• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.3
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• pp.520-528
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• 2001

This paper investigates a resonant microaccelerometer that measures acceleration using a built-in micromechanical resonator, whose resonant frequency is changed by the acceleration-induced axial force. A set of design equations for the resonant microaccelerometer has been developed, including analytic formulae for resonant frequency, sensitivity, nonlinearity and maximum stress. On this basis, the sizes of the accelerometer are designed for the sensitivity of 10$^3$Hz/g in the detection range of 5g, while satisfying the conditions for the maximum nonlinearity of 5%, the minimum shock endurance of 100g and the size constraints placed by microfabrication process. A set of the resonant accelerometers has been fabricated by the combined use of bulk-micromachining and surface-micromachining techniques. From a static test of the cantilever beam resonant accelerometer, a frequency shift of 860Hz has been measured for the proof-mass deflection of 4.3${\pm}$0.5$\mu\textrm{m}$; thereby resulting in the detection sensitivity of 1.10${\times}$10$^3$Hz/g. Uncertainty analysis of the resonant frequency output has been performed to identify important issues involved in the design, fabrication and testing of the resonant accelerometer.

• International Journal of Precision Engineering and Manufacturing
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• v.5 no.4
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• pp.61-68
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• 2004

Necessity to address engineering system uncertainties in design processes has long been acknowledged. To obtain quality of product, a safety factor is traditionally used by many design engineers due to its easy of use and comprehension. However, the safety factor approach often yields either conservative or unreliable designs, since it ignores the type of probability distribution and the mechanism of uncertainty propagation from the input to the output. For a consistent reliability-based design, two fundamental issues must be investigated thoroughly. First, the design-decision process that clearly identifies a mechanism of uncertainty propagation under system uncertainties needs to be developed, which must be an efficient and accurate process. To identify the mechanism more effectively, an adaptive probability analysis is proposed by adaptively setting probability levels through a posteriori error estimation. The second is to develop the design process that not only yields a high quality design but also a cost-effective optimum design from manufacturing point of view. As a result, a response surface methodology is specially developed for RBDO, thus enhancing numerical challenges of efficiency and complicatedness. Side crashworthiness application is used to demonstrate the integrated design process for product and manufacturing process design.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.3
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• pp.295-300
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• 1998

In this paper, we point out the possibility of the divergence of control input caused by the estimation error of delay-time when general iterative learning algorithms are applied to a class of linear dynamic systems with time-delay in which delay-time is not exactly measurable, and then propose a new type of iterative learning algorithm in order to solve this problem. To resolve the uncertainty of delay-time, we propose an algorithm using holding mechanism which has been used in digital control system and/or discrete-time control system. The control input is held as constant value during the time interval of which size is that of the delay-time uncertainty. The output of the system tracks a given desired trajectory at discrete points which are spaced auording to the size of uncertainty of delay-time with the robust property for estimation error of delay-time. Several numerical examples are given to illustrate the effeciency of the proposed algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.513-521
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• 2017

Robust optimal operation of a microgrid is required since the increase of the penetration level of renewable generators in the microgrid raises uncertainty due to their intermittent power output. In this paper, an application of probabilistic optimization method to economical operation of a microgrid is studied. To simplify the treatment of the uncertainties of renewable generations and load, the new 'band of virtual equivalent load variation' is introduced considering their uncertainties. A simplified robust optimization methodology to generate the scenarios within the band of virtual equivalent load variation and to obtain the optimal solution for the worst scenario is presented based on Monte Carlo method. The microgrid to be studied here is composed of distributed generation system(DGs), battery systems and loads. The distributed generation systems include combined heat and power(CHP) and small generators such as diesel generators and the renewable energy generators such as photovoltaic(PV) systems and wind power systems. The modeling of the objective function for considering interruption cost by the penalty function is presented. Through the case study for a microgrid with uncertainties, the validity of proposed robust optimization methodology is evaluated.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.270-271
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• 2011

Due to integration of wind power, its unpredictable uncertainty can be a very lethal factor in generation dispatch problem. To handle such uncertainty of wind power output, a profit maximization problem is formulated and random wind speed is modeled by Weibull distribution in this paper. A case study is calculated through profit maximization approach with random wind speed. The effect of case study results is evaluated on how the uncertain wind power integration into the power system affects on the generation dispatch.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1159-1166
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• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• Korean Management Science Review
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• v.29 no.1
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• pp.1-14
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• 2012

The goal of the multi-reservoir operation planning is to provide an optimal release plan that maximize the reservoir storage and hydropower generation while minimizing the spillages. However, the reservoir operation is difficult due to the uncertainty associated with inflows. In order to consider the uncertain inflows in the reservoir operating problem, we present a Stochastic Dynamic Programming (SDP) model based on the markov decision process (MDP). The objective of the model is to maximize the expected value of the system performance that is the weighted sum of all expected objective values. With the SDP model, multi-reservoir operating rule can be derived, and it also generates the steady state probabilities of reservoir storage and inflow as output. We applied the model to the Geum-river basin in Korea and could generate a multi-reservoir monthly operating plan that can consider the uncertainty of inflow.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.494-496
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• 1999

In this paper, we consider the design of a state feedback $H_{\infty}$ controller for uncertain linear systems with saturating actuators. We consider a general saturating actuator and employ the additive decomposition to deal with it effectively. And the considered uncertainty is the unstructured uncertainty which is only known its norm bound. Based on Linear Matrix Inequality(LMI) techniques, we present a condition on designing a controller that guarantees the $L_2$ gain, from the noise to the output, is not greater than a given value. A controller is obtained by checking the feasibility of three LMI's, and this can be easily done by well-known control package. Finally, we show the usefulness of our result by a numerical example.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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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 Aeronautical and Space Sciences
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• v.16 no.4
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• pp.510-519
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• 2015

Concerning the issue of high-dimensions, hybrid uncertainties of randomness and intervals including implicit and highly nonlinear limit state function, reliability analysis based on the hybrid uncertainty reliability mode combining with back propagation neural network (HU-BP neural network) is proposed in this paper. Random variables and interval variables are as input layer of the neural network, after the training and approximation of the neural network, the response variables are obtained through the output layer. Reliability index is calculated by solving the optimization model of the most probable point (MPP) searching in the limit state band. Two numerical cases are used to demonstrate the method proposed in this paper, and finally the method is employed to solving an engineering problem of the aerospace friction plate. For this high nonlinear, small failure probability problem with interval variables, this method could achieve a good analysis result.