• The KIPS Transactions:PartC
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• v.18C no.6
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• pp.441-444
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• 2011

In this study, we present a method for efficiently aggregating network state information. It is especially useful for aggregating links that have both delay and bandwidth in an asymmetric network. Proposed method reduces the information distortion of logical link by integration process after similar measure and grouping of logical links in multi-level topology transformation to reduce the space complexity. It is applied to transform the full mesh topology whose Service Boundary Line (SBL) serves as its logical link into a spanning tree topology. Simulation results show that aggregated information accuracy and query response accuracy are higher than that of other known method.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.195-202
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• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• Wind and Structures
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• v.20 no.4
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• pp.549-564
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• 2015

The three-dimensional unsteady incompressible Reynolds-averaged Navier-Stokes equations and k-${\varepsilon}$ double equations turbulent model were used to investigate the effect on the measurements of anemometers due to a passing high-speed train. Sliding mesh technology in Fluent was utilized to treat the moving boundary problem. The high-speed train considered in this paper was with bogies and inter-carriage gaps. Combined with the results of the wind tunnel test in a published paper, the accuracy of the present numerical method was validated to be used for further study. In addition, the difference of slipstream between three-car and eight-car grouping models was analyzed, and a series of numerical simulations were carried out to study the influences of the anemometer heights, the train speeds, the crosswind speeds and the directions of the induced slipstream on the measurements of the anemometers. The results show that the influence factors of the train-induced slipstream are the passing head car and tail car. Using the three-car grouping model to analyze the train-induced flow is reasonable. The maxima of horizontal slipstream velocity tend to reduce as the height of the anemometer increases. With the train speed increasing, the relationship between $V_{train}$ and $V_{induced\;slipstream}$ can be expressed with linear increment. In the absence of natural wind conditions, from the head car arriving to the tail car leaving, the induced wind direction changes about $330^{\circ}$, while under the crosswind condition the wind direction fluctuates around $-90^{\circ}$. With the crosswind speed increasing, the peaks of $V_X,{\mid}V_{XY}-V_{wind}{\mid}$ of the head car and that of $V_X$ of the tail car tend to enlarge. Thus, when anemometers are installed along high-speed railways, it is important to study the effect on the measurements of anemometers due to the train-induced slipstream.