• International Journal of CAD/CAM
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• v.7 no.1
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• pp.1-10
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• 2007

The compression of CAD models is a key technology for realizing Internet-based collaborative product development because big model sizes often prohibit us to achieve a rapid product information transmission. Although there exist some algorithms for compressing discrete CAD models, original precise CAD models are focused on in this paper. Here, the characteristics of hierarchical structures in CAD models and the distribution of their redundant data are exploited for developing a novel data encoding method. In the method, different encoding rules are applied to different types of data. Geometric data is a major concern for reducing model sizes. For geometric data, the control points of B-spline curves and surfaces are compressed with the second-order predictions in a local coordinate system. Based on analysis to the distortion induced by quantization, an efficient method for computation of the distortion is provided. The results indicate that the data size of CAD models can be decreased efficiently after compressed with the proposed method.

• Geomechanics and Engineering
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• v.6 no.4
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• pp.359-376
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• 2014

Thermal conductivity of ground has a great influence on the performance of Ground Heat Exchangers (GHEs). In general, the ground thermal conductivity significantly depends on the density (or porosity) and the moisture content since they are decisive factors that determine the interface area between soil particles which is available for heat transfer. In this study, a large number of thermal conductivity experiments were conducted for soils of varying porosity and moisture content, and a database of thermal properties for the weathered granite soils was set up. Based on the database, a 3D Curved Surface Model and an Artificial Neural Network Model (ANNM) were proposed for estimating the thermal conductivity. The new models were validated by comparing predictions by the models with new thermal conductivity data, which had not been used in developing the models. As for the 3D CSM, the normalized average values of training and test data were 1.079 and 1.061 with variations of 0.158 and 0.148, respectively. The predictions became somewhat unreliable in a low range of thermal conductivity values in considering the distribution pattern. As for the ANNM, the 'Logsig-Tansig' transfer function combination with nine neurons gave the most accurate estimates. The normalized average values of training data and test data were 1.006 and 0.954 with variations of 0.026 and 0.098, respectively. It can be concluded that the ANNM gives much better results than the 3D CSM.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.629-632
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• 1989

The scattered light intensity from a spherical particle passing through the cross-over region of two coherent laser beams, varies periodically. Photodetection of this light beams produces a periodic signal of varying amplitude. The phase of the signal varies with the particle size and refractive index, the beam crossing angle and wavelength, and the position and size of the scattered ligth collecting aperture. In this paper the phase variation with respect to the particle absorptive index of retraction, collecting lens size and beam crossing angle is calculated using both Mie scattering theory and reflection theory. The two theories show good agreement in phase predictions, especially for large absorptive indices and for small collection lenses. Both theories predict phase to be inversely proportional to the beam crossing angle.

• Bioinformatics and Biosystems
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• v.2 no.2
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• pp.52-57
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• 2007

The computational discovery of transcription factor binding site is one of the important tools in the genetic and genomic analysis. Rough prediction of gene regulation network and finding possible co-regulated genes are typical applications of the technique. Countless motif-discovery algorithms have been proposed for the past years. However, there is no dominant algorithm yet. Each algorithm does not give enough accuracy without extensive information. In this paper, we explore the possibility of combining multiple algorithms for the one integrated result in order to improve the performance and the convenience of researchers. Moreover, we apply new high order information that is reorganized from the set of basis predictions to the final prediction.

• Journal of Mechanical Science and Technology
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• v.14 no.5
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• pp.569-581
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• 2000

A cycle simulation program is developed and its predictions are compared with the test bed measurements of a direct injection (DI) diesel engine. It is based on the mass and energy conservation equations with phenomenological models for diesel combustion. Two modeling approaches for combustion have been tested; a multi-zone model by Hiroyasu et al (1976) and the other one coupled with an in-cylinder flow model. The results of the two combustion models are compared with the measured imep, pressure trace and NOx and soot emissions over a range of the engine loads and speeds. A parametric study is performed for the fuel injection timing and pressure, the swirl ratio, and the squish area. The calculation results agree with the measured data, and with intuitive understanding of the general operating characteristics of a DI diesel engine.

• ETRI Journal
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• v.43 no.2
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• pp.272-287
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• 2021

This article presents a multi-dimensional spatial pattern analysis of crime events in San Francisco. Our analysis includes the impact of spatial resolution on hotspot identification, temporal effects in crime spatial patterns, and relationships between various crime categories. In this work, crime prediction is viewed as a classification problem. When predictions for a particular category are made, a binary classification-based model is framed, and when all categories are considered for analysis, a multiclass model is formulated. The proposed crime-prediction model (HotBlock) utilizes spatiotemporal analysis for predicting crime in a fixed spatial region over a period of time. It is robust under variation of model parameters. HotBlock's results are compared with baseline real-world crime datasets. It is found that the proposed model outperforms the standard DeepCrime model in most cases.

• Advanced Composite Materials
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• v.16 no.4
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• pp.377-384
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• 2007

'Green' composites were fabricated from poly lactic acid (PLA) and bamboo fibers by using a conventional hot pressing method. The insulating properties of the PLA-bamboo fiber 'green' composites were evaluated by determination of the thermal conductivity, which was measured using a hot-wire method. The thermal conductivity values were compared with theoretical estimations. It was demonstrated that thermal conductivity of PLA-bamboo fiber 'green' composites is smaller than that of conventional composites, such as glass fiber reinforced plastics (GFRPs) and carbon fiber reinforced plastics (CFRPs). The thermal conductivity of PLA-bamboo fiber 'green' composites was significantly influenced by their density, and was in fair agreement with theoretical predictions based on Russell's model. The PLA-bamboo fiber composites have low thermal conductivity comparable with that of woods.

• KIEE International Transactions on Electrophysics and Applications
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• v.4C no.4
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• pp.155-159
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• 2004

This work reports the theoretical and experimental investigations of capillary bust valves to regulate liquid flow in microchannels. The theoretical analysis uses the Young-Laplace equation and geometrical considerations to predict the pressure at the edge of the valve opening. Numerical simulations are employed to calculate the meniscus shape evolution while the interface is pinned at the valve edge. Microchannels and valves are fabricated using soft lithography. A wafer-rotating system, which can adjust the driving pressure by rotational speed, induces a liquid flow. Experimentally measured valve-bursting pressure agrees with theoretical predictions.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.46.2-46.2
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• 2021

Presolar silicon carbide (SiC) grains form around in the envelopes of asymptotic giant branch (AGB) stars by satisfying C/O>1 which is an optimal condition for SiC grains to condense in the stellar outflows. Ruthenium (Ru) isotopes are locked into the SiC grains during the condensation of SiC grains. We investigate the isotopic compositions of Ru in the stellar winds by using the NuGrid data, which are obtained by nucleosynthesis calculations during the stellar evolution. We compare the isotopic compositions of Ru obtained from the NuGrid data with measurements and the predictions obtained from different codes. Our results present a piece of evidence that SiC grains in the presolar system came from low-mass and low-metallicity AGB stars, also confirming that they were not from massive stars. We also suggest a new scenario in which the total stellar yields are also considered because SiC grains can condense during the collapse of molecular clouds.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.28 no.1
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• pp.13-26
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• 2016

Sea water level variations are generally influenced by a variety of factors such as tides, meteorological forces, water temperature, salinity, wave, and topography, etc. Among non-tidal conditions, atmospheric pressure is one of the major factors causing water level changes. In the East Sea, due to small tidal range which is opposite to large tidal range of the Yellow Sea, it is difficult to predict water level changes using a numerical model, which consider tidal forcing only. This study focuses on the effects of atmospheric pressure variations on sea level predictions along the eastern coast of Korea. Telemac-2D model is simulated with the Inverted Barometer Effect(IBE), and then its results are analyzed. In comparison between observed data and predictions, the correlation of prediction with IBE and tide is better than that of tide-only case. Therefore, IBE is strongly suggested to be considered for the numerical simulations of sea level changes in the East Sea.