• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.87-92
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• 2002

Placement is an important step in the physical design of VLSI circuits. It is the problem of placing a set of circuit modules on a chip to optimize the circuit performance. The most popular algorithms for placement include the cluster growth, simulated annealing and integer linear programming. In this paper we propose a stochastic evolution algorithm searching solution space for the placement problem, and then compare it with simulated annealing by analyzing the results of each implementation.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.881-885
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• 2005

Scheduling repetitive projects under limitations on the amounts of available resources (labor and equipment) has been an active subject because of its practical relevance. Traditionally, the limitation is specified as a deterministic (fixed) number, such as 1000 labor-hours. The limitation, however, is often exposed to uncertainty and variability, especially when the project is lengthy. This paper presents a stochastic optimization model to treat the situations where the limitations of resources are expressed as probability functions in lieu of deterministic numbers. The proposed model transfers each deterministic resource constraint into a corresponding stochastic one and then solves the problem by the use of a chance-constrained programming technique. The solution is validated by comparison with simulation results to show that it can satisfy the resource constraints with a probability beyond the desired confidence level.

• Proceedings of the KIEE Conference
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• 1989.11a
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• pp.191-193
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• 1989

This paper presents a optimal Var allocation algorithm for minimizing power loss and improving voltage profile in a given system. In this paper, nodal input data is considered as Gaussian distribution with their mean value and their variance. A stochastic Linear Programming technique based on chance constrained method is applied to solve the probabilistic constraint. The test result in IEEE-14 Bus model system showes that the voltage distribution of load buses is improved and the power loss is more reduced than before Var allocation.

• Journal of the Korea Society of Computer and Information
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• v.21 no.9
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• pp.91-100
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• 2016

In this research, emergency vehicle dispatching problems faced with in the wake of massive natural disasters are considered. Here, the emergency vehicle dispatching problems can be regarded as a single machine stochastic scheduling problems, where the processing times are independently and identically distributed random variables, are considered. The objective of minimizing the expected number of tardy jobs, with distinct job due dates that are independently and arbitrarily distributed random variables, is dealt with. For these problems, optimal static-list policies can be found by solving corresponding assignment problems. However, for the special cases where due dates are exponentially distributed random variables, using a proposed dynamic programming approach is found to be relatively faster than solving the corresponding assignment problems. This so-called Pivot Dynamic Programming approach exploits necessary optimality conditions derived for ordering the jobs partially.

• Bulletin of the Korean Mathematical Society
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• v.32 no.2
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• pp.287-296
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• 1995

Wets ([4],[5],[6]) considered single objective linear two-stage programming problem under uncertainty with complete recourse. Artstein, Dupacova, Romisch, Schultz and Wets studied stability of this problem id depth. But in many real world problems to make best decision, we need multiple objective functions. So we consider the following multiple objective two-stage programming problems with complete fixed recourse.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.346-354
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• 1992

In the power-system, the active and reactive power levels of load bus randomly vary over days, months, and years which are stochastic in nature. This paper presents an algorithm for optimal Var planning considering the uncertainties of loads. The optimization problem is solved by a stochastic linear programming technique which can handle stochastic constraints to evaluate optimal Var requirement at load bus to maintain the voltage profile which results in probabilistic density function by stochastic Load Flow analysis within admissible range. The effectiveness of the proposed algorithm has been verified by the test on the IEEE-30 bus system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2004.05a
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• pp.685-688
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• 2004

본 연구에서는 저수지군 연계운영을 위한 표본 추계학적 선형 계획(SSLP, Sampling Stochastic Linear Programming) 모형을 제안한다. 일반적 추계학적 모형은 과거 자료로부터 확률변수의 확률분포를 추정하고 이를 몇 개 구간으로 나누어 이산 확률 값을 산정하여 기댓값이 최대가 되는 운영방안을 도출하지만 저수지 유입량 예측시 고려되어야할 지속성 효과(Persistemcy Effect)와 유역간 또는 시점별 공분산 효과(The joint spatial and temporal correlations)를 반영하는데 많은 한계가 있다. 이를 극복하기 위하여 과거자료 자체를 유입량 시나리오로 적용하여 시${\cdot}$공간적 상관관계를 유지하는 표본 추계학적(Sampling Stochastic)기법을 바탕으로 Simple Recourse Model로 구성한 추계학적 선형 계획 모형을 제시한다. 이 모형은 미국 기상청(NWS)에서 발생 가능한 유입량의 시나리오를 예측하는 방법인 앙상블 유량 예측(ESP, Ensemble Streamflow Prediction)을 통한 시나리오를 적용함으로써 좀더 신뢰성 있는 저수지군 연계운영 계획을 도출 할 수 있을 것으로 기대된다.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.80-89
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• 2014

A stochastic optimal power flow (S-OPF) model considering uncertainties of load and wind power is developed based on chance constrained programming (CCP). The difficulties in solving the model are the nonlinearity and probabilistic constraints. In this paper, a limit relaxation approach and an iterative learning control (ILC) method are implemented to solve the S-OPF model indirectly. The limit relaxation approach narrows the solution space by introducing regulatory factors, according to the relationship between the constraint equations and the optimization variables. The regulatory factors are designed by ILC method to ensure the optimality of final solution under a predefined confidence level. The optimization algorithm for S-OPF is completed based on the combination of limit relaxation and ILC and tested on the IEEE 14-bus system.

• Journal of Korea Multimedia Society
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• v.11 no.6
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• pp.807-815
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• 2008

A simple efficient method of spotting and recognizing hand gestures in video is presented using a network of hidden Markov models and dynamic programming search algorithm. The description starts from designing a set of isolated trajectory models which are stochastic and robust enough to characterize highly variable patterns like human motion, handwriting, and speech. Those models are interconnected to form a single big network termed a spotting network or a spotter that models a continuous stream of gestures and non-gestures as well. The inference over the model is based on dynamic programming. The proposed model is highly efficient and can readily be extended to a variety of recurrent pattern recognition tasks. The test result without any engineering has shown the potential for practical application. At the end of the paper we add some related experimental result that has been obtained using a different model - dynamic Bayesian network - which is also a type of stochastic model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2A
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• pp.139-148
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• 2011

This paper presents the minimum-cost network selection scheme which determines the transmission instance in the multi-band maritime communication system, so that the shipment-related real-time information can be transmitted within the maximum allowed period. The transmission instances and the corresponding network selection process are modeled by a Markov Decision Process (MDP), for the channel model in the 2-state Markov chain, which can be solved by stochastic dynamic programming. It derives the minimum-cost network selection rule, which can reduce the network cost significantly as compared with the straight-forward scheme with a periodic transmission.