• Smart Structures and Systems
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• v.20 no.2
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• pp.207-217
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• 2017

Because of the inevitable uncertainties such as structural parameters, external excitations and measurement noises, the effects of uncertainties should be taken into consideration in structural damage detection. In this paper, two probabilistic structural damage detection approaches are proposed to account for the underlying uncertainties in structural parameters and external excitation. The first approach adopts the statistical moment-based structural damage detection (SMBDD) algorithm together with the sensitivity analysis of the damage vector to the uncertain parameters. The approach takes the advantage of the strength SMBDD, so it is robust to measurement noise. However, it requests the number of measured responses is not less than that of unknown structural parameters. To reduce the number of measurements requested by the SMBDD algorithm, another probabilistic structural damage detection approach is proposed. It is based on the integration of structural damage detection using temporal moments in each time segment of measured response time history with the sensitivity analysis of the damage vector to the uncertain parameters. In both approaches, probability distribution of damage vector is estimated from those of uncertain parameters based on stochastic finite element model updating and probabilistic propagation. By comparing the two probability distribution characteristics for the undamaged and damaged models, probability of damage existence and damage extent at structural element level can be detected. Some numerical examples are used to demonstrate the performances of the two proposed approaches, respectively.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.329-340
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• 2011

The probability concepts mainly used for rainfall or flood frequency analysis in water resources planning are the frequentist viewpoint that defines the probability as the limit of relative frequency, and the unknown parameters in probability model are considered as fixed constant numbers. Thus the probability is objective and the parameters have fixed values so that it is very difficult to specify probabilistically the uncertianty of these parameters. This study constructs the uncertainty evaluation model using Bayesian MCMC and Metropolis -Hastings algorithm for the uncertainty quantification of parameters of probability distribution in rainfall frequency analysis, and then from the application of Bayesian MCMC and Metropolis- Hastings algorithm, the statistical properties and uncertainty intervals of parameters of probability distribution can be quantified in the estimation of probability rainfall so that the basis for the framework configuration can be provided that can specify the uncertainty and risk in flood risk assessment and decision-making process.

• The Journal of Korea Robotics Society
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• v.17 no.1
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• pp.76-85
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• 2022

One of the most fundamental challenges when designing controllers for dynamic systems is the adjustment of controller parameters. Usually the system model is used to get the initial controller, but eventually the controller parameters must be manually adjusted in the real system to achieve the best performance. To avoid this manual tuning step, data-driven methods such as machine learning were used. Recently, reinforcement learning became one alternative of this problem to be considered as an agent learns policies in large state space with trial-and-error Markov Decision Process (MDP) which is widely used in the field of robotics. However, on initial training step, as an agent tries to explore to the new state space with random action and acts directly on the controller parameters in real systems, MDP can lead the system safety-critical system failures. Therefore, the issue of 'safe exploration' became important. In this paper we meet 'safe exploration' condition with Control Barrier Function (CBF) which converts direct constraints on the state space to the implicit constraint of the control inputs. Given an initial low-performance controller, it automatically optimizes the parameters of the control law while ensuring safety by the CBF so that the agent can learn how to predict and control unknown and often stochastic environments. Simulation results on a quadrotor UAV indicate that the proposed method can safely optimize controller parameters quickly and automatically.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1846-1850
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• 2005

This paper presents a constrained-based routing (CBR) algorithm called, Dynamic Possible Path per Link (D-PPL) routing algorithm, for MultiProtocol Label Switching (MPLS) networks. In MPLS on-line routing, future traffics are unknown and network resource is limited. Therefore many routing algorithms such as Minimum Hop Algorithm (MHA), Widest Shortest Path (WSP), Dynamic Link Weight (DLW), Minimum Interference Routing Algorithm (MIRA), Profiled-Based Routing (PBR), Possible Path per Link (PPL) and Residual bandwidth integrated - Possible Path per Link (R-PPL) are proposed in order to improve network throughput and reduce rejection probability. MIRA is the first algorithm that introduces interference level avoidance between source-destination node pairs by integrating topology information or address of source-destination node pairs into the routing calculation. From its results, MIRA improves lower rejection probability performance. Nevertheless, MIRA suffer from its high routing complexity which could be considered as NP-Complete problem. In PBR, complexity of on-line routing is reduced comparing to those of MIRA, because link weights are off-line calculated by statistical profile of history traffics. However, because of dynamic of traffic nature, PBR maybe unsuitable for MPLS on-line routing. Also, both PPL and R-PPL routing algorithm we formerly proposed, are algorithms that achieve reduction of interference level among source-destination node pairs, rejection probability and routing complexity. Again, those previously proposed algorithms do not take into account the dynamic nature of traffic load. In fact, future traffics are unknown, but, amount of previous traffic over link can be measured. Therefore, this is the motivation of our proposed algorithm, the D-PPL. The D-PPL algorithm is improved based on the R-PPL routing algorithm by integrating traffic-per-link parameters. The parameters are periodically updated and are dynamically changed depended on current incoming traffic. The D-PPL tries to reserve residual bandwidth to service future request by avoid routing through those high traffic-per-link parameters. We have developed extensive MATLAB simulator to evaluate performance of the D-PPL. From simulation results, the D-PPL improves performance of MPLS on-line routing in terms of rejection probability and total throughput.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.59-66
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• 2010

This paper is proposed an adaptive fuzzy bilinear synchronization design for uncertain $L\ddot{u}$ chaos system. It is assumed that the $L\ddot{u}$ chaos system has unknown parameters. First, The $L\ddot{u}$ chaos system can be reconstructed via TS fuzzy bilinear modeling. We design an adaptive fuzzy bilinear synchronization control scheme based on TS fuzzy bilinear $L\ddot{u}$ chaos system with uncertain parameters. Lyapunov theory is employed to guarantee the stability of error dynamic system between TS fuzzy bilinear $L\ddot{u}$ chaos system and the proposed slave system and to derive the adaptive laws for estimating unknown parameters. Simulation results is given to demonstrate the validity of our proposed synchronization scheme.

• Journal of Electrical Engineering and Technology
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• v.4 no.3
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• pp.365-369
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• 2009

Identification is considered to be among the main applications of inverse theory and its objective for a given physical system is to use data which is easily observable, to infer some of the geometric parameters which are not directly observable. In this paper, a parameter identification method using inverse problem methodology is proposed. The minimisation of the objective function with respect to the desired vector of design parameters is the most important procedure in solving the inverse problem. The conjugate gradient method is used to determine the unknown parameters, and Tikhonov's regularization method is then used to replace the original ill-posed problem with a well-posed problem. The simulation and experimental results are presented and compared.

• Journal of Power System Engineering
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• v.13 no.4
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• pp.68-73
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• 2009

The mathematical model has a different response character with the real system because this mathematical model has the modeling errors and the imprecise value of system's parameters. Therefore to find the value of system parameters as possible as near by real value in the model is necessary to design the controlled system. This study concern about the identification method to estimate the parameter for the magnetic bearing system with RCGA(Real Coded Genetic Algorithm). Firstly, we will get the mathematical model from the current amplifier circuit and the magnetic bearing system. Secondly we will get the step response data in this circuit and system. Finally, we will estimate the unknown parameter's value from the data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.6 s.177
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• pp.1593-1600
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• 2000

A method based on frequency domain approaches is presented for the nonlinear parameters identification of structure having nonlinear joints. The finite element model of linear substructure is us ed to calculating its frequency response functions needed in parameter identification process. This method is easily applicable to a complex real structure having nonlinear elements since it uses the frequency response function of finite element model. Since this method is performed in frequency domain, the number of equations required to identify the unknown parameters can be easily increased as many as it needed, just by not only varying excitation amplitude but also selecting excitation frequencies. The validity of this method is tested numerically and experimentally with a cantilever beam having the nonlinear element. It was verified through examples that the method is useful to identify the nonlinear parameters of a structure having arbitary nonlinear boundaries.

• The Transactions of the Korean Institute of Power Electronics
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• v.10 no.5
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• pp.468-480
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• 2005

The overall performance of AC servo system is greatly affected the uncertainties of unpredictable mechanical parameter variations and external load disturbances. To overcome this problem, it is necessary to know different parameters and load disturbances subjected to position/speed control. This paper proposes an on-line identification method of mechanical parameters/load disturbances for AC servo system using support vector regression(SVR). The experimental results demonstrate that the proposed SVR algorithm is appropriate for control of unknown servo systems even with time-varying/nonlinear parameters.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.05a
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• pp.194-197
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• 2004

In the tube hydroforming process, a tube is placed into the die cavity and the ends of the tube are sealed by fixing the axial cylinder piston into the ends. Then the tube is pressurized with a hydraulic fluid and simultaneously the axial cylinders move to feed the material into the expansion zone. Therefore, the quantitative relationship between process parameters such as internal pressure and feeding amount and hydroformabillity, is hard to establish because of their high complexity and many unknown factors. In this study, the empirical and the quantitative relationship between process parameters and hydroformabillity are analyzed by fuzzy rules. Fuzzy expert system is an advanced expert system which uses fuzzy rule and approximate reasoning. Many process parameters are converted to the quantitative relationship by use of approximate reasoning of fuzzy expert system. The comparison between experimentally measured hydroformabillity from hydroforming experiments and the predicted values by fuzzy expert system shows a good agreement.