• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.1-8
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• 2007

In this paper, research on joint optimization of the image spatial registration and the exposure compensation is conducted. The exposure compensation is performed in a frame work of the intensity compensation based on the polynomial approximation of the relationship between images. This compensation is jointly combined with the registration problem employing the Gauss-Newton nonlinear optimization method. In this paper, to perform for a simple and stable optimization, the block-coordinate method is combined with the Gauss-Newton optimization and extensively compared with the traditional approaches. Furthermore, regression analysis is considered in the compensation part for a better stable performance. By combining the block-coordinate method with the Gauss-Newton optimization, we can obtain a compatible performance reducing the computational complexity and stabilizing the performance. In the numerical result for a particular image, we obtain a satisfactory result for 10 repeats of the iteration, which implies a 50% reduction of the computational complexity. The error is also further reduced by 1.5dB compared to the ordinary method.

• Journal of the Korea Society of Computer and Information
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• v.16 no.4
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• pp.15-22
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• 2011

Principal component analysis (PCA) is a well-known method for dimensionality reduction and feature extraction while maintaining most of the variation in data. Although PCA has been applied in many areas successfully, it is sensitive to outliers and only valid for Gaussian distributions. Several variants of PCA have been proposed to resolve noise sensitivity and, among the variants, improved robust fuzzy PCA (RF-PCA2) demonstrated promising results. RF-PCA, however, is still a linear algorithm that cannot accommodate non-Gaussian distributions. In this paper, a non-linear algorithm that combines RF-PCA2 and kernel PCA (K-PCA), called improved robust kernel fuzzy PCA (RKF-PCA2), is introduced. The kernel methods make it to accommodate non-Gaussian distributions. RKF-PCA2 inherits noise robustness from RF-PCA2 and non-linearity from K-PCA. RKF-PCA2 outperforms previous methods in handling non-Gaussian distributions in a noise robust way. Experimental results also support this.

• Journal of Korean Society of Transportation
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• v.19 no.3
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• pp.75-88
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• 2001

Signal optimization model for alternative use of lanes at a signalized intersection with an stop-line added backward was presented in this paper. The simulation results shot-ed that the traffic fed from the stop-line passed the intersection in each specified phasing interval for left and through traffic. The experimental results indicated that the proposed model was much superior to traditional signal optimization methodology in reducing delay, fuel consumption, and disutility index for delay and stops. The effects for reducing delay were greater than those for doing fuel consumption and disutility index due to the added stop-line. The proposed model is expected to alleviate traffic congestion at intersections, both which have no left turn pocket, and which have large left turn volume. The model is recommended to adapted for intersections spaced long among them with no near driveway.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.7
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• pp.75-84
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• 2003

To reduce the effects of the uncertainties due to the modeling error and aerodynamic coefficients, a nonlinear adaptive control system based on neural networks is proposed . Neural networks parameters are adjusted by using an adaptive law. The sliding mode control scheme is used to compensate for the effect of the approximation error of neural networks. Control parameters and neural networks structures are optimized to obtain better performance by using the genetic algorithm. By introducing the concept of multi-groups of populations, the genetic algorithm is modified so that individuals and groups can be simultaneously evolved . To verify the performance of the pro posed algorithm, the optimized neural networks control system is applied to an aircraft longitudinal dynamics.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.46 no.3
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• pp.15-21
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• 2009

In this paper, we consider the design problem of robust stabilization and robust guaranteed cost state feedback controller for discrete-time singular systems with parameter uncertainties by LMI(linear matrix inequality) approach without semi-definite condition and decomposition of system matrices. The objective of robust stabilization controller is to construct a state feedback controller such that the closed-loop system is regular, causal, and stable. In the case of robust guaranteed cost control, the optimal value of guaranteed cost and controller design method are presented on the basis of robust stabilization control technique. Finally, a numerical example is provided to show the validity of the design methods.

• Aerospace Engineering and Technology
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• v.11 no.1
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• pp.98-102
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• 2012

These days, Uncooled IR Systems are more popular in the area of defense and aerospace than before. Uncooled IR Systems are widely used as core technology for making unmanned systems and detecting enemy objects during the day and night in the distance. Recently, researches on TEC-less IRFPA have been increased to minimize the power consumption and to make a smaller system than before. For this, it needs to find adequate NUC(Non-Uniformity Correction) coefficients as FPA(Focal Plane Array) temperature changes. In this paper, we propose a new NUC coefficient estimating technique, DCCE-LI(Dynamic Calibration Coefficients Estimation with Linear Interpolation), for TEC-less IRFPA. It is based on a linear interpolation method and it can estimate NUC coefficients in real-time. So, by testing and evaluating it with some IR images, we conclude that the quality of IR images using proposed method is better than applying static coefficients.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1647-1655
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• 2006

본 연구는 무선 및 사용자요구사항 등 환경의 변화에 따라 부반송파(subcarrier) 할당, 변조방식 및 전력량을 조절하는 적응형 다중사용자 OFDM 시스템을 다룬다. 먼저 사용자별 전송요구 비트수 및 전체 사용가능한 전력량에 대한 제약 조건하에서 총 데이터 전송량(Throughput)을 최대화하는 최적화 문제를 모형화한다. 이 문제는 대부분의 기존 연구들을 포함할 수 있을 정도로 포괄적이지만 미분이 불가능한 비선형 최적화 문제이면서도 빠른 계산시간을 요구한다. 본 연구는 쌍대간격(Duality Gap)이 0이라는 제안 문제의 특성을 바탕으로 하여 Subgradient 쌍대최적화(Dual Optimization) 기법을 적용한다. 또한 이러한 접근법은 사용자 또는 무선환경의 변화에 적극적으로 적응하는 무선자원 할당 메커니즘을 제시한다.

• Aerospace Engineering and Technology
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• v.7 no.1
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• pp.61-67
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• 2008

To design flight control law of an unmanned aerial vehicle, automated control gain determination program was developed. The procedure for determination of control gain was formulated as the control gains were designed from the optimal solutions of the optimization problem. PSO algorithm, which is one of the evolutionary computation method, and SQP algorithm, which is one of the nonlinear programming method, are used as optimization problem solver. Thru this technique, computation time required for finding the optimal solution is decreased to 1/5 of that of PSO algorithm and more accurate optimal solution is obtained.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.177-182
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• 2021

Generally, the linear programming (LP) with O(n⁴) time complexity is applied to mixture optimization problem that can be produce the given ingredients grade product with minimum cost from mixture of various raw materials. This paper suggests heuristic algorithm with O(n log n) time complexity to obtain the solution of this problem. The proposed algorithm meets the content range of the components required by the alloy steel plate while obtaining the minimum raw material cost, decides the quantity of raw material that is satisfied with ingredients grade for ascending order of unit cost. Although the proposed algorithm applies simple decision technique with O(n log n) time complexity, it can be obtains same solution as or more than optimization technique of linear programing.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.9
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• pp.681-688
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• 2013

Several problems caused by inevitable increment of airplane have been issued. The most effective solution to solve the issues is considered as establishing appropriate Air Traffic Management (ATM) that reduces aircraft's delay at an airport and intensify the airport's capacity. The purpose of this paper is to produce the optimum aircraft schedules that maximize the aircraft throughput by smooth air traffic flow near terminal area of an airport In this paper, mathematical formulations of the scheduling problem are firstly specified. Based on the mathematical modelling, an Optimization-Based Flight Scheduler that provides the optimum flight schedules for arriving aircraft is developed by introducing the Mixed Integer Linear Programming(MILP) and the Genetic Algorithms(GA). With this scheduler, we calculated the optimum schedules to compare to real schedule data from an Incheon Airport. As a result, it is validated that aircraft throughput produced by the optimum schedule is much better than that of the schedule from the Incheon airport. The optimization-based flight scheduler is expected to deal with problems due to the aircraft saturation in near future.