• Journal of Advanced Marine Engineering and Technology
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• v.21 no.5
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• pp.571-578
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• 1997

This paper presents a method for designing a control system to stand upright inverted triangle. A linearized model is obtained form the nonlinear system by Taylor series expansion and a state controller is designed based on the model. After implementing the control system which is combined control law and estimator with reference input, experiments are carried out to stand upright inverted triangle at any angluar position.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.873-876
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• 2002

This paper presents the effect of internal corrosion, external corrosion, material properties, operation condition, earthquake, traffic load and design thickness in pipeline on the failure prediction using a failure probability model. A nonlinear corrosion is used to represent the loss of pipe wall thickness with time. The effects of environmental, operational, and design random variables such as a pipe diameter, earthquake, fluid pressure, a corrosion rate, a material yield stress and a pipe thickness on the failure probability are systematically investigated using a failure probability model for the corrosion pipeline.

• Journal of Ocean Engineering and Technology
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• v.5 no.2
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• pp.51-57
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• 1991

불규칙 해상에서 선박의 큰 횡요각의 예측이 중요한 과제로 대두 되고 있다. 본 논문에서는 통계적 해석에 의한 이의 예측 방법을 제시한다. 즉 주어진 비 선형 횡요운동 방정식으로 부터 배의 횡요각과 각속도의 결합 확률 밀도 함수를 구하는 방법을 도입하고 각종 계수들의 값의 변화에 따른 예측 결과를 다른 논문에서 제시한 시뮬레이션 결과와 비교하였다.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.448-449
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• 2008

본 논문에서는 이동 로봇의 경로 추종을 위해서, 비선형 모델 예측 슬라이딩 모드 제어(nonlinear model predictive strung mode control) 기법을 제안한다. 본 논문에서 제안한 방법에서는 미래의 슬라이딩 평면을 예측하고, 이에 따른 최적화된 제어기를 유도함으로써 슬라이딩 모드 제어기 단독으로 사용하는 제언 시스템에 비해 성능을 향상시킬 수 있다. 마지막으로 컴퓨터 시뮬레이션을 통해 본 논문에서 제안한 제어기의 성능을 검증하고자한다.

• Journal of Korean Society for Atmospheric Environment
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• v.7 no.2
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• pp.127-136
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• 1991

This paper is concerned with the development and application of a stochastic model for daily sulphur dioxide $(SO_2)$ concentrations in urban area (Seoul). For this, the characteristics of the regression trend, periodicity and dependence of the daily $SO_2$ concentration are investigated by a statistisical analysis of the daily average $SO_2$ values measured in Seoul area during 1989 $\sim$ 1990. Based on these, nonlinear regression time series model for the prediction of daily $SO_2$ concentrations is derived. A statistical procedure for using the model to predict the concentration level is also proposed.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.198-201
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• 2006

In order to evaluate the seismic performance of reinforced concrete bridge piers, an inelastic time-dependent element is proposed. The proposed element enables increased characteristics due to structural intervention (i.e., repair and retrofitting) to be accurately reflected to the degraded strength and stiffness of the members. Comparative studies are conducted for reinforced concrete bridge columns being repaired and retrofitted and show good correlation between analytical prediction and experimental results. In addition, a nonlinear time-history analysis of a reinforced concrete bridge under multiple earthquakes confirms the applicability and effectiveness of the present development.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.999-1005
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• 2011

This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.89.3-89
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• 2002

Plasma processing plays a crucial role in fabricating integrated circuits (ICs). Manufacturing ICs in a cost effective way, it is increasingly demanded a computer model that predicts plasma properties to unknown process inputs. Physical models are limited in the prediction accuracy since they are subject to many assumptions. Expensive computation time is another hindrance that prevents their widespread used in manufacturing site. To circumvent these difficulties inherent in physical models, neural networks have been used to learn nonlinear plasma data. A generalized regression neural network (GRNN) [I] is one of the architectures that have been widely used to analyze complex chemical data. I...

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.46.2-46
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• 2002

Plasma processing plays a crucial role in fabricating integrated circuits (ICs). Manufacturing ICs in a cost effective way, it is increasingly demanded a computer model that predicts plasma properties to unknown process inputs. Physical models are limited in the prediction accuracy since they are subject to many assumptions. Expensive computation time is another hindrance that prevents their widespread used in manufacturing site. To circumvent these difficulties inherent in physical models, neural networks have been used to learn nonlinear plasma data [1]. Among many types of networks, a backpropagation neural network (BPNN) is the most widely used architecture. Many training variables are...

• Proceedings of the Korean Reliability Society Conference
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• 2002.06a
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• pp.403-410
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• 2002

This paper presents the effect of internal corrosion, external corrosion, material properties, operation condition, earthquake, traffic load and design thickness in pipeline on the failure prediction using a failure probability model. A nonlinear corrosion is used to represent the loss of pipe wall thickness with time. The effects of environmental, operational, and design random variables such as a pipe diameter, earthquake, fluid pressure, a corrosion rate, a material yield stress and a pipe thickness on the failure probability are systematically investigated using a failure probability model for the corrosion pipeline.