• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.205-216
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• 2019

Diagnostic tests in medical fields detect or diagnose a disease with results measured by continuous or discrete ordinal data. The performance of a diagnostic test is summarized using the receiver operating characteristic (ROC) curve and the area under the curve (AUC). The diagnostic test is considered clinically useful if the outcomes in actually-positive cases are higher than actually-negative cases and the ROC curve is concave. In this study, we apply the stochastic ordering method in a Bayesian hierarchical model to estimate the proper ROC curve and AUC when the diagnostic test results are measured in discrete ordinal data. We compare the conventional binormal model and binormal model under stochastic ordering. The simulation results and real data analysis for breast cancer indicate that the binormal model under stochastic ordering can be used to estimate the proper ROC curve with a small bias even though the sample sizes were small or the sample size of actually-negative cases varied from actually-positive cases. Therefore, it is appropriate to consider the binormal model under stochastic ordering in the presence of large differences for a sample size between actually-negative and actually-positive groups.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.3
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• pp.149-155
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• 2005

In this paper, we optimize simulation model of a manufacturing system using the real-coded genetic algorithm. Because the manufacturing system expressed by simulation model has stochastic process, the objective functions such as the throughput of a manufacturing system or the resource utilization are not optimized by simulation itself. So, in order to solve it, we apply optimization methods such as a genetic algorithm to simulation method. Especially, the genetic algorithm is known to more effective method than other methods to find global optimum, because the genetic algorithm uses entity pools to find the optimum. In this study, therefore, we apply the real-coded genetic algorithm to simulation optimization of a manufacturing system, which is known to more effective method than the binary-coded genetic algorithm when we optimize the constraint problems. We use the reproduction operator of the applied real-coded genetic algorithm as technique of the remainder stochastic sample with replacement and the crossover operator as the technique of simple crossover. Also, we use the mutation operator as the technique of the dynamic mutation that configures the searching area with generations.

• Journal of the Society of Naval Architects of Korea
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• v.32 no.1
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• pp.37-41
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• 1995

In order to analyze the rolling motion of a ship in random beam waves we use the partial stochastic linearization method. The quadratic damping and the nonlinear restoring moments given by the odd polynomials up to the 11th order are added to a single degree of freedom linear equation of roll motion. The irregular excitation moment is assumed to be the Gaussian white noise. The statistical characteristics of the response by the partial stochastic linearization method is compared with results by the equivalent linearization method and Monte Carlo simulation. It is fecund that the partial stochastic linearization method is not necessarily superior to the equivalent linearization method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.19-30
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• 2013

Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochastic optimal control problems cannot be solved exactly except in very simple cases, approximations are required in most practical problems to obtain good suboptimal policies. In this paper, we present a procedure for finding a suboptimal solution to the constrained index tracking problem based on approximate dynamic programming. Illustrative simulation results show that this procedure works well when applied to a set of real financial market data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.13 no.1
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• pp.55-63
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• 1993

Finite element analyses are conducted with stochastic elastic moduli when truss structures are subjected to static loads of a deterministic nature. Stochastic stiffness matrix is derived from stochastic shape functions and numerical analyses are performed within the framework of the Monte Carlo method. Analysis methods are verified for the space truss and applied to cable stayed bridge for determining the cable force.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.525-528
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• 2005

Much of the study has been dong on the design of dual controller that guarantee the stability and improvement of the system performance. A dual controller concept is proposed to consist of first controller estimates the control law and second controller suppresses the combined noises due to numerical error and internal noise as well. These irregular disturbances are not only increasing the fatigue but also destabilize the system because of unwanted output performance. The 'stochastic controller' is used to suppress the irregular random disturbance. Simulation is conducted to reveal that the proposed dual stochastic controller is highly efficient one to control a system hybrid noises.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.6 s.99
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• pp.757-763
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• 2005

Much of the study has been done on the design of dual controller that guarantee the stability and improvement of the system performance. A dual controller concept is proposed to consist of first controller estimates the control law and second controller suppresses the combined noises due to numerical error and internal noise as well. These Irregular disturbances are not only increasing the fatigue but also destabilize the system because of unwanted output Performance. The 'stochastic controller' is used to suppress the irregular random disturbance. Simulation is conducted to reveal that the proposed dual stochastic controller is highly efficient one to control a system hybrid noises.

• Korean Management Science Review
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• v.15 no.1
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• pp.33-47
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• 1998

The facility location problem considered here is to determine facility location sites under future's uncertain demand. The objective of this paper is to propose a solution method and algorithm for a two-stage stochastic facility location problem. utilizing the Benders decomposition method. As a two-stage stochastic facility location problem is a large-scale and complex to solve, it is usually attempted to use a mean value problem rather than using a stochastic problem. Thus, the other objective is to study the relative error of objective function values between a stochastic problem and a mean value problem. The simulation result shows that the relative error of objective function values between two problems is relatively small, when a feasibility constraint is added to a facility location model.

• Journal of Korean Institute of Industrial Engineers
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• v.43 no.1
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• pp.1-11
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• 2017

Stochastic resource-constrained project scheduling problem is an extension of resource-constrained project scheduling problem such that activity duration has stochastic nature. In real situation where activity duration is not known until the activity is finished, open-loop based static policies such as activity-based policy and priority-based policy will not well cope with duration variability. Then, a dynamic policy based on closed-loop decision making will be regarded as an alternative toward achievement of minimal makespan. In this study, a dynamic policy designed to select activities to start at each decision time point is illustrated. The performance of static and dynamic policies based on variable neighborhood search is evaluated under the discrete-event simulation environment. Experiments with J120 sets in PSPLIB and several probability distributions of activity duration show that the dynamic policy is superior to static policies. Even when the variability is high, the dynamic policy provides stable and good solutions.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1999.10a
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• pp.351-358
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• 1999

The structural responses of underground structures are examined in probability by using the elasto-plastic stochastic finite element method in which the spatial distributions of material properties are assumed to be stochastic fields. In addition, the adaptive importance sampling method using the response surface technique is used to improve simulation efficiency. The method is found to provide appropriate information although the nonlinear Limit State involves a large number of basic random variables and the failure probability is small. The probability of plastic local failures around an excavated area is effectively evaluated and the reliability for the limit displacement of the ground is investigated. It is demonstrated that the adaptive importance sampling method can be very efficiently used to evaluate the reliability of a large scale stochastic finite element model, such as the underground structures located in the multi-layered ground.