• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.5
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• pp.417-425
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• 2003

H.264, which is the latest video coding standard of both ITU-T(International Telecommunication Union-Telecommunication standardization sector) and MPEG(Moving Picture Experts Group), adopts new video coding tools such as variable block size motion estimation, multiple reference frames, quarter-pel motion estimation/compensation(ME/MC), 4${\times}$4 Integer DCT(Discrete Cosine Transform), and Rate-Distortion Optimization, etc. These new video coding tools provide good coding of efficiency compared with existing video coding standards as H.263, MPEG-4, etc. However, these new coding tools require the increase of encoder complexity. Therefore, in order to apply H.264 to many real applications, fast algorithms are required for H.264 coding tools. In this paper, when encoder MacroBlock(MB) mode is decided by rate-distortion optimization tool, fast mode decision algorithm by skipping variable block size ME/MC and spatial-predictive coding, which occupies most encoder complexity, is proposed. In terms of computational complexity, the proposed method runs about 4 times as far as JM(Joint Model) 42 encoder of H.264, while the PSNR(peak signal-to-noise ratio)s of the decoded images are maintained.

• Transactions of the Korean Society of Mechanical Engineers
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• v.15 no.2
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• pp.668-676
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• 1991

Turbulent flows around tube banks and in the diffuser were studied using a non-orthogonal boundary fitted coordinate system and the modified K-.epsilon. turbulence model. In these cases, many problems emerge which stem from the geometrical complexity of the flow domain and the physical complexity of turbulent flow itself. To treat the complex geometry, governing equations were reformulated in a non-orthogonal coordinate system with Cartesian velocity components and discretised by the finite volume method with a non-staggered variable arrangement. The modified K-.epsilon. model of Hanjalic and Launer was applied to solve above two cases under the condition of strong and mild pressure gradient. The results using the modified K-.epsilon. model results in both test cases.

• Journal of KSNVE
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• v.6 no.6
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• pp.791-800
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• 1996

Linear LQG controller has been investigated to control flexible link manipulators. The performance and complexity of these depend largely on the model upon which the controller is designed. In this study, the flexible modes of the link manipulator are considered to have uncertain parameters, which can be represented by random variable and these parameters are reflected on the weighting of performance. In this method, the exact modelling for the flexible modes is not necessary. The order of the resulting controller is much lower than the one based on a full model. Through numerical study, it is shown that the performance and the stability-robustness of the proposed controller reaches reasonably the one based on the full model.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.43-50
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• 1997

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. This makes it very dificult to apply conventional control methods to the combustion control of the refuse. So most of the refuse incineration plant are operated by operators. In this paper, an multi-variable fuzzy model predictive controller is proposed for the combustion control of the re:fuse. Adaptive network based fuzzy inference system is used for modeling of the refuse incineration plant and multi-variable fuzzy model predictive controller is designed based on the identified fuzzy model. And computer simulation was carried out to evaluate performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.20-23
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• 1997

Refuse incineration plant operations involve many kinds of uncertain factors, such as the variable physical properties of refuse as fuel and the complexity of the burning phenomenon. That makes it very difficult to apply conventional control methods to the combustion control of the refuse. In this paper, an adaptive fuzzy model predictive controller is proposed for the combustion control of the refuse. And computer simulation was carried out to evaluate performance of the proposed controller.

• Asia pacific journal of information systems
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• v.34 no.1
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• pp.209-225
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• 2024

This research aims to use machine learning technology in human resource management to predict employees' work-life balance. The study utilized a dataset from IBM Watson Analytics in the IBM Community for the machine learning analysis. Multinomial dependent variables concerning workers' work-life balance were examined, categorized into continuous and categorical types using the Generalized Linear Model. The complexity of assessing variable roles and their varied impact based on the type of model used was highlighted. The study's outcomes are academically and practically relevant, showcasing how machine learning can offer further understanding of psychological variables like work-life balance through analyzing employee profiles.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.1 s.139
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• pp.189-197
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• 2005

The purpose of this study was to find factors affecting sun protective behavior and intention to buy UV-protective clothing among Korean golfers. Health belief (HB) model and diffusion theory(DT) were used for the study. Dependent variable of HB model was sun protective behaviors (SPBs) and dependent variable of DT model was intention to buy (ITB) UV-protective clothing. Independent variables for HB model were cancer perception, perceived benefits, behavioral/psychological barriers and cues to actions, while independent variables of DT model were relative advantage, compatibility, complexity, friability, and observability, besides demographic variables. Perceived benefits and cues to action variables in addition to gender and age were significant determinants of SPB for Korean golfers. Also, relative advantage and compatibility. behavioral barriers and cues to action were significant in affecting intention to buy UV-protective clothes. Both HB model and extended DT model were useful to predict sun protective behavior of Korean golfers.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.301-309
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• 2012

Prior research has shown empirically that organizational and career commitment may also be highly compatible with each other. The purpose of this study is to examine the effects of social welfare's organizational structure variable on the dual commitment to the organization and career. Dual commitment is analyzed by using multiple regression analysis of parallel model. For the research, organizational structure was consisted of complexity, formalization and decentralization. The results showed that social worker have high correlation to organizational commitment and career commitment. Especially, decentralization and complexity have a positive effects on the dual commitment to organizational and career commitment. This study finally discusses theoretical implications for future study and practical implications for structure design strategies on the results.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.6A
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• pp.473-479
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• 2012

In this paper, blind equalization technique using Gaussian two-cluster model is proposed. The proposed approach, by modeling the received M-QAM signals as Gaussian distributed two-cluster, minimizes the computational complexity and enhances the reliability of the signal estimates. In addition, by using a nonlinear estimator with variable parameters to estimate the transmitted signal, and by selectively applying the reduced constellation and the original constellation when estimating the signals, the reliability of the signal estimation was further improved. As a result, the proposed approach has improved the performance while reducing the complexity of the equalizer. Through computer simulations for blind equalization of higher-order signals of 64-QAM, it was confirmed that the proposed method showed better performance than traditional approaches.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.10
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• pp.1299-1306
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• 2011

In this study, crack-growth model parameters subjected to variable amplitude loading are estimated in the form of a probability distribution using the method of Bayesian parameter estimation. Huang's model is employed to describe the retardation and acceleration of the crack growth during the loadings. The Markov Chain Monte Carlo (MCMC) method is used to obtain samples of the parameters following the probability distribution. As the conventional MCMC method often fails to converge to the equilibrium distribution because of the increased complexity of the model under variable amplitude loading, an improved MCMC method is introduced to overcome this shortcoming, in which a marginal (PDF) is employed as a proposal density function. The model parameters are estimated on the basis of the data from several test specimens subjected to constant amplitude loading. The prediction is then made under variable amplitude loading for the same specimen, and validated by the ground-truth data using the estimated parameters.