• Journal of the Korea Safety Management & Science
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• v.22 no.4
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• pp.65-74
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• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• Journal of the military operations research society of Korea
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• v.25 no.2
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• pp.15-30
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• 1999

A number of combat simulation models are scattered and the analytic solution approaches have experienced very difficult computational efforts. Today´s computer communication technology let people to do many unrealistic things possible and the use of those technologies is becoming increasingly prevalent throughout the military operation. Both DIS and ADS are welled defined computer aided military simulations. This study discusses a simulation of stochastic combat network modeling through Internet space. We have developed two separate simulation models, one for clients and another for server, and validated for conducting studies with these models. The object-oriented design was necessary to define the system entities and their relationship, to partition functionality into system entities, and to transform functional metrics into realizations derived from system component behaviors. Heterogeneous forces for each side are assumed at any battle node. The time trajectories for mean number of survivors and combat history at each node, some important combat measures, and relative difference computations between models were made. We observe and may conclude that the differences exit and some of these are significant based on a limited number of experiments.

• International journal of advanced smart convergence
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• v.5 no.1
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• pp.34-46
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• 2016

Networked Control System (NCS) has evolved in the past decade through the advances in communication technology. The problems involved in NCS are broadly classified into two categories namely network issues due to network and control performance due to system network. The network problems are related to bandwidth allocation, scheduling and network security, and the control problems deal with stability analysis and delay compensation. Various delays with variable length occur due to sharing a common network medium. Though most delays are very less and mostly neglected, the network induced delay is significant. It occurs when sensors, actuators, and controllers exchange data packet across the communication network. Networked induced delay arises from sensor to controller and controller to actuator. This paper presents an adaptive delay compensation process for efficient control. Though Smith predictor has been commonly used as dead time compensators, it is not adaptive to match with the stochastic behavior of network characteristics. Time delay adaptive compensation gives an effective control to solve dead time, and creates a virtual environment using the plant model and computed delay which is used to compensate the effect of delay. This approach is simulated using TrueTime simulator that is a Matlab Simulink based simulator facilitates co-simulation of controller task execution in real-time kernels, network transmissions and continuous plant dynamics for NCS. The simulation result is analyzed, and it is confirmed that this control provides good performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.279-288
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• 2017

The Material Transportation Capability Analysis Method in North Korea includes adversary's activities such as destruction of bridge which is one kind of choke points in the road network and surprise attack against resupply march unit. Also, the amount of damage on choke points in the road network and repair time depending on repair unit commitment must be reflected. In this study, a scenario encompassing plausible resupply transportation circumstances while counterattacking into NK will be established. Then, based on such scenario, a simulation model will be established and the result of simulation will be compared to the results of numeric example which has been used in the ROK Army. We demonstrate, through a certain Corps operation area, that the Scenario-based Simulation Model results predict the performance of resupply operation very well. Therefore, it makes sustainment planners and commanders do activities which is suitable for battlefield and should be used in the real situation. It is also a stochastic model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5B
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• pp.809-819
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• 2000

This paper deals with an optimal capacity allocation problem and performance analysis in Advanced Information Communication Processing System(AICPS). AICPS is a gateway system interconnection PSTN(Public Switched Telephone Network), ISDN(Intergrated Services Digital Network), PSDN(Packet Switched Data Network), internet, Frame Relay and ATM together. This study considers not only ISDN and Internet but also user behavior of On-line service which is analyzed by Markov process. A call blocking probability of TNAS and INAS is computed by Erlang's formula. Then, PNAS and WNAS's call blocking probability are computed by Stochastic knapsack modeling. The result is compared with result of simulation. Finally, we allocate an optimal capacity minimizing total call blocking probability.

• Smart Structures and Systems
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• v.30 no.4
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• pp.421-432
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• 2022

Regarding the high significance of correct pan evaporation modeling, this study introduces two novel neuro-metaheuristic approaches to improve the accuracy of prediction for this parameter. Vortex search algorithms (VSA), sunflower optimization (SFO), and stochastic fractal search (SFS) are integrated with a multilayer perceptron neural network to create the VSA-MLPNN, SFO-MLPNN, and SFS-MLPNN hybrids. The climate data of Arcata-Eureka station (operated by the US environmental protection agency) belonging to the years 1986-1989 and the year 1990 are used for training and testing the models, respectively. Trying different configurations revealed that the best performance of the VSA, SFO, and SFS is obtained for the population size of 400, 300, and 100, respectively. The results were compared with a conventionally trained MLPNN to examine the effect of the metaheuristic algorithms. Overall, all four models presented a very reliable simulation. However, the SFS-MLPNN (mean absolute error, MAE = 0.0997 and Pearson correlation coefficient, R_P = 0.9957) was the most accurate model, followed by the VSA-MLPNN (MAE = 0.1058 and R_P = 0.9945), conventional MLPNN (MAE = 0.1062 and R_P = 0.9944), and SFO-MLPNN (MAE = 0.1305 and R_P = 0.9914). The findings indicated that employing the VSA and SFS results in improving the accuracy of the neural network in the prediction of pan evaporation. Hence, the suggested models are recommended for future practical applications.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.124-129
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• 2002

This paper demonstrates that the largest Lyapunov exponent λ of recurrent neural networks can be controlled efficiently by a stochastic gradient method. An essential core of the proposed method is a novel stochastic approximate formulation of the Lyapunov exponent λ as a function of the network parameters such as connection weights and thresholds of neural activation functions. By a gradient method, a direct calculation to minimize a square error (λ - λ$\^$obj/)$^2$, where λ$\^$obj/ is a desired exponent value, needs gradients collection through time which are given by a recursive calculation from past to present values. The collection is computationally expensive and causes unstable control of the exponent for networks with chaotic dynamics because of chaotic instability. The stochastic formulation derived in this paper gives us an approximation of the gradients collection in a fashion without the recursive calculation. This approximation can realize not only a faster calculation of the gradient, but also stable control for chaotic dynamics. Due to the non-recursive calculation. without respect to the time evolutions, the running times of this approximation grow only about as N$^2$ compared to as N$\^$5/T that is of the direct calculation method. It is also shown by simulation studies that the approximation is a robust formulation for the network size and that proposed method can control the chaos dynamics in recurrent neural networks efficiently.

• Journal of Electrical Engineering and Technology
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• v.3 no.2
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• pp.285-291
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• 2008

Active Queue Management(AQM) has been widely used for congestion avoidance in Transmission Control Protocol(TCP) networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level, most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. To alleviate these problems, we introduce an AQM technique based on a dynamic neural network using the Back-Propagation(BP) algorithm. The dynamic neural network is designed to perform as a robust adaptive feedback controller for TCP dynamics after an adequate training period. We evaluate the performances of the proposed neural network AQM approach using simulation experiments. The proposed approach yields superior performance with faster transient time, larger throughput, and higher link utilization compared to two existing schemes: Random Early Detection(RED) and Proportional-Integral(PI)-based AQM. The neural AQM outperformed PI control and RED, especially in transient state and TCP dynamics variation.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1154-1162
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• 2014

In the restructured electricity market, Performance-Based Regulation (PBR) regime has been introduced to the distribution network. To ensure the network stability, this regime is used along with quality regulations. Quality regulation impose new financial risks on distribution system operators (DSOs). The poor quality of the network will result in reduced revenues for DSOs. The mentioned financial risks depend on the quality indices of the system. Based on annual variation of these indices, the cost of quality regulation will also vary. In this paper with regard to reclosing fault in distribution network, we develop a risk-based method to assess the financial risks caused by quality regulation for DSOs. Furthermore, in order to take the stochastic behavior of the distribution network and quality indices variations into account, time-sequential Monte Carlo simulation method is used. Using the proposed risk method, the effect of taking reclosing time into account will be examined on system quality indicators and the cost of quality regulation in Swedish rural reliability test system (SRRTS). The results show that taking reclosing fault into consideration, affects the system quality indicators, particularly annual average interruption frequency index of the system (SAIFI). Moreover taking reclosing fault into consideration also affects the quality regulations cost. Therefore, considering reclosing time provides a more realistic viewpoint about the financial risks arising from quality regulation for DSOs.

• Journal of electromagnetic engineering and science
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• v.10 no.4
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• pp.290-295
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• 2010

In this paper, an interference analysis method with a site-specific path loss model for a wireless personal area network (WPAN) is proposed. The site-specific path loss model is based on geometrical optics and geometric probability to consider both site-specific radio propagation characteristics and a closed-form expression to obtain the mean interference from which the uniformly distributed multiple interferers are derived. Therefore, the proposed interference analysis method can achieve more computational simplicity than the Monte-Carlo (MC) simulation, which uses the ray-tracing (RT) technique. In addition, better accuracy than the conventional interference analysis model that uses stochastic method can also be achieved. To evaluate the proposed method, a signal to the interference-noise ratio with a mean interference concept for uniformly distributed interferers is calculated and compared in two simulation scenarios. As a result, the proposed method produces not only better matched results with the MC simulation using the RT technique than the conventional interference analysis model, but also simpler and faster calculation, which is due to the site-specific path loss model and closed-form expression for interference calculation.