• Proceedings of the IEEK Conference
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• 2001.06a
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• pp.281-284
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• 2001

In this paper, we design the Sorting-Banyan network with an efficient buffer and sorting management schema that makes switch be capable of supporting delay sensitive as well as loss sensitive. The proposed switching network is remodeled that based on Batcher-banyan network that have eight input and output ports The structure of designed switching network is constructed of modified banyan network with 2-way routing paths and two plane sorting networks. we have analysed the maximum throughput of the switch, under the uniform random traffic load, the FIFO discipline has increased by about 11% when we compare the switching system with the input buffering system.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.572-574
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• 1998

In this paper, we are proposed design of ATM networks to transfer for electric power system informations, proposed transport networks is partitioned management part and functional part, management part is partitioned edge network, core network, local network, authority network, functional part is partitioned core network, access network, edge area. It is based on laying and partitioning by ITU-T G.805, we also proposed ATM network requirements for Carrier Relay traffic acceptability in electric power system information.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.120-128
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• 1993

In this paper, an implementation of high-speed parallel processing system for neural network design on the multicomputer network is presented. Linear speedup expandability is increased by reducing the synchronization penalty and the communication overhead. Also, we presented the parallel processing models and their performance evaluation models for each of the parallization methods of the neural network. The results of the experiments for the character recognition of the neural network bases on the proposed system show that the proposed approach has the higher linear speedup expandability than the other systems. The proposed parallel processing models and the performance evaluation models could be used effectively for the design and the performance estimation of the neural network on the multicomputer network.

• International Journal of High-Rise Buildings
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• v.8 no.1
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• pp.49-56
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• 2019

Against the background of the rapid development of the Shanghai Metro network, this paper attempts to establish an analytical approach to evaluate the impact of multiple transport network accessibilities on commercial space use in metro station areas. Ten well-developed metro station areas in central Shanghai are selected as samples. Commercial space floor area and visitors in these areas are collected. Using ArcGIS and Spatial Design Network Analysis, the Shanghai Metro network and road network are modeled to compute diversified transport accessibilities. Evidence from land use and commercial space floor area within a 0-to-500-meter buffer zone of stations is consistent with location and land-use theory: commercial land use is concentrated closer to stations. Correlation analysis suggests that hourly visitors to the shopping mall are mainly influenced by metro network accessibility, while retail stores and restaurants are affected by both metro and pedestrian accessibility.

• Tunnel and Underground Space
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• v.3 no.1
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• pp.63-79
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• 1993

The design of tunnels in rock masses often demands more informations on geologic features and rock mass properties than acquired by usual field survey and laboratory testings. In practice, the situation that a perfect set of geological and mechanical input data is given to geomechanics design engineer is rare, while the engineers are asked to achieve a high level of reliability in their design products. This study presents an artificial neural network which is developed to resolve the difficulties encountered in conventional design techniques, particulary the problem of deteriorating the confidence of existing numerical techniques such as the finite element, boundary element and distinct element methods due to the incomplete adn vague input data. The neural network has inferring capabilities to identify the possible failure modes, support requirements and its timing for underground openings, from previous case histories. Use of the neural network has resulted in a better estimate of the correlation between systems of rock mass classifications such as the RMR and Q systems. A back propagation learning algorithm together with a multi-layer network structure is adopted to enhance the inferential accuracy and efficiency of the neural network. A series of experiments comparing the results of the neural network with the actual field observations are performed to demonstrate the abilities of the artificial neural network as a new tunnel design assistance system.

• Journal of the Korea Society for Simulation
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• v.7 no.2
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• pp.1-16
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• 1998

This paper presents an automated methodology for campus network design and performance analysis using the rule-based SES and DEVS modeling & simulation techniques. Proposed methodology for structural design and performance analysis can be utilized not only in the early stage of network design for selecting configurable candidate from all possible design alternatives, but also in simulation verification for generating performance data. Our approach supercedes conventional methodologies in that, first, it can support the configuration automation by utilizing the knowledge of design expert ; second, it can provide the simulation-based performance evaluation ; third, it is established on the basis of the well-formalized framework so that it can support a hierarchical and modular system design. Several simulation tests performed on a campus network example will illustrate our technique.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2021.05a
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• pp.223-224
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• 2021

Recently, the demand for high fluidity concrete has been increased due to skyscrapers. However, it has its own limits. First of all, high fluidity concrete has large variation and through trial & error it costs lots of money and time. Neural network model has repetitive learning process which can solve the problem while training the data. Therefore, the purpose of this study is to predict optimum mix design of 40MPa, 60MPa high fluidity concrete by using neural network model and verifying compressive strength by applying real data. As a result, comparing collective data and predicted compressive strength data using MATLAB, 40MPa mix design error rate was 1.2%~1.6% and 60MPa mix design error rate was 2%~3%. Overall 40MPa mix design error rate was less than 60MPa mix design error rate.

• Korean Journal of Hydrosciences
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• v.6
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• pp.107-118
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• 1995

The water distribution system problem consists of finding a minimum cost system design subject to hydraulic and operation constraints. The design of new branchin network in a paddy irrigation system is presented here. The program based on the linear programming formulation is aimed at finding the optimal economical combination of two main factors : the capital cost of pipe network and the energy cost. Two loading conditions and booster pumps for design of pipe network are considered to obtain the least cost design.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.17-20
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• 2001

In this paper, we design a nonlinear adaptive controller using wavelet neural network. The method proposed in this paper performs for a nonlinear system with unknown parameters, identification with using a wavelet neural network, and then a nonlinear adaptive controller is designed with those identified informations. The advantage of the proposed control method is simple to design a controller for unknown nonlinear systems, because we use the identified informations and design parameters are positioned within a negative real part of s-plane. The simulation results showed the effectiveness of proposed controller design method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.21 no.5
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• pp.727-734
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• 1997

In practice, the design of forging processes is performed based on an experience-oriented technology, that is designer's experience and expensive trial and errors. Using the finite element simulation and the artificial neural network, we propose an optimal die geometry satisfying the design conditions of final product. A three-layer neural network is used and the back propagation algorithm is employed to train the network. An optimal die geometry that satisfied the same between inner extruded rib and outer extruded one is determined by applying the ability of function approximation of neural network. The neural networks may reduce the number of finite element simulation for determine the optimal die geometry of forging products and further they are usefully applied to physical modelling for the forging design.