• 한국군사과학기술학회지
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• 제15권3호
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• pp.322-333
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• 2012

AESA radar is able to instantaneously and adaptively position and control the beam, and such adaptive beam pointing of AESA radar enables to remarkably improve the multi-mission capability. For this reason, Radar Resource Management(RRM) becomes new challenging issue. RRM is a technique efficiently allocating finite resources, such as energy and time to each task in an optimal and intelligent way. Especially radar beam scheduling is the most critical component for the success of RRM. In this paper, we proposed a rule-based scheduling algorithm and Simulated Annealing(SA) based scheduling algorithm, which are alternatively selected and applied to beam scheduler according radar load status in real-time. The performance of the proposed algorithm was evaluated on the multi-function radar scenario. As a result, we showed that our proposed algorithm can process a lot of beams at the right time with real time capability, compared with applying only rule-based scheduling algorithm. Additionally, we showed that the proposed algorithm can save scheduling time remarkably, compared with applying only SA-based scheduling algorithm.

• 한국정보통신학회논문지
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• 제10권12호
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• pp.2283-2288
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• 2006

최근 생명 정보학 기술의 발달로 마이크로 단위의 실험조작이 가능해짐에 따라 하나의 chip상에서 전체 genome의 expression pattern을 관찰할 수 있게 되었고, 동시에 수 만개의 유전자들 간의 상호작용도 연구 가능하게 되었다. 이처럼 DNA 마이크로어레이 기술은 복잡한 생물체를 이해하는 새로운 방향을 제시해주게 되었다. 따라서 이러한 기술을 통해 얻어진 대량의 유전자 정보들을 효과적으로 분석하는 방법이 시급하다. 본 논문에서는 실험용 데이터로 하버드대학교의 바이오인포메틱스 코어 그룹의 샘플데이터 이용하여 마이크로어레이 실험에서 다양한 원인에 의해 발생하는 잡음(noise)을 줄이거나 제거하는 과정인 표준화 과정을 거쳐 특징 추출방법인 베이지안 알고리즘 ASA(Adaptive Simulated Annealing) 방법을 이용하여 데이터를 2개의 클래스로 나누고, 정확도를 평가하는 시스템을 설계하고 구현하였다. Lowess 표준화 후 98.23%의 정확도를 보였다.

• Journal of Electrical Engineering and Technology
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• 제5권2호
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• pp.179-190
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• 2010

This paper describes a new stochastic heuristic algorithm in engineering problem optimization especially in power system applications. An improved particle swarm optimization (PSO) called adaptive particle swarm optimization (APSO), mixed with simulated annealing (SA), is introduced and referred to as APSO-SA. This algorithm uses a novel PSO algorithm (APSO) to increase the convergence rate and incorporate the ability of SA to avoid being trapped in a local optimum. The APSO-SA algorithm efficiency is verified using some benchmark functions. This paper presents the application of APSO-SA to find the optimal location, type and size of flexible AC transmission system devices. Two types of FACTS devices, the thyristor controlled series capacitor (TCSC) and the static VAR compensator (SVC), are considered. The main objectives of the presented method are increasing the voltage stability index and over load factor, decreasing the cost of investment and total real power losses in the power system. In this regard, two cases are considered: single-type devices (same type of FACTS devices) and multi-type devices (combination of TCSC, SVC). Using the proposed method, the locations, type and sizes of FACTS devices are obtained to reach the optimal objective function. The APSO-SA is used to solve the above non.linear programming optimization problem for better accuracy and fast convergence and its results are compared with results of conventional PSO. The presented method expands the search space, improves performance and accelerates to the speed convergence, in comparison with the conventional PSO algorithm. The optimization results are compared with the standard PSO method. This comparison confirms the efficiency and validity of the proposed method. The proposed approach is examined and tested on IEEE 14 bus systems by MATLAB software. Numerical results demonstrate that the APSO-SA is fast and has a much lower computational cost.

• International Journal of Control, Automation, and Systems
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• 제4권5호
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• pp.575-591
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• 2006

In a previous research, we presented the Grid Method and confirmed it as a systematic and efficient problem formulation method for the task-oriented design of robot kinematics. However, our previous research was limited in two ways. First, it gave only a local optimum due to its use of a local optimization technique. Second, it used constant weights for a cost function chosen by the manual weights tuning algorithm, thereby showing low efficiency in finding an optimal solution. To overcome these two limitations, therefore, this paper presents a global optimization technique and an adaptive weights tuning algorithm to solve a formulated problem using the Grid Method. The efficiencies of the proposed algorithms have been confirmed through the kinematic design examples of various robot manipulators.

• Advances in aircraft and spacecraft science
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• 제10권5호
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• pp.393-418
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• 2023

Drones are increasingly used in logistics delivery due to their low cost, high-speed and straight-line flight. Considering the small cargo capacity, limited endurance and other factors, this paper optimized the pickup and delivery vehicle routing problem with time windows in the mode of "truck+drone". A mixed integer programming model with the objective of minimizing transportation cost was proposed and an improved adaptive large neighborhood search algorithm is designed to solve the problem. In this algorithm, the performance of the algorithm is improved by designing various efficient destroy operators and repair operators based on the characteristics of the model and introducing a simulated annealing strategy to avoid falling into local optimum solutions. The effectiveness of the model and the algorithm is verified through the numerical experiments, and the impact of the "truck+drone" on the route cost is analyzed, the result of this study provides a decision basis for the route planning of "truck+drone" mode delivery.

• 한국통신학회논문지
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• 제25권1B호
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• pp.77-88
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• 2000

본 논문은 블록 단위의 비디오 코더 (Video coder)에서 최적 모드들을 적절하게 선택할 수 있는 방법을 설명한다. 비트율의 제어에 관한 문제는 버퍼가 차 있는 양에 따라 양자화 레벨이 변하는 방법이 여러 학자들로부터 연구되어 왔다. 제시된 방법은 각각의 매크로블록(macroblock)에 관한 최적 모드를 검색하지 않고 최적 결정 곡선을 검색한다는 점에서 이전의 것, 즉[4]와는 다르다. 제시된 방법은 비트율의 제어를 위하여 운동보상 또는 비 운동 보상 및 intra/inter 코딩에 관한 결정 방법까지 포함된다. 이러한 결정 방법은 주어진, 즉 고장된 결정 곡선을 이용하는 것 보다 주어진 프레임에서 설정된 비트율(bit-rate)에 따른 총체적인 왜곡을 최소화시키는 유동적인 결정 곡선을 이용하는 것이 더 바람직하다. 가열냉각 최적화기술 (annealing optimization technique)의 모의실험을 통해 곡선을 도출할 수 있다. 알고리즘은 H.261 비디오 코텍(video codec)범위에서 수행되고 모의실험 또한 그 범위에서 결과들을 보여주고 이들을 여타의 접근방법들과 비교한다.

• 산업경영시스템학회지
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• 제45권4호
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• pp.233-239
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• 2022

The injection molding process is a process in which thermoplastic resin is heated and made into a fluid state, injected under pressure into the cavity of a mold, and then cooled in the mold to produce a product identical to the shape of the cavity of the mold. It is a process that enables mass production and complex shapes, and various factors such as resin temperature, mold temperature, injection speed, and pressure affect product quality. In the data collected at the manufacturing site, there is a lot of data related to good products, but there is little data related to defective products, resulting in serious data imbalance. In order to efficiently solve this data imbalance, undersampling, oversampling, and composite sampling are usally applied. In this study, oversampling techniques such as random oversampling (ROS), minority class oversampling (SMOTE), ADASYN(Adaptive Synthetic Sampling), etc., which amplify data of the minority class by the majority class, and complex sampling using both undersampling and oversampling, are applied. For composite sampling, SMOTE+ENN and SMOTE+Tomek were used. Artificial neural network techniques is used to predict product quality. Especially, MLP and RNN are applied as artificial neural network techniques, and optimization of various parameters for MLP and RNN is required. In this study, we proposed an SA technique that optimizes the choice of the sampling method, the ratio of minority classes for sampling method, the batch size and the number of hidden layer units for parameters of MLP and RNN. The existing sampling methods and the proposed SA method were compared using accuracy, precision, recall, and F1 Score to prove the superiority of the proposed method.

• Advances in Computational Design
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• 제1권4호
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• pp.315-327
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• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.104-108
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• 2003

Finite element (FE) model updating is a procedure to minimize the differences between analytical and experimental results, which can be usually posed as an optimization problem. This paper aims to introduce a hybrid optimization algorithm (GA-SA), which consists of a Genetic algorithm (GA) stage and an Adaptive Simulated Annealing (ASA) stage, to FE model updating for a shrunk shaft. A good agreement of the first four natural frequencies has been achieved obtained from GASA based updated model (FEgasa) and experiment. In order to prove the validity of GA-SA, comparisons of natural frequencies obtained from the initial FE model (FEinit), GA based updated model (FEga) and ASA based updated model (FEasa) are carried out. Simultaneously, the FRF comparisons obtained from different FE models and experiment are also shown. It is concluded that the GA, ASA, GA-SA are powerful optimization techniques which can be successfully applied to FE model updating, the natural frequencies and FRF obtained from all the updated models show much better agreement with experiment than that obtained from FEinit model. However, FEgasa is proved to be the most reasonable FE model, and also FEasa model is better than FEga model.

• 조명전기설비학회논문지
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• 제20권9호
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• pp.32-39
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• 2006

In this paper, an iterative subspace-based calibration algorithm for a CDMA-based antenna array in the presence of unknown gain and phase error is presented. The algorithm does not depend on the array geometry and does not require a prior knowledge of the Directions Of Arrival (DOA) of the signals. The method requires the code sequence of a reference user only. The proposed algorithm is based on the subspace method and root finding approach, and it provides estimates of the calibration vector, the DOA and the channel impulse response, by using the code sequence of a reference user. The performance of the proposed algorithm was investigated by means of computer simulations and was verified using field data measured through a custom-built W-CDMA test-bed. The data show that experimental results match well with the theoretical calibration algorithm. Also, teh study propose an efficient algorithm using the simulated annealing technique. This algorithm overcomes the requirement of initial guessing in the subspace-based approach.