• International conference on construction engineering and project management
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• 2009.05a
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• pp.907-913
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• 2009

Pipeline construction is a highly repetitive and resource-intensive process that is exposed to various constraints and uncertainties in the working environment. Effective look-ahead scheduling based on the most recent project performance data can greatly improve project execution and control. This study enhances the traditional linear scheduling method with stochastic simulation to incorporate activity performance uncertainty in look-ahead scheduling. To facilitate the use of this stochastic method, a computer program, Stochastic Linear Scheduling Method (SLSM), was designed and implemented. Accurate look-ahead scheduling can help schedulers to better anticipate problem areas and formulate new plans to improve overall project performance.

• Journal of applied mathematics & informatics
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• v.42 no.2
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• pp.223-235
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• 2024

In this paper, our primary focus revolves around the examination of a set of fractional stochastic models. Through our investigation, we can establish the presence of a solution and its distinctiveness. Additionally, we employ a moment-based algorithm to estimate the coefficients within these models and provide evidence that these estimations maintain their asymptotic characteristics. To support this claim, we conduct experimental studies using simulations and numerical examples.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.1-10
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• 2003

This paper presents a high-precision magnetically levitated (maglev) stage to meet demanding motion specifications in the next-generation precision manufacturing and nanotechnology. Characterization of dynamic behaviors of such a motion stage is a crucial task. In this paper, we address the issues related to the stochastic modeling of the stage including transfer function identification, and noise/disturbance analysis and prediction. Provided are test results on precision dynamics, such as fine settling, effect of optical table oscillation, and position ripple. To deal with the dynamic coupling in the platen, we designed and implemented a multivariable linear quadratic regulator, and performed time-optimal control. We demonstrated how the performance of the current maglev stage can be improved with these analyses and experimental results. The maglev stage operates with positioning noise of 5 nm rms in $\chi$ and y, acceleration capabilities in excess of 2g(20 $m/s^2$), and closed-loop crossover frequency of 100 Hz.

• Environmental and Resource Economics Review
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• v.20 no.2
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• pp.357-379
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• 2011

The problem of invasive species has been recently emerged as one of complicated issues due to increasing globalisation and its consequence of species immigrations. Since in most cases of invasive species it is less likely to fully eradicate them through human efforts, it is often interested in reducing the possibility of ecological disaster caused by the invasive species. This paper provides an optimal control model to minimize such possibility while allowing the stochastic nature of biological growth of the invasive species. Conditions under which the partial eradication effort is optimal are derived. Simple numerical illustration is provided using H1N1 data which is categorized as an invasive disease in microorganism level.

• The Journal of the Korea Contents Association
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• v.3 no.4
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• pp.55-62
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• 2003

In this paper, a call admission contro(cac) technique is proposed to reduce the dropping probabilities of handoff calls in wireless networks while guaranteeing QoS to the users. The proposed technique is based on the estimated effective load for the target eel if a call is accepted. When the estimated effective load is higher than a predetermined threshold, a nu call is blocked and a handoff call is queued irrespective of the availability channels. The SRN, an extended Stochastic Petri Net, modes are constructed to compare the performance of the techniques. The SRN uses rewards concepts instead of the complicate numerical analysis required for the Markov chain modes. As a result, the SRN modeling techniques provide an easier way to carry out performance analysis for call admission control and channel allocation.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.298-303
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• 2004

This paper deals with a stochastic stabilization of observer-based output-feedback control Takagi-Sugeno (T-S) fuzzy system with Markovian input delay. The finite Markovian process is adopted to model the input delay of the overall control system. It is assumed that the zero and hold devices are used for control input. The continuous-time T-S fuzzy system with the Markovian input delay is discretized for easy handling delay, accordingly, the discretized T-S fuzzy system is represented by a discrete-time T-S fuzzy system with jumping parameters. The stochastic stabilizability of the jump T-S fuzzy system is derived and formulated in terms of linear matrix inequalities (LMIs). The usefulness of the proposed algorithm is also certificated by simulation of 2 degree of freedom helicopter model.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.3
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• pp.79-89
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• 1998

The Error Back-Propagation(EBP) algorithm is widely applied to train a multi-layer perceptron, which is a neural network model frequently used to solve complex problems such as pattern recognition, adaptive control, and global optimization. However, the EBP is basically a gradient descent method, which may get stuck in a local minimum, leading to failure in finding the globally optimal solution. Moreover, a multi-layer perceptron suffers from locking a systematic determination of the network structure appropriate for a given problem. It is usually the case to determine the number of hidden nodes by trial and error. In this paper, we propose a new algorithm to efficiently train a multi-layer perceptron. OUr algorithm uses stochastic perturbation in the weight space to effectively escape from local minima in multi-layer perceptron learning. Stochastic perturbation probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the probabilistically re-initializes weights associated with hidden nodes to escape a local minimum if the EGP learning gets stuck to it. Addition of new hidden nodes also can be viewed asa special case of stochastic perturbation. Using stochastic perturbation we can solve the local minima problem and the network structure design in a unified way. The results of our experiments with several benchmark test problems including theparity problem, the two-spirals problem, andthe credit-screening data show that our algorithm is very efficient.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.7
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• pp.637-648
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• 2007

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.107-110
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• 1996

Continuing advances in the formulation and solution of risk-sensitive control problems have reached a point at which this topic is becoming one of the more intriguing modern paradigms of feedback thought. Despite a prevailing atmosphere of close scrutiny of theoretical studies, the risk-sensitive body of knowledge is growing. Moreover, from the point of view of applications, the detailed properties of risk-sensitive design are only now beginning to be worked out. Accordingly, the time seems to be right for a survey of the historical underpinnings of the subject. This paper addresses the beginnings and the evolution, over the first quarter-century or so, and points out the close relationship of the topic with the notion of optimal cost cumulates, in particular the cost variance. It is to be expected that, in due course, some duality will appear between these notions and those in estimation and filtering. The purpose of this document is to help to lay a framework for that eventuality.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1225-1228
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• 1987

It is shown that the singularly perturbed continuous-time system is led to two different discrete versions according to slow or fast sampling rates. The design of stabilizing feedback control of singularly perturbed discrete-time stochastic system is decomposed into the design of slow and fast controllers, which is combined to form the composite control. Composite control law is derived for the case of both single rate measurement and multirate measurement.