• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.5
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• pp.977-982
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• 2017

Recently, the effort that converge the sport and ICT continues together with developing ICT. The game is a representative example. The digital sport is developing as mixed type that are composed with sport and digital such as screen golf and screen bowling. The addiction problem exist in the digital sport like the addiction problem exist in general sport. In this paper, we propose the addiction model in the digital sport as fractional-order. We represent time series and phase portrait for nonlinear behavior from proposed fractional-order model and we confirms the difference between them.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.35 no.11
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• pp.990-998
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• 2007

Adaptive output feedback control technique using Neural Networks(NN) is proposed for uncertain nonlinear Multi-Input Multi-Output(MIMO) systems. Modified Dynamic Inversion Model(MDIM) is introduced to decouple uncertain nonlinearities from inversion-based control input. MDIM consists of approximated dynamic inversion model and inversion model error. One NN is applied to compensate the MDIM of the system. The output of the NN augments the tracking controller which is based upon a filtered error approximation with online weight adaptation laws which are derived from Lyapunov's direct method to guarantee tracking performance and ultimate boundedness. Several numerical results are illustrated in the simulation of Van der Pol system and unmanned helicopter with model uncertainties.

• Journal of the Korea Concrete Institute
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• v.21 no.5
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• pp.649-660
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• 2009

A nonlinear column model of an internally confined hollow (ICH) reinforced concrete (RC) column was suggested and a parametric study was performed. The suggested column model considered the confining effect and the material nonlinearity of concrete. To verify the suggested column model, its analysis results were compared with the test results from previous researchers and a quasi static test performed in this study. They showed that the suggested column model was reasonable and had acceptable accuracy. The results from parametric studies showed that the thickness of the internal tube, concrete strength, and the hollow ratio of the ICH RC column affected its behavior.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.976-987
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• 1998

In this paper, the effect of nonllinear distortion, caused by a high-power amplifier(HPA) in an orthogonal frequency division multiplexing (OFDM) system, on the receiver part is analyzed. Since the HPA, which can be modeled by a memeoryless Volterra system, distorts OFDM signals in a nonlinear fashion, the received signal at each subchannel includes the multiplicative distortion of 1-st order as well as additive nonlinear distortion of high-order. the nonlinear distortion can be viewed as a nonlinear interchannel interference (NICI) since it consists of harmonic distortions and intermodulation distortions, produced by oother subchannels affecting the subchannel of interest. In this paper, we analytically derive the variance of NICI in terms of average input power using the volterra model for HPA, and then calculate the bit-effor rate(BER) performance of an OFDM system. Also, we propose a simple method to compensate for the phase distortion in OFDM system amplified by HPA, OFDM system employing 16-QAM constellation input.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.37 no.1
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• pp.32-42
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• 2000

This paper presents an on-line learning algorithm for sequential construction of generalized radial basis function networks (GRBFNs) to model nonlinear systems from empirical data. The GRBFN, an extended from of standard radial basis function (RBF) networks with constant weights, is an architecture capable of representing nonlinear systems by smoothly integrating local linear models. The proposed learning algorithm has a two-stage learning scheme that performs both structure learning and parameter learning. The structure learning stage constructs the GRBFN model using two construction criteria, based on both training error criterion and Mahalanobis distance criterion, to assign new hidden units and the linear local models for given empirical training data. In the parameter learning stage the network parameters are updated using the gradient descent rule. To evaluate the modeling performance of the proposed algorithm, simulations and their results applied to two well-known benchmarks are discussed.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.87-91
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• 2005

The cascade-correlation (CC) learning algorithm of Fahlman and Lebiere is one of the most influential constructive algorithm in a neural network. Cascading the hidden neurons results in a network that can represent very strong nonlinearities. Although this power is in principle useful, it can be a disadvantage if such strong nonlinearity is not required to solve the problem. 3 models are presented and compared empirically. All of them are based on valiants of the cascade architecture and output neurons weights training of the CC algorithm. Empirical results indicate the followings： (1) In the pattern classification, the model that train only new hidden neuron to output layer connection weights shows the best predictive ability; (2) In the function approximation, the model that removed input-output connection and used sigmoid-linear activation function is better predictability than CasCor algorithm.

• Nuclear Engineering and Technology
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• v.27 no.3
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• pp.336-343
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• 1995

The Self Tuning Regulator(STR) method which is an approach of adaptive control theory, is ap-plied to design the fully automatic power controller of the nonlinear reactor model. The adaptive control represent a proper approach to design the suboptimal controller for nonlinear, time-varying stochastic systems. The control system is based on a third­order linear model with unknown, time-varying parameters. The updating of the parameter estimates is achieved by the recursive extended least square method with a variable forgetting factor. Based on the estimated parameters, the output (average coolant temperature) is predicted one-step ahead. And then, a weighted one-step ahead controller is designed so that the difference between the output and the desired output is minimized and the variation of the control rod position is small. Also, an integral action is added in order to remove the steady­state error. A nonlinear M plant model was used to simulate the proposed controller of reactor power which covers a wide operating range. From the simulation result, the performances of this controller for ramp input (increase or decrease) are proved to be successful. However, for step input this controller leaves something to be desired.

• Magazine of the Korea Concrete Institute
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• v.10 no.3
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• pp.175-183
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• 1998

고강도콘크리트의 역학적 특성은 그 압축강도의 증가 이외에도 여러 가지 변화를 갖게 된다. 본 연구에서는 이와 같은 여러 특성의 변화 중 철근과의 부착특성에 관한 해석적 접근을 통하여 고강도콘크리트부재의 부착설계를 위한 이론적인 접근을 시도하였다. 해석의 변수로는 콘크리트의 압축강도, 부착길이 및 피복두께 등 3가지의 변수를 선정하였으며, 해석의 목적은 본 연구에 앞서 실시된 실험의 결과를 예측할 수 있는 단순화된 모델을 개발하고 이를 이용하여 부착실험의 결과를 해석적으로 분석하도록 하였다. 이에 따라 사용된모델은 실험에서 사용한 보단부형 부착시험체의철근과 콘크리트 부착부분의 기하학적 형상을 비교적 실제와 유사하게 모델링시킨 2차원의 평면모델을 사용하였다. 본 연구의 주요결과를 살펴보면 고강도콘크리트의 부착강도는 콘크리트의 피복두께에는 선형으로 비례하게 되나 부착길에는 비례하지 않는 것으로 나타났다. 이와 같은 결고는 기존의 실험결과와도일치하고있으며, 그 원인은 콘크리트의 강성증가에 따라 하중단측에 응력이 집중됨으로써 보통강도콘크리트의 경우와 같이 응력의 균등한 배분을 기대할 수 없기 때문으로 나타났다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.231-236
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• 2009

본 연구에서는 집이나 사무실과 같은 일상 공간에서 발생할 수 있는 연간의 일상생활행위 (ADL: Activities of Daily Living) 들을 인지하는 분산 모델을 제시한다. 사용자의 환경 정보, 위치 정보 및 행위 정보를 간단한 센서들이 부착된 가정용 기기들과 가구, 식기들을 통해 무선 센서 네트워크를 통해 수집하며 분석한다. 하지만 이와 같은 다양한 기기의 활용과 충분히 분석되어지지 않은 데이터들은 본 논문에서 제시하는 일상 환경에서 고차원의 ADL 모델을 구축하기 어렵게 한다. 그러나 ADL들이 생성하는 센서 데이터들과 센서 데이터들의 순서들은 어떤 행위가, 이루어지고 있는지 인지할 수 있도록 도와준다. 따라서 이 센서 데이터들의 순서를 특정 행위 패턴을 분석하는 데 활용하고, 이를 통해 분산 선형 시간 추론 알고리즘을 제안한다. 이 알고리즘은 센서 네트워크와 같은 소규모 시스템에서 행위를 인지하는 데 적절하다.

• 한국해양학회지
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• v.27 no.1
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• pp.35-45
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• 1992

This paper is concerned with the development and application of a stochastic model for predicting E1 nino phenomenon. For this, first a general criterion for determining E1 nino phenomenon, including period and strength, which is based on partial sum of monthly sea surface temperatures (SST) anomalies, is proposed, Secondly, the annual fluctuations, periodicity and dependence of monthly mean of equatorial Pacific SST during the period 1951-1990 are analyzed. Based on these, time series nonlinear regression model for the prediction of SST have been derived. A statistical procedure for using the model to predict the SST have been derived. A statistical procedure for using the model to predict the SST level is also proposed.