• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.152-155
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• 1994

The presence of joint elasticity or the arm flexibility causes low damped oscillatory position error along a desired trajectory. We utilize a stochastic model for describing the fast dynamics and the approximation error. A second order shaping filter is synthesized such that its spectrum matches that of the fast dynamics. Augmenting the state vector of slow part with that of shaping filter, we obtain a nonlinear dynamics to which a Gaussian white noise is injected. This modeling approach leads us to the design of an extended Kalman filter(KEF) and a linear quadratic Gaussian(LQG) control scheme. We present the simulation results of this control method. The simulation results show us that our Kalman filtering approach is one of prospective methods in controlling the flexible arms.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1992.04b
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• pp.117-126
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• 1992

This study proposes a new interactive multicriteria method for determining the best levels of the decision variables needed to optimize a stochastic computer simulation with multiple response variables. The method, called the Pairwise Comparison Stochastic Cutting Plane (PCSCP) method, combines good features from interactive multiple objective mathematical programming methods and response surface methodology. The major characteristics of the PCSCP algorithm are: (1) it interacts progressively with the decision maker (DM) to obtain his preferences, (2) it uses good experimental design to adequately explore the decision space while reducing the burden on the DM, and (3) it uses the preference information provided by the DM and the sampling error in the responses to reduce the decision space. This paper presents the basic concepts of the PCSCP method along with its performance for solving randomly selected test problems.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.4
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• pp.633-649
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• 1996

An interactive multicriteria method, which is called the Modified Pairwise Comparison Stochastic Cutting Plane (MPCSCP) method, is proposed for determining the best levels of the integer decision variables needed to optimize a stochastic computer simulation with multiple response functions. MPCSCP combines good features from interactive tradeoff cutting plane methods and response surface methodologies. The proposed method uses a simple pairwise man-machine interaction and searches an integer space uniformly by using the experimental design which evaluates the decision space centering around an integer center point. The characteristics of the proposed method are investigated through an extensive computational study. The parameter configurations examined in the study are (1) variability of the sampling errors, (2) the size of experimental design, (3) the relaxation of cutting planes, and (4) the levels of decision maker's inconsistency.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.852-856
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• 2015

Demand side management (DSM) program has been frequently used for reducing the system peak load because it gives utilities and independent system operator (ISO) a convenient way to control and change amount of electric usage of end-use customer. Planning and operating methods are needed to efficiently manage a DSM program. This paper presents a planning method for DSM program. A planning method for DSM program should include an electric load forecasting, because this is the most important factor in determining how much to reduce electric load. In this paper, load forecasting with the temperature stochastic modeling and the sensitivity to temperature of the electric load is used for improving load forecasting accuracy. The proposed planning method can also estimate the required day, hour and total capacity of DSM program using Monte-Carlo simulation. The results of case studies are presented to show the effectiveness of the proposed planning method.

• Computational Structural Engineering
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• v.7 no.1
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• pp.125-134
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• 1994

Response variability of reinforced concrete frame subjected to material property randomness has been evaluated with the aid of the finite element method. The spatial variation of Young's modulus is assumed to be a two-dimensional homogeneous stochastic process. Young's Modulus of concrete material has been investigated based on the uiaxial strength of concrete cylinder. Direct Monte Carlo simulation method is used to investigate the response of reinforced concrete frame due to the variation of Young's modulus with the Neumann expansion method and the pertubation method. The results by three analytic methods are compared with those by deterministic finite element analysis. These stochastic technique may be an efficient tool for evaluating the structural safety and reliability of reinforced concrete structures.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1994.04a
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• pp.437-450
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• 1994

The problem of testing for a change in the parameter of a stochastic process is particularly important in simulation studies. In studies of the steady state characteristics of a simulation model, it is important to identify initialization bias and to evaluate efforts to control this problem. A simulation output have the characteristics of chaotic behavior because of sensitive dependence on initial conditions. For that reason, we will apply Lyapunov exponent for diagnosis of chaotic motion to simulation output analysis. This paper proposes two methods for diagnosis of steady state in simulation output. In order to evaluate the performance and effectiveness of these methods using chaos theory, M/M/I(.inf.) queueing model is used for testing point estimator, average bias.

• Korean Management Science Review
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• v.12 no.3
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• pp.77-95
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• 1995

This paper shows the case study of risk analysis in an weapon system research and development project. For risk analysis, an advanced stochastic networking technique-VERT (Venture Evaluation and Review Technique) is used. Assumptions for activities of network diagram and conversion methods from PERT ( Program Evaluation and Review Technique) to VERT are discussed. Also, simulation result is presented and discussed.

• Journal of the Korean Data and Information Science Society
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• v.25 no.1
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• pp.245-254
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• 2014

The traditional methods of simulating daily precipitation have paid little attention to the inherent dependence structure between the total precipitation amount and the precipitation frequency for a fixed period of time. To address this issue, we propose a new simulation algorithm using copula in order to incorporate the dependence into the traditional methods. The algorithm consists of two parts: First, while reflecting the observed dependence, we generate the total precipitation amount (S) and the frequency (N) during the period of interest; then we simulate the daily precipitation whose aggregation matches the pair of (N; S) generated in the first part. Our result shows that the proposed method substantially improves the traditional methods.

• ETRI Journal
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• v.35 no.5
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• pp.849-858
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• 2013

In this paper, we propose a new adaptive single model to track a maneuvering target with abrupt accelerations. We utilize the stochastic differential equation to model acceleration of a maneuvering target with stochastic volatility (SV). We assume the generalized autoregressive conditional heteroscedasticity (GARCH) process as the model for the tracking procedure of the SV. In the proposed scheme, to track a high maneuvering target, we modify the Kalman filtering by introducing a new GARCH model for estimating SV. The proposed tracking algorithm operates in both the non-maneuvering and maneuvering modes, and, unlike the traditional decision-based model, the maneuver detection procedure is eliminated. Furthermore, we stress that the improved performance using the GARCH acceleration model is due to properties inherent in GARCH modeling itself that comply with maneuvering target trajectory. Moreover, the computational complexity of this model is more efficient than that of traditional methods. Finally, the effectiveness and capabilities of our proposed strategy are demonstrated and validated through Monte Carlo simulation studies.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.665-679
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• 2017

When measuring productive efficiency, often it is necessary to have knowledge of the production frontier function that shows the maximum possible output of production units as a function of inputs. Canonical parametric forms of the frontier function were initially considered under the framework of stochastic frontier model; however, several additional nonparametric methods have been developed over the last decade. Efforts have been recently made to impose shape constraints such as monotonicity and concavity on the non-parametric estimation of the frontier function; however, most existing methods along that direction suffer from unnecessary non-smooth points of the frontier function. In this paper, we propose methods to estimate the smooth frontier function with monotonicity for stochastic frontier models and investigate the effect of imposing a monotonicity constraint into the estimation of the frontier function and the finite dimensional parameters of the model. Simulation studies suggest that imposing the constraint provide better performance to estimate the frontier function, especially when the sample size is small or moderate. However, no apparent gain was observed concerning the estimation of the parameters of the error distribution regardless of sample size.