• IE interfaces
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• v.13 no.3
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• pp.320-327
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• 2000

In this research report, we consider multi stage supply chain optimization modeling techniques, and propose a new integrated model. The stages that are normally associated with a supply chain include procurement, production and distribution. In general, there are two distinct set of approaches which manage whole supply chain. One approach is the plant decision and subsequently distribution and inventory decision while the second approach is to address all decisions simultaneously through the integrated model. First, we present a survey of existing models that are treated by independently. And then, we propose an integrated model that is in a moderate size and easy to implement in practice. Finally, we address possible solution methodologies and present some computational test results using CPLEX to see the computational burden from which new algorithmic insight might be come up.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2262-2267
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• 2018

In electric machine design, there is a large computation cost for finite element analyses (FEA) when analyzing nonlinear characteristics in the machine Therefore, for the optimal design of an electric machine, designers commonly use an optimization algorithm capable of excellent convergence performance. However, robustness consideration, as this factor can guarantee machine performances capabilities within design uncertainties such as the manufacturing tolerance or external perturbations, is essential during the machine design process. Moreover, additional FEA is required to search robust optimum. To address this issue, this paper proposes a computationally efficient robust optimization algorithm. To reduce the computational burden of the FEA, the proposed algorithm employs a useful technique which termed static analysis assisted technique (SAAT). The proposed method is verified via the effective robust optimal design of electric machine to reduce cogging torque at a reasonable computational cost.

• Journal of Korea Multimedia Society
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• v.16 no.11
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• pp.1338-1347
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• 2013

Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher's linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.

• The Journal of Engineering Research
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• v.8 no.1
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• pp.51-57
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• 2006

We propose efficient methods for implementing 3-D reconstruction algorithms for Compton camera. Since reconstructing Compton scattered data involves the surface integral over the cone associated with the measurement bin, it is crucial to develop a computationally efficient surface integration method. In this work we assume that a cone is made up of a series of ellipses (or circles) stacked up one o top of the other. In order to reduce computational burden for tracing ellipses formed by the intersection of a cone and an image plane, we construct a series of imaginary planes perpendicular to the cone axis so that each plane contains a circle, not an ellipse. In this case the surface integral can be performed by adding uniform samples along each circle. The experimental results demonstrate that our method using imaginary planes significantly improves computational efficiency while keeping reconstruction accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10b
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• pp.1228-1233
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• 1990

This paper proposes a robust on-line fault detection method for uncertain systems. It is based on the fault detection method [10] accounting for modelling errors, which is shown to have superior performance over traditional methods but has some computational problems so that it is hard to be applied to on-line problems. The proposed method in this paper is an on-line version of the fault detection method suggested in [10]. Thus the method has the same detection performance robust to model uncertainties as that of [10]. Moreover, its computational burden is shown to be considerably lessened so that it is applicable to on-line fault detection problems.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.865-872
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• 2008

Spacecraft attitude estimation using the nonlinear unscented filter is addressed to fully utilize capabilities of the unscented transformation. To release significant computational load, an efficient technique is proposed by reasonably removing correlation between random variables. This modification introduces considerable reduction of sigma points and computational burden in matrix square-root calculation for most nonlinear systems. Unscented filter technique makes use of a set of sample points to predict mean and covariance. The general QUEST(QUaternion ESTimator) algorithm preserves explicitly the quaternion normalization, whereas extended Kalman filter(EKF) implicitly obeys the constraint. For spacecraft attitude estimation based on quaternion, an approach to computing quaternion means from sampled quaternions with guarantee of the quaternion norm constraint is introduced applying a constrained optimization technique. Finally, the performance of the new approach is demonstrated using a star tracker and rate-gyro measurements.

• IE interfaces
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• v.11 no.1
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• pp.183-190
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• 1998

This paper is concerned with the bicriteria vehicle routing problems with time window constraints(BVRPTW). The BVRPTW is to determine the most favorable vehicle routes that minimize the total vehicle travel time and the total customer wait time which are, more often than not, conflicting. We construct a linear goal programming (GP) model for the BVRPTW and propose a heuristic algorithm to relieve a computational burden inherent to the application of the GP model. The heuristic algorithm consists of a parallel insertion method for clustering and a sequential linear goal programming procedure for routing. The results of computational experiments showed that the proposed algorithm finds successfully more favorable solutions than the Potvin an Rousseau's method that is known as a very good heuristic for the VRPs with time window constraints, through the change of target values and the decision maker's goal priority structure.

• Structural Engineering and Mechanics
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• v.8 no.1
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• pp.1-18
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• 1999

Procedures are investigated by which nonlinear finite element shell analysis algorithms can be simplified to provide more cost effective approximate analyses of orthogonally-reinforced concrete flat plate structures. Two alternative effective stiffness formulations, and an unbalanced force formulation, are described. These are then implemented into a nonlinear shell analysis algorithm. Nonlinear geometry, three-dimensional layered stress analyses, and other general formulations are bypassed to reduce the computational burden. In application to standard patch test problems, these simplified approximate analysis procedures are shown to provide reasonable accuracy while significantly reducing the computational effort. Corroboration studies using various simple and complex test specimens provide an indication of the relative accuracy of the constitutive models utilized. The studies also point to the limitations of the approximate formulations, and identify situations where one should revert back to full nonlinear shell analyses.

• Journal of the Korean Institute of Telematics and Electronics
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• v.25 no.12
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• pp.1548-1558
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• 1988

In this paper, a segmentation algorithm for color images based on the scale-space filter and the Fuzzy c-means (FCM) techniques is proposed. The methodology uses a coarse-fine concept to reduce the computational burden required for the FCM. The coarse segmentation attempts to segment coarsely using a thresholding technique, while a fine segmentation assigns the unclassified pixels by a coarse segmentation to the closest class using the FCM. Attempts also have been made to compare the performance of the proposed algorithm with other algorithms such as Ohlander's, Rosenfeld's, and Bezdek's. Intensive computer simulations has been done and the results are discussed in the paper. The simulation results indicate that the proposed algorithm produces the most accurate segmentation on the O-K-S color coordinate while requiring a reasonable amount of computational effort.

• Korean Journal of Computational Design and Engineering
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• v.7 no.3
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• pp.170-176
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• 2002

To exchange CAD data between heterogeneous CAD systems, we generally use a neutral format especially STEP, which is the international standard (ISO-10303) for product model data exchange. AP214 (Application Protocol) for the automotive industry not only takes into account geometry and organizational data, but also provides a classification mechanism for product modeling. When reading a STEP file during a design process that is exported from other CAD systems, it is a burden to a designer to go through the tedious process of removing duplicate or non-manifold entities, adjusting parts, and rearranging text. We analyze the structure of AP214 and develop a healing tool to solve the following problem. Without the assembly information in the Master workspace of CATIA, or to read a STEP file from Pro/Engineer, a designer should do a repetitive process of disintegrating an assembly into parts one by one. We have developed a post-processing tool for STEP AP214 that separates out a part from an assembly model and adjusts superfluous or useless entities using the ACIS kernel.