• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.5-8
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• 2003

In this paper, we propose that the chaotic behavior analysis in the mobile robot of embedding Chua's equation with obstacle. In order to analysis of chaotic behavior in the mobile robot, we apply not only qualitative analysis such as time-series, embedding phase plane, but also quantitative analysis such as Lyapunov exponent in the mobile robot with obstacle. In the obstacle, we only assume that all obstacles in the chaos trajectory surface in which robot workspace has an unstable limit cycle with Van der Pol equation

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 추계학술대회 논문집 학회본부
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• pp.154-156
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• 1995

The effect of a periodic and a chaotic' behaviour in the Bonhoeffer-Van der Pol(BVP) oscillation of the nerve membrane driven by a periodic stimulating current $A_1=cos\;{\omega}\;t$ are investigated by numeric analysis and hardware Implementation. To control the chaotic motion, we are suggested by temperature parameter c, $c=c(1+\eta\;cos\;{\Omega}\;t)$ which the values of $\eta,\;Omega$ varied respectively. The feasibilities of chaotic and periodic phenomena were analysed by phase plane and time series.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.282-282
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• 2000

The analysis of the radiation effect on matter has been performed using stochastic methods. Recently, It was discovered that the detector pulses of radiation can be analysed using deterministic method that utilizes the chaotic behaviour with an attractor found in a noise region. We acquired a time series for pulse tram of Am-241 using scintillation detector and reconstructed a phase space, then performed new analysis for the radiation detection signal by applying embedding theory, Lyapunov exponent, correlation dimension, autocorrelation dimension, and power spectrum.

• 지능정보연구
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• 제4권1호
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• pp.133-147
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• 1998

Recently, many researchers have been involved in finding deterministic equations which can accurately predict future event, based on chaotic theory, or fractal theory. The theory says that some events which seem very random but internally deterministic can be accurately predicted by fractal equations. In contrast to the conventional methods, such as AR model, MA, model, or ARIMA model, the fractal equation attempts to discover a deterministic order inherent in time series data set. In discovering deterministic order, researchers have found that neural networks are much more effective than the conventional statistical models. Even though prediction accuracy of the network can be different depending on the topological structure and modification of the algorithms, many researchers asserted that the neural network systems outperforms other systems, because of non-linear behaviour of the network models, mechanisms of massive parallel processing, generalization capability based on adaptive learning. However, recent survey shows that prediction accuracy of the forecasting models can be determined by the model structure and data structures. In the experiments based on actual economic data sets, it was found that the prediction accuracy of the neural network model is similar to the performance level of the conventional forecasting model. Especially, for the data set which is deterministically chaotic, the AR model, a conventional statistical model, was not significantly different from the MLP model, a neural network model. This result shows that the forecasting model. This result shows that the forecasting model a, pp.opriate to a prediction task should be selected based on characteristics of the time series data set. Analysis of the characteristics of the data set was performed by fractal analysis, measurement of Hurst index, and measurement of Lyapunov exponents. As a conclusion, a significant difference was not found in forecasting future events for the time series data which is deterministically chaotic, between a conventional forecasting model and a typical neural network model.

• 디지털콘텐츠학회 논문지
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• 제18권1호
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• pp.217-224
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• 2017

최근에 카오스 이론을 사회과학의 한 분야의 사랑 모델에 적용하고자 하는 노력을 지속하고 있다. 로미오와 줄리엣으로 표현하는 미분 방정식에서 카오스 거동을 만들기 위해서 외력을 인가한다. 그러나 이 외력은 사람의 감정을 정확하게 표현하지 못하는 단점을 가진다. 본 논문에서는 이러한 단점을 해결하기 위하여, 로미오와 줄리엣의 사랑모델에서 외력을 사람의 말이나 행동에 가장 유사한 형태로 제공하기 위해 퍼지 소속 함수를 도입하고 이를 삼각 퍼지 소속 함수를 제시하였다. 또한 제시된 퍼지 소속 함수를 가진 로미오와 줄리엣의 사랑모델에서 카오스 거동을 확인하기 위하여, 시계열과 위상공간을 이용하였으며 이를 통하여 카오스 거동의 존재를 확인한다.

• Journal of Korean Neurosurgical Society
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• 제63권3호
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• pp.279-313
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• 2020

This review summarises the classification, anatomy and embryogenesis of complex spinal cord lipomas, and describes in some detail the new technique of total lipoma resection and radical reconstruction of the affected neural placode. Its specific mission is to tackle two main issues surrounding the management of complex dysraphic lipomas : whether total resection confers better long term benefits than partial resection, and whether total resection does better than conservative treatment, i.e., no surgery, for asymptomatic lipomas. Accordingly, the 24 years progression-free survival data of the author and colleagues' series of over 350 cases of total resection are compared with historical data from multiple series (including our own) of partial resection, and total resection data specifically for asymptomatic lesions are compared with the two known series of non-surgical treatment of equivalent patients. These comparisons amply support the author's recommendation of total resection for most complex lipomas, with or without symptoms. The notable exception is the asymptomatic chaotic lipoma, whose peculiar anatomical relationship with the neural tissue defies even our aggressive surgical approach, and consequently projects worse results (admittedly of small number of cases) than for the other two lipoma subtypes of dorsal and transitional lesions. Prophylactic resection of asymptomatic chaotic lipomas is therefore not currently endorsed.

• 한국수자원학회논문집
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• 제39권3호
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• pp.253-259
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• 2006

기존의 거의 모든 수문학적 연구에 있어서, 시스템의 특성을 파악한 뒤 예측을 실시하는 표준접근법이 채택되어왔다. 그러나 최근 들어 시스템의 특성분석에 앞서 예측을 실시하고, 상태공간 매개변수가 시스템의 특성분석단계가 아닌 예측단계에서 평가되는 가역접근법이 제안되었다. 본 연구에서는 최근에 제안된 가역접근법과 기존에 널리 적용되어온 표준접근법을 이론적 카오스 시계열과 Idaho주 Bear강의 일유출량 자료에 적용함으로써, 가역접근법의 적용성을 검토하고 카오스 시계열의 특성을 알아보았으며, 카오스이론이 적용된 비선형 예측기법으로는 부분근사화 기법을 이용하였다. 카오스 특성 분석을 통해, 이론적 카오스 시계열과 Idaho주 Bear강의 일유출량 시계열 자료 모두에서 카오스 특성이 나타남을 알 수 있었다. 200일에 대한 1, 3, 5일 예측 결과, 가역접근법이 표준접근법에 비해 우수함을 알 수 있었다.

• 한국습지학회지
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• 제21권3호
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• pp.236-243
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• 2019

현재까지 많은 수문 시계열은 전통적인 선형 모형을 이용하여 분석되고 예측되어 왔다. 하지만, 자연현상과 수문시계열의 패턴 및 변동과 관련하여 비선형적 구조의 증거가 발견되고 있다. 따라서 시계열 분석 및 예측을 위한 기존의 선형 모형은 비선형적 특성에 적합하지 않을 수 있다. 본 연구에서는 미국 플로리다 코코아 지역 인근에 있는 St.Johns 강의 일유량 자료에 대한 카오스 분석을 수행하였고, 그 결과 낮은 차원의 비선형 동역학적 특성을 가진 흥미로운 결과가 나타났지만 한국의 소양강댐 일유량 자료는 확률적 특성을 보여주었다. 카오스 특성을 토대로한 DVS(결정론적 vs 추계학적) 알고리즘을 이용해 두 시계열 시스템의 특성을 파악하였고 단기 예측을 수행하였다. 또한 본 연구에서는 일 유량 시계열 예측을 위해 인공신경망 방법을 사용하였고, DVS 알고리즘에 의한 예측을 비교 분석하였다. 분석 결과, 카오스 특성을 갖는 시계열 자료가 보다 정확한 예측성을 보였다.

• 한국항해학회지
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• 제24권3호
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• pp.147-155
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• 2000

In this paper, we propose the Chaos Fuzzy controller to analyze the chaotic character of time series obtained from the specific plant and to predict the short-term for power consumption of the plant using the Fuzzy controller. We compared the predicted data with the active ones and checked the error generated by them after we time series of supplied power to the proposed controller. As a result of the simulation, we obtained a admirable consequence that the proposed controller can be advanced through various and accurate data acquisition, and continuous analysis of the resident and industrial environment.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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• pp.168-175
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• 1999

In this paper, a modified RBF(Radial Basis Function) network structure is suggested for the prediction of a time-series with non-linear, non-stationary characteristics. Coventional RBF network predicting time series by using past outputs sense the trajectory of the time series and react when there exists strong relation between input and hidden activation function's RBF center. But this response is highly sensitive to level and trend of time serieses. In order to overcome such dependencies, hidden activation functions are modified to react to the increments of input variable and multiplied by increment(or dectement) for prediction. When the suggested structure is applied to prediction of Macyey-Glass chaotic time series, Lorenz equation, and Rossler equation, improved performances are obtained.