• Journal of muscle and joint health
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• v.20 no.3
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• pp.235-246
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• 2013

Purpose: This study systematically reviewed the research literature on nurse job satisfaction to suggest directions for further research. Methods: A total of 98 articles published in the journals registered in National Research Foundation of Korea (NRF) from January 2000 to August 2013 were included for the final analysis. Results: The number of published papers related to nurse job satisfaction has increased annually. The most frequently used instrument to measure nurse job satisfaction was 'The Index of Work Satisfaction'developed by Slavitt et al. (1978). 'Personal Perception/Attitude' was the strongest predictor influencing on nurse job satisfaction (41 times, 38.0%). Organizational commitment, turnover intention, nursing performance, job stress, and organizational performance were commonly used as outcome variables related to nurse job satisfaction. Conclusion: Further research is needed to analyze diverse influential factors on nurse job satisfaction. In addition, it is also needed to develop an intervention program which can improve nurse job satisfaction.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.7
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• pp.819-825
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• 1988

An 1-bit DPCM image coding method is presented. Our method is specially designed to reduce the slope overload which seems to be the major performance degradation factor in 1-bit DPCM. In the present algorithm, based on the classification of neighborhoods by its flatness, slope strength and direction, predictor and quantizer operate adaptively through switching action. Compared with some other methods by computer simulation, proposed method shows improved performance in image quality as well as in signal to noise ratio. This gain mainly comes from the reduced slope overload and seems large to compensate the increased complexity in prediction. As a post processing, Lee filter is used to reduce the granular noise subjectively annoying in flat region.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.1115-1124
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• 2005

The usual distinction made in the literature is between decision facilitating and decision influencing. Because time only runs one way, both uses of cost information are problematic. In the first instance, a cost description (measurement or estimate) is provided to decision makers before a decision is made. Unfortunately, costs can only be measured after the fact. This means that cost analysts must estimate the costs of the alternatives under consideration. In some cases, measured cost is a reliable predictor of future costs; in other cases it is not. Regardless of its reliability, however, it is all we have or can have. Consequently, managers must be informed as to how their performance will be measured and how measured performance will affect outcomes they care about -- promotion, pay, esteem, etc. I will outline the basic approaches to cost measurement and then show how cost measures can be used to facilitate and to influence decisions of the railway industry.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.2
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• pp.49-55
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• 2008

To address the convergence issue of power control algorithms, a number of algorithms have been developed hat shape the dynamics of up-link power control for cellular network. Power algorithms based on fixed point iterations can be accelerated by the use of various methods, one of the simplest being the use of Newton iterations, however, this method has the disadvantage which not only needs derivatives of the cost function but also may be weak to noisy environment. we showed performance of the power control schemes to solve the fixed point problem under static or stationary channel. They proved goof performance to solve the fixed point problem due to their predictor based optimal control and quadratic convergence rate. Here, we apply the proposed power control schemes to the problem of the dynamic channel or to dynamic time varying link gains. The rigorous simulation results demonstrated the validity of our approach.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.10-14
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• 2004

We present a predictive control algorithm combined with the robust robot control that is constructed on the Lyapunov min-max approach. Since the control design of a real manipulator system may often be made on the basis of the imperfect knowledge about the model, it is an important trend to design a robust control law that guarantees the desired properties of the manipulator under uncertain elements. In the preceding robust control work, we need to tune several control parameters in the admissible set where the desired stability can be achieved. By introducing an optimal predictive control technique in robust control we can find out much more deterministic controller for both the stability and the performance of manipulators. A new class of robust control combined with an optimal predictive control is constructed. We apply it to a simple type of 2-link robot manipulator and show that a desired performance can be achieved through the computer simulation.

• Journal of information and communication convergence engineering
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• v.8 no.6
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• pp.640-644
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• 2010

When we talk with hands-free in a car or noisy lobby, the performance of the echo canceller degrade because background noise added to echo caused by the distance from mouth to microphone is relatively long. It gives a reason for necessity of noise-robust and high convergence speed adaptive algorithm. And if acoustic echo canceller operated not perfectly, residual signal going through the echo canceller to far-end speaker remains residual echo, which degrade quality of talk. To solve this problem, post-processing needed to remove residual echo ones more. In this paper, we propose a new acoustic echo canceller, which has noise robust and high convergence speed, linked with linear predictor as a post-processor. By computer simulation, it is confirmed that the proposed algorithm shows better performance from acoustic interference cancellation (AIC) viewpoint.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2690-2692
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• 2002

This paper describes a technique for human sensibility evaluation using 10-channel EEG(electroencephalogram). The proposed method uses the linear predictor coefficients as EEG feature parameters and a neural network as sensibility pattern classifier. For subject independent system, multiple templates are stored and the most similar template can be selected. EEG signals corresponding to 4 emotions such as, relaxation, joy, sadness and anger are collected from 5 armature performers. The states of relaxation and joy are considered as positive sensibility and those of sadness and anger as negative. The classification performance using the proposed method is about 72.6%. This will be promising performance in the human sensibility evaluation.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.8 no.1
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• pp.65-75
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• 1996

A pilot plant, which is simplified the hot water heating control system of a large scale residential building, is used to investigate the effects of control methods and operating conditions on the system performance and to compare control characteristics. Digital variable structure controller(DVSC) and digital PI controller are implemented to control the speed of the circulating pump for the pilot plant using PC. For the DVSC, a control algorithm is suggested, which using a nonlinear sliding surface and a PID sliding surface outside and inside of output error boundary layer, respectively. Smith predictor algorithm is used for the compensation of long dead time. The suggested DVSC yields improved control performance compared with existing DVSC using linear sliding surface only. the system responses with the suggested DVSC shows good responses without overshoot for various operating conditions and robust under external disturbances compared with digital PI controller.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.65-75
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• 2004

This paper presents a generalized predictive control method based on a fuzzy neural network(FNN) model, which uses the on-line multi-step prediction, fur the intelligent control of chaotic nonlinear systems whose mathematical models are unknown. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of FNN are determined adaptively during the operation of the system. In order to design a generalized predictive controller effectively, this paper describes computing procedure for each of the two important parameters. Also, we introduce a projection matrix to determine the control input, which deceases the control performance function very rapidly. Finally, in order to evaluate the performance of our controller, the proposed method is applied to the Doffing and Henon systems, which are two representative continuous-time and discrete-time chaotic nonlinear systems, res reactively.

• Journal of information and communication convergence engineering
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• v.11 no.2
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• pp.118-123
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• 2013

This paper proposes an efficient hardware architecture for high efficiency video coding (HEVC), which is the next generation video compression standard. It adopts several new coding techniques to reduce the bit rate by about 50% compared with the previous one. Unlike the previous H.264/AVC 6-tap interpolation filter, in HEVC, a one-dimensional seven-tap and eight-tap filter is adopted for luma interpolation, but it also increases the complexity and gate area in hardware implementation. In this paper, we propose a parallel architecture to boost the interpolation performance, achieving a luma $4{\times}4$ block interpolation in 2-4 cycles. The proposed architecture contains shared operations reducing the gate count increased due to the parallel architecture. This makes the area efficiency better than the previous design, in the best case, with the performance improved by about 75.15%. It is synthesized with the MagnaChip $0.18{\mu}m$ library and can reach the maximum frequency of 200 MHz.