• International Journal of Contents
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• v.4 no.3
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• pp.20-28
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• 2008

To obtain realistic performance measures for wireless networks, one should consider changes in performance due to failure related behavior. In performability analysis, simultaneous consideration is given to both pure performance and performance with failure measures. SRN is an extension of stochastic Petri nets and provides compact modeling facilities for system analysis. In this paper, a new methodology to model and analyze performability based on stochastic reward nets (SRN) is presented. Composite performance and availability SRN models for wireless handoff schemes are developed and then these models are decomposed hierarchically. The SRN models can yield measures of interest such as blocking and dropping probabilities. These measures are expressed in terms of the expected values of reward rate functions for SRNs. Numerical results show the accuracy of the hierarchical model. The key contribution of this paper constitutes the Petri nets modeling techniques instead of complicate numerical analysis of Markov chains and easy way of performance analysis for channel allocation under SRN reward concepts.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.431-434
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• 2001

Knowledge discovery in databases(KDD) is the process for extracting valid, novel, potentially useful and understandable knowledge form real data. There are many academic and industrial activities with new technologies and application areas. Particularly, data mining is the core step in the KDD process, consisting of many algorithms to perform clustering, pattern recognition and rule induction functions. The main goal of these algorithms is prediction and description. Prediction means the assessment of unknown variables. Description is concerned with providing understandable results in a compatible format to human users. We introduce an efficient data mining algorithm considering predictive and descriptive capability. Reasonable pattern is derived from real world data by a revised neural network model and a proposed fuzzy rule extraction technique is applied to obtain understandable knowledge. The proposed neural network model is a hierarchical self-organizing system. The rule base is compatible to decision makers perception because the generated fuzzy rule set reflects the human information process. Results from real world application are analyzed to evaluate the system\`s performance.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.217-220
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• 2001

The fuzzy equalization method does not require the usual learning step for generating fuzzy rules. However it is heavily depend on the given input-output data set. So, we adapt an hierarchical scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership functions obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to the Rice taste data and got better results than previous ones.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.2097-2098
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• 2006

In this study, we introduce the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA). HFCGA is a kind of multi-populations of Parallel Genetic Algorithms(PGA), and it is used for structure optimization and parameter identification of fuzzy set model. It concerns the fuzzy model-related parameters as the number of input variables, a collection of specific subset of input variables, the number of membership functions, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. The structural optimization is realized via HFCGA method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• 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.

• Genomics & Informatics
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• v.7 no.2
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• pp.122-130
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• 2009

A systems biology approach for the identification of perturbed molecular functions is required to understand the complex progressive disease such as breast cancer. In this study, we analyze the microarray data with Gene Ontology terms of molecular functions to select perturbed molecular functional modules in breast cancer tissues based on the definition of Gene ontology Functional Code. The Gene Ontology is three structured vocabularies describing genes and its products in terms of their associated biological processes, cellular components and molecular functions. The Gene Ontology is hierarchically classified as a directed acyclic graph. However, it is difficult to visualize Gene Ontology as a directed tree since a Gene Ontology term may have more than one parent by providing multiple paths from the root. Therefore, we applied the definition of Gene Ontology codes by defining one or more GO code(s) to each GO term to visualize the hierarchical classification of GO terms as a network. The selected molecular functions could be considered as perturbed molecular functional modules that putatively contributes to the progression of disease. We evaluated the method by analyzing microarray dataset of breast cancer tissues; i.e., normal and invasive breast cancer tissues. Based on the integration approach, we selected several interesting perturbed molecular functions that are implicated in the progression of breast cancers. Moreover, these selected molecular functions include several known breast cancer-related genes. It is concluded from this study that the present strategy is capable of selecting perturbed molecular functions that putatively play roles in the progression of diseases and provides an improved interpretability of GO terms based on the definition of Gene Ontology codes.

• Journal of Families and Better Life
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• v.24 no.2 s.80
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• pp.149-163
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• 2006

The purposes of this study were to explore ecological variables that affect family functions and to analyze relative magnitudes of significant predictors. The study employed ecological model. Data were collected from 143 mothers of mildly handicapped children enrolled in integrated kindergartens in Seoul, Korea. The results of the study were as follows : 1. The level of family functions with mildly handicapped children were found to be moderate(M=3.21, SD=.49). 2. The hierarchical regression analysis yielded Model V as the most powerful model, explaining 79%$(Adjusted\;R^2=.787)$ of the variance. 3. The most powerful predictors throughout Model I to V were found to be maternal efficacy $({\beta}=.578,\;p<.001)$, maternal satisfaction with parent-education and counseling programs $({\beta}=.249,\;p<.001)$, husband's helps $({\beta}=.207,\;p<.01)$, and the availability of assistance other than family members $({\beta}=.232,\;p<.05)$ in the order.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.11
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• pp.463-473
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• 2016

The purpose of this study was to investigate the relations between the cognitive functions, health conditions, activities of daily living, depression and sleep states among the elderly at care facilities. The subjects include 204 elderly people aged 65. The data collected were analyzed using descriptive statistics, the t-test, ANOVA, Pearson correlation, and hierarchical multiple regression. The findings show that the cognitive functions of the elderly at care facilities are related to their engagement in regular exercise, duration of residency, grade of care, lack of physical freedom, listening ability, state of teeth, urinary incontinence, activities of daily living, and state of sleep. Their cognitive functions had positive correlations with activities of daily living and negative correlations with state of sleep. Their cognitive impairment was significantly influenced by their engagement in regular exercise, duration of residency, grade of care, listening ability, ADL, IAD dependency, and sleep disorder. In short, the cognitive functions of the elderly at care facilities are highly related to their health conditions as perceived and felt by them. It is thus required to develop, apply, and consistently assess and manage cognitive rehabilitation training programs to provide interventions for the factors that influence their cognitive impairment.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.40-46
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• 1990

This paper introduces a geometric modelling system adopted in a newly developed preprocessor for finite element analysis of three dimensional structures. The formulation is characterized by hierarchical construction of structural model which consists of control points, curves, surfaces and solids. Various surface and solid modeling schemes based on blending functions and boundary representation are systematized for finite element mesh generation. The modeling system is integrated with model synthesis and operations which facilitate modelling of complex structures.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.11
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• pp.514-521
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• 2001

This paper uses the distributed heating thin metal moving at constant velocity which are modeled as distributed parameter systems, and applies the Paley order of Walsh functions to high order partial differential equations and matrix partial differential equations. This thesis presents a new algorithm which usefully exercises the optimal control in the distributed parameter systems. In this paper, the excellent consequences are found without using the existing decentralized control or hierarchical control method.