• Proceedings of KOSOMES biannual meeting
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• 2017.11a
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• pp.211-211
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• 2017

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.11a
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• pp.296-297
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• 2022

A smart port refers to a port built based on digital technologies such as IoT, big data, AI, and block chain, and refers to a port that minimizes waste of time, space and resources as the only means of survival of the port. Sustainability refers to 'environmental, economic, and social characteristics that enable people to continue to use the environment, ecosystem, or publicly used resources'. It contains the meaning of 'future sustainability' that can be maintained in the future. In the face of the 4th industrial revolution, interest and realization of smart port construction and sustainability are actively progressing around the world. In this study, core indicators of the ESG (Enviornment, Social, Governance) area, which are key elements of sustainable smart ports, were developed,

• Journal of Korean Port Research
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• v.9 no.1
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• pp.1-17
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• 1995

According to the analysis results, first, the purpose of information system is usually internal-oriented (decrease of employees and increase of productivity) in the port-logistics industry. Second, the success factor of information system for internal strategy group is different for its external strategy group. Third, the external enviornment factors affect on the information strategy and the success factor in the port-logistics industry. Therefore, I think that it is necessary for the port-logistics industry to consider the information strategy direction and its affecting factors in the port-MIS.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.11
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• pp.2875-2882
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• 2000

Typically, the ultrasonic sensors can be used in navigation systems for modeling of the enviornment, obstacle avoidance, and map building. In this paper, we tried to approach an object classification method using the range data of the ultrasonic sensors. A characterization of the sonar scan is described that allows the differentiation of planes, corners, edges, cylindrical and rectangular pillars by processing the scanned data from three sonars. To use the data from the ultrasonic sensors as input to the neural networks, we have introduced a clustering, threshold, and bit operation algorithm for the obtained raw data, After repeated training of the neural network, the performance of the proposed method was obtained through experiments. Also, the recognition ranges of the proposed method were investigated. As a result of experiments, we found that the proposed method successfully recognized the objects within the accuracy of 78%.