• Earthquakes and Structures
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• 제14권2호
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• pp.143-153
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• 2018

Correlation among different factors must be considered for selection of influencing factors in safety monitoring of high dam including positive correlation of variables. Therefore, a new factor selection method was constructed based on Copula entropy and mutual information theory, which was deduced and optimized. Considering the small sample size in high dam monitoring and distribution of daily monitoring samples, a computing method that avoids causality of structure as much as possible is needed. The two-dimensional normal information diffusion and fuzzy reasoning of pattern recognition field are based on the weight theory, which avoids complicated causes of the studying structure. Hence, it is used to dam safety monitoring field and simplified, which increases sample information appropriately. Next, a complete system integrating high dam monitoring and uncertainty prediction method was established by combining Copula entropy theory and information diffusion theory. Finally, the proposed method was applied in seepage monitoring of Nuozhadu clay core-wall rockfill dam. Its selection of influencing factors and processing of sample data were compared with different models. Results demonstrated that the proposed method increases the prediction accuracy to some extent.

• 한국지하수토양환경학회지:지하수토양환경
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• 제16권6호
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• pp.1-9
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• 2011

A bibliographical review of Gy sampling theory for particulate materials was conducted to provide readers with useful means to reduce errors in soil contamination investigation. According to the Gy theory, the errors caused by the heterogeneous nature of soil include; the fundamental error (FE) caused by physical and chemical constitutional heterogeneity, the grouping and segregation error (GE) aroused from gravitational force, long-range heterogeneous fluctuation error ($CE_2$), the periodic heterogeneity fluctuation error ($CE_3$), and the materialization error (ME) generated during physical process of sample treatment. However, the accurate estimation of $CE_2$ and $CE_3$ cannot be estimated easily and only increasing sampling locations can reduce the magnitude of the errors. In addition, incremental sampling is the only method to reduce GE while grab sampling should be avoided as it introduces uncertainty and errors to the sampling process. Correct preparation and operation of sampling tools are important factors in reducing the incremental delimitation error (DE) and extraction error (EE) which are resulted from physical processes in the sampling. Therefore, Gy sampling theory can be used efficiently in planning a strategy for soil investigations of non-volatile and non-reactive samples.

• 대한환경공학회지
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• 제22권4호
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• pp.699-712
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• 2000

주암호는 광주 전남지역의 주민생활이나 공 농업활동에 기반이 되는 중요한 수자원의 역할을 하고 있으나, 주암호에 유입되는 오염물질로 말미암아 호수의 수질은 점점 악화되고 있는 실정이다. 따라서 주암호 수질개선 전략은 시급히 마련되어야 할 것이다. 호수의 수질개선 전략으로 하나가 아닌 다수의 대안이 개발될 경우에 대안별 평가 및 최적순위 결정을 위해서는 목표수질의 달성도, 사회 기술적 적용성 및 경제성 측면을 고려한 평가기준들이 선정된다. 그러나 평가기준들의 수가 많고 각 기준이 추구하는 목적이 서로 상충될 때 의사결정은 어려워진다. 특히, 각 평가기준에 대한 대안별 기준값이 불확실성을 내포하는 경우에는 의사결정이 더욱 곤란하게 된다. 이러한 불확실성의 정도가 크면 클수록 의사결정은 점점 어려워지는데, 각 기준값의 불확실성이 발생하는 주요 원인은 활용할 수 있는 정보의 부족, 미래 상황의 불확실 또는 전문가 지식의 한계 때문이다. 본 논문에서는 의사결정권자가 불확실성 하에서도 호수의 수질개선 대안들을 평가하는데 이용할 수 있는 퍼지 의사결정법이 보여질 것이다. 이 방법은 퍼지이론을 응용하여 대안별 평가기준 값의 불확실성 정도를 해석하고, 이를 그대로 의사결정 과정에 반영하기 때문에 불확실성을 고려하지 않는 방법들에 비해 합리적이고 현실성있는 최적의 수질관리 대안이 선정될 수 있다.

• Transactions on Control, Automation and Systems Engineering
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• 제2권1호
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• pp.1-6
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• 2000

This paper presents a control of active suspension for quarter car model with two degree of freedom by using H$\infty$ method. Absolute velocity of car body is measured for feedback. The system parameter variations are treated with multiplicative uncertainty model. Simulation results show that the H$\infty$ control provides good trade-off between ride quality, suspension packaging and road holding constraints. The experiment with a front wheel suspension system was done to verify the simulation results.

• Journal of Magnetics
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• 제19권1호
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• pp.78-83
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• 2014

This paper proposes an efficient reliability-based optimization method for designing a superconducting magnetic energy system in presence of uncertainty. To evaluate the probability of failure of constraints, samplingbased reliability analysis method is employed, where Monte Carlo simulation is incorporated into dynamic Kriging models. Its main feature is to drastically reduce the numbers of iterative designs and computer simulations during the optimization process without sacrificing the accuracy of reliability analysis. Through comparison with existing methods, the validity of the proposed method is examined with the TEAM Workshop Problem 22.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.384-387
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• 2003

Most of the real world phenomena change over time. The ability to represent and to reason geographic data becomes crucial. A large amount of non-standard applications are dealing with data characterized by spatial, temporal and/or uncertainty features. Non-standard data like spatial and temporal data have an inner complex structure requiring sophisticated data representation, and their operations necessitate sophisticated and efficient algorithms. Current GIS technology is inefficient to model and to handle complex geographic phenomena, which involve space, time and uncertainty dimensions. This paper concentrates on developing a fuzzy spatio-temporal data model based on fuzzy set theory and relational data models. Fuzzy spatio-temporal operators are also provided to support dynamic query.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제16권2호
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• pp.140-146
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• 2016

This paper proposes a control method of the Segway with unknown control coefficient and input saturation. To design a simple controller for the Segway with the model uncertainty, the prescribed performance function is used. Furthermore, an auxiliary variable is introduced to deal with unknown time-varying control coefficient and input saturation problem. Due to the auxiliary variable, function approximators are not used in this paper although all model uncertainties are unknown. Thus, the controller can be simple. From the Lyapunov stability theory, it is proved that all errors of the proposed control system remain within the prescribed performance bounds. Finally, the simulation results are presented to demonstrate the performance of the proposed scheme.

• International Journal of Contents
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• 제10권1호
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• pp.18-22
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• 2014

Hidden nodes have a key role in the information processing of feed-forward neural networks in which inputs are processed through a series of weighted sums and nonlinear activation functions. In order to understand the role of hidden nodes, we must analyze the effect of the nonlinear activation functions on the weighted sums to hidden nodes. In this paper, we focus on the effect of nonlinear functions in a viewpoint of information theory. Under the assumption that the nonlinear activation function can be approximated piece-wise linearly, we prove that the entropy of weighted sums to hidden nodes decreases after piece-wise linear functions. Therefore, we argue that the nonlinear activation function decreases the uncertainty among hidden nodes. Furthermore, the more the hidden nodes are saturated, the more the entropy of hidden nodes decreases. Based on this result, we can say that, after successful training of feed-forward neural networks, hidden nodes tend not to be in linear regions but to be in saturated regions of activation function with the effect of uncertainty reduction.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.548-552
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• 1994

The robust compensation controller, which has been proposed by one of the authors and is based on the fundamental principle of making the plant follow the reference model, consists of the reference model and the robust compensator. The reference model is constructed by using the nominal model of the plant and determines the input-output properties of the resultant system. The robust compensator is obtained as a solution of the mixed sensitivity problem in H infinity control theory. Therefore the resultant system is of low sensitivity and robust stability. In the case where uncertainty does not occur in the plant, the plant follows perfectly the reference model. Therefore, in the case where uncertainty occurs in the plant, we propose the system configuration which improves the following accuracy without replacing the 개bust compensator but by identifying, the plant and reconstructing the reference model.

• International Journal of Control, Automation, and Systems
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• 제4권4호
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• pp.495-505
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• 2006

A class of robust control for flexible joint manipulators with nonlinearity mismatched uncertainty is designed based on Lyapunov approach. The uncertainties are unknown but their values are within certain prescribed sets. No statistic information of the uncertainties is imposed. The control which utilizes state transformation via virtual control is proposed. The resulting robust control guarantees practical stability for the transformed system and later the stability for the original system is proved. The designed robust control is implemented by experiments in a 2-link flexible joint manipulator.