• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.701-703
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• 2022

In this paper, we present a multi-class image scene classification method based on map learning. We were able to learn from the convolutional neural network model in the dataset, classify facial scene images of multiclass people, and classify the optimized CNN model into the Google image dataset in the experiment with significant results.

• Smart Structures and Systems
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• v.21 no.4
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• pp.435-448
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• 2018

This paper reports the development of a theoretically rigorous method for permanent way engineers to assess the condition of railway ballast under a concrete sleeper with the potential to be extended to a smart system for long-term health monitoring of railway ballast. Owing to the uncertainties induced by the problems of modeling error and measurement noise, the Bayesian approach was followed in the development. After the selection of the most plausible model class for describing the damage status of the rail-sleeper-ballast system, Bayesian model updating is adopted to calculate the posterior PDF of the ballast stiffness at various regions under the sleeper. An obvious drop in ballast stiffness at a region under the sleeper is an evidence of ballast damage. In model updating, the model that can minimize the discrepancy between the measured and model-predicted modal parameters can be considered as the most probable model for calculating the posterior PDF under the Bayesian framework. To address the problems of non-uniqueness and local minima in the model updating process, a two-stage hybrid optimization method was developed. The modified evolutionary algorithm was developed in the first stage to identify the important regions in the parameter space and resulting in a set of initial trials for deterministic optimization to locate all most probable models in the second stage. The proposed methodology was numerically and experimentally verified. Using the identified model, a series of comprehensive numerical case studies was carried out to investigate the effects of data quantity and quality on the results of ballast damage detection. Difficulties to be overcome before the proposed method can be extended to a long-term ballast monitoring system are discussed in the conclusion.

• Imaging Science in Dentistry
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• v.50 no.2
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• pp.105-115
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• 2020

Purpose: This study investigated the position of the hyoid bone and its relationship with airway dimensions in different skeletal malocclusion classes using cone-beam computed tomography (CBCT). Materials and Methods: CBCT scans of 180 participants were categorized based on the A point-nasion-B point angle into class I, class II, and class III malocclusions. Eight linear and 2 angular hyoid parameters(H-C3, H-EB, H-PNS, H-Me, H-X, H-Y, H-[C3-Me], C3-Me, H-S-Ba, and H-N-S) were measured. A 3-dimensional airway model was designed to measure the minimum cross-sectional area, volume, and total and upper airway length. The mean crosssectional area, morphology, and location of the airway were also evaluated. Data were analyzed using analysis of variance and the Pearson correlation test, with P values <0.05 indicating statistical significance. Results: The mean airway volume differed significantly among the malocclusion classes(P<0.05). The smallest and largest volumes were noted in class II (2107.8±844.7 ㎣) and class III (2826.6±2505.3 ㎣), respectively. The means of most hyoid parameters (C3-Me, C3-H, H-Eb, H-Me, H-S-Ba, H-N-S, and H-PNS) differed significantly among the malocclusion classes. In all classes, H-Eb was correlated with the minimum cross-sectional area and airway morphology, and H-PNS was correlated with total airway length. A significant correlation was also noted between H-Y and total airway length in class II and III malocclusions and between H-Y and upper airway length in class I malocclusions. Conclusion: The position of the hyoid bone was associated with airway dimensions and should be considered during orthognathic surgery due to the risk of airway obstruction.

• Journal of Korean Elementary Science Education
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• v.31 no.1
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• pp.71-83
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• 2012

The purpose of this study was to explore ecological concepts, epistemological reasoning and reasoning processes through constructing 'food web and food pyramid' in ecology. We conducted classes which involved a 'food web and food pyramid' for $6^{th}$ grade students. Each class is constructed of small groups to do modeling and epistemological reasoning through communication. The researcher had videotaped and recorded each class and have made transcription about classes. We analysed patterns of 'food web and food pyramid models' and reasoning processes according to scientific epistemology using transcription data and student outputs. As a result, students represented phenomenon-based reasoning, relation-based reasoning and model-based reasoning in scientific epistemology from their modeling. Students usually did relation-based reasoning and model-based reasoning in food web which explains ecological phenonenon, while they usually did model-based reasoning in food pyramid which expects ecological phenomenon. Student's reasoning can be limited when they have misconception of scientific knowledge and are limited by fragmentary knowledge. This represents that students has to do relation-based reasoning and model-based reasoning is beneficial in their ecological model. It also suggests that students need to define correct-conception related to ecological modeling(food web, food pyramid).

• Proceedings of the Korean Statistical Society Conference
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• 2000.11a
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• pp.5-12
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• 2000

Meta-analysis refers to quantitative methods for combining results from independent studies in order to draw overall conclusions. We consider hierarchical models including selection models under a skewed heavy tailed error distribution and it is shown to be useful in such Bayesian meta-analysis. A general class of skewed elliptical distribution is reviewed and developed. These rich class of models combine the information of independent studies, allowing investigation of variability both between and within studies, and weight function. Here we investigate sensitivity of results to unobserved studies by considering a hierarchical selection model and use Markov chain Monte Carlo methods to develop inference for the parameters of interest.

• Journal of the Korean Statistical Society
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• v.29 no.4
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• pp.407-422
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• 2000

This article is concerned with the selecting predictor variables to be included in building a class of binary response t-link regression models where both probit and logistic regression models can e approximately taken as members of the class. It is based on a modification of the stochastic search variable selection method(SSVS), intended to propose and develop a Bayesian procedure that used probabilistic considerations for selecting promising subsets of predictor variables. The procedure reformulates the binary response t-link regression setup in a hierarchical truncated normal mixture model by introducing a set of hyperparameters that will be used to identify subset choices. In this setup, the most promising subset of predictors can be identified as that with highest posterior probability in the marginal posterior distribution of the hyperparameters. To highlight the merit of the procedure, an illustrative numerical example is given.

• Journal of Korean Institute of Industrial Engineers
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• v.25 no.2
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• pp.274-281
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• 1999

In this paper, we determine the required storage capacity of a unit-load automated storage/retrieval system(AS/RS) under random storage assignment(RAN) and n-class turnover-based storage assignment(CN) policies. For each of the storage policies, an analytic model to determine the optimal storage capacity of the AS/RS is formulated so that the total cost related to storage space and space shortage is minimized while satisfying a desired service level. A closed form of optimal solutions for the RAN policy is derived from the model. For the CN policy, an optimal storage capacity is shown to be determined by applying the existing iterative search algorithm developed for the full turnover-based storage(FULL) policy. Finally, an application of the approach to the standard economic-order-quantity inventory model is provided.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.207-212
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• 2003

In this paper, we propose a class of imputed estimators using response probability. The proposed estimator can be justified under the response probability model and thus is robust against the failure of the assumed imputation model. We also propose a variance estimator that is justified under the response probability model.

• Journal of Korea Multimedia Society
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• v.21 no.9
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• pp.1068-1075
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• 2018

We propose a method for driving loudspeakers from class D audio power amplifiers in current mode, instead of in conventional voltage mode, which was impossible with the feedback circuitry. Unlike analog audio amplifiers, Class D audio power amplifiers have signal delay between the input and output signals, which makes it difficult to apply the feedback circuitry for current-mode driving. The idea of the pre-distortion scheme used for the compensation of the non-linearity of RF power amplifiers is adapted to remedy the impedance variation effect of the loudspeakers for current driving. The method uses the speaker model for the pre-distorter to compensate for the speaker impedance variation with frequency. The simulation and test results confirms the validity of the proposed method.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.05b
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• pp.94-99
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• 2004

This paper presents experimental data from model windings with different arrangement of coil in order to provide information to design a 22.9 kV class HTS transformer. Before experiment, the composite insulation of two different type of HTS transformers are investigated. The first basic of investigation is a breakdown characteristic of liquid nitrogen and flashover characteristic on the GFRP surface under ac and impulse, The second investigation is insulation design, manufacture and test of model windings. These include a AC withstand voltage test of 50 kV rms and a lighting impulse test of 150 kV at peak.