• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.272-277
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• 2003

In order to predict vibration during end-milling process, the cutting dynamics was modelled by using neural network and combined with structural dynamics by considering dynamic cutting states. Specific cutting constants of the cutting dynamics model were obtained by averaging cutting forces and tool diameter, cutting speed, feed, axial depth radial depth were considered as machining factors. Cutting farces by test and by neural network simulation were compared and the vibration during end-milling was simulated.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.10
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• pp.1341-1347
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• 2002

In order to improve efficiency of a system with three-dimensional flow characteristics, this paper presents a new method that overcomes three-dimensional effects by using two-dimensional CFD and neural network. The method was applied to shape optimization of cut-off in a multi-blade fan/scroll system. As the entrance conditions of two-dimensional CFD, the experimental values at the positions out of the inactive zone were used. The distributions of velocity and pressure obtained by two-dimensional CFD were compared with those of three-dimensional CFD and experimental results. It was found that the distributions of velocity and pressure have qualitative similarity. The results of two-dimensional CFD were used for teaming as target values of neural network. The optimal angle and radius of cut-off were determined as 71$^{\circ}$and 0.092 times the outer diameter of impeller, respectively. It is quantified in the previous report that the optimal angle and radius of cut-off are approximately 72$^{\circ}$and 0.08 times the outer diameter of impeller, respectively.

• Design & Manufacturing
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• v.16 no.3
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.56-63
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• 1997

This paper presents a hybrid network model(HY-PIPENET) implementing a minimum cost spanning tree(MCST) network algorithm to be able to determine optimum path and constrained derivative(CD) method to select optimum Pipe diameter. The HY-PIPENET has been validated with the published data of 6-node/7-pipe network. Networking system and also this system has been optimized with MCST-CD method. As a result, it was found that the gas can be sufficiently supplied at the lower pressure with the smaller diameters of pipe compared to the original system in 6-node/7-pipe network. Hence, the construction cost was reduced about $40\%$ in the optimized system. The hybrid networking model has been also applied to a complicated domestic gas pipeline network in metropolitan area, Korea. In this simulation, parametric study was peformed to understand the role of each individual parameter such as source pressure, flow rate, and pipe diameter on the optimized network. From the results of these simulations, we have proposed the optimized network as tree-type structure with optimum pipe diameter and source pressure in metropolitan area, Korea, however, this proposed system does not consider the environmental problems or safety concerns.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.1_2
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• pp.78-85
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• 2004

Given a set S of n points in the plane, a minimum-diameter spanning tree(MDST) for the set might have a degree up to n-1. This might cause the degradation of the network performance because the node with high degree should handle much more requests than others relatively. Thus it is important to construct a spanning tree network with small degree and diameter. This paper presents an algorithm to construct a spanning tree for S satisfying the following four conditions: (1) the degree is controled as an input, (2) the tree diameter is no more than constant times the diameter of MDST, (3) the tree is monotone (even if arbitrary point is fixed as a root of the tree) in the sense that the Euclidean distance from the root to any node on the path to any leaf node is not decreasing, and (4) there are no crossings between edges of the tree. The monotone property will play a role as an aesthetic criterion in visualizing the tree in the plane.

• Proceedings of the Korea Information Processing Society Conference
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• 2002.11b
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• pp.1023-1026
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• 2002

The demand for Wireless LAN (WLAN) access to use their network and the Internet is surged dramatically over the past year. Since WLAN provides users' access from anywhere in the workplace without having to plug in, it therefore leads the WLAN market to grow steadily. Unfortunately, the first WLAN implementation designed primarily for home networking did little to address these security issues. Moreover, although the 802.11b standard published by IEEE in 1999 improved WLAN connections LAN-equivalent speed and security from the 802.11 standard. However, there still are several flaws such as the weaknesses in the Authentication and WEP encryption schemes in the IEEE 802.11 WLAN standard. In this paper, we propose WLAN architecture for providing the strong centralized authentication, encryption, and dynamic key distribution on a WLAN. Additionally, this proposed architecture is able to support roaming users and is flexible and extensible to future developments in the network security.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.58-60
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• 2004

최근에 하이퍼큐브의 망비용을 개선한 하이퍼-스타 연결망이 제안되었다. 본 논문에서는 하이퍼-스타 연결망 HS(2n, n)의 container를 이용하여 k-wide diameter가 dist(u, v)＋4이하임과 HS(2n, n)의 고장지름이 D(HS(2n, n))＋2 이하임을 보인다.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.4
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• pp.479-487
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• 2013

This study was to investigate characteristics for the pressure wave propagation and the maximum pressure near a rapid closure valve which was installed the end of multi piping network. The multi piping network consists of one inlet and three outlet with straight pipes. The diameter of the pipes including the valve was 100 mm, 80 mm, 80 mm respectively. The valve was rapidly closed with the instantaneous time which was 0.023s in the level for the water hammer. For the simulation, the influence of the pipe thickness and deformation due to pressure-wave-propagation was not considered. CFD was conducted under the following condition : the initial pressure was 1bar in the inlet and the mass flow rate was 7.83 kg/s in the outlet(the velocity in the pipe with 100 mm diameter was 1 m/s). As the valve have conditions that were status with and without fluid flow in the pipe after valve closing, the maximum pressure change and the frequency analysis were examined. As the results, the case that was status with fluid flow appeared the higher maximum pressure than another's, the maximum frequency band was about 10 ~ 11 Hz.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.467-470
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• 1998

In this paper, we propose a matrix star graph which improves the network cost of the well-known star grah as an interconnection network. We analyze its characteristics in terms of the network parameters, such as degree, scalability, routing, and diameter. The proposed matrix star graph MS2,n has the half degrees of a star graph S2n with the same number of nodes and is an interconnection network with the properties of node symmetry, maximum fault tolerance, and recursive structure. In network cost, a matrix star graph MS2,n and a star graph S2n are about 3.5n2 and 6n2 respectively which means that the former has a better value by a certain constant than the latter has.

• Tribology and Lubricants
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• v.14 no.3
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• pp.74-80
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• 1998

The morphologies of the wear particles are directly indicative of wear process occuring in the machine. The analysis of wear particle morphology can therefore provide very early detection of a fault and can also ofen facilitate a dignosis. For this work, the neural network was applied to identify friction coefficient through four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris generated from the machine. The averages of these parameters were used as inputs to the network. It is shown that collect identification of friction coefficient depends on the ranges of these shape parameters learned. The various kinds of the wear debris had a different pattern characteristics and recognized relation between the friction condition and materials very well by neural network. We discuss how the network determines difference in wear debris feature, and this approach can be applied for machine condition monitoring and fault diagnosis.