• Transactions of Materials Processing
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• v.27 no.1
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• pp.28-36
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• 2018

Electromagnetic forming is a type of high-speed forming process to deform a workpiece through a Lorentz force. As the high strain rate in an electromagnetic-forming simulation causes infeasibility in determining constitutive parameters, we employed inverse parameter estimation in the previous study. However, the inverse parameter estimation process required us to spend considerable time, which leads to an increase in computational cost. To overcome the computational obstacle, in this research, we applied two types of surrogate modeling methods and compared them to each other to evaluate which model is best for the electromagnetic-forming simulation. We exploited an artificial neural network and we reduced-order modeling methods. During the construction of a reduced-order model, we extracted orthogonal bases with proper orthogonal decomposition and predicted basis coefficients by utilizing an artificial neural network. After the construction of the surrogate models, we verified the artificial neural network and reduced-order models through training and testing samples. As a result, we determined the artificial neural network model is slightly more accurate than the reduced-order model. However, the construction of the artificial neural network model requires a considerably larger amount of time than that of the reduced-order model. Thus, a reduced order modeling method is more efficient than an artificial neural network for estimating the electromagnetic forming and for the rapid approximation of structural simulations which needs repetitive runs.

• Journal of Soil and Groundwater Environment
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• v.20 no.3
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• pp.51-64
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• 2015

In the present study, we develop two history matching techniques based on Markov chain Monte Carlo method where radial basis function and Gaussian distribution generated by unconditional geostatistical simulation are employed as the random walk transition kernels. The Bayesian inverse methods for aquifer characterization as the developed models can be effectively applied to the condition even when the targeted information such as hydraulic conductivity is absent and there are transient hydraulic head records due to imposed stress at observation wells. The model which uses unconditional simulation as random walk transition kernel has advantage in that spatial statistics can be directly associated with the predictions. The model using radial basis function network shares the same advantages as the model with unconditional simulation, yet the radial basis function network based the model does not require external geostatistical techniques. Also, by employing radial basis function as transition kernel, multi-scale nested structures can be rigorously addressed. In the validations of the developed models, the overall predictabilities of both models are sound by showing high correlation coefficient between the reference and the predicted. In terms of the model performance, the model with radial basis function network has higher error reduction rate and computational efficiency than with unconditional geostatistical simulation.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2010.05a
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• pp.886-889
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• 2010

Power consumption measurement in sensor network is difficult to proceed by survey in real field. Thus, through simulation, the power consumption is estimated and replacement time of nodes are decided. A simulation tool simulates various facts such as power consumption, packet transmission traffic, network topology and etc. In this paper, it suggests sensor node power model to simulate power consumption which has large importance among simulation facts in sensor network. This model omits calculating expressions that the data originally surveyed can substitute with, according to power consumption property of each functions in sensor node in order to minimize calculations in simulation. In this case accuracy of power consumption estimation will be reduced, but can simulate it faster due to reduced calculation. Suggested model is fitted to analyze power consumption difference between two or more sensor network algorithms with rapid simulation speed rather than accurate simulation.

• Water for future
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• v.25 no.1
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• pp.101-110
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• 1992

In this paper for simulating spatially and temporally varied moving storm in a watershed a distributed model was developed. The model is conducted by two major flow simulations which overland flow simulation and channel network flow simulation. Two dimensional continuity equation and momentum equation of kinematic approximation are used in the overland flow simulation. On the other hand, in the channel networks simulation two types of governing equations which are one dimensional continuity and momentum equations between two adjacent sections in a channel, and continuity and energy equations at a channel junction are applied. The finite element formulations were used in the overland flow simulation and the implicit finite difference formulations were used in the channel network simulation. The finite element formulations for the overland flow are analyzed by the Gauss elimination method and the finite difference formulations for the channel network flow are analyzed by the double sweep method having advantages of computational speed and reduced computer storages. Several recurrent coefficient equations for channel network simulation are suggested in the paper.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8B
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• pp.897-903
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• 2011

In this paper, we consider a future u-Army network with VoIPv6 and evaluate the performance based on the large-scale simulation. The target model chosen in our paper is extended to an division network from several unit networks in which the scope of the performance simulation was limited with the small experiment network[l]. The simulation model is based on several practical scenarios and the actual condition of the division network with some assumption. The evaluated performance results will help to predict whether a future u-Army network meet the real-time service or not, and to design the network suitable for military condition and requirements.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.119-126
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• 2003

Computer simulation has used to a area of military training from about several years ago. War game model(or computer simulation) endow a military man with field training such as combat experience without operating combat strength or capabilities. To samely construct simulation environment against actual combat environment is to well construct DB to operate war game model, associate among federates on network. Thus, we construct virtual combat environment enabling to efficiently manage network traffic among federates(or active nodes) on active network that construct virtual military training space such as urgent combat field needed to rapidly transfer combat information including image and video.

• Fire Science and Engineering
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• v.10 no.2
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• pp.28-39
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• 1996

A recently developed electro-thermal simulation methodology is used to analyze the behavior of a PWM(Pulse-Width-Modulated) voltage source inverter which uses IGBT(Insulated Gate Bipolar Transistor) as the switching devices. In the electro-thermal network simulation methdology, the simulator solves for the temperature distribution within the power semiconductor devices(IGBT electro-thermal model), control logic circuitry, the IGBT gate drivers, the thermal network component models for the power silicon chips, package, and heat sinks as well as the current and voltage within the electrical network. The thermal network describes the flow of heat form the chip surface through the package and heat sink and thus determines the evolution of the chip surface temperature used by the power semiconductor device models. The thermal component model for the device silicon chip, packages, and heat sink are developed by discretizing the nonlinear heat diffusion equation and are represented in component from so that the thermal component models for various package and heat sink can be readily connected to on another to form the thermal network.

• 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.32 no.5B
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• pp.277-285
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• 2007

In this paper, we present a simulation model of Slammer worm propagation process which caused serious disruptions on Internet in the you of 2003 and analyze the process of Slammer by using NS-2. Recently introduced NS-2 modeling called "Detailed Network-Abstract Network Model" had enabled packet level analysis. However, it had deficiency of accommodating only small sized network. By extending the NS-2 DN-AN model to AN-AN model (Abstract Network-Abstract Network model), it is effectively simulated that the whole process from the initial infection to the total network congestion on hourly basis not only for the Korean network but also for the rest of the world networks. Furthermore, the progress of the propagation from Korean network to the other country was also simulated through the AN-AN model. 8,848 hosts in Korean network were infected in 290 second and 66,152 overseas hosts were infected in 308 second. Moreover, the scanning traffics of the worm at the Korean international gateway saturated the total bandwidth in 154 seconds for the inbound traffic and in 135 seconds for the outbound one.