In the event of a maritime accident, surveying the maximum area efficiently in the least amount of time is crucial for rescuing survivors. Increasingly, unmanned aerial vehicles (UAVs) are being used in search and rescue operations. This study proposes a method to generate a search path that covers all generated nodes in the shortest amount of time with multiple heterogeneous UAVs. The proposed model, which is a mixed-integer linear programming (MILP) model based on a hexagonal grid-based decomposition method, was verified through a simulation analysis based on the performance of an actual UAV. This study presents both the optimization technique’s calculation time as a function of the search area size and the various UAV routes derived as the search area grows. The results of this study can have wide-ranging applications for emergency search and rescue operations.

Governments around the world are planning to ban sales of vehicles running on petroleum-based fuels as an effort to reduce greenhouse gas emissions, and electric vehicles surfaced as a solution to decrease pollutants produced by the transportation sector. As a result, wireless power transfer technology has recently gained much attention as a convenient and practical method for charging electric vehicles. In this paper, patent analysis is conducted to identify emerging and vacant technology areas of wireless power transfer. Topics are first extracted from patents by text mining, and the topics with similar semantics are grouped together to form clusters. Then, the process of identifying emerging and vacant technology areas is improved by applying a time series analysis and innovation cycle of technology to the clustering result. Lastly, the results of clustering, time series, and innovation cycle are compared to minimize the possibility of misidentifying emerging and vacant technology areas, thus improving the accuracy of the identification process and the validity of the identified technology areas. The analysis results revealed that one emerging technology area and two vacant technology areas exist in wireless power transfer. The emerging technology area identified is circuitries consisting of transmitter coils and receiver coils for wireless power transfer, and the two vacant technology areas identified are wireless charging methods based on resonant inductive coupling and wireless power transfer condition monitoring methods or devices.

The rapid increase in international trade volume has caused frequent fluctuation of the vessels’ arrival time in container terminals. In order to solve this problem, this study proposes a methodology for rescheduling berth and quay cranes caused by updated information on the arrival time of vessels. A mixed-integer linear programming model was formulated for the berth allocation and crane assignment problem, and we solved the problem using a rolling-horizon approach. Numerical experiments were conducted under three scenarios with empirical data from a container terminal located in Busan, Korea. The experiments revealed that the proposed model reduces penalty costs and overall delayed departure time compared to the results of the terminal planner.

Object detection in the maritime domain is crucial for ensuring the safety and navigation of ships. However, there is still a lack of publicly available large-scale datasets in this domain. To address this challenge, we introduce KOLOMVERSE, an open large-scale image dataset for object detection in the maritime domain. We gathered 5,845 hours of video data captured from 21 territorial waters of South Korea. Through rigorous data quality assessment, we manually gathered around 186,419 4K resolution images from the video data. The KOLOMVERSE includes five classes (ship, buoy, fishnet buoy, lighthouse and wind farm) for maritime object detection. The dataset comprises images with dimensions of $3840\times 2160$ pixels and, to the best of our knowledge, it is by far the largest publicly available dataset for object detection in the maritime domain. We conducted object detection experiments and evaluated our dataset using several state-of-the-art pre-trained architectures to demonstrate its effectiveness and usefulness. The dataset is available at: https://github.com/MaritimeDataset/KOLOMVERSE.

Recently, last mile delivery has emerged as an essential process that greatly affects the opportunity of obtaining delivery service market share due to the rapid increase in the business-to-consumer (B2C) service market. Express delivery companies are investing to expand the capacity of hub terminals to handle increasing delivery volume. As for securing massive delivery quantity by investment, companies must examine the profitability between increasing delivery quantity and price. This study proposes two strategies for a company’s decision making regarding the adjustment of market density and price by developing a pricing and collaboration model based on the delivery time of the last mile process. A last mile delivery time function of market density is first derived from genetic algorithm (GA)-based simulation results of traveling salesman problem regarding the market density. The pricing model develops a procedure to determine the optimal price, maximizing the profit based on last mile delivery time function. In addition, a collaboration model, where a multi-objective integer programming problem is developed, is proposed to sustain long-term survival for small and medium-sized companies. In this paper, sensitivity analysis demonstrates the effect of delivery environment on the optimal price and profit. Also, a numerical example presents four different scenarios of the collaboration model to determine the applicability and efficiency of the model. These two proposed models present managerial insights for express delivery companies.