• Korean Journal of Computational Design and Engineering
• /
• v.13 no.1
• /
• pp.1-7
• /
• 2008

Data compression becomes increasingly an important issue for reducing data storage spaces as well as transmis-sion time in network environments. In 3D geometric models, the normal vectors of faces or meshes take a major portion of the data so that the compression of the vectors, which involves the trade off between the distortion of the images and compression ratios, plays a key role in reducing the size of the models. So, raising the compression ratio when the normal vector is compressed and minimizing the visual distortion of shape model's shading after compression are important. According to the recent papers, normal vector compression is useful to heighten com-pression ratio and to improve memory efficiency. But, the study about distortion of shading when the normal vector is compressed is rare relatively. In this paper, new normal vector compression method which is clustering normal vectors and assigning Representative Normal Vector (RNV) to each cluster and using the angular deviation from actual normal vector is proposed. And, using this new method, Visually Undistinguishable Lossy Compression (VULC) algorithm which distortion of shape model's shading by angular deviation of normal vector cannot be identified visually has been developed. And, being applied to the complicated shape models, this algorithm gave a good effectiveness.

• The KSFM Journal of Fluid Machinery
• /
• v.13 no.6
• /
• pp.42-50
• /
• 2010

The present analysis deals with a numerical procedure for optimizing the shape of elliptical dimples in a cooling channel. The three-dimensional Reynolds-averaged Navier-Stokes (RANS) analysis is employed in conjunction with the SST model for predictions of the turbulent flow and the heat transfer. Three non-dimensional geometric design variables, such as the ellipse dimple diameter ratio, ratio of the dimple depth to the average diameter, and ratio of the distance between dimples to the pitch are considered in the optimization. Twenty-one experimental points within design space are selected by Latin Hypercube Sampling. Each objective function values at these points are evaluated by RANS analysis and producing optimal point using surrogate model. The linear combination of heat transfer coefficient and friction loss related terms with a weighting factor is defined as the objective function. The results show that the optimized elliptical dimple shape improves considerably the heat transfer performance than the circular dimple shape.

• Journal of Astronomy and Space Sciences
• /
• v.21 no.4
• /
• pp.361-370
• /
• 2004

The DSN(Deep Space Network) measurement model for interplanetary navigations which is essential for precise orbit determination has been developed. The DSN measurement model produces fictitious DSN observables such as range, doppler and angular data, containing the potential observational errors in geometric data obtained from orbit propagator. So the important part of this research is to model observational errors in DSN observation and to characterize the errors. The modeled observational errors include the range delay effect caused by troposphere, ionosphere, antenna offset, and angular refraction effect caused by troposphere. Non-modeled errors are justified as the parameters. All of these results from developed models show about $10\%$ errors compared to the JPL's reference results, that are within acceptable error range.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.2
• /
• pp.100-109
• /
• 2007

Object recognition of service robots is very important for most of services such as delivery, and errand. Conventional methods are based on the geometric models in static industrial environments, but they have limitations in indoor environments where the condition is changable and the movement of service robots occur because the interesting object can be occluded or small in the image according to their location. For solving these uncertain situations, in this paper, we propose the method that exploits observed objects as context information for predicting interesting one. For this, we propose the method for modeling domain knowledge in probabilistic frame by adopting Bayesian networks and ontology together, and creating knowledge model dynamically to extend reasoning models. We verify the performance of our method through the experiments and show the merit of inductive reasoning in the probabilistic model

• Smart Structures and Systems
• /
• v.25 no.5
• /
• pp.605-617
• /
• 2020

The existence of damages in structures causes changes in the physical properties by reducing the modal parameters. In this paper, we develop a two-stages approach based on normalized Modal Strain Energy Damage Indicator (nMSEDI) for quick applications to predict the location of damage. A two-dimensional IsoGeometric Analysis (2D-IGA), Machine Learning Algorithm (MLA) and optimization techniques are combined to create a new tool. In the first stage, we introduce a modified damage identification technique based on frequencies using nMSEDI to locate the potential of damaged elements. In the second stage, after eliminating the healthy elements, the damage index values from nMSEDI are considered as input in the damage quantification algorithm. The hybrid of Teaching-Learning-Based Optimization (TLBO) with Artificial Neural Network (ANN) and Particle Swarm Optimization (PSO) are used along with nMSEDI. The objective of TLBO is to estimate the parameters of PSO-ANN to find a good training based on actual damage and estimated damage. The IGA model is updated using experimental results based on stiffness and mass matrix using the difference between calculated and measured frequencies as objective function. The feasibility and efficiency of nMSEDI-PSO-ANN after finding the best parameters by TLBO are demonstrated through the comparison with nMSEDI-IGA for different scenarios. The result of the analyses indicates that the proposed approach can be used to determine correctly the severity of damage in beam structures.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2013.08a
• /
• pp.277.2-277.2
• /
• 2013

Petri dishes and glass slides have been widely used as general substrates for in vitro mammalian cell cultures due to their culture viability, optical transparency, experimental convenience, and relatively low cost. Despite the aforementioned benefit, however, the flat two-dimensional substrates exhibit limited capability in terms of realistically mimicking cellular polarization, intercellular interaction, and differentiation in the non-physiological culture environment. Here, we report a protocol of culturing embryonic rat hippocampal neurons on the electro-spun polymeric network and the results from examination of neuronal cell behavior and network formation on this culture platform. A combinatorial method of laser-scanning confocal fluorescence microscopy and live-cell imaging technique was employed to track axonal outgrowth and synaptic connectivity of the neuronal cells deposited on this model culture environment. The present microfiber-based scaffold supports the prolonged viability of three-dimensionally-formed neuronal networks and their microscopic geometric parameters (i.e., microfiber diameter) strongly influence the axonal outgrowth and synaptic connection pattern. These results implies that electro-spun fiber scaffolds with fine control over surface chemistry and nano/microscopic geometry may be used as an economic and general platform for three-dimensional mammalian culture systems, particularly, neuronal lineage and other network forming cell lines.

• The Journal of Engineering Geology
• /
• v.3 no.2
• /
• pp.125-148
• /
• 1993

Groundwater flow in fractured rock masses is controlled by combined effects of fracture networks, state of geostafic stresses and crossflow between fractures and rock matrix. Furthermore the scaie dependent, anisotropic properties of hydraulic parameters results mainly from irregular paftems of fracture system, which can not be evaluated properly with the methods available at present. The basic assumpfion of discrete fracture network model is that groundwater flows only along discrete fractures and the flow paths in rock mass are determined by geometric paftems of interconnected fractures. The characteristics of fracture distribution in space and fracture hydraulic parameters are represented as the probability density functions by stochastic simulation. The discrete fracture network modelling was aftempted to characterize the groundwater flow in the vicinity of existing large cavems located in Wonjeong-ri, Poseung-myon, Pyeungtaek-kun. The fracture data of $1\textrm{km}^2$ area were analysed. The result indicates that the fracture sets evaluated from an equal area projection can be grouped into 6 sets and the fracture sizes are distributed in longnormal. The conductive fracture density of set 1 shows the highest density of 0.37. The groundwater inflow into a carvem was calculated as 29ton/day with the fracture transmissivity of $10^{-8}\textrm{m}^2/s$. When the fracture transmissivity increases in an order, the inflow amount estimated increases dramatically as much as fold, i.e 651 ton/day. One of the great advantages of this model is a forward modelling which can provide a thinking tool for site characterization and allow to handle the quantitative data as well as qualitative data.

• Journal of Information Technology Applications and Management
• /
• v.18 no.4
• /
• pp.81-93
• /
• 2011

By using GOSST theory, this paper models SSCFLP taking FLP, capacity of the facilities, single source capacitated limitation level and service enhancement issues into consideration. GOSST theory is strongly suggested as the solution procedure for these issues. We have used clustering of Center of Gravity method using the case study of the company S and then, took a heuristic GOSST measure in the alternative selection process. As a result, the research finds an alternative solution that both meets the satisfactory level of service and achieves consistent distribution capacity. When using this modeling, especially, to select the location of the logistics distribution center, the efficiency of current facilities is maximized while offering the minimum geometric distance for the alternative. Also, we can expect that the illustrated model and alternative solution can be applied to architecture of distribution system, to selection of telecommunication system locations for wireless network and to relocation of related facilities due to their sensitivities to location and weight.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.410-413
• /
• 2007

Water vapor in the atmosphere is an influential factor of the hydrosphere cycle, which exchanges heat through phase change and is essential to precipitation. Because of its significance in altering weather, the estimation of water vapor amount and distribution is crucial to determine the precision of the weather forecasting and the understanding of regional/local climate. It is shown that it is reliable to measure precipitable water (PW) using long baseline (500-2000km) GPS observations. However, it becomes infeasible to derive absolute PW from GPS observations in Taiwan due to geometric limitation of relatively short-baseline network. In this study, a method of deriving Near-Real-Time PW from short baseline GPS observations is proposed. This method uses a reference station to derive a regression model for wet delay, and to interpolate the difference of wet delay among stations. Then, the precipitable water is obtained by using a conversion factor derived from radiosondes. The method has been tested by using the reference station located on Mt. Ho-Hwan with eleven stations around Taiwan. The result indicates that short baseline GPS observations can be used to precisely estimate the precipitable water in near-real-time.

• The Journal of Engineering Geology
• /
• v.8 no.1
• /
• pp.1-12
• /
• 1998

The fracture size among geometric parameters of the fracture system is treated as one of the most important factors in the geotechnical and hydrogeological analysis. However, several uncertainties in data acquisition and analysis pmcess about the fracture size are not clear yet. This study presents the current status on the estimation of the fracture size and verifies the estimating method using forward modeling approach. The factors considered are the variation of fracture intersection probabilities with different assumptions on the orientation of sampling planes and fracture size by using a simulated tleee dimensional fracture network model. If it is possible to analyze precisely the fracture intersection probabilities and the characteristics of probabilistic distnbution fiom cavern walls, outcrops or boreholes,the actual fracture size developed in rock rnass can be estimated confidently.