• Korean Journal of Computational Design and Engineering
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• v.1 no.3
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• pp.224-233
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• 1996

An automatic mesh generation scheme with triangular finite elements on three-dimensional surfaces has been developed. The surface triangulation process is performed as follows. To begin, surfaces with key nodes are transformed to two-dimensional planes and the meshes with triangular elements are constructed in these planes. Finally, the constructed meshes are transformed back to the original 3D surfaces. For the mesh generation, an irregular mesh generation scheme is employed in which local mesh densities are assigned by the user along the boundaries of the analysis domain. For this purpose a looping algorithm combined with an advancing front technique using basic operators has been developed, in which the loops are recursively subdivided into subloops with the use of the best split lines and then the basic operators generate elements. Using the split lines, the original boundaries are split recursively until each loop contains a certain number of key nodes, and then using the basic operators such as type-1 and type-2, one or two triangular elements are generated at each operation. After the triangulation process has been completed for each meshing domain, the resulting meshes are finally improved by smoothing process. Sample meshes are presented to demonstrate the versatility of the algorithm.

• ETRI Journal
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• v.30 no.2
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• pp.183-193
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• 2008

In this paper, we propose quality of service mechanisms for flow-based routers which have to handle several million flows at wire speed in high-speed networks. Traffic management mechanisms are proposed for guaranteed traffic and non-guaranteed traffic separately, and then the effective harmonization of the two mechanisms is introduced for real networks in which both traffic types are mixed together. A simple non-work-conserving fair queuing algorithm is proposed for guaranteed traffic, and an adaptive flow-based random early drop algorithm is proposed for non-guaranteed traffic. Based on that basic architecture, we propose a dynamic traffic identification method to dynamically prioritize traffic according to the traffic characteristics of applications. In a high-speed router system, the dynamic traffic identification method could be a good alternative to deep packet inspection, which requires handling of the IP packet header and payload. Through numerical analysis, simulation, and a real system experiment, we demonstrate the performance of the proposed mechanisms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Journal of Mechanical Science and Technology
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• v.17 no.12
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• pp.1969-1976
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making for the product concepts, and the best alternative can be selected based on its estimated LCA and benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts need a new approach for the environmental analysis. This paper explores a new approximate LCA methodology for the product concepts by grouping products according to their environmental characteristics and by mapping product attributes into environmental impact driver (EID) index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then, a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for newly designed products. The training is generalized by using product attributes for an EID in a group as well as another product attributes for the other EIDs in other groups. The neural network model with back propagation algorithm is used, and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines for the design of environmentally conscious products in conceptual design phase.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.221-229
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• 2003

In the early phases of the product life cycle, Life Cycle Assessment (LCA) is recently used to support the decision-making fer the conceptual product design and the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA fur a various range of design concepts need the new approach fer the environmental analysis. This paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes into impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. Then a neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. The training is generalized by using product attributes for an ID in a group as well as another product attributes for another IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace the full LCA but it would give some useful guidelines fer the design of environmentally conscious products in conceptual design phase.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.5
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• pp.361-367
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• 2003

We propose an algorithm to detect the face region using a variable ellipsoidal mask and a neural network. Since outlines of human faces are similar to ellipsoid, the ellipsoidal mask that has the fixed ratio of major and minor axis can be used to detect the candidate area. The positions of eyes and lips are extracted in this candidate area, and then the morphological analysis is applied to make features which are consist of six parameters, such as the geometrical ratio of eyes and lips. A back-propagation neural network is used as a classifier to determine the most possible face region. The experimental result is conducted to verify its efficiency compared with those of previous works.

• Geomechanics and Engineering
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• v.9 no.4
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• pp.465-481
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• 2015

Three-dimensional simulation of flow through dam foundation is performed using finite element (Seep3D model) and artificial neural network (ANN) models. The governing and discretized equation for seepage is obtained using the Galerkin method in heterogeneous and anisotropic porous media. The ANN is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning, using the water level elevations of the upstream and downstream of the dam, as input variables and the piezometric heads as the target outputs. The obtained results are compared with the piezometric data of Shahid Abbaspour's Dam. Both calculated data show a good agreement with available measurements that demonstrate the effectiveness and accuracy of purposed methods.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2002.04a
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• pp.573-578
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• 2002

In this paper, it Is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-negro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.04a
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• pp.125-130
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• 1997

In this paper, it is presents a new approach to the design of neural control system using digital signal processors in order to improve the precision and robustness. Robotic manipulators have become increasingly important in the field of flexible automation. High speed and high-precision trajectory tracking are indispensable capabilities for their versatile application. The need to meet demanding control requirement in increasingly complex dynamical control systems under significant uncertainties, leads toward design of intelligent manipulation robots. The TMS320C3x is used in implementing real time neural control to provide an enhanced motion control for robotic manipulators. In this control scheme, the networks introduced are neural nets with dynamic neurons, whose dynamics are distributed over all the network nodes. The nets are trained by the distributed dynamic back propagation algorithm. The proposed neural network control scheme is simple in structure, fast in computation, andsuitable for implementation of robust control.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.10a
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• pp.122-127
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• 2001

In this paper, it is presented a new scheme of adaptive-neuro control system to implement real-time control of robot manipulator. Unlike the well-established theory for the adaptive control of linear systems, there exists relatively little general theory for the adaptive control of nonlinear systems. Adaptive control technique is essential for providing a stable and robust performance for application of robot control. The proposed neuro control algorithm is one of learning a model based error back-propagation scheme using Lyapunov stability analysis method. Through simulation, the proposed adaptive-neuro control scheme is proved to be a efficient control technique for real-time control of robot system using DSPs.