• Title/Summary/Keyword: Spline interpolation method

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A Study on the Gray Images Morphing Using Morphology and Spline (그레이영상에서의 모폴로지와 스플라인기법을 적용한 영상모핑에 관한 연구)

  • 정은숙;허창우;류광렬
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2003.05a
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    • pp.161-164
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    • 2003
  • A study on the gray images morphing using morphology and spline interpolation is realized. The adapted method is that join the 2 broken line together with the dilation of morphology to the morphing image, remove the holes to be a soft edge at the image boundaries and extract the featuring points with the cubic spline interpolation. As a result of experiment the method elicits more smoothing image morphing in detail and speed up the interframe processing.

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Modelling of the noise-added saturated steam table using neural networks (노이즈가 포함된 포화증기표의 신경회로망 모델링)

  • Lee, Tae-Hwan;Park, Jin-Hyun
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.15 no.2
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    • pp.413-418
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    • 2011
  • The thermodynamic properties of steam table are obtained by measurement or approximate calculation under appropriate assumptions. Therefore they are supposed to have basic measurement errors. And thermodynamic properties should be modeled through function approximation for using in numerical analysis. In order to make noised thermodynamic properties corresponding to measurement errors, random numbers are generated, adjusted to appropriate magnitudes and added to original thermodynamic properties. Both neural networks and quadratic spline interpolation method are introduced for function approximation of these modified thermodynamic properties in the saturated water based on pressure and temperature. In analysis spline interpolation method gives much less relative errors than neural networks at both ends of data. Excluding the both ends of data, the relative errors of neural networks is generally within ${\pm}0.2%$ and those of spline interpolation method within ${\pm}0.5$~1.5%. This means that the neural networks give smaller relative errors compared with quadratic spline interpolation method within range of use. From this fact it was confirmed that the neural networks trace the original values better than the quadratic interpolation method and neural networks are more appropriate method in modelling the saturated steam table.

Location Prediction of Mobile Objects using the Cubic Spline Interpolation (3차 스플라인 보간법을 이용한 이동 객체의 위치 추정)

  • 안윤애;박정석;류근호
    • Journal of KIISE:Databases
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    • v.31 no.5
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    • pp.479-491
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    • 2004
  • Location information of mobile objects is applied to vehicle tracking, digital battlefields, location based services, and telematics. Their location coordinates are periodically measured and stored in the application systems. The linear function is mainly used to estimate the location information that is not in the system at the query time point. However, a new method is needed to improve uncertainties of the location representation, because the location estimation by linear function induces the estimation error. This paper proposes an application method of the cubic spline interpolation in order to reduce deviation of the location estimation by linear function. First, we define location information of the mobile object moving on the two-dimensional space. Next, we apply the cubic spline interpolation to location estimation of the proposed data model and describe algorithm of the estimation operation. Finally, the precision of this estimation operation model is experimented. The experimentation comes out more accurate results than the method by linear function, although the proposed location estimation function uses the small amount of information. The proposed method has an advantage that drops the cost of data storage space and communication for the management of location information of the mobile objects.

Investigation on the Description Method of Extrusion Die Surface using B-Spline Surface Scheme (B-스플라인 곡면기법을 이용한 압출금형 곡면의 표현방법에 관한 연구)

  • 유동진;임종훈
    • Transactions of Materials Processing
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    • v.12 no.2
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    • pp.142-150
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    • 2003
  • To construct the extrusion die surface, a B-Spline surface scheme based on the cubic B-Spline curve interpolation method is proposed in the present work. The inlet and outlet profiles are described with B-Spline curves by using the centripetal method for uniform parameterization. The interior control points of surface are generated using the derivative characteristics of B-Spline curve. A complete B-Spline surface is constructed by using appropriate coordinate transformation and knot deletion. In the present study, a quantitative measure for the control of surface is suggested by introducing the tangential vector and inclination angles at the inlet and outlet sections. To verify the validity of the proposed method, automatic surface generation is carried out for the various types of extrusion die surface.

Application of Curve Interpolation Algorithm in CAD/CAM to Remove the Blurring of Magnified Image

  • Lee Yong-Joong
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 2005.05a
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    • pp.115-124
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    • 2005
  • This paper analyzes the problems that occurred in the magnification process for a fine input image and investigates a method to improve the problems. This paper applies a curve interpolation algorithm in CAD/CAM for the same test images with the existing image algorithm in order to improve the problems. As a result. the nearest neighbor interpolation. which is the most frequently applied algorithm for the existing image interpolation algorithm. shows that the identification of a magnified image is not possible. Therefore. this study examines an interpolation of gray-level data by applying a low-pass spatial filter and verifies that a bilinear interpolation presents a lack of property that accentuates the boundary of the image where the image is largely changed. The periodic B-spline interpolation algorithm used for curve interpolation in CAD/CAM can remove the blurring but shows a problem of obscuration, and the Ferguson's curve interpolation algorithm shows a more sharpened image than that of the periodic B-spline algorithm. For the future study, hereafter. this study will develop an interpolation algorithm that has an excel lent improvement for the boundary of the image and continuous and flexible property by using the NURBS. Ferguson's complex surface. and Bezier surface used in CAD/CAM engineering based on. the results of this study.

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A Study on the Interpolation Algorithm to Improve the Blurring of Magnified Image (확대 영상의 몽롱화 현상을 제거하기 위한 보간 알고리즘 연구)

  • Lee, Jun-Ho
    • Journal of the Korean Society of Manufacturing Technology Engineers
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    • v.19 no.4
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    • pp.562-569
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    • 2010
  • This paper analyzes the problems that occurred in the magnification process for a fine input image and investigates a method to improve the blurring of magnified image. This paper applies a curve interpolation algorithm in CAD/CAM for the same test images with the existing image algorithm in order to improve the blurring of magnified image. As a result, the nearest neighbor interpolation, which is the most frequently applied algorithm for the existing image interpolation algorithm, shows that the identification of a magnified image is not possible. Therefore, this study examines an interpolation of gray-level data by applying a low-pass spatial filter and verifies that a bilinear interpolation presents a lack of property that accentuates the boundary of the image where the image is largely changed. The periodic B-spline interpolation algorithm used for curve interpolation in CAD/CAM can remove the blurring but shows a problem of obscuration, and the Ferguson' curve interpolation algorithm shows a more sharpened image than that of the periodic B-spline algorithm. For the future study, hereafter, this study will develop an interpolation algorithm that has an excellent improvement for the boundary of the image and continuous and flexible property by using the NURBS, Ferguson' complex surface, and Bezier surface used in CAD/CAM engineering based on the results of this study.

Comparison Error of Signal Interpolation Methods for Vibration Signal Analysis of Revolution Machine (회전체의 진동신호분석을 위한 신호보간의 오차분석)

  • Park Jun-Yong;Park Chong-Yeon
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.53 no.12
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    • pp.820-826
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    • 2004
  • In this paper, studied error of various signal interpolation methods in vibration signal analysis with digital order tracking. Because, interpolation errors are related with sampling rate and amount of calculation. Appled Signal interpolation methods are Lagrange, Newton and Cubic-spline. This paper proposed more proper interpolation method. Also, we suggest guideline for adaptive application of signal interpolation methods with Calculated results.

A non-symmetric non-periodic B3-spline finite strip method

  • Kim, Kyeong-Ho;Choi, Chang-Koon
    • Structural Engineering and Mechanics
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    • v.18 no.2
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    • pp.247-262
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    • 2004
  • In the earlier application of the spline finite strip method(FSM), the uniform B3-spline functions were used in the longitudinal direction while the conventional interpolation functions were used in the transverse direction to construct the displacement filed in a strip. To overcome the shortcoming of the uniform B3-spline, non-periodic B-spline was developed as the displacement function. The use of non-periodic B3-spline function requires no tangential vectors at both ends to interpolate the geometry of shell and the Kronecker delta property is also satisfied at the end boundaries. Recently, non-periodic spline FSM which was modified to have a multiple knots at the boundary was developed for the shell analysis and applied to the analysis of bridges. In the formulation of a non-symmetric spline finite strip method, the concepts of non-periodic B3-spline and a stress-resultant finite strip with drilling degrees of freedom for a shell are used. The introduction of non-symmetrically spaced knots in the longitudinal direction allows the selective local refinement to improve the accuracy of solution at the connections or at the location of concentrated load. A number of numerical tests were performed to prove the accuracy and efficiency of the present study.

Spline Finite Strip Shell Analysis of Prestressed Concrete Box-Girder Bridges (프리스트레스트 콘크리트 박스 거더 교량의 Spline 유한대판 쉘 해석)

  • 최창근;김경호;홍현석
    • Proceedings of the Computational Structural Engineering Institute Conference
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    • 2001.04a
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    • pp.18-25
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    • 2001
  • Analysis of prestressed box-girder bridges using the spline finite strip method is presented. In the present study, the spline finite strip method(FSM) is modified using the non-periodic B-spline interpolation. In the analysis of the prestressed box girder bridges, each tendon force is evaluated by summation of the adjacent segment forces. Once the equivalent forces acting on the structure at the tendon points are found, they are transformed into statically equivalent forces at the adjacent node or joints. Several examples were analyzed to verify the performance of present method using the non-periodic B-spline FSM. Good agreements were obtained when compared with the previous study.

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Comparison of the neural networks with spline interpolation in modelling superheated water (물의 과열증기 모델링에 대한 신경회로망과 스플라인법 비교)

  • Lee, Tae-Hwan;Park, Jin-Hyun;Kim, Bong-Hwan
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2007.10a
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    • pp.246-249
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    • 2007
  • In numerical analysis for phase change material, numerical values of thermodynamic properties such as temperature, pressure, specific volume, enthalpy and entropy are required. But the steam table or diagram itself cannot be used without modelling. In this study applicability of neural networks in modelling superheated vapor region of water was examined by comparing with the quadratic spline. neural network consists of an input layer with 2 nodes, two hidden layers and an output layer with 3 nodes. Quadratic spline interpoation method was also applied for comparison. Neural network model revealed smaller percentage error to quadratic spline interpolation. From these results, it is confirmed that the neural networks could be powerful method in modelling the superheated range of the steam table.

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