기존 온·습도 센서와 여러 가스센서에 의해 측정 및 제어되는 돈사환경제어시스템에 돼지의 체온조 절행동에 근거한 생체정보를 이용하여 외부 환경정보를 보정한다면 보다 정밀한 축사 환경제어를 할 수 있다. 이를 위한 본 연구는 ICT기술을 접목한 스마트돈사의 정밀환경제어를 위한 기초연구로 획득된 이 미지를 바탕으로 돼지의 행동특성을 3가지로 분류하기 위한 영상처리시스템 알고리즘을 제시하고자 한 다. 공시재료는 실험돈사에서 사육되고 있는 육돈용 자돈(F2, 36~40kg) 3마리를 이용하였으며, 영상처 리를 수행하고자 천정에 설치된 카메라를 통해 획득된 이미지를 이용하였다. 영상처리를 위한 프로그램 은 Visual Studio C과 다양한 영상처리를 위해 개발된 오픈 소스 라이브러리인 OpenCV Library를 이 용하여 구현하였다. 행동분류 알고리즘은 각 돼지의 중심점 데이터, 돼지가 차지하는 면적, 돼지 사이 의 거리를 구하고자 전처리를 수행한 이미지를 RGB 색상계에서 YCrCb 색상계로 변환하였으며, 히스토 그램 평활화(Histogram Equalization), cvBlob함수를 사용하여 Labeling 알고리즘을 수행하였다. 영상 처리 결과, 검증 이미지를 대상으로 군집형태 A로 판단된 결과는 면적만 고려한 것과 거리와 면적을 같 이 고려하였을 때 인식률 95%를 나타내었다. 군집형태 B의 경우 면적만을 고려하였을 경우 65%, 면적 과 거리를 모두 고려하였을 경우 95%로 나타났다. 군집형태 C의 경우 면적만을 고려하였을 경우 25%, 면적과 거리를 모두 고려하였을 경우 100%로 나타나 환경정보 보정자료로 활용이 가능한 것으로 판단 되었다.

This paper proposes an improved standard genetic algorithm (GA) of making a near optimal schedule for integrated process planning and scheduling problem (IPPS) considering tool flexibility and tool related constraints. Process planning involves the selection of operations and the allocation of resources. Scheduling, meanwhile, determines the sequence order in which operations are executed on each machine. Due to the high degree of complexity, traditionally, a sequential approach has been preferred, which determines process planning firstly and then performs scheduling independently based on the results. The two sub-problems, however, are complicatedly interrelated to each other, so the IPPS tend to solve the two problems simultaneously. Although many studies for IPPS have been conducted in the past, tool flexibility and capacity constraints are rarely considered. Various meta-heuristics, especially GA, have been applied for IPPS, but the performance is yet satisfactory. To improve solution quality against computation time in GA, we adopted three methods. First, we used a random circular queue during generation of an initial population. It can provide sufficient diversity of individuals at the beginning of GA. Second, we adopted an inferior selection to choose the parents for the crossover and mutation operations. It helps to maintain exploitation capability throughout the evolution process. Third, we employed a modification of the hybrid scheduling algorithm to decode the chromosome of the individual into a schedule, which can generate an active and non-delay schedule. The experimental results show that our proposed algorithm is superior to the current best evolutionary algorithms at most benchmark problems.

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.

Companies are pursuing the management of small quantity batch production or JIT(Just-in-time) system for improving the delivery response and LOB(Line Balancing) in order to satisfy consumers’ increasing demands in the current global economic recession. And in order to improve the growth of production for reducing manufacturing cost, improvements of the Load Balancing have become an important reformation factor. Thus this paper is aimed at warehouse which procures materials on the assembly line in procurement logistics of automotive logistics and proceed with research on heuristic algorithm development which can increase the Load Balancing of workers. As a result of this study, when applied the primary target value, it was verified that the whole workers decreased from 28 to 24. Furthermore, when specified the secondary target value and applied algorithm once more, it was verified that the Load Balance Ratio was improved from 44.96% to 91.7%.

This research focused on deciding optimal manufacturing WIP (Work-In-Process) limit for a small production system. Reducing WIP leads to stable capacity, better manufacturing flow and decrease inventory. WIP is the one of the important issue, since it can affect manufacturing area, like productivity and line efficiency and bottlenecks in manufacturing process. Several approaches implemented in this research. First, two strategies applied to decide WIP limit. One is roulette wheel selection and the other one is elite strategy. Second, for each strategy, JIT (Just In Time), CONWIP (Constant WIP), Gated Max WIP System and CWIPL (Critical WIP Loops) system applied to find a best material flow mechanism. Therefore, pull control system is preferred to control production line efficiently. In the production line, the WIP limit has been decided based on mathematical models or expert’s decision. However, due to the complexity of the process or increase of the variables, it is difficult to obtain optimal WIP limit. To obtain an optimal WIP limit, GA applied in each material control system. When evaluating the performance of the result, fitness function is used by reflecting WIP parameter. Elite strategy showed better performance than roulette wheel selection when evaluating fitness value. Elite strategy reach to the optimal WIP limit faster than roulette wheel selection and generation time is short. For this reason, this study proposes a fast and reliable method for determining the WIP level by applying genetic algorithm to pull system based production process. This research showed that this method could be applied to a more complex production system.

For several years, keyboard and mouse have been used as the main interacting devices between users and computer games, but they are becoming outdated. Gesture-based human-computer interaction systems are becoming more popular owing to the emergence of virtual reality and augmented reality technologies. Therefore research on these systems has attracted a significant attention. The researches focus on designing the interactive interfaces between users and computers. Human-computer interaction is an important factor in computer games because it affects not only the experience of the users, but also the design of the entire game. In this research, we develop an particle filter-based face tracking method using color distributions as features, for the purpose of applying to gesture-based human-computer interaction systems for computer games. The experimental results proved the efficiency of particle filter and color features in face tracking, showing its potential in designing human-computer interactive games.

본 논문에서는 인접 4화소의 형태에 기반한 고속 방향성 영상보간 알고리즘을 제시한다. 제안한 알고리즘의 기 본 개념은 비간축 이산 웨이브릿 변환에 기초하지만, 실제로 변환이 수행되는 것이 아니라 경계방향 검출을 위해 인접 4화소의 값만 비교된다. 2×2 화소의 형태는 8개의 종류로 분류되고 각 형태에 따라 방향성 보간이 수행된다. 그러므로 제안한 알고리즘은 매우 단순하기 때문에, 1차 선형보간과 비슷한 수행시간을 나타내지만 성능은 기존의 영상보간 기 법들 보다 우수한 결과 품질을 보여준다.

본 연구에서는 철근콘크리트 건물에 대한 유전자 알고리즘 기반의 최적구조설계기법을 제시하고자 한다. 목적함수는 구조 물의 비용과 이산화탄소 배출량을 동시에 각각 최소화하는 것이다. 비용 및 인산화탄소 배출량은 구조설계안에서 얻을 수 있는 단면치수, 부재길이, 재료강도, 철근량 등과 같은 설계정보를 통해 계산한다. 즉, 구조물의 물량을 기초로 하여 비용과 이산화탄소 배출량을 평가한다. 재료의 운반, 시공 및 건물 운영 단계에서 발생하는 비용 및 이산화탄소 배출량은 본 연구에 서 제외한다. 제약조건은 철근콘크리트 건물을 구성하는 기둥과 보 부재의 강도조건과 층간변위조건이 고려된다. 제약조건 을 평가하기 위해 OpenSees를 활용한 선형정적해석이 수행된다. 제약조건을 만족시키면서 목적함수에 대해 최소의 값을 제 시하는 설계안을 찾기 위해 유전자 알고리즘이 사용된다. 제시한 알고리즘의 적용성을 검증하기 위해 4층 철근콘크리트 모 멘트 골조 예제에 제시하는 기법을 적용하여 검증한다.

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

For air express service providers offering various express delivery services such as overnight delivery and next-business day delivery services, establishing quickly cargo loading plans is one of important issues owing to the characteristics of air express business, i.e., a short amount of time is available to complete all cargo loading operations before flight departure after receiving air express containers, pallets and bulks. On the other hand, one of major concerns in the air cargo loading planning is to make a plan that insures the stability of an aircraft to avoid take-off, flight, and landing accidents. To this end, this paper considers an air cargo loading planning problem, which is the problem of determining locations in the aircraft cargo space where air containers, pallets and bulks to be loaded while insuring the aircraft stability, motivated from DHL and Air Hong Kong. The objective of the problem is to maximize the total revenue gained from loading air express containers, pallets and bulks. To solve the problem, this paper suggests a simulated annealing algorithm to overcome impracticality of the integer programming model developed by a previous study requiring excessive computation time. The results of computational experiments show that the heuristic algorithm is a viable tool for establishing express cargo loading plans as giving robust and good solutions in a short amount of computation time. Scenario analyses are performed to investigate the effect of the current activities of air express carriers on the revenue change and to draw practical implications for air express service providers.

The genetic algorithm (GA), one of the artificial intelligence (AI), is developed based on Darwin's theory of evolution, i.e., the mating of randomly selected objects. If more optimal solution is generated, then it is better to repeat the process of setting the optimum value. In this paper, the method of background music using the genetic algorithm is exploited when the computer game is executed each time. As a result, it has created several music that can be used in the actual game, and it could be confirmed that the other music that is created is different music when performed each time.

본 연구에서는 철골모멘트골조의 보-힌지 붕괴모드를 유도하는 최적 내진설계기법을 제안한다. 이는 유전자알고리즘을 사용하며, 기둥의 소성힌지 발생을 억제하는 제약조건을 설정하여 보-힌지 붕괴모드를 유도한다. 제안하는 기법은 구조물량를 최소화하고 에너지소산능력을 최대화하는 목적함수를 사용한다. 제안하는 기법은 9층 철골모멘트골조 예제 적용을 통해 검증한다. 예제 적용을 통해 철골모멘트골조의 보-힌지 붕괴모드를 유도하기 위해 요구되는 기둥-보 강도비를 평가한다. 패널존에 대한 3가지 모델링 기법을 각각 적용하여 모델링 조건에 따른 휨강도비 영향이 추가적으로 검토된다.

This paper considers the allocation and engagement scheduling problem of interceptor missiles, and the problem was formulated by using MIP (mixed integer programming) in the previous research. The objective of the model is the maximization of total intercept altitude instead of the more conventional objective such as the minimization of surviving target value. The concept of the time window was used to model the engagement situation and a continuous time is assumed for flying times of the both missiles. The MIP formulation of the problem is very complex due to the complexity of the real problem itself. Hence, the finding of an efficient optimal solution procedure seems to be difficult. In this paper, an efficient genetic algorithm is developed by improving a general genetic algorithm. The improvement is achieved by carefully analyzing the structure of the formulation. Specifically, the new algorithm includes an enhanced repair process and a crossover operation which utilizes the idea of the PSO (particle swarm optimization). Then, the algorithm is throughly tested on 50 randomly generated engagement scenarios, and its performance is compared with that of a commercial package and a more general genetic algorithm, respectively. The results indicate that the new algorithm consistently performs better than a general genetic algorithm. Also, the new algorithm generates much better results than those by the commercial package on several test cases when the execution time of the commercial package is limited to 8,000 seconds, which is about two hours and 13 minutes. Moreover, it obtains a solution within 0.13 ~33.34 seconds depending on the size of scenarios.

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.

Maritime transport is now regarded as one of the main contributors to global climate change by virtue of its CO2 emissions. Meanwhile, slow steaming, i.e., slower ship speed, has become a common practice in the maritime industry so as to lower CO2 emissions and reduce bunker fuel consumption. The practice raised various operational decision issues in terms of shipping companies: how much ship speed is, how much to bunker the fuel, and at which port to bunker. In this context, this study addresses an operation problem in a shipping companies, which is the problem of determining the ship speed, bunkering ports, and bunkering amount at the ports over a given ship route to minimize the bunker fuel and ship time costs as well as the carbon tax which is a regulatory measure aiming at reducing CO2 emissions. The ship time cost is included in the problem because slow steaming increases transit times, which implies increased in-transit inventory costs in terms of shippers. We formulate the problem as a nonlinear lot-sizing model and suggest a Lagrangian heuristic to solve the problem. The performance of the heuristic algorithm is evaluated using the data obtained from reliable sources. Although the problem is an operational problem, the heuristic algorithm is used to address various strategic issues facing shipping companies, including the effects of bunker prices, carbon taxes, and ship time costs on the ship speed, bunkering amount and number of bunkering ports. For this, we conduct sensitivity analyses of these factors and finally discuss study findings.