• Journal of the Korean earth science society
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• v.38 no.1
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• pp.80-90
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• 2017

To build tetrahedra mesh for FEM numerical analysis, Boundary Representation (B-Rep) model is required, which provides the efficient volume description of an object. In engineering, the parametric solid modeling method is used for building B-Rep model. However, a geological modeling generally adopts discrete modeling based on the triangulated surface, called a Sealed Geological Model, which defines geological domain by using geological interfaces such as horizons, faults, intrusives and modeling boundaries. Discrete B-Rep model is incompatible with mesh generation softwares in engineering because of discrepancies between discrete and parametric technique. In this research we have developed a converting program from Sealed Geological Model to Gmsh and COMSOL software. The developed program can convert complex geological model built by geomodeling software to user-friendly FEM software and it can be applied to geoscience simulation such as geothermal, mechanical rock simulation etc.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.55-60
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• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.31-38
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• 2009

Most 3D models found in the literature focus on theoretical topology for exterior 3D volumes. Although there are a few indoor models such as CityGML or IFC, implementing a full topology for the indoor spaces is either less practical due to the complexity or not even necessary in some application domains. Moreover, current spatial DBMSs do not support functionalities explicitly for 3D topological relations. In this study, an alternative method to build a 3D indoor model with less complexity ernativespatial DBMS is suggested. Focusnation the fact that semantic attributes can be storedion the floor surface, we suggestivemulti-layered 3D model for indoor spaces. We show the process to build the proposed model in the PostGIS, a spatial DBMS. And, then, as an example application, we illustrate the process to build and run a campus building information system.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.51-58
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• 2023

Blockchains have recently struggled to design for the ideal distributed trust networks by solving scalability trilemma. However, local conflicts between some countries lead to imbalance on energy distribution. Besides, blockchain networks (e.g., Bitcoin) currently consume enormous energy for transaction and mining. The existing data volume based trust model evaluated an increasing blockchain size better than Lubin's trust model in scalability trilemma. In this paper, we propose a scalability based energy model to evaluate sustainability for blockchain networks, considering energy consumption for transaction, time duration, and the blockchain size of growing blockchain networks. Through the rigorous numerical analysis, we compare the proposed scalability based energy model with the existing model for the satisfaction and optimal blockchain size. Thus, the scalability based energy model will provide an assessment tool to choose the proper blockchain networks to solve scalability trilemma problem and prove sustainability.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.65-70
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• 2020

With the appearance of Bitcoin that builds peer-to-peer networks for transaction of digital content and issuance of cryptocurrency, lots of blockchain networks have been developed to improve transaction performance. Recently, Joseph Lubin discussed Decentralization Transaction per Second (DTPS) against alleviating the value of biased TPS. However, this Lubin's trust model did not enough consider a security issue in scalability trilemma. Accordingly, we proposed a trust metric based on blockchain size, stale block rate, and average block size, using a sigmoid function and convex optimization. Via numerical analysis, we presented the optimal blockchain size of popular blockchain networks and then compared the proposed trust metric with the Lubin's trust model. Besides, Bitcoin based blockchain networks such as Litecoin were superior to Ethereum for trust satisfaction and data volume.

• Tunnel and Underground Space
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• v.18 no.3
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• pp.202-213
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• 2008

In this study, theoretical approach of 3D seismic traveltime tomography was investigated. To guarantee the successful field application of 3D tomography, appropriate control of problem associated with blind zone is pre-requisite. To overcome the velocity distortion of the reconstructed tomogram due to insufficient source-receiver array coverage, the algorithm of 3D seismic traveltime tomography based on the Fresnel volume was developed as a technique of ray-path broadening. For the successful reconstruction of velocity cube, 3D traveltime algorithm was explored and employed on the basis of 2nd order Fast Marching Method(FMM), resulting in improvement of precision and accuracy. To prove the validity and field application of this algorithm, two numerical experiments were performed for globular and layered models. The algorithm was also found to be successfully applicable to field data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1644-1652
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• 2022

In the case of safety training where practical training is impossible due to risk problems, training contents using realistic media such as virtual reality or augmented reality are becoming a new alternative. In this paper, we propose a smoke modeling method that can be applied to safety-training contents implemented with realistic media technology. When an accident occurs in a hazardous area such as a petrochemical plant, visibility is not secured due to gas leakage and fire. In order to create such a situation, it is important to realistically express smoke. The proposed method is a smoke model implementation technique that can be effectively applied to the background of complex passages and devices such as petrochemical plants. In the proposed method, the smoke is expressed using volumetric rendering in the post-processing stage for the resulting image of scene rendering. Implementation results in the background of the factory show that the proposed method produces models that can express the smoke realistically.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.110-119
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• 2010

In this paper, we propose an automatic segmentation of the prostate using intensity distribution and gradient information in MR images. First, active shape model using adaptive intensity profile and multi-resolution technique is used to extract the prostate surface. Second, hole elimination using geometric information is performed to prevent the hole from occurring by converging the surface shape to the local optima. Third, the surface shape with large anatomical variation is corrected by using 2D gradient information. In this case, the corrected surface shape is often represented as rugged shape which is generated by the limited number of vertices. Thus, it is reconstructed by using surface modelling and smoothing. To evaluate our method, we performed the visual inspection, accuracy measures and processing time. For accuracy evaluation, the average distance difference and the overlapping volume ratio between automatic segmentation and manual segmentation by two radiologists are calculated. Experimental results show that the average distance difference was 0.3${\pm}$0.21mm and the overlapping volume ratio was 96.31${\pm}$2.71%. The total processing time of twenty patient data was 16 seconds on average.

• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1075-1085
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• 2012

In clinical research using medical images, the image segmentation is one of the most important processes. Especially, the hippocampal atrophy is helpful for the clinical Alzheimer diagnosis as a specific marker of the progress of Alzheimer. In order to measure hippocampus volume exactly, segmentation of the hippocampus is essential. However, the hippocampus has some features like relatively low contrast, low signal-to-noise ratio, discreted boundary in MRI images, and these features make it difficult to segment hippocampus. To solve this problem, firstly, We selected region of interest from an experiment image, subtracted a original image from the negative image of the original image, enhanced contrast, and applied anisotropic diffusion filtering and gaussian filtering as preprocessing. Finally, We performed an image segmentation using two level set methods. Through a variety of approaches for the validation of proposed hippocampus segmentation method, We confirmed that our proposed method improved the rate and accuracy of the segmentation. Consequently, the proposed method is suitable for segmentation of the area which has similar features with the hippocampus. We believe that our method has great potential if successfully combined with other research findings.

• Journal of Digital Convergence
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• v.12 no.11
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• pp.209-214
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• 2014

Despite the recession of the global market, the domestic electronic appliance companies dominated TV market in North America. They took both the premium and mid-priced product market and achieved both profitability and volume due to strong product competitiveness and brand power. Despite doing well in the North American market, the domestic TV manufacturers are worried about product development, marketing and sales strategies to remain the continuous competitiveness in the TV market. This study proposes the a sales prediction model of electronic appliances using sales data of S company from the North American market. We develop the sales prediction models based on multiple regression analysis and artificial neural network and compare two models. Especially, this study analyzes the relevance between the TV sales and TV main features in order to improve the price competitiveness or improve the value of TV products.