Engineering and Mechanics
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• v.84 no.6
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• pp.723-731
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• 2022

Functionally graded materials (FGMs) have been spotlighted as an advanced composite material, accordingly the intensive studies have focused on FGMs to examine their mechanical behaviors. Among them is thermal buckling which has been a challenging subject, because its behavior is connected directly to the safety of structural system. In this context, this paper presents the numerical analysis of thermal buckling of metal-ceramic functionally graded (FG) plates. For an accurate and effective buckling analysis, a new numerical method is developed by making use of (1,1,0) hierarchical model and 2-D natural element method (NEM). Based on 3-D elasticity theory, the displacement field is expressed by a product of 1-D assumed thickness monomials and 2-D in-plane functions which are approximated by NEM. The numerical method is compared with the reference solutions through the benchmark test, from which its numerical accuracy has been verified. Using the developed numerical method, the critical buckling temperatures of metal-ceramic FG plates are parametrically investigated with respect to the major design parameters.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.11
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• pp.135-146
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• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• Genomics & Informatics
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• v.18 no.1
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• pp.11.1-11.3
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• 2020

In genome-wide association studies, pathway-based analysis has been widely performed to enhance interpretation of single-nucleotide polymorphism association results. We proposed a novel method of hierarchical structural component model (HisCoM) for pathway analysis of common variants (HisCoM for pathway analysis of common variants [HisCoM-PCA]) which was used to identify pathways associated with traits. HisCoM-PCA is based on principal component analysis (PCA) for dimensional reduction of single nucleotide polymorphisms in each gene, and the HisCoM for pathway analysis. In this study, we developed a HisCoM-PCA software for the hierarchical pathway analysis of common variants. HisCoM-PCA software has several features. Various principle component scores selection criteria in PCA step can be specified by users who want to summarize common variants at each gene-level by different threshold values. In addition, multiple public pathway databases and customized pathway information can be used to perform pathway analysis. We expect that HisCoM-PCA software will be useful for users to perform powerful pathway analysis.

• Communications for Statistical Applications and Methods
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• v.31 no.5
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• pp.601-612
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• 2024

A functional clustering analysis is a crucial machine learning technique in functional data analysis. Many functional clustering methods have been developed to enhance clustering performance. Moreover, due to the phase variability between functions, elastic functional clustering methods, such as applying the Fisher-Rao metric, which can manage phase variation during clustering, have been developed to improve model performance. However, aligning functions without considering the phase variation can distort functional information because phase variation can be a natural characteristic of functions. Hence, we propose a state-of-the-art functional hierarchical clustering that can manage phase and amplitude variations of functional data. This approach is based on the phase and amplitude separation method using the norm-preserving time warping of functions. Due to its invariance property, this representation provides robust variability for phase and amplitude components of functions and improves clustering performance compared to conventional functional hierarchical clustering models. We demonstrate this framework using simulated and real data.