• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.525-530
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• 2003

This paper focuses on design of nonlinear power plant controller using immune based multiobjective fuzzy approach. The thermal power plant is typically regulated by the fuel flow rate, the spray flow rate, and the gas recirculation flow rate. However, Strictly maintaining the steam temperature can be difficult due to heating value variation to the fuel source, time delay changes in the main steam temperature. the change of the dynamic characteristics in the steam-turbine system. Up to the present time, PID Controller has been used to operate this system. However, it is very difficult to achieve an optimal PID gain with no experience, since the gain of the PID controller has to be manually tuned by trial and error. These parameters tuned by multiobjective based on immune network algorithms could be used for the tuning of nonlinear power plant.

• ETRI Journal
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• v.34 no.4
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• pp.492-502
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• 2012

We consider an overlaid broadcast service, where a spread spectrum (SS)-based broadcast signal is overlaid onto the existing terrestrial Digital Multimedia Broadcasting (T-DMB) band. The system is similar to the augmented data transmission in the ATSC DTV, for which it was investigated mostly in terms of link level performance, such as bit error rate. Our focus in this paper is on the system-level performances. More specifically, utilizing both a large scale path loss and a small scale fading channel model, the primary objective is to explore the tradeoff between the coverage and the achievable rate of the overlaid service and, finally, to determine the achievable rate in the overlaid service for marginal coverage reduction in the existing broadcast service. The analytical and simulation results show that an SS-based add-on service of 10 kbps to 20 kbps can co-exist with the T-DMB service while resulting in only a marginal degradation in T-DMB coverage (for example, less than one percent reduction).

• JSTS:Journal of Semiconductor Technology and Science
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• v.13 no.6
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• pp.608-616
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• 2013

This paper presents an adaptive sampling method for electrocardiogram (ECG) signal detection. First, by employing the strings matching process with compression dictionary, we recognize each segment of ECG with different characteristics. Then, based on the non-uniform sampling strategy, the sampling rate is determined adaptively. As the results of simulation indicated, our approach reconstructed the ECG signal at an optimized sampling rate with the guarantee of ECG integrity. Compared with the existing adaptive sampling technique, our approach acquires an ECG signal at a 30% lower sampling rate. Finally, the experiment exhibits its superiority in terms of energy efficiency and memory capacity performance.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2003.04a
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• pp.302-305
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• 2003

The purpose of this study was to assess the effect of surfactant on the rate of NAPLs(non-aqueous phase liquids) solubilization. The experimental variables were surfactant type, NAPLs type and water type. The main experimental designs were consists of two phases. The solubilization rate is sensitive to surfactant type based on this test. Used aqueous surfactants were solubilized and removed 72.77 to 89.90% of toluene, PCE(tetrachloroethylene) from the contaminated water during the test, respectively. T60 has higher and stable recovery ratio than SDS in surfactant type but, the micelle of the T60 is more weaker than that of SDS based on this study's results. And the solubilization rate in used water type was almost same.(deionized water, surface water).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.9
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• pp.547-553
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• 2003

In this paper, we designed the PC-based analyzer of the power spectral density that could estimate the heart rate variability from time series data of R-R interval. The power spectral density estimated that it applied the autoregressive model to the measured electrocardiogram during a short period. Also, the characteristics of the designed analyzer are that it could process of the signal filtering, the generation and recomposition of time series and the feature extraction at the same time. Especially the analyzer reconstructed which applied the lowpass filter of the time series composed by the linear interpolation so as to enhance the signal-to-noise feature. We could estimate the power spectral density that confirmed a variety of power peak with low frequency range and high frequency rang of autonomic nerve by the heart rate variability.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.21 no.1
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• pp.1-10
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• 2015

Enzyme kinetics-based respiration model can be effectively used for estimating respiration rate in $O_2$ consumption and $CO_2$ production of fresh produce as a function of $O_2$ and $CO_2$ concentrations. Arrhenius equation can be applied to describe the temperature dependence of the respiration rate. Parameters of enzyme kinetics-based respiration model and activation energy of Arrhenius equation were compiled from analysis of literature data and closed system experiment. They enable to estimate the respiration rate for any modified atmosphere conditions at temperature of interest and thus can be used for design of modified atmosphere packaging of fresh produce.

• Proceedings of the IEEK Conference
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• 2002.06a
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• pp.405-408
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• 2002

To support Internet traffic efficiently over ATM \networks, Guaranteed Frame Rate(GFR) has been proposed in tile ATM Forum to ensure minimum rate guarantees to VCs. In this paper, we proposed fuzzy logic based buffer management algorithm that provides MCR guarantee and fair sharing to GFR VCs. A key feature of proposed algorithm is its ability to accept or drop a new incoming packet dynamically based on buffer condition and load rate of VCs. This is achieved by using fuzzy logic controller for the production of a drop factor. Simulation result shows that proposed scheme significantly has improved fairness and TCP throughput compared with previous schemes.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.11
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• pp.476-484
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• 2006

The failure prediction and preventive maintenance for the equipment of nuclear power plant area using reliability-centered maintenance have been grown. On the other hand, the maintenance for power distribution system consists of time-based maintenance mainly. In this paper, the new maintenance algorithms for power distribution system are developed considering reliability indices. First of all, Time-varying failure rates are extracted from data accumulated at KEPCO using exponential distribution function and weibull distribution function. Next, based on the extracted failure rate, reliability for real power distribution system is evaluated for applying the effective maintenance algorithm which is the analytic method deciding the maintenance point of time and searching the feeder affecting the specific customer. Also the algorithm deciding the maintenance priority order are presented based on sensitivity analysis and equipment investment plan are analyzed through the presented algorithm at real power distribution system.

• Journal of information and communication convergence engineering
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• v.3 no.1
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• pp.56-61
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• 2005

In this paper, we present a video rate control scheme for storage and delivery in which the time-varying viewing interests are controlled by human gaze. To track the gaze, the pupil's movement is detected using the three-step process : detecting face region, eye region, and pupil point. To control bit rates, the quantization parameter (QP) is changed by considering the static parameters, the video object priority derived from the pupil tracking, the target PSNR, and the weighted distortion value of the coder. As results, we achieved human interfaced visual model and corresponding region-of-interest rate control system.

• Journal of information and communication convergence engineering
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• v.17 no.1
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• pp.41-48
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• 2019

In recent years, sentiment analysis research has become popular. The research results of sentiment analysis have achieved remarkable results in practical applications, such as in Amazon's book recommendation system and the North American movie box office evaluation system. Analyzing big data based on user preferences and evaluations and recommending hot-selling books and hot-rated movies to users in a targeted manner greatly improve book sales and attendance rate in movies [1, 2]. However, traditional machine learning-based sentiment analysis methods such as the Classification and Regression Tree (CART), Support Vector Machine (SVM), and k-nearest neighbor classification (kNN) had performed poorly in accuracy. In this paper, an improved kNN classification method is proposed. Through the improved method and normalizing of data, the purpose of improving accuracy is achieved. Subsequently, the three classification algorithms and the improved algorithm were compared based on experimental data. Experiments show that the improved method performs best in the kNN classification method, with an accuracy rate of 11.5% and a precision rate of 20.3%.