• 한국CDE학회논문집
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• 제5권4호
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• pp.301-311
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• 2000

For effective part modifications which is necessary in the design process frequently, variational geometric modeling with constraint management being used in a wide. Most variational geometric modeling methods, however, manage just the constraints about sketch elements used for generation of primitives. Thus, not only constraint propagation but also re-build of various modeling operations stored in the modeling history is necessary iota part geometry modifications. Especially, re-build of high-cost Boolean operations is apt to deteriorate overall modeling efficiency abruptly. Therefore, in this paper we proposed an algorithm that can handle all geometric entities of the part directly. For this purpose, we introduced eight type geometric constraints to the various geometric calculations about all geometric entities in sweepings and Boolean operations as well as the existing constraints of the sketch elements. The algorithm has a merit of rapid part geometric modifications through only constraint propagation without rebuild of modeling operations which are necessary in the existing variational geometric modeling method.

• 지구물리와물리탐사
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• 제6권1호
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• pp.13-22
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• 2003

TSP탐사는 터널 및 지하공간의 건설 시 굴착 진행 중인 터널 주위의 지질구조를 파악하기 위하여 VSP탐사를 터널 내에 적용한 물리탐사 기법이다. 하지만 터널 굴착예정 지역의 지질구조에 따른 터널 주위에서 파의 전파특성을 보여줄 수 있는 인공 탄성파합성 알고리듬이 부족한 실정이다. 이에 본 연구에서는 기존의 2차원 유한 요소 탄성파 모델링 알고리듬을 발전시켜 터널 전방에 여러 형태의 지질구조가 존재하는 경우에 대하여 2차원 시간영역 탄성파 모델링을 수행하였다. 그리고 이로부터 얻은 인공합성 탄성파단면과 파면단면을 통하여 파의 전파특성을 규명하고자 하였다. 우선 TSP탐사와 동일한 형태의 송수신 배열을 한 후, 균질 매질에 대한 모델링을 수행하여 인공합성 탄성파단면과 파면단면을 동시에 구해 직접파의 전파특성을 분석하였다. 그리고 터널 굴착방향과 수직인 반사면과 평행인 반사면이 존재하는 경우에 대해 각각 모델링을 수행하여 균질 매질의 경우와의 비교, 분석을 통해 반사파들의 전파특성을 고찰하였다. 또한 송신원을 수진기의 앞쪽과 뒤쪽에 배열한 두 가지 경우에 대하여 모델링을 수행하여, 두 방법의 특성과 장단점을 고찰하였다. 이와 같은 모델링을 통하여 TSP탐사자료의 해석, 역산 알고리듬의 개발 및 송수신 개발 등에 도움을 줄 수 있을 것으로 사료된다.

• 제어로봇시스템학회논문지
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• 제17권4호
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• pp.321-327
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• 2011

This paper proposes an equipment and an algorithm for modeling the magnetic force of electromagnets in magnetic levitation systems. We assume that the magnetic force model is represented in terms of a 2D lookup table. The 2D lookup table is constructed by applying noncausal filtering and interpolation to data measured by the proposed modeling equipment. The proposed modeling equipment is designed such that it can measure the magnetic force exerted on the levitation object while it changes the voltage applied to the electromagnet and position of the levitation object. The algorithm of making a 2D lookup table has two stages. The data measured by the proposed modeling equipment is smoothed by a noncausal filter and then the 2D lookup table is obtained by interpolating filtered data. The proposed modeling method has advantages of time-saving, model consistency, and chance of automation for mass production. We show the validity of proposed method through control experiments.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제1권1호
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• pp.87-94
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• 2001

Silicon nitride films grown by plasma-enhanced chemical vapor deposition (PECVD) are useful for a variety of applications, including anti-reflecting coatings in solar cells, passivation layers, dielectric layers in metal/insulator structures, and diffusion masks. PECVD systems are controlled by many operating variables, including RF power, pressure, gas flow rate, reactant composition, and substrate temperature. The wide variety of processing conditions, as well as the complex nature of particle dynamics within a plasma, makes tailoring SiN film properties very challenging, since it is difficult to determine the exact relationship between desired film properties and controllable deposition conditions. In this study, SiN PECVD modeling using optimized neural networks has been investigated. The deposition of SiN was characterized via a central composite experimental design, and data from this experiment was used to train and optimize feed-forward neural networks using the back-propagation algorithm. From these neural process models, the effect of deposition conditions on film properties has been studied. A recipe synthesis (optimization) procedure was then performed using the optimized neural network models to generate the necessary deposition conditions to obtain several novel film qualities including high charge density and long lifetime. This optimization procedure utilized genetic algorithms, hybrid combinations of genetic algorithm and Powells algorithm, and hybrid combinations of genetic algorithm and simplex algorithm. Recipes predicted by these techniques were verified by experiment, and the performance of each optimization method are compared. It was found that the hybrid combinations of genetic algorithm and simplex algorithm generated recipes produced films of superior quality.

• Advances in materials Research
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• 제11권4호
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• pp.351-374
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• 2022

Softening function is the primary input for modeling the fracture of concrete when the cohesive crack approach is used. In this paper, based on the laboratory data on notched beams, an inverse algorithm is proposed that can accurately find the softening curve of the concrete. This algorithm uses non-linear finite element analysis and the damage-plasticity model. It is based on the kinematics of the beam at the late stages of loading. The softening curve, obtained from the corresponding algorithm, has been compared to other softening curves in the literature. It was observed that in determining the behavior of concrete, the usage of the presented curve made accurate results in predicting the peak loads and the load-deflection curves of the beams with different concrete mixtures. In fact, the proposed algorithm leads to softening curves that can be used for modeling the tensile cracking of concrete precisely. Moreover, the advantage of this algorithm is the low number of iterations for converging to an appropriate answer.

• Composite Materials and Engineering
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• 제3권3호
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• pp.221-239
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• 2021

This paper presents a multiscale modeling method for sheet molding compound (SMC) composites through a novel bundle packing reconstruction algorithm based on a micro-CT (Computed Tomography) image processing. Due to the complex flow pattern during the compression molding process, the SMC composites show a spatially varying orientation and overlapping of fiber bundles. Therefore, significant inhomogeneity and anisotropy are commonly observed and pose a tremendous challenge to predicting SMC composites' properties. For high-fidelity modeling of the SMC composites, the statistical distributions for the fiber orientation and local volume fraction are characterized from micro-CT images of real SMC composites. After that, a novel bundle packing reconstruction algorithm for a high-fidelity SMC model is proposed by considering the statistical distributions. A method for evaluating specimen level's strength and stiffness is also proposed from a set of high-fidelity SMC models. Finally, the proposed multiscale modeling methodology is experimentally validated through a tensile test.

• 한국정보시스템학회지:정보시스템연구
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• 제31권4호
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• pp.25-45
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• 2022

Purpose In this paper, we propose the method to extend topic modeling techniques in order to derive data-based research hypotheses when establishing research hypotheses for social sciences, As a concept in contrast to the existing deductive hypothesis establishment methodology for the social science research, the topic modeling technique was expanded to enable the so-called inductive hypothesis establishment methodology, and an analysis case of the Seongsan Ilchulbong online review based on the proposed methodology was presented. Design/methodology/approach In this paper, an extension architecture and extension algorithm in the form of extending the existing topic modeling were proposed. The extended architecture and algorithm include data processing method based on topic ratio in document, correlation analysis and regression analysis of processed data for topics derived by existing topic modeling. In addition, in this paper, an analysis case of the online review of Seongsan Ilchulbong Peak was presented by applying the extended topic modeling algorithm. An exploratory analysis was performed on the Seongsan Ilchulbong online reviews through the basic text analysis. The data was transformed into 5-point scale to enable correlation and regression analysis based on the topic ratio in each online review. A regression analysis was performed using the derived topics as the independent variable and the review rating as the dependent variable, and hypotheses could be derived based on this, which enable the so-called inductive hypothesis establishment. Findings This paper is meaningful in that it confirmed the possibility of deriving a causal model and setting an inductive hypothesis through an extended analysis of topic modeling.

• 한국BIM학회 논문집
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• 제13권3호
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• pp.1-11
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• 2023

In order to use BIM as a tool for improving the productivity and quality of products in the construction industry, a BIM model must be created from the design stage first. Infrastructure structures such as bridges and tunnels are mainly created based on three-dimensional alignment in the generation of BIM models. Especially, generation of BIM models based on three-dimensional linearity has high task difficulty and algorithms for automating BIM modeling for railway infra structures have been suggested in previous studies. This study improved the BIM modeling automation algorithm of railway infrastructures and developed a system based on the algorithm so that it can be easily used by ordinary users. The system was built as an add-in system of Autodesk's Revit. As an improvement first, it is possible to arrange different libraries for each pattern, enabling various uses. In addition, it can be created models of several members with a single process and the system can automatically places structures that are added periodically, such as Rock Bolt and Fore Polling. Finally, 3D length information and volume for each pattern are automatically calculated for more accurate 3D-based volume calculation. This study contributes to increasing user accessibility by building a BIM modeling automation algorithm into a system. The system is expected to improve the efficiency of BIM modeling creation of linear-based infra structures, including railway infrastructure.

• Journal of Computational Design and Engineering
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• 제2권1호
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• pp.67-72
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• 2015

Recently, along with the improvements of geometry modeling methods using sketch-based interface, there have been a lot of developments in research about generating surface model from 3D curves. However, surfacing a 3D curve network remains an ambiguous problem due to the lack of geometric information. In this paper, we propose a new algorithm for estimating the normal vectors of the 3D curves which accord closely with user intent. Bending energy is defined by utilizing RMF(Rotation-Minimizing Frame) of 3D curve, and we estimated this minimal energy frame as the one that accords design intent. The proposed algorithm is demonstrated with surface model creation of various curve networks. The algorithm of estimating geometric information in 3D curves which is proposed in this paper can be utilized to extract new information in the sketch-based modeling process. Also, a new framework of 3D modeling can be expected through the fusion between curve network and surface creating algorithm.

• 대한전기학회논문지
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• 제45권1호
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.