With the continuous development of science and technology, unmanned ship has gradually become a hot spot in the field of marine research. In practical applications, unmanned ships need to have long-range navigation and high efficiency, so that they can accurately perform tasks in the marine environment. As one of the key technologies of unmanned ship, path planning is of great significance to improve the endurance of unmanned ship. In order to meet the requirements, this paper proposes a path planning method for long distance unmanned ships based on reinforcement learning angle precedence ant colony improvement algorithm. Firstly, canny operator is used to automatically extract navigation environment information, and then MAKLINK graph theory is applied for environment modelling. Finally, the basic ant colony algorithm is improved and applied to the path planning of unmanned ship to generate an optimal path. The experimental results show that, compared with the traditional ant colony algorithm, the path planning method based on the improved ant colony algorithm can achieve a voyage duration of nearly 7 km for unmanned ships under the same sailing environment, which has certain practicability and popularization value.

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.

With the development of the global marine transportation industry, marine accidents frequently occur due to the complex and changeable climate environment, and maritime search and rescue work has thus received much attention. To improve marine search and rescue operations, an algorithm for environmental modeling and search path optimization based on an ant colony system is proposed. First, MAKLINK is selected to build an ecological model. Secondly, the relevant parameters of the ant colony system algorithm are established, and the search and rescue route is designed. Finally, simulations of the environmental model and route design are constructed in search and rescue waters in Zhoushan, Zhejiang Province, using MATLAB. Experimental results prove the validity of this algorithm.

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.

A colony of Solenopsis invicta was first intercepted on Gamman pier, Pusan port in Korea at September, 2017 by Animal and Plant Quarantine Agency. The mitochondrial DNA (mt-DNA) of workers was analyzed and compared with vary libraries of mt-DNA haplotypes to elucidate the origin of the introduced colony with the trade pattern of the Gamman pier. The mt-DNA fragment of 768 bp, which is part of the Cytochrome oxidae I gene, was amplified and sequenced. The results showed that the mt-DNA was in the clade of haplotype 5, which is endemic in southern USA, China, Taiwan, and Australia. More than 60% of containers are imported from China into Gamman pier, it may be possible to assume that the colony was inadvertently invaded through containers from China.

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.

In korea, only small amount of nonstructural lightweight concrete is being used through indirect effects such as heat insulation property and soundproofing rather than structural elements due to lack of structural lightweight aggregates and lack of understanding about lightweight concrete development, etc. That`s why structural lightweight concrete to reduce weight has not been put to practical use. This study is a part of high strength lightweight aggregate concrete researches using lightweight aggregates and the purpose of this study is to find out the basic physical characteristics and tension cracking fracture characteristics of lightweight concrete. Crack Mouth Opening Displacement is measured through 3 point flexure experiment about notch beam. Load-CMOD characteristics are examined through rules of countries, characteristics of lightweight concrete and tension cracking fracture experiments. The degree of tensile characteristic alteration according to size changes of specimen and the characteristics about crack surface are analyzed. The changes of softening curve are analyzed and fracture energy is drawn through inverse analysis by the obtained Load-CMOD curve. To decide fracture energy and analysis parametric, inverse analysis is conducted and Ant Colony Method is conducted for optimization and then a way to find out optimal parameterization fracture energy is suggested.

고객의 요구에 대한 빠른 대응과 유연하고 효율적으로 새로운 제품을 적기에 개발하기 위해서는 제품 플랫폼에 기초한 대량 맞춤이 절실히 요구된다. 이러한 목적을 달성하기 위하여 기업들은 상대적으로 생산비용을 낮게 유지하면서 대량생산의 이점을 유지하고 동시에 고객의 요구사항을 만족시키기 위해, product family를 도입하고 가능하면 작은 변화를 통하여 제품의 다양성을 유지하고자 한다. Product family를 설계할 때 중요한 이슈 중에 하나는 제