Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation.

Quantum-inspired Genetic Algorithm (QGA) is a probabilistic search optimization method combined quantum computation and genetic algorithm. In QGA, the chromosomes are encoded by qubits and are updated by quantum rotation gates, which can achieve a genetic search. Asset-based weapon target assignment (WTA) problem can be described as an optimization problem in which the defenders assign the weapons to hostile targets in order to maximize the value of a group of surviving assets threatened by the targets. It has already been proven that the WTA problem is NP-complete. In this study, we propose a QGA and a hybrid-QGA to solve an asset-based WTA problem. In the proposed QGA, a set of probabilistic superposition of qubits are coded and collapsed into a target number. Q-gate updating strategy is also used for search guidance. The hybrid-QGA is generated by incorporating both the random search capability of QGA and the evolution capability of genetic algorithm (GA). To observe the performance of each algorithm, we construct three synthetic WTA problems and check how each algorithm works on them. Simulation results show that all of the algorithm have good quality of solutions. Since the difference among mean resulting value is within 2%, we run the nonparametric pairwise Wilcoxon rank sum test for testing the equality of the means among the results. The Wilcoxon test reveals that GA has better quality than the others. In contrast, the simulation results indicate that hybrid-QGA and QGA is much faster than GA for the production of the same number of generations.

Loan consultants assist clients with loan application processing and loan decisions. Their duties may include contacting people to ask if they want a loan, meeting with loan applicants and explaining different loan options. We studied the efficiency of service quality of loan consultants contracted to a bank in Korea. They do not work as a team, but do work independently. Since he/she is not an employee of the bank, the consultant is paid solely in proportion to how much he/she sell loans. In this study, a consultant is considered as a decision making unit (DMU) in the DEA (Data Envelopment Analysis) model. We use a principal component analysis-data envelopment analysis (PCA-DEA) model integrated with Shannon’s Entropy to evaluate quality efficiency of the consultants. We adopt a three-stage process to calculate the efficiency of service quality of the consultants. In the first stage, we use PCA to obtain 6 synthetic indicators, including 4 input indicators and 2 output indicators, from survey results in which questionnaire items are constructed on the basis of SERVQUAL model. In the second stage, 3 DEA models allowing negative values are used to calculate the relative efficiency of each DMU. In the third stage, the weight of each result is calculated on the basis of Shannon’s Entropy theory, and then we generate a comprehensive efficiency score using it. An example illustrates the proposed process of evaluating the relative quality efficiency of the loan consultants and how to use the efficiency to improve the service quality of the consultants.

본 연구는 다중빔 음향측심 자료를 바탕으로 침몰선박의 정확한 형상 및 해저지형에 대한 정보를 분석하였다. 다양한 영상 자료처리를 통해 현재 침몰선박의 상태를 분석하였다. 해당 자료와 과거 조사 자료의 비교를 통하여 침몰선박 상태 변화 및 주변지형의 변화를 해석하였다. 분석대상 선박 중 퍼시픽프랜드호의 경우 조류에 의한 침·퇴적으로 인해 선수-선미의 지형변화가 뚜렷하게 나타나는 특징을 알 수 있었다. 그리고 제7해성호의 경우 2011년 국립해양조사원 영상자료에서는 선수 일부가 유지된 상태였으나, 2015년 조사 영상에서는 선수 일부가 붕괴된 것을 확인할 수 있었다. 이는 오랜 기간 선박이 해저에 방치되면서 조류 및 화물의 하중 등과 지속적인 부식으로 선체가 붕괴된 것으로 추정된다. 따라서 잔존연료 유출 및 주변 해양오염이 발생할 수 있어 지속적인 모니터링이 필요할 것으로 판단된다. 침몰선박의 영상분석을 통해 침몰선박이 수심과 조류에 의한 영향을 많이 받은 지역일 경우 지질학적 특성과 퇴적물의 침·퇴적 양상에 따라 침몰선박의 구조 안전성에 상당한 영향을 줄 것으로 판단되며, 부식으로 인한 선체의 변화는 계속적으로 변화될 것으로 추정된다. 따라서 지질학적 특성을 고려한 잔존연료 유출 및 주변 환경 변화에 대하여 침몰선박의 변화에 따라 예측‧대응하는 기술의 개발이 필요하다.

본 연구에서는 침몰선박 위해도 평가항목 및 평가지수를 개정하고 신규항목을 도출하기 위해 전문가를 대상으로 AHP 기법의 설문을 실시하였다. 설문조사 결과, 사고원인 및 조류영향 등 두개의 신규항목을 도출하였으며 하나의 그룹으로 평가되었던 독성액체물질과 연료유적재량, 폭발성가스는 각각 평가하는 것이 적합한 것으로 나타났다. 이에 따라 기존 일곱 개 평가항목을 열한 개 항목으로 조정하여 평가항목별 지수를 분석한 결과, 독성액체물질, 유출가능성, 폭발성가스, 연료유적재량, 해역환경민감도, 해상교통환경, 사고원인, 조류, 여유수심, 선박종류, 선박규모 순으로 평가지수가 나타났다. 특히, 신규 도출된 평가항목 지수를 기존 위해도 평가항목과 비교해 보았을 때 해역환경민감도와 유출가능성 항목은 기존 평가지수 보다 높게 나타났고, 여유수심 항목의 평가지수는 더 낮게나타났다.

A missile defense system is composed of radars detecting incoming missiles aiming at defense assets, command control units making the decisions on weapon target assignment, and artillery batteries firing of defensive weapons to the incoming missiles. Although, the technology behind the development of radars and weapons is very important, effective assignment of the weapons against missile threats is much more crucial. When incoming missile targets toward valuable assets in the defense area are detected, the asset-based weapon target assignment model addresses the issue of weapon assignment to these missiles so as to maximize the total value of surviving assets threatened by them. In this paper, we present a model for an asset-based weapon assignment problem with shoot-look-shoot engagement policy and fixed set-up time between each anti-missile launch from each defense unit. Then, we show detailed linear approximation process for nonlinear portions of the model and propose final linear approximation model. After that, the proposed model is applied to several ballistic missile defense scenarios. In each defense scenario, the number of incoming missiles, the speed and the position of each missile, the number of defense artillery battery, the number of anti-missile in each artillery battery, single shot kill probability of each weapon to each target, value of assets, the air defense coverage are given. After running lpSolveAPI package of R language with the given data in each scenario in a personal computer, we summarize its weapon target assignment results specified with launch order time for each artillery battery. We also show computer processing time to get the result for each scenario.