• Journal of Korea Port Economic Association
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• v.36 no.2
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• pp.1-18
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• 2020

The Internet of things (IoT) has been applied to a variety of industrial uses such as public service sectors, medical industries, automotive industries, and so on. Led by smart cities, this is typical. However, from a logistics perspective, the level of application is insufficient. This study examines the applicability of IoT-related technology in a container terminal, an object of the present invention, to derive an applicable plan. Analytic network process (ANP) analysis reveals the following results for IoT applications in container terminals: operating systems (26.7%), safety/environmental/security systems (26.4%), equipment maintenance systems (25.3%), and facility maintenance systems (21.6 %). The second ANP analysis reveals the following results: Economy (40.2%), productivity (21.1%), service level (19.5%), and utilizing technology level (19.2%). The application or standard of evaluation is important when applying IoT technology to container terminals; however, it is not concentrated in a certain area. It is desirable to build each container system with linkage and efficiency from a macroscopic view.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.979-995
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• 2023

As wildfires are difficult to predict, real-time monitoring is crucial for a timely response. Geostationary satellite images are very useful for active fire detection because they can monitor a vast area with high temporal resolution (e.g., 2 min). Existing satellite-based active fire detection algorithms detect thermal outliers using threshold values based on the statistical analysis of brightness temperature. However, the difficulty in establishing suitable thresholds for such threshold-based methods hinders their ability to detect fires with low intensity and achieve generalized performance. In light of these challenges, machine learning has emerged as a potential-solution. Until now, relatively simple techniques such as random forest, Vanilla convolutional neural network (CNN), and U-net have been applied for active fire detection. Therefore, this study proposed an active fire detection algorithm using state-of-the-art (SOTA) deep learning techniques using data from the Advanced Himawari Imager and evaluated it over East Asia and Australia. The SOTA model was developed by applying EfficientNet and lion optimizer, and the results were compared with the model using the Vanilla CNN structure. EfficientNet outperformed CNN with F1-scores of 0.88 and 0.83 in East Asia and Australia, respectively. The performance was better after using weighted loss, equal sampling, and image augmentation techniques to fix data imbalance issues compared to before the techniques were used, resulting in F1-scores of 0.92 in East Asia and 0.84 in Australia. It is anticipated that timely responses facilitated by the SOTA deep learning-based approach for active fire detection will effectively mitigate the damage caused by wildfires.