• ETRI Journal
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• 제41권3호
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• pp.316-325
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• 2019

Accurate suppressive jamming is a prominent problem faced by radar equipment. It is difficult to solve signal detection problems for extremely low signal to noise ratios using traditional signal processing methods. In this study, a joint sensing dictionary based compressed sensing and adaptive iterative optimization algorithm is proposed to counter suppressive jamming in information domain. Prior information of the linear frequency modulation (LFM) and suppressive jamming signals are fully used by constructing a joint sensing dictionary. The jamming sensing dictionary is further adaptively optimized to perfectly match actual jamming signals. Finally, through the precise reconstruction of the jamming signal, high detection precision of the original LFM signal is realized. The construction of sensing dictionary adopts the Pei type fast fractional Fourier decomposition method, which serves as an efficient basis for the LFM signal. The proposed adaptive iterative optimization algorithm can solve grid mismatch problems brought on by undetermined signals and quickly achieve higher detection precision. The simulation results clearly show the effectiveness of the method.

• Advances in aircraft and spacecraft science
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• 제6권5호
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• pp.349-369
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• 2019

In the design of flight control systems there are issues that deserve special consideration and attention such as external perturbations or systems failures. A Simple Adaptive Controller (SAC) that does not require a-priori knowledge of the faults is proposed in this paper with the aim of realizing a fault tolerant flight control system capable of leading the pitch motion of an aircraft. The main condition for obtaining a stable adaptive controller is the passivity of the plant; however, since real systems generally do not satisfy such requirement, a properly defined Parallel Feedforward Compensator (PFC) is used to let the augmented system meet the passivity condition. The design approach used in this paper to synthesize the PFC and to tune the invariant gains of the SAC is the Population Decline Swarm Optimization ($P_DSO$). It is a modification of the Particle Swarm Optimization (PSO) technique that takes into account a decline demographic model to speed up the optimization procedure. Tuning and flight mechanics results are presented to show both the effectiveness of the proposed $P_DSO$ and the fault tolerant capability of the proposed scheme to control the aircraft pitch motion even in presence of elevator failures.

• 한국구조물진단유지관리공학회 논문집
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• 제12권5호
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• pp.125-132
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• 2008

구조최적화는 최근 CAD와 컴퓨터 기술이 발전하면서 구조설계부분에 널리 이용되고 있다. 본 연구에서는 30층의 강구조물을 대상으로 유한요소해석 및 어댑티브 시뮬레이티드 어닐링 알고리즘을 이용하여 최적중량설계를 구현하였다. 최적설계는 모든 설계상수와 설계하중들이 주어졌을 때, 목적함수가 최소로 됨과 동시에 모든 설계제약조건을 만족시키는 설계변수를 결정하는 설계법이라고 정의할 수 있다. 최적설계 구현을 통해 건설 측면에 있어 성능 향상과 신뢰도 향상 효과를 가져 올 수 있을 것이다.

• 한국산업융합학회 논문집
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• 제23권5호
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• pp.799-807
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• 2020

This paper deals with identification of probabilistic design using reliability based robust optimization in structure design of automatic salt collector. The thickness sizing variables of main structure member in the automatic salt collector were considered the random design variables including the uncertainty of corrosion that would be an inevitable hazardousness in the saltern work environment. The probabilistic constraint functions were selected from the strength performances of the automatic salt collector. The reliability based robust optimum design problem was formulated such that the random design variables were determined by minimizing the weight of the automatic salt collector subject to the probabilistic strength performance constraints evaluating from reliability analysis. Mean value reliability method and adaptive importance sampling method were applied to the reliability evaluation in the reliability based robust optimization. The three sigma level quality was considered robustness in side constraints. The probabilistic optimum design results according to the reliability analysis methods were compared to deterministic optimum design results. The reliability based robust optimization using the mean value reliability method showed the most rational results for the probabilistic optimum structure design of the automatic salt collector.

• 한국항공우주학회지
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• 제38권6호
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• pp.557-563
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• 2010

본 논문은 Kringing 근사모델이 제공하는 확률정보를 이용하여 순차적으로 전역 최적해를 찾는 내용을 담고 있다. 적응 전역 최적화란 소수의 실험 점으로 구성한 근사모델의 예측 값과 불확실성을 고려하여 다음 실험 점을 찾고, 이를 이용하여 근사모델을 개선함으로써 순차적으로 해를 찾는 방식이다. 본 연구에서는 근사모델에서 도출한 기대값을 이용하여 개선시킬 필요가 없는 구속함수나 목적함수를 식별함으로써 계산효율을 증대시키는 기법을 제안한다. 다음 단계의 후보 실험점이 유용영역의 비활성일 가능성이 있을 경우 또는 목적함수를 개선시킬 가능성이 희박할 경우, 이 점은 근사함수를 개선하는 데 사용하지 않았다. 본 기법을 비선형성이 강한 시험문제에 적용한 결과, 제안하는 기법이 정밀도는 보장하면서 계산 효율을 증대시키는 것을 확인할 수 있었다.

• 한국음향학회지
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• 제20권5호
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• pp.61-68
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• 2001

본 논문에서는 목적함수에 대한 구배계산을 위한 해석적인 방법과 차분근사법을 점배열음원과 최적화 알고리즘(DFP법: davidon-fletcher-powell)을 조합한 적응형 초음파 트랜스듀서의 지향성 최적화에 적용하였다. 위의 두 방법에 대한성능을 비교하기 위하여 준이상 빔 (quasi-ideal beam)과 회전 빔을 목적지향성으로 설정하였다. 수치적인 실험 결과, 차분근사법은 부엽 (side lobe) 레벨의 억압능력은 물론 수렴과정에서의 안정성, 수렴속도, 적응성 면에서 해석적인 계산법에 비해 탁월함을 나타내었다.

• 한국항공우주학회지
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• 제32권6호
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• pp.49-55
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• 2004

컴퓨터 성능의 향상으로 통계학적 최적화기법이 구조설계최적화에 시도되고 있다. 통계학적 최적화 가법 중 시뮬레이티드 애닐링 기법도 구조 최적화 분야에 적용되기 시작하였다. 시뮬레이티드 애닐링, 불쯔만 애닐링, 패스트 애닐링, 어댑티브 시뮬레이티드 애닐링을 적용하여, 트러스 구조물의 최적화에 적용시켜 목적함수를 향상시키고 함수계산량을 감소시키도록 하였다. 특히 알고리즘 중에서 수렴성과 목적함수의 우수성을 구현하기 위하여 어댑티브 시뮬레이티드 애닐링의 냉각스케줄의 냉각변수를 다양하게 적용하여 트러스 구조물을 최적화하였다. 또한 볼쯔만 애닐링과 어댑티브 시뮬레이티드 애닐링을 복합한 냉각스캐줄로 10부재와 25부재 트러스 구조물의 최적화도 수행하여 보았다.

• 제어로봇시스템학회논문지
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• 제7권10호
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• pp.819-826
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• 2001

Genetic algorithms are becoming more popular because of their relative simplicity and robustness. Genetic algorithms are global search techniques for nonlinear optimization. However, traditional genetic algorithms, though robust, are generally not the most successful optimization algorithm on any particular domain because they are poor at hill-climbing, whereas simulated annealing has the ability of probabilistic hill-climbing. Therefore, hybridizing a genetic algorithm with other algorithms can produce better performance than using the genetic algorithm or other algorithms independently. In this paper, we propose an efficient hybrid optimization algorithm named the adaptive random signal-based learning. Random signal-based learning is similar to the reinforcement learning of neural networks. This paper describes the application of genetic algorithms and simulated annealing to a random signal-based learning in order to generate the parameters and reinforcement signal of the random signal-based learning, respectively. The validity of the proposed algorithm is confirmed by applying it to two different examples.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.323-326
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• 2005

Several video coding standards, such as MPEG-4 and H.263, have been investigated to reduce the resulting number of bits while pursuing the maximum video quality. The recent video coding standard, H.264, provides higher coding efficiency than previous coding standards by using the mode decision scheme. For mode decision, H.264 chooses the best macroblock mode among the several candidates using Lagrangian cost function which reflects both the rate and the distortion. H.264 employs only one rate-distortion optimization (RDO) model for all macroblocks. Since the characteristics of each macroblock is different, each macroblock should have its own RDO model. In this paper, we propose an adaptive rate-distortion optimization algorithm for H.264. We regulate the Lagrangian multiplier considering the picture type and characteristics of each macroblock.

• 대한기계학회논문집
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• 제19권3호
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• pp.713-723
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• 1995

In intelligent machine tools, a computer based control system, which can adapt the machining parameters in an optimal fashion based on sensor measurements of the machining process, should be incorporated. In this paper, the technology for adaptively optimizing the cutting conditions to maximize the material removal rate in face milling operations is proposed using the exterior penalty function method combined with multilayered neural networks. Two neural networks are introduced ; one for estimating tool were length, the other for mapping input and output relations from experimental data. Then, the optimization of cutting conditions is adaptively implemented using tool were information and predicted process output. The results are demonstrated with respect to each level of machining such as rough, fine and finish cutting.