• 전자공학회논문지B
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• 제30B권8호
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• pp.54-63
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• 1993

This paper proposes a method for improving the performances of the neural network for optimization by an optimal estimation of initial states. The optimal initial state that leads to the global minimum is estimated by using the stochastic approximation. And then the update rule of Hopfield model, which is the high speed deterministic algorithm using the steepest descent rule, is applied to speed up the optimization. The proposed method has been applied to the tavelling salesman problems and an optimal task partition problems to evaluate the performances. The simulation results show that the convergence speed of the proposed method is higher than conventinal Hopfield model. Abe's method and Boltzmann machine with random initial neuron output setting, and the convergence rate to the global minimum is guaranteed with probability of 1. The proposed method gives better result as the problem size increases where it is more difficult for the randomized initial setting to give a good convergence.

• 전자공학회논문지S
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• 제34S권9호
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• pp.91-98
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• 1997

In this paper, a new pixel decimation algorithm for the estimation of motion vector is proposed. In traditional methods, the computational cost can be reduced since only part of the pixels are used for motion vector calculation. But these methods limits the accuracy ofmotion vector because of the same reason. We derive a selection criteria of subsampled pixels that can reduce the probablity of false motion vector detection based on stochastic point of view. By using this criteria, a new pixel decimation algorithm that can reduce the prediction error with similar computational cost is presented. The simulation results applied to standard images haveshown that the proposed algorithm has less mean absolute prediction error than conventional pixel decimation algorithm.

• 전기학회논문지
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• 제56권2호
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• pp.248-253
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• 2007

Traditional time based preventive maintenance is used to constant maintenance interval for equipment life. In order to consider economic aspect for time based preventive maintenance, preventive maintenance is scheduled by RCM(Reliability-Centered Maintenance) evaluation. So, Markov state model is utilized considering stochastic state in RCM. In this paper, a Markov state model which can be used for scheduling and optimization of maintenance is presented. The deterioration process of system condition is modeled by a Markov model. In case study, simulation results about RCM are used to the real historical data of combustion turbine generating units in Korean power systems.

• Journal of applied mathematics & informatics
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• 제35권3_4호
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• pp.387-397
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• 2017

In order to react the dynamic behavior of the system more actually, it is necessary to solve the first problem of synchronization for Markovian jump complex network system in practical engineering problem. In this paper, the problem of local stochastic synchronization for Markovian nonlinear coupled neural network system is investigated, including nonlinear coupling terms and mode-dependent delays, that is less restriction to other system. By designing the Lyapunov-Krasovskii functional and applying less conservative inequality, we get a new criterion to ensure local synchronization in mean square for Markovian nonlinear coupled neural network system. The criterion introduced some free matrix variables, which are less conservative. The simulation confirmed the validity of the conclusion.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 1998년도 춘계학술발표회 논문집
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• pp.90-100
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• 1998

There is a limitation for a ship which is sailing on sea to gather weather and seastate informations. To make up for this weakness , land organizations can gather wider variety of information and evaluate the seakeeping performance on ship. and supply this information to the ship. In this study, calculated the response amplitude of ship motions with the weather information provided in real time, the norminal speed loss with obtaining increased of resistance caused by wave and stochastic process of the seakeeping performance elements. And the results have been achieved to develop a system whichcan evaluate the synthetic seakeeping performance. Using this system , the results have been studied to determine the feasibility of using this simulation inactual operation onboard ship.

• 한국국방경영분석학회지
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• 제24권1호
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• pp.110-131
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• 1998

A stochastic modeling of combat that takes more realistic situations into account has been studied with deep concern. Either the firing strategies or network formations are very important elements in the analysis of combat. The first objective of this study is to evaluate how the different strategies affect the outcomes of combat. An analytical approach has been used in an attempt to understand a small-sized battle. The results are validated and compared with existing simulation models. Extending to the moderate size of battle may be achieved with ease. Secondly, an attempt has been made to study and investigate a way to solve combat in a different fashion. We divided a two-on-two battle into two separate one-on-one battles and connected them into a network. New elements considered such as delay time of starting a firefight on a particular node or search time for the next target when a kill occurs are defined and used as the input parameters. The discussions are made to validate the hypothesized model and ask if the results are meaningful and useful in the analysis of combat operations or not.

• 한국경영과학회지
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• 제30권1호
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• pp.17-26
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• 2005

There is the uncertainty of demands at each retailer in the supply chain. To satisfy customers' demand, retailer must have enough inventory. Nevertheless, stockout is occurred for some retailers. A lateral transshipment policy can be effectively used to deal with stockout. The new lateral transshipment policy, referred to service level adjustment (SLA), is suggested. The difference between SLA and previous policies is the integration of an emergency lateral 'transshipment with a preventive lateral transshipment to efficiently respond customers' demand in the proposed policy. Additionally, the service level to decide the quantity of products is considered. Simulation experiment is executed to treat stochastic factors in the two-echelon supply chain. The proposed policy can reduce total cost and is more effective to the change of demand, penalty cost, and ordering cost than the currently used policies.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 춘계학술대회 논문집
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• pp.262-266
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• 1995

This paper presents a new adaptive-neuro control scheme to control the velocity and position of SCARA robot with parameter uncertainties. The adaptive control of linear system found wiedly in many areas of control application. While techniques for the adaptive control of linear systems have been well-established in the literature, there are a few corresponding techniques for nonlinear systems. In this paper an attempt is made to present a newcontrol scheme for theadaptive control of ponlinear robot based on a feedforward neural network. The proposed approach incorporates a neuro controller used within a reinforcement learning framework, which reduces the problem to one of learning a stochastic approximation of an unknown average error surface Emphasis is focused on the fact that the adaptive-neuro controoler dose not need any input/output information about the controlled system. The simulation result illustrates the effectiveness of the proposed adaptive-neuro control scheme.

• 소음진동
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• 제10권6호
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• pp.941-948
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• 2000

Active noise control is an approach to reduce the noise by utilizing a secondary noise source that destructively interferes with the unwanted noise. In general, active noise control systems rely on multiple sensors to measure the unwanted noise field and the effect of the cancellation. This paper develops an approach that utilizes a single sensor. The noise field is modeled as a stochastic process, and an adaptive algorithm is used to adaptively estimate the parameters of the process. Based on these parameter estimates, a canceling signal is generated. Oppenheim assumed that transfer function characteristics from the canceling source to the error sensor is only a propagation delay. This paper proposes a modified Oppenheim algorithm by considering transfer characteristics of speaker-path-sensor This transfer characteristics is adaptively cancelled by the proposed adaptive modeling technique. Feasibility of the proposed method is proved by computer simulations with artificially generated random noises and sine wave noise. The details of the proposed architecture. and theoretical simulation of the noise cancellation system for three dimension enclosure are presented in the Paper.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권11호
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• pp.4387-4404
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• 2015

The energy-efficient design of sensing-based spectrum sharing of a multi-input and multi-output (MIMO) cognitive radio (CR) system with imperfect multiple antenna spectrum sensing is investigated in this study. Optimal resource allocation strategies, including sensing time and power allocation schemes, are studied to maximize the energy efficiency (EE) of the secondary base station under the transmit power and interference power constraints. EE problem is formulated as a nonlinear stochastic fractional programming of a nonconvex optimal problem. The EE problem is transformed into its equivalent nonlinear parametric programming and solved by one-dimension search algorithm. To reduce searching complexity, the search range was founded by demonstration. Furthermore, simulation results confirms that an optimal sensing time exists to maximize EE, and shows that EE is affected by the spectrum detection factors and corresponding constraints.