자기공명(magnetic resonance, MR)영상에서 주로 발생하는 Rician 노이즈는 영상의 화질을 저하하는 주요 요소 중의 하나이다. 본 연구에서는 노이즈 제거에 효율적이라고 잘 알려진 총변이(total variation, TV) 알고리즘을 모 델링하여 Rician 노이즈 레벨에 따른 파라미터를 최적화하고자 한다. 시스템은 8채널 기반의 3.0 T 장치를 활용하였 고 물 팬텀 영상을 획득하여 각각 Rician 노이즈를 0.05, 0.10, 0.15, 그리고 0.20 값을 부가하였다. TV 알고리즘 은 Rudin-Osher-Fatemi 모델을 기반으로 모델링하였고 최적화를 수행하기 위하여 반복수 파라미터를 조정하여 획득된 영상에 적용하였다. 결과적으로 Rician 노이즈 레벨을 0.05, 0.10, 0.15, 그리고 0.20을 사용하였을 때 각 각 30, 40, 80, 그리고 120 반복수를 기반으로 한 TV 노이즈 알고리즘에서 가장 우수한 신호 대 잡음비(signal to noise ratio, SNR)와 대조도 대 잡음비(contrast to noise ratio, CNR) 결괏값이 도출되었다. 또한, 최적화된 반복수를 적용한 TV 알고리즘을 사용한 MR 영상에서 기존의 위너 및 중간값 필터를 사용하였을 때 비하여 SNR과 CNR 모두 우수한 값을 획득할 수 있었다. 특히 기본적으로 획득된 MR 영상보다 최적화된 TV 알고리즘을 적용한 영상의 평균 SNR과 CNR은 각각 3.11 및 3.31배 향상됨이 증명되었다. 결론적으로, 노이즈 제거 효율이 우수한 TV 알고리즘의 최적화된 파라미터를 활용한다면 MR 영상에서의 활용 가능성이 클 것으로 기대한다.

In order to solve the problem of improper thrust distribution of each thruster of underwater vehicle, the PSO optimization algorithm is used to solve the problem of thrust distribution. According to the spatial layout of the thruster, the algorithm model of the underwater vehicle propulsion system is established. The thrust input is carried out under the broken line search trajectory, and the simulation verifies the thrust allocation results of the PSO algorithm and the traditional pseudo-inverse method. The simulation results show that compared with the traditional algorithm. First of all, the PSO algorithm can set the physical threshold for each thruster to prevent the thruster from having too much thrust. Secondly, it can ensure that the thruster can turn with a reasonable torque to prevent the robot from drifting due to the large thrust gap. This paper provides a theoretical reference for thrust distribution of underwater salvage robot, and has practical engineering significance.

A sample size calculation algorithm was developed in a prototype version to select inspection samples in domestic bulk handling facilities. This algorithm determines sample sizes of three verification methods satisfying target detection probability for defected items corresponding to one significant quantity (8 kg of plutonium, 75 kg of uranium 235). In addition, instead of using the approximation equation-based algorithm presented in IAEA report, the sample size calculation algorithm based on hypergeometric density function capable of calculating an accurate non-detection probability is adopted. The algorithm based the exact equation evaluates non-detection probability more accurately than the existing algorithm based on the approximation equation, but there is a disadvantage that computation time is considerably longer than the existing algorithm due to the large amount of computational process. It is required to determine sample size within a few hours using laptop-level performance because sample size is generally calculated with an inspector’s portable laptop during inspection activity. Therefore, it is necessary to improve the calculation speed of the algorithm based on the exact equation. In this study, algorithm optimization was conducted to improve computation time. In order to determine optimal sample size, the initial sample size is calculated first, and the next step is to perform an iterative process by changing the sample size to find optimal result. Most of the computation time occurs in sample size optimization process performing iterative computation. First, a non-detection probability calculation algorithm according to the sample sizes of three verification methods was improved in the iterative calculation process for optimizing sample size. A computation time for each step within the algorithm was reviewed in detail, and improvement approaches were derived and applied to some areas that have major effects. In addition, the number of iterative process to find the optimal sample size was greatly reduced by applying the algorithm based on the bisection method. This method finds optimal value using a large interval at the beginning step and reduces the interval size whenever the number of repetitions increases, so the number of iterative process is less than the existing algorithm using unit interval size. Finally, the sample sizes were calculated for 219 example cases presented by the IAEA report to compare computation time. The existing algorithm took about 15 hours, but the improved algorithm took only about 41 minutes using high performance workstation (about 22 times faster). It also took 87 minutes for calculating the cases using a regular laptop. The improved algorithm through this study is expected to be able to apply the sample size determination process, which was performed based on the approximate equation due to the complexity and speed issues of the past calculation process, based on the accurate equation.

An Ant Colony Optimization Algorithm(ACO) is one of the frequently used algorithms to solve the Traveling Salesman Problem(TSP). Since the ACO searches for the optimal value by updating the pheromone, it is difficult to consider the distance between the nodes and other variables other than the amount of the pheromone. In this study, fuzzy logic is added to ACO, which can help in making decision with multiple variables. The improved algorithm improves computation complexity and increases computation time when other variables besides distance and pheromone are added. Therefore, using the algorithm improved by the fuzzy logic, it is possible to solve TSP with many variables accurately and quickly. Existing ACO have been applied only to pheromone as a criterion for decision making, and other variables are excluded. However, when applying the fuzzy logic, it is possible to apply the algorithm to various situations because it is easy to judge which way is safe and fast by not only searching for the road but also adding other variables such as accident risk and road congestion. Adding a variable to an existing algorithm, it takes a long time to calculate each corresponding variable. However, when the improved algorithm is used, the result of calculating the fuzzy logic reduces the computation time to obtain the optimum value.

블록 매칭 및 3D 필터링(BM3D) 알고리즘은 단일 필터의 문제점을 보완하기 위하여 non-local means 기반으로 만들 어진 융합형 노이즈 제거 알고리즘이다. 하지만, 그 수식 인자의 조절에 관한 연구는 이루어지지 않고 있어 본 연구에서는 자기공명영상에서 발생하는 Rician 노이즈를 제거하기 위해 BM3D 알고리즘의 평활화 정도를 결정하는 노이즈 전력 스펙 트럼 밀도(noise power spectrum density,  )에 대한 최적화를 진행하고자 하였다. MRiLab 시뮬레이션 프로그램을 이 용하여 뇌 조직을 모사할 수 있는 뇌척수액(cerebrospinal fluid, CSF)/회색질(gray matter, GM)/백질(white matter, WM) 팬텀의 T1 강조영상을 획득하였고, 노이즈 레벨이 0.1, 0.15, 0.2, 0.25, 그리고 0.3인 Rician 노이즈를 각각 부가 한 후, BM3D 알고리즘의  값을 0.01부터 0.99까지 0.01씩 증가시키며 각각의 노이즈가 부가된 영상에 적용하였다. 정량 적 평가를 통해 최적화 값을 선정하기 위하여 CSF, GM, WM, 그리고 배경 영역에 관심 영역을 설정한 후 조직별 신호 대 잡음비(signal to noise ratio, SNR), 총 변동계수(coefficient of variation, COV), 그리고 평균 제곱근 오차(root mean square error, RMSE)를 측정하였다. 결과적으로, 조직별로 계산된 SNR, COV, 그리고 RMSE를 종합적으로 평가 했을 때 모든 조직에서 노이즈 레벨 0.1부터 0.3까지 증가함에 따라  값 또한 함께 증가하는 경향이 나타났으며 일정  값 이상에서는 노이즈뿐만 아니라 영상신호까지 함께 제거되어 개선 폭이 감소하는 것으로 관찰되었으며, 노이즈 레벨에 따라 각각 0.09, 0.13, 0.17, 0.21, 그리고 0.25의  값이 설정된 BM3D 알고리즘이 적용되었을 때 가장 합리적인 영상 특성을 보이는 것으로 나타났다. 결론적으로, 효과적인 노이즈 제거를 위해서 고정된 값이 아닌 노이즈 레벨에 따른 적합한 값을 적용해야 함을 증명할 수 있었다.

A tilted tall building is actively constructed as landmark structures around world to date. Because lateral displacement responses of a tilted tall building occurs even by its self-weight, reduction of seismic responses is very important to ensure structural safety. In this study, a smart tuned mass damper (STMD) was applied to the example tilted tall building and its seismic response control performance was investigated. The STMD was composed of magnetorheological (MR) damper and it was installed on the top floor of the example building. Control performance of the STMD mainly depends on the control algorithn. Fuzzy logic controller (FLC) was selected as a control algorithm for the STMD. Because composing fuzzy rules and tuning membership functions of FLC are difficult task, evolutionary optimization algorithm (EOA) was used to develop the FLC. After numerical simulations, it has been seen that the STMD controlled by the EOA-optimized FLC can effectively reduce seismic responses fo the tilted tall building.

A60 급 갑판 관통 관은 선박과 해양플랜트에서 화재사고가 발생할 경우 화염의 확산을 방지하고 인명을 보호하기 위해 수평구조에 설치되는 방화장치이다. 본 연구에서는 다양한 대리모델과 다중 섬유전자 알고리즘을 이용하여 A60 급 갑판 관통 관의 방화설계에 대한 이산변수 근사최적화를 수행하였다. A60 급 갑판 관통 관의 방화설계는 과도 열전달해석을 통해 평가하였다. 근사최적화에서 관통 관의 길이, 지름, 재질, 그리고 단열재의 밀도는 이산설계변수로 적용하였고, 제한조건은 온도, 생산성 및 가격을 고려하였다. 대리모델 기반의 근사최적설계 문제는 제한조건을 만족하면서 A60 급 갑판 관통 관의 중량을 최소화할 수 있는 이산설계변수를 결정하도록 정식화 하였다. 반응표면모델, 크리깅, 그리고 방사기저함수 신경망과 같은 다양한 대리모델이 근사최적화에 사용되었다. 근사최적화의 정확도를 검토하기 위해 최적해의 결과는 실제 계산 결과와 비교하였다. 근사최적화에 사용된 대리모델 중 방사기저함수 신경망 모델이 A60 급 갑판 관통 관의 방화설계에 대해 가장 정확한 최적설계 결과를 나타내었다.

This paper proposes a methodology for gantry route optimization in order to maximize the productivity of a odd-type surface mount device (SMD). A odd-type SMD is a machine that uses a gantry to mount electronic components on the placement point of a printed circuit board (PCB). The gantry needs a nozzle to move its electronic components. There is a suitability between the nozzle and the electronic component, and the mounting speed varies depending on the suitability. When it is difficult for the nozzle to adsorb electronic components, nozzle exchange is performed, and nozzle exchange takes a certain amount of time. The gantry route optimization problem is divided into the mounting order on PCB and the allocation of nozzles and electronic components to the gantry. Nozzle and electronic component allocation minimized the time incurred by nozzle exchange and nozzle-to-electronic component compatibility by using an mixed integer programming method. Sequence of mounting points on PCB minimizes travel time by using the branch-and-price method. Experimental data was made by randomly picking the location of the mounting point on a PCB of 800mm in width and 800mm in length. The number of mounting points is divided into 25, 50, 75, and 100, and experiments are conducted according to the number of types of electronic components, number of nozzle types, and suitability between nozzles and electronic components, respectively. Because the experimental data are random, the calculation time is not constant, but it is confirmed that the gantry route is found within a reasonable time.

The estimation of heat source model is very important for heat transfer analysis with finite element method. Part I of this study used adaptive simulated annealing which is one of the global optimization algorithm for anticipating the parameters of the Goldak model. Although the analysis with 3D model which depicted the real situation produced the correct answer, that took too much time with moving heat source model based on Fortran and Abaqus. This research suggests the procedure which can reduce time with maintaining quality of analysis. The lead time with 2D model is reduced by 90% comparing that of 3D model, the temperature distribution is similar to each other. That is based on the saturation of heat transfer among the direction of heat source movement. Adaptive simulated annealing with 2D model can be used to estimate more proper heat source model and which could enhance to reduce the resources and time for experiments.

Anticipation of welding deformation with finite element method is a very interested topic in the industries, adequate heat source model is essential for concluding reasonable results. This study is related to estimate the parameters of Goldak heat source model, and global optimization algorithm is applied to this research. The heat affected zone (HAZ) boundary line of bead on plate (BOP) welding is used as the target, parameters of heat sources are used as the variables. Adaptive simulated annealing is applied and the optimal result is obtained out of 1,000 candidates. The convergence of finite element method and the global optimization is meaningful for estimation of welding deformation, which could enhance to reduce the resources and time for experiments.

본 논문에서는 AISC 표준 단면을 설계 변수로 하는 캔틸레버 타입 헬리데크 모델의 유전 알고리즘 최적설계를 소개한다. AISC 표준 단면을 단면 형상별로 분류하고 단면적 순으로 정렬한 후 정수 단면 번호를 부여하여 설계 변수로 최적설계를 수행하였다. 이 과정을 통하여 이산화된 설계 변수를 가지는 최적설계 문제를 해결하기 위해 유전 알고리즘을 적용하였다. 또한, 제약조건으로 허용응력 및 허용응력비 검사 조건을 모두 고려하여 구조물의 구조 안정성을 고려한 설계를 수행하였다. 최적설계 과정중 매 반복계산 마다 수행되는 구조해석 시간을 단축시키기 위해 선형 중첩법을 사용하였고, 이를 통해 구조 해석 시간을 약 75% 감소시킬 수 있었다. 또한 헬리데크 최적설계의 경량 효과를 높이기 위해 부재 그룹 세분화를 하였고, 그 결과를 선행 연구 모델, 기존의 부재 그룹 모델과 비교하였다. 그 결과 선행연구 대비 약 30톤의 부재를 절감할 수 있었으며, 구조적으로도 보다 안전한 헬리데크 설계를 얻을 수 있었다.

It is one of the known methods to obtain the optimal solution using the Ant Colony Optimization Algorithm for the Traveling Salesman Problem (TSP), which is a combination optimization problem. In this paper, we solve the TSP problem by proposing an improved new ant colony optimization algorithm that combines genetic algorithm mutations in existing ant colony optimization algorithms to solve TSP problems in many cities. The new ant colony optimization algorithm provides the opportunity to move easily fall on the issue of developing local optimum values of the existing ant colony optimization algorithm to global optimum value through a new path through mutation. The new path will update the pheromone through an ant colony optimization algorithm. The renewed new pheromone serves to derive the global optimal value from what could have fallen to the local optimal value. Experimental results show that the existing algorithms and the new algorithms are superior to those of existing algorithms in the search for optimum values of newly improved algorithms.

다양한 활용용수 생산을 위한 막 소재 및 막여과 공정개발이 고도화됨에 따라 점차적으로 유지관리 측면의 연구개발 필요성이 부각되고 있다. 그 중에서 유지관리 측면의 막 손상 검지와 수명예측은 제도적으로 정립이 미흡한 실정이며, 막여과 공정 운영에서 경제성과 안정성을 고려한 막 손상 검지 기술개발은 막 특성 및 시설규모에 따른 오차보상이 불가피하게 수반되어야 하는 연구이다. 따라서 분리막의 물리/화학적 노출강도에 따른 특성 변화를 관찰하여 내구년한 산정 및 수명예측 알고리즘을 구축하고자 한다. 또한 분리막의 특성과 모듈의 배열에 따라 최적화된 막 손상 검지를 위한 기체-액체 치환율을 기반으로 물리적 한계도출과 병행하여 이를 극복하기 위한 센싱융합 기술을 소개하고자 한다.

The object of research is based on 1.5 MW wind turbine blade. This paper has carried out the aerodynamic shape optimization design of wind turbine blade. Based on the aerodynamic basic theory of wind turbine blade design and combined with particle swarm optimization algorithm(PSO), the design optimization model of the aerodynamic shape of blade is established. Through this study, the optimization results of the angle inducing ′ and tangential inducing  were obtained. The calculation programs are written and calculated chord length and torsion angle of the blade used by ′ and . The calculation result for the optimized wind turbine was 1.38 MW when the wind speed was 16 m/s. The 8 % error could be considered as an engineering acceptable error and the calculated values can be proved the correctness of the design value.

Airline schedules are highly dependent on various factors of uncertainties such as unfavorable weather conditions, mechanical problems, natural disaster, airport congestion, and strikes. If the schedules are not properly managed to cope with such disturbances, the operational cost and performance are severely affected by the delays, cancelations, and so forth. This is described as a disruption. When the disruption occurs, the airline requires the feasible recovery plan returning to the normal operations in a timely manner so as to minimize the cost and impact of disruptions. In this research, an Ant Colony Optimization (ACO) algorithm with re-timing strategy is developed to solve the recovery problem for both aircraft and passenger. The problem consists of creating new aircraft routes and passenger itineraries to produce a feasible schedule during a recovery period. The suggested algorithm is based on an existing ACO algorithm that aims to reflect all the downstream effects by considering the passenger recovery cost as a part of the objective function value. This algorithm is complemented by re-timing strategy to effectively manage the disrupted passengers by allowing delays even on some of undisrupted flights. The delays no more than 15 minutes are accepted, which does not influence on the on-time performance of the airlines. The suggested method is tested on the real data sets from 2009 ROADEF Challenge, and the computational results are compared with the existing ones on the same data sets. The method generates the solution for most of problem set in 10 minutes, and the result generated by re-timing strategy is discussed for its impact.

The object of research in Based on 1.5MW wind turbine blade. This paper has carried out the aerodynamic shape optimization design of wind turbine blade. Based on the aerodynamic basic theory of wind turbine blade design and combined with particle swarm optimization algorithm, the design optimization model of the aerodynamic shape of blade is established. The calculation programs are written by use of MATLAB and calculate chord length and torsion angle of the blade. Then the shape of wind turbine blade is obtained. As research we can know that the chord length is decreased after optimization design of wind turbine blade, The optimized blade not only meets the actual manufacturing requirement, but also has the largest wind energy utilization coefficient.

Recently, owing to the development of ICT industry and wide spread of smart phone, the number of people who use car sharing service are increased rapidly. Currently two-way car sharing system with same rental and return locations are mainly operated since this system can be easily implemented and maintained. Currently the demand of one-way car sharing service has increase explosively. But this system have several obstacle in operation, especially, vehicle stock imbalance issues which invoke vehicle relocation. Hence in this study, we present an optimization approach to depot location and relocation policy in one-way car sharing systems. At first, we modelled as mixed-integer programming models whose objective is to maximize the profits of a car sharing organization considering all the revenues and costs involved and several constraints of relocation policy. And to solve this problem efficiently, we proposed a new method based on particle swarm optimization, which is one of powerful meta-heuristic method. The practical usefulness of the approach is illustrated with a case study involving satellite cities in Seoul Metrolitan Area including several candidate area where this kind systems have not been installed yet and already operating area. Our proposed approach produced plausible solutions with rapid computational time and a little deviation from optimal solution obtained by CPLEX Optimizer. Also we can find that particle swarm optimization method can be used as efficient method with various constraints. Hence based on this results, we can grasp a clear insight into the impact of depot location and relocation policy schemes on the profitability of such systems.

This paper deals with the production plan for the foaming process, the core part of the refrigerator manufacturing process. In accordance with this change, the refrigerator manufacturing process has also been converted into the mixed-model production system and it is necessary to optimize the production release pattern for the foaming process. The pattern optimization is to create a mixed-model combination which can minimize the number of setup operations and maintain mixed-model production. The existing method is a simple heuristic that depends on the demand priority. Its disadvantages are low mixed-model configuration rate and high setup frequency. Therefore, demand partitioning occurs frequently. In this study, we introduce the tolerance concept and propose a new pattern optimization algorithm based the large neighborhood search (LNS). The proposed algorithm was applied to a refrigerator plant and it was found that mixed-model configuration rate can be improved without demand partitioning.