• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.35 no.2
• /
• pp.163-168
• /
• 2011

Robust optimization or reliability-based design optimization are some of the methodologies that are employed to take into account the uncertainties of a system at the design stage. For applying such methodologies to solve industrial problems, accurate and efficient methods for estimating statistical moments and failure probability are required, and further, the results of sensitivity analysis, which is needed for searching direction during the optimization process, should also be accurate. The aim of this study is to employ the function approximation moment method into the sensitivity analysis formulation, which is expressed as an integral form, to verify the accuracy of the sensitivity results, and to solve a typical problem of reliability-based design optimization. These results are compared with those of other moment methods, and the feasibility of the function approximation moment method is verified. The sensitivity analysis formula with integral form is the efficient formulation for evaluating sensitivity because any additional function calculation is not needed provided the failure probability or statistical moments are calculated.

• The Journal of Information Technology
• /
• v.3 no.2
• /
• pp.73-85
• /
• 2000

The efficient algorithms are suggested in this study for solving the multicommodity network flow problems applied to Communications Systems. These problems are typical NP-complete optimization problems that require integer solution and in which the computational complexity increases numerically in appropriate with the problem size. Although the suggested algorithms are not absolutely optimal, they are developed for computationally efficient and produce near-optimal and primal integral solutions. We supplement the traditional Lagrangian method with a price-directive decomposition. It proceeded as follows. First, A primal heuristic from which good initial feasible solutions can be obtained is developed. Second, the dual is initialized using marginal values from the primal heuristic. Generally, the Lagrangian optimization is conducted from a naive dual solution which is set as ${\lambda}=0$. The dual optimization converged very slowly because these values have sort of gaps from the optimum. Better dual solutions improve the primal solution, and better primal bounds improve the step size used by the dual optimization. Third, a limitation that the Lagrangian decomposition approach has Is dealt with. Because this method is dual based, the solution need not converge to the optimal solution in the multicommodity network problem. So as to adjust relaxed solution to a feasible one, we made efficient re-allocation heuristic. In addition, the computational performances of various versions of the developed algorithms are compared and evaluated. First, commercial LP software, LINGO 4.0 extended version for LINDO system is utilized for the purpose of implementation that is robust and efficient. Tested problem sets are generated randomly Numerical results on randomly generated examples demonstrate that our algorithm is near-optimal (< 2% from the optimum) and has a quite computational efficiency.

• Proceedings of the KSR Conference
• /
• 2008.11b
• /
• pp.416-421
• /
• 2008

Recent high-speed trains around the world have achieved remarkable improvement in speed. In Korea, the new high-speed train with maximum speed of 400km/h has been developing through the 'Future High-Speed Rail System Project'. The improvement in train speed brings numerous aerodynamic problems such as strong aerodynamic resistance, noise, drastic pressure variation due to the crosswind or passing by, micro-pressure wave at tunnel exit, and so on. Especially, the nose shape of high-speed train is closely related to the most of the aerodynamic problems. Also the pantograph has to be considered for noise prevention and detachment problems. In this paper, the project, 'Research on the Aerodynamic Technology Advancement of the High-Speed EMU' is introduced briefly, which is one of the efforts for the speed improvement of the 'HEMU400x'. Finally, two main results of train nose and pantograph will be shown. First, the optimization of the cross-sectional area distribution of the high-speed train nose to reduce tunnel micro-pressure wave, and second, robust design optimization of the panhead shape of a pantograph.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.22 no.6
• /
• pp.113-119
• /
• 2014

For the robust design of Motor Driven Power Steering (MDPS) systems, it is important to consider energy efficiency from every aspect such as system configuration and current flow, etc. If design optimization is not considered, it has many problems on a vehicle. For example, when evaluating steering test, particularly the Catch-up test which turning the steering wheel left or right quickly, steering effort should be increased rapidly. Also a vehicle might have poor fuel efficiency. In this study, it is calculated energy consumption for each component of the steering system and analyzed factors of energy consumption. As a result, this paper redefines a method to estimate steering input torque using characteristics of vehicle electric components and then conducts an analysis of contribution for the Catch-up.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.39 no.5
• /
• pp.8-17
• /
• 2002

This paper considers the problems of robust decentralized control for uncertain discrete-time large-scale systems with delays in interconnections and state feedback gain perturbations. Based on the Lyapunov method, the state feedback control design for robust stability is given in terms of solutions to a linear matrix inequality (LMI), and the measure of non-fragility in controller is presented. The solutions of the LMI can be easily obtained using efficient convex optimization techniques. A numerical example is included to illustrate the design procedures.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.4
• /
• pp.237-250
• /
• 2015

This paper describes a hardware-oriented algorithm and its conceptual implementation in a real-time speed limit traffic sign detection system on an automotive-oriented field-programmable gate array (FPGA). It solves the training and color dependence problems found in other research, which saw reduced recognition accuracy under unlearned conditions when color has changed. The algorithm is applicable to various platforms, such as color or grayscale cameras, high-resolution (4K) or low-resolution (VGA) cameras, and high-end or low-end FPGAs. It is also robust under various conditions, such as daytime, night time, and on rainy nights, and is adaptable to various countries' speed limit traffic sign systems. The speed limit traffic sign candidates on each grayscale video frame are detected through two simple computational stages using global luminosity and local pixel direction. Pipeline implementation using results-sharing on overlap, application of a RAM-based shift register, and optimization of scan window sizes results in a small but high-performance implementation. The proposed system matches the processing speed requirement for a 60 fps system. The speed limit traffic sign recognition system achieves better than 98% accuracy in detection and recognition, even under difficult conditions such as rainy nights, and is implementable on the low-end, low-cost Xilinx Zynq automotive Z7020 FPGA.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.6
• /
• pp.2388-2398
• /
• 2017

In this study, we propose a robust face recognition system to pose variations based on automatic pose estimation. Radial basis function neural network is applied as one of the functional components of the overall face recognition system. The proposed system consists of preprocessing and recognition modules to provide a solution to pose variation and high-dimensional pattern recognition problems. In the preprocessing part, principal component analysis (PCA) and 2-dimensional 2-directional PCA ($(2D)^2$ PCA) are applied. These functional modules are useful in reducing dimensionality of the feature space. The proposed RBFNNs architecture consists of three functional modules such as condition, conclusion and inference phase realized in terms of fuzzy "if-then" rules. In the condition phase of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by the Fuzzy C-Means (FCM) algorithm. In conclusion phase of rules, the connections (weights) are realized through four types of polynomials such as constant, linear, quadratic and modified quadratic. The coefficients of the RBFNNs model are obtained by fuzzy inference method constituting the inference phase of fuzzy rules. The essential design parameters (such as the number of nodes, and fuzzification coefficient) of the networks are optimized with the aid of Particle Swarm Optimization (PSO). Experimental results completed on standard face database -Honda/UCSD, Cambridge Head pose, and IC&CI databases demonstrate the effectiveness and efficiency of face recognition system compared with other studies.

• Journal of Plant Biotechnology
• /
• v.45 no.4
• /
• pp.322-327
• /
• 2018

Post-translational modification of proteins regulates signaling cascades in eukaryotic system, including plants. Among these modifications, phosphorylation plays an important role in modulating the functional properties of proteins. Plants perceive environmental cues that directly affect the phosphorylation status of many target proteins. To determine the effect of environmentally induced phosphorylation in plants, in vivo methods must be developed. Various in vitro methods are available but, unlike in animals, there is no optimized methodology for detecting protein phosphorylation in planta. Therefore, in this study, a robust, and easy to handle Phos-Tag Mobility Shift Assay (PTMSA) is developed for the in vivo detection of protein phosphorylation in plants by empirical optimization of methods previously developed for animals. Initially, the detection of the phosphorylation status of target proteins using protocols directly adapted from animals failed. Therefore, we optimized the steps in the protocol, from protein migration to the transfer of proteins to PVDF membrane. Supplementing the electrophoresis running buffer with 5mM $NaHSO_3$ solved most of the problems in protein migration and transfer. The optimization of a fast and robust protocol that efficiently detects the phosphorylation status of plant proteins was successful. This protocol will be a valuable tool for plant scientists interested in the study of protein phosphorylation.

• International Journal of CAD/CAM
• /
• v.4 no.1
• /
• pp.33-45
• /
• 2004

In this work, a rational procedure has been formulated for the selection of points approximating slice contours cut in LOM (Laminated Object manufacturing) with first order approximation. It is suggested that the number of points representing a slice contour can be 'minimised' or 'optmised' by equating the horizontal chordal deviation (HCD) to the user-defined surface form tolerance. It has been shown that such optimization leads to substantial reduction in slice height calculations and NC codes file size for cutting out the slices. Due to optimization, the number of contour points varies from layer to layer, so that points on successive layer contours have to be matched by four sided ruled surface patches and triangular patches. The technological problems associated with the cutting out of triangular patches have been addressed. A robust algorithm has been developed for the determination of slice height for optimum and arbitrary numbers of contour points with different strategies for error calculations. It has been shown that optimisation may even lead to detection and appropriate representation of elusive surface features. An index of optimisation has been defined and calculations of the same have been tabulated.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.15 no.2
• /
• pp.305-313
• /
• 2002

The genetic algorithm(GA), an optimization technique based on the theory of natural selection, has proven to be relatively robust means to search for global optimum. It is converged near to the global optimum point without auxiliary information such as differentiation of function. When studying some optimization problems with continuous variables, it was found that premature saturation was reached that is no further improvement in the object function could be found over a set of iterations. Also, the general GA oscillates in the region of the new global optimum point so that the speed of convergence is decreased. This paper is to propose the concept of restarting and elitist preserving strategy as a measure to overcome this difficulty. Some benchmark examples are studied involving 3-bar truss and cantilever beam with plane stress elements. The modifications to GA improve the speed of convergence.