• Journal of Korean Society for Quality Management
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• v.20 no.1
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• pp.59-67
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• 1992

This paper deals with preventive replacements models for the item whose failures are dependent on their wear level. When measuring the item wear level is very costly, it may be economical to use substitutive characteristics that are correlated with the item wear level and relatively inexpensive to measure. In this paper, replacement policies based on such substitutive characteristics are proposed. The optimal level of substitutive characteristic to replace the item, which minimizes total cost, is obtained. Some numerical examples are also given.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.51-54
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• 1990

A parametrization for a linear system is presented to design a direct model reference adaptive pole placement controler. This parametrized model is one of the structured nonminimal models. The exponentially weighted least-squres algorithm is employed to estimate the control parameters. The direct adaptive controller has the exponential weighting properties by the proposed method of selecting the characteristic polynomials of the sensitivity function filters in connection with the reference models.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.16 no.5
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• pp.404-408
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• 2003

The characterization for several multi-layer embedded inductors with different structures was investigated. The optimized equivalent circuit models for several test structures were obtained from HSPICE. Building blocks are modeled using Partial element equivalent circuit method. The mean and the standard deviation of model parameters were extracted and predictive modeling was performed on different test structure. From this study, the characteristic of multi-layer inductors can be predicted.

• Proceedings of the KSPS conference
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• 2003.05a
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• pp.183-186
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• 2003

Speech recognition performance depends on various factors. One of the factors is the characteristic of a microphone which is used when speech data is collected. Thus, in the present experiment speech databases for tests are created through varying types of microphones. Then, acoustic models are built based on these databases, and each of the acoustic models is assessed by the data to determine recognition performance depending on various microphones.

• Imaging Science in Dentistry
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• v.42 no.3
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• pp.175-182
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• 2012

Purpose: The aim of this study was to provide sex-matched three-dimensional (3D) statistical shape models of the mandible, which would provide cephalometric parameters for 3D treatment planning and cephalometric measurements in orthognathic surgery. Materials and Methods: The subjects used to create the 3D shape models of the mandible included 23 males and 23 females. The mandibles were segmented semi-automatically from 3D facial CT images. Each individual mandible shape was reconstructed as a 3D surface model, which was parameterized to establish correspondence between different individual surfaces. The principal component analysis (PCA) applied to all mandible shapes produced a mean model and characteristic models of variation. The cephalometric parameters were measured directly from the mean models to evaluate the 3D shape models. The means of the measured parameters were compared with those from other conventional studies. The male and female 3D statistical mean models were developed from 23 individual mandibles, respectively. Results: The male and female characteristic shapes of variation produced by PCA showed a large variability included in the individual mandibles. The cephalometric measurements from the developed models were very close to those from some conventional studies. Conclusion: We described the construction of 3D mandibular shape models and presented the application of the 3D mandibular template in cephalometric measurements. Optimal reference models determined from variations produced by PCA could be used for craniofacial patients with various types of skeletal shape.

• Asia-Pacific Journal of Business
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• v.13 no.2
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• pp.107-122
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• 2022

Purpose - This study aims to develop machine learning models to predict administrative issue designation in KOSDAQ Market using financial data. Design/methodology/approach - Employing four classification techniques including logistic regression, support vector machine, random forest, and gradient boosting to a matched sample of five hundred and thirty-six firms over an eight-year period, the authors develop prediction models and explore the practicality of the models. Findings - The resulting four binary selection models reveal overall satisfactory classification performance in terms of various measures including AUC (area under the receiver operating characteristic curve), accuracy, F1-score, and top quartile lift, while the ensemble models (random forest and gradienct boosting) outperform the others in terms of most measures. Research implications or Originality - Although the assessment of administrative issue potential of firms is critical information to investors and financial institutions, detailed empirical investigation has lagged behind. The current research fills this gap in the literature by proposing parsimonious prediction models based on a few financial variables and validating the applicability of the models.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4359-4372
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• 2022

Turbulent mixing in buoyant flows is an essential mechanism involved in many scenarios related to nuclear safety in nuclear power plants. Comprehensive understanding and accurate predictions of turbulent buoyant flows in the reactor are of crucial importance, due to the function of mitigating the potential detrimental consequences during postulated accidents. The present study uses URANS methodology to investigate the buoyancy-influenced flows in the reactor pressure vessel under the main steam line break accident scenarios. With a particular focus on the influence of turbulent heat flux closure models, various combinations of two turbulence models and three turbulent heat flux models are utilized for the numerical simulations of three ROCOM tests which have different characteristic features in terms of the flow rate and fluid density difference between loops. The simulation results are compared with experimental measurements of the so-called mixing scalar in the downcomer and at the core inlet. The study shows that the anisotropic turbulent heat flux models are able to improve the accuracy of the predictions under conditions of strong buoyancy whilst in the weak buoyancy case, a major role is played by the selected turbulence models with essentially a negligible influence of the turbulent heat flux closure models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.626-648
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• 2011

Energy consumption of wireless access networks is in permanent increase, which necessitates development of more energy-efficient network management approaches. Such management schemes must result with adaptation of network energy consumption in accordance with daily variations in user activity. In this paper, we consider possible energy savings of wireless local area networks (WLANs) through development of a few integer linear programming (ILP) models. Effectiveness of ILP models providing energy-efficient management of network resources have been tested on several WLAN instances of different sizes. To cope with the problem of high computational time characteristic for some ILP models, we further develop several heuristic algorithms that are based on greedy methods and local search. Although heuristics obtains somewhat higher results of energy consumption in comparison with the ones of corresponding ILP models, heuristic algorithms ensures minimization of network energy consumption in an amount of time that is acceptable for practical implementations. This confirms that network management algorithms will play a significant role in practical realization of future energy-efficient network management systems.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.77-81
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• 2020

In this paper, we create scaffold defect classification models using machine learning based data. We extract the characteristic from collected scaffold external images using USB camera. SVM, KNN, MLP algorithm of machine learning was using extracted features. Classification models of three type learned using train dataset. We created scaffold defect classification models using test dataset. We quantified the performance of defect classification models. We have confirmed that the SVM accuracy is 95%. So the best performance model is using SVM.

• BMB Reports
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• v.56 no.4
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• pp.225-233
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• 2023

Hepatocellular carcinoma (HCC) is a very common form of cancer worldwide and is often fatal. Although the histopathology of HCC is characterized by metabolic pathophysiology, fibrosis, and cirrhosis, the focus of treatment has been on eliminating HCC. Recently, three-dimensional (3D) multicellular hepatic spheroid (MCHS) models have provided a) new therapeutic strategies for progressive fibrotic liver diseases, such as antifibrotic and anti-inflammatory drugs, b) molecular targets, and c) treatments for metabolic dysregulation. MCHS models provide a potent anti-cancer tool because they can mimic a) tumor complexity and heterogeneity, b) the 3D context of tumor cells, and c) the gradients of physiological parameters that are characteristic of tumors in vivo. However, the information provided by an multicelluar tumor spheroid (MCTS) model must always be considered in the context of tumors in vivo. This mini-review summarizes what is known about tumor HCC heterogeneity and complexity and the advances provided by MCHS models for innovations in drug development to combat liver diseases.