• 한국CDE학회논문집
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• 제8권3호
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• pp.150-156
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• 2003

Direct Metal Deposition (DMD) is a new additive process producing three-dimensional metal components or tools directly from CAD data, which aims to take mold making and metalworking in an entirely new direction. It is the blending of five common technologies: lasers, CAD, CAM, sensors and materials. In the resulting process, alternatively called laser cladding, an industrial laser is used to locally heat a spot on a tool-steel work piece or platform, forming a molten pool of metal. A small stream of powdered tool-steel metal is then injected into the metal pool to increase the size of the molten pool. By moving the laser beam back and forth, under CNC control, and tracing out a pattern determined by a computerized CAD design, the solid metal part is eventually built line-by-line, one layer at a time. DMD produces improved material properties in less time and at a lower cost than is possible with traditional fabrication technologies.

• 대한안전경영과학회:학술대회논문집
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• 대한안전경영과학회 1999년도 추계학술대회
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• pp.565-574
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• 1999

In reverse engineering area, it is rapidly developing reconstruction of surfaces from scanning or digitizing data, but geometric models of existing objects unavailable many industries. This paper describes new methodology of reverse engineering area, good strategies and important algorithms in reverse engineering area. Furthermore, proposing reconstruction of surface technique is presented. A method find base geometry and blending surface between them. Each based geometry is divided by triangular patch which are compared their normal vector for face grouping. Each group is categorized analytical surface such as a part of the cylinder, the sphere, the cone, and the plane that mean each based geometry surface. And then, each based geometry surface is implemented infinitive surface. Infinitive average surface's intersections are trimmed boundary representation model reconstruction. This method has several benefits such as the time efficiency and automatic functional modeling system in reverse engineering. Especially, it can be applied 3D scanner and 3D copier.

• Nuclear Engineering and Technology
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• 제34권5호
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• pp.482-493
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• 2002

The MOX fuel for LWR is fabricated either by direct mechanical blending of UO$_2$ and PuO$_2$ or by two stage mixing. Hence Pu-rich particles, whose Pu concentrations are higher than pellet average one and whose size distribution depends on a specific fabrication method, are inevitably dispersed in MOX pellet. Due to the inhomogeneous microstructure of MOX fuel, the thermal conductivity of LWR MOX fuel scatters from 80 to 100 % of UO$_2$ fuel. This paper describes a mechanistic thermal conductivity model for MOX fuel by considering this inhomogeneous microstructure and presents an explanation for the wide scattering of measured MOX fuel＇s thermal conductivity. The developed model has been incorporated into a KAERI＇s fuel performance code, COSMOS, and then evaluated using the measured in-pile data for MOX fuel. The database used for verification consists of homogeneous MOX fuel at beginning-of-life and inhomogeneous MOX fuel at high turnup. The COSMOS code predicts the thermal behavior of MOX fuel well except for the irradiation test accompanying substantial fission gas release. The over-prediction with substantial fission gas release seems to suggest the need for the introduction of a recovery factor to a term that considers the burnup effect on thermal conductivity.

• 품질경영학회지
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• 제40권3호
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• pp.347-358
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• 2012

Purpose: Given the mixture components - process variables experimental data, we propose the strategy to find the proper combined model. Methods: Process variables are factors in an experiment that are not mixture components but could affect the blending properties of the mixture ingredients. For example, the effectiveness of an etching solution which is measured as an etch rate is not only a function of the proportions of the three acids that are combined to form the mixture, but also depends on the temperature of the solution and the agitation rate. Efficient designs for the mixture components - process variables experiments depend on the mixture components - process variables model which is called a combined model. We often use the product model between the canonical polynomial model for the mixture and process variables model as a combined model. Results: First we choose the reasonable starting models among the class of admissible product models and practical combined models suggested by Lim(2011) based on the model selection criteria and then, search for candidate models which are subset models of the starting model by the sequential variables selection method or all possible regressions procedure. Conclusion: Good candidate models are screened by the evaluation of model selection criteria and checking the residual plots for the validity of the model assumption. The strategy to find the proper combined model is illustrated with examples in this paper.

• Smart Structures and Systems
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• 제17권4호
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• pp.647-667
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• 2016

In this paper, dynamic displacement is estimated with high accuracy by blending high-sampling rate acceleration data with low-sampling rate displacement measurement using a two-stage Kalman estimator. In Stage 1, the two-stage Kalman estimator first approximates dynamic displacement. Then, the estimator in Stage 2 estimates a bias with high accuracy and refines the displacement estimate from Stage 1. In the previous Kalman filter based displacement techniques, the estimation accuracy can deteriorate due to (1) the discontinuities produced when the estimate is adjusted by displacement measurement and (2) slow convergence at the beginning of estimation. To resolve these drawbacks, the previous techniques adopt smoothing techniques, which involve additional future measurements in the estimation. However, the smoothing techniques require more computational time and resources and hamper real-time estimation. The proposed technique addresses the drawbacks of the previous techniques without smoothing. The performance of the proposed technique is verified under various dynamic loading, sampling rate and noise level conditions via a series of numerical simulations and experiments. Its performance is also compared with those of the existing Kalman filter based techniques.

• 한국연초학회지
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• 제22권2호
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• pp.184-189
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• 2000

This study was conducted to develop the objective evaluation method for tobacco smoke. The evaluation was carried out by using the data of cut or blended tobacco components, smoke components, electric nose system (ENS), and sensory test. By using the statistical methods, such as cluster analysis, discriminant analysis, factor analysis, correlation analysis, and multiple regression analysis, the relationship among the data of tobacco, smoke, ENS, and sensory evaluation was studied. By the results of cluster analysis, the data from smoke analysis by GC and ENS were able to select the difference of tobacco leaf characteristics. As the results of discriminant analysis, grouping by the components of tobacco leaves and smoke was possible and the results of GC analysis of smoke could be used for discrimination of tobacco leaves. In the results of factor analysis, nicotine, tar, CO, puff No and pH in the smoke were the factors effecting on the tobacco leaf characteristics. From the correlation analysis, aroma, taste, irritation, and smoke volume of sensory test had high relation to tar, p-cresol threonolatone, levoglucosane, and quinic acid- ${\gamma}$ -lactone of smoke. The ENS data showed high efficiency for discriminant analysis and cluster analysis, but it was not good for factor analysis, and correlation analysis. It was possible to estimate tobacco leaves and their blending characteristics by the analytical data of tobacco leaves, smoke, ENS, and sensory test results. By the multiple regression analysis, some correlation among selected chemical components and sensory evaluation were found. This study strongly indicated that the some chemical analysis data was available for the objective evaluation of tobacco sensory attributes.

• 한국인터넷방송통신학회논문지
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• 제19권2호
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• pp.111-117
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• 2019

본 논문에서는 레미콘 현장검사 시 스마트폰으로 검사와 대응이 가능하도록 모바일 레미콘 품질 관리 시스템을 제안하였다. 그리고 현장검사를 디지털화하기 위한 실시간 슬럼프 데이터 처리 부분과 서버와의 데이터 교환을 위한 XML 포맷을 제안하였다. 스마트폰을 이용하여 레미콘 현장검사의 영상을 실시간으로 전송하여 오류를 판단하였으며, 이렇게 함으로써 현장과 본사에서 레미콘 제품의 이상 여부를 공유하였다. 개발 대상 기술은 실제 결과 값을 기반으로 영상 이미지 분석 등을 통하여 제품에 대한 이상 여부가 판단되며 회사 및 현장에 적합한 정보 제공이 가능하다. 레미콘 품질 데이터 분석을 기반으로 원자재 관리 데이터와 연동함으로써 레미콘 업종의 가장 큰 문제 사항인 원자재 반입에 따른 실시간 최적의 배합비율 제시가 가능하다.

• 한국연초학회지
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• 제27권1호
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• pp.134-140
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• 2005

The purpose of this study was the discrimination of different tobacco types by the E-Nose/tongue and the analysis of what human sensory attributes are correlated with e-instrument's sensors. Samples were made from five groups of Korean domestic tobacco leaves, aged burley and not aged, aged flue-cured and not aged and blending types of the four. Instrumental tests were conducted to discriminate characteristics among different tobacco samples by the E-Nose and the E-Tongue. Sensory attributes of tobacco tastes were impact, irritation, bitterness, hay-like, tobacco taste, smoke volume, smoke pungent and mouth cleanness. STATISTICA software was used to analyze correlation between the human sensory data and the raw data of e-instruments. Discrimination analysis can be achieved using principal components analysis (PCA) and discriminant factorial analysis(DFA). As a result, impact, bitterness, irritation, smoke volume and smoke pungent of human sensory attributes were correlated with data from the several clustered E-Nose sensors(p < 0.10). And bitterness, irritation, and smoke pungent of human sensory attributes were correlated with data from the E-Tongue sensors(p < 0.10). PCA plot by the E-Nose shows that aged tobacco and not aged were discriminated and DFA plot shows that three groups(aged burley, not aged burley and flue-cured) were discriminated. PCA plot by the E- Tongue shows that flue-cured tobacco was separated from burley. Our results indicated that the e-instruments are sensitive enough to distinguish among tobacco types and their several sensors are reacted to the human sensory attributes.

• International Journal of CAD/CAM
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• 제13권1호
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• pp.1-11
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• 2013

Just by adjusting the control points iteratively, progressive iterative approximation (PIA) presents an intuitive and straightforward scheme such that the resulting limit curve (surface) can interpolate the original data points. In order to obtain more flexibility, adjusting only a subset of the control points, a new method called local progressive iterative approximation (LPIA) has also been proposed. But to this day, there are two problems about PIA and LPIA: (1) Only an approximation process is discussed, but the accurate convergence curves (surfaces) are not given. (2) In order to obtain an interpolating curve (surface) with high accuracy, recursion computations are needed time after time, which result in a large workload. To overcome these limitations, this paper gives an explicit matrix expression of the control points of the limit curve (surface) by the PIA or LPIA method, and proves that the column vector consisting of the control points of the PIA's limit curve (or surface) can be obtained by multiplying the column vector consisting of the original data points on the left by the inverse matrix of the collocation matrix (or the Kronecker product of the collocation matrices in two direction) of the blending basis at the parametric values chosen by the original data points. Analogously, the control points of the LPIA's limit curve (or surface) can also be calculated by one-step. Furthermore, the $G^1$ joining conditions between two adjacent limit curves obtained from two neighboring data points sets are derived. Finally, a simple LPIA method is given to make the given tangential conditions at the endpoints can be satisfied by the limit curve.

• 대기
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• 제20권2호
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• pp.73-89
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• 2010

An updateable model output statistics (UMOS) system for daily maximum and minimum temperature ($T_M$ and $T_m$) over South Korea based on the Canadian UMOS system were developed and validated. RDAPS (regional data assimilation and prediction system) and KWRF (Korea WRF) which have quite different physics and dynamics were used for the development of UMOS system. The 20 most frequently selected potential predictors for each season, station, and forecast projection time from the 68 potential predictors of the MOS system, were used as potential predictors of the UMOS system. The UMOS equations were developed through the weighted blending of the new and old model data, with weights chosen to emphasize the new model data while including enough old model data to ensure stable equations and a smooth transition of dependency from the old model to the new model. The UMOS equations are being updated by every 7 days. The validation results of $T_M$ and $T_m$ showed that seasonal mean bias, RMSE, and correlation coefficients for the total forecast projection times are -0.41-0.17 K, 1.80-2.46 K, and 0.80-0.97, respectively. The performance is slightly better in autumn and winter than in spring and summer. Also the performance of UMOS system are clearly dependent on location, better at the coastal region than inland area. As in the MOS system, the performance of UMOS system is degraded as the forecast day increases.