• Proceedings of the KSR Conference
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• 2005.05a
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• pp.1105-1108
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• 2005

In oder to develope a train system for parcel service of high speed railway, which had been proposed for the operation of a high speed train, its conceptual design was build. Through this system, we are able to expect the fulfillment of customer needs for high speed rail market segments, and enhancement for cost-effective rail network operation of the mixed traffic on infrastructure cost-reduction in national logistics.

• The Transactions of the Korea Information Processing Society
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• v.7 no.3
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• pp.909-922
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• 2000

High sped messaging layer for application's feeling of low level network performance is needed by Clustering System based on high speed network fabrics. It should have the mechanism to directly pass messages between network card and application space, and provide flexible affodabilities for many diverse applications. In this paper, CROWN (Clustering Resources On Workstations' Network) which is designed and implemented for multi-purpose clustering system will be introduced briefly, and CLCP(CROWN Lean Communication Primitives)which is the high speed messaging layer for CROWN will be followed. CLCP consists of a firmware for controlling Myrinet card, device drier, and user libraries. CLCP supports various application domains as a result of pooling and interrupt receive mechanism. In case of polling based receive, 8 bytes short message, and no other process, CLCP has 262 micro-second response time between two nodes, and IM bytes large message, it shows 442Mbps bandwidth.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.99-107
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• 2009

Since a long work piece influences the natural frequency of the entire system with a miniature high speed spindle, a bar-feeder is used for a long work piece to improve the vibration characteristics of a spindle system. Therefore, it is very important to design optimally support positions between a bar-feeder and a long work piece for a miniature high speed spindle system. The goal of the current paper is to present an optimization method for the design of support positions between a bar-feeder and a long work piece. This optimization method is effectively composed of the method of design of experiment (DOE), the artificial neural network (ANN) and the genetic algorithm (GA). First, finite element models which include a high speed spindle, a long work piece and the support conditions of a bar-feeder were generated from the orthogonal array of the DOE method, and then the results of natural vibration analysis using FEM were provided for the learning inputs of the neural network. Finally, the design of bar-feeder support positions was optimized by the genetic algorithm method using the neural network approximations.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1433-1435
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• 2000

Currently KERI is participating a project whose goal is to develop the Korean High-Speed Train(KHST) with maximum speed of 350kph. KERI's responsibility is the electrical system engineering that includes engineering design of an on-board computer system for diagnosis and control of train set and electrical/mechanical devices. A system engineering approach of the design is performed in order to guarantee the passenger safety and economically viable train for on-board control system construction, operation and maintenance. This paper presents the draft engineering des on-board computer system that ensures the s and reliability of KHST. The draft is focuse network interfaced distributed processing system.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.6
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• pp.247-251
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• 2021

We propose a protocol for Ethernet-based industrial real-time communication networks. In the protocol, the master periodically transmits control frames to all slaves, and the ring-type network topology is selected to achieve high-speed transmission speed. The proposed protocol is implemented in the form of both firmware and Linux kernel modules. To improve the transmission speed, the MAC address table is disabled in the firmware implementation, and the NAPI function of the Ethernet driver is removed in the Linux kernel module implementation. A network experiment environment is built with four ARM processor-based embedded systems and network operation experiments are performed for various frame sizes. From the experimental results, it is verified that the proposed protocol normally operates, and the firmware implementation shows better transmission speed than the Linux kernel module implementation.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• Wind and Structures
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• v.36 no.6
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• pp.423-434
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• 2023

Aerodynamic force coefficients are generally obtained from traditional wind tunnel tests or computational fluid dynamics (CFD). Unfortunately, the techniques mentioned above can sometimes be cumbersome because of the cost involved, such as the computational cost and the use of heavy equipment, to name only two examples. This study proposed to build a deep neural network model to predict the aerodynamic force coefficients based on data collected from CFD simulations to overcome these drawbacks. Therefore, a series of CFD simulations were conducted using different geometric parameters to obtain the aerodynamic force coefficients, validated with wind tunnel tests. The results obtained from CFD simulations were used to create a dataset to train a multilayer perceptron artificial neural network (ANN) model. The models were obtained using three optimization algorithms: scaled conjugate gradient (SCG), Bayesian regularization (BR), and Levenberg-Marquardt algorithms (LM). Furthermore, the performance of each neural network was verified using two performance metrics, including the mean square error and the R-squared coefficient of determination. Finally, the ANN model proved to be highly accurate in predicting the force coefficients of similar bridge sections, thus circumventing the computational burden associated with CFD simulation and the cost of traditional wind tunnel tests.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.963-968
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• 2003

A feedback control implementation for a high speed train pressurization system is proposed based on CAN (Controller Area Network). Firstly, system model including network latencies by CAN arbitration mechanisms is proposed, and an analytical compensation method of control parameters based on the system model is proposed for the network latencies. For the practical implementation of the control, global synchronization is adopted for controller to measure network latencies and to utilize them for the compensation of the control parameters. Simulation results are shown with practical tunnel data response. The proposed method is evaluated to be the most effective for the system through the control performances comparing among a controller not considering network latencies, other two off-line compensation methods, and the proposed method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.111-118
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• 2012

When broadcasters want immediately a variety of VOD files in a distributed server of them data centers and away contents provider, CPs of different platform to exchange high-quality HD, 3DTV video and other video files over the IP networks of high-performance that can be transferred quickly and must be configured quickly. This paper, by using an optional encryption method to complement a QoS and security of public network, suggests high speed and secure content transmission protocol such as VPN(Virtual Private Network) for large video files and big data. As configured high performance VPN, end to end devices use the best of available resources over public network by parallel transfer protocol and the secure content delivery network.

• Korean Journal of Computational Design and Engineering
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• v.8 no.4
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• pp.222-230
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• 2003

Artificial intelligence has been used in the configuration design stage of high-speed train. The traction system of a high-speed train is composed of transformers, motor blocks, and traction motors of which locations and number in the trainset should be determined in the early stage of the train conceptual design. Components of the traction system are heavy parts in the train, so it gives strong influence to the top speeds and overall train configuration of high-speed trains. Top speeds have been predicted using the neural network with the associated data of the traction system. The neural networks have been learned with data sets of many commercially operated high-speed trains, and the predicted results have been compared with the actual values. The configuration design of the train set of a high-speed train determines the basic specification of the train and layout of the traction system. The neural networks is a useful design tool when there is not sufficient data for the configuration design and we need to use the existing data of other train for the prediction of trainset in development.