• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.2
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• pp.28-36
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• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Fire Science and Engineering
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• v.28 no.4
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• pp.1-7
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• 2014

Several complex devices and equipments are installed in the car's engine room, including various kind of oils or other flammable materials. So re-ignition is very likely to take place in it. In addition, it is restrictive for the driver or the occupant to open the bonnet and to spray the fire extinguisher in the engine room due to the high possibility of explosion. Therefore, a fire extinguishing system, which can detect a fire and inject the fire extinguishing agent to extinguish it, and fire extinguishing agents including HFC-227ea, which can stand the high temperature within the engine room and hold the viscosity sufficient to keep it in the kind of foam, were developed and tested. And the suffocation effect and the cooling effect come from the fire extinguishing principle of the foam fire extinguishing agent and the inhibiter catalyst effect come from the one of HFC-227ea was led simultaneously, and fire extinguishing agents without the secondary damage caused by residuals after the fire extinguishment like a case of the powder fire extinguishing agent, were developed. And experiments using a vehicle collision after the discharge is complete, foreign material can be removed without extinguishing the advantage that experimental results obtained.

• Journal of KIISE:Databases
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• v.30 no.2
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• pp.197-208
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• 2003

Abstract Workflow is not appropriately applied to coordinated execution of applications(steps) that comprise business process such as a collaborative series of tasks because of the lacks of network infra, standard of information exchange and data consistency management with conflict mode of shared data. Particularly we have not mentioned the problem which can be occurred by shared data with conflict mode. In this paper, to handle data consistency in the process of rollback for failure handling or recovery policy, we have classified rollback dependency into three types such as implicit rollback dependency in a transactional workflow, implicit rollback dependency in collaborative transactional workflows and explicit rollback dependency in collaborative transactional workflows. Also, we have proposed the rollback dependency compiler that determines above three types of rollback dependency. A workflow designer specifies the workflow schema and the resources accessed by the steps from a global database of resources. The rollback dependency compiler generates the enhanced workflow schema with the rollback dependency specification. The run-time system interprets this specification and executes the rollback policy with data consistency if failure of steps is occurred. After all, this paper can offer better correctness and performance than state-of-the-art WFMSs.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.12
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• pp.468-474
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• 2019

Korea had the highest accident rate among OECD countries in 2018, with 8.4 per 100,000 population, ranking 4th among 35 countries. In addition, the accident rate of traffic with children and the elderly was also high. This study reviewed the relevant literature and analyzed the traffic-accident analysis system. Customized traffic safety facilities were developed. In addition, by measuring the visibility of the traffic safety facilities by installing a test bed, this study measured the forward driving frequency and vehicle driving speed while driving. As a result of applying the "pedestrian pedestrian model" collision test model, the possibility of serious injury after installing the facility was reduced greatly to 4.6%. In this study, the visibility of traffic safety facilities and the effect of reducing the traffic speed were verified through test beds. Recognizing traffic safety facilities will reduce traffic accidents.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.59-68
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• 2021

As artificial intelligence(AI) technologies, which have made rapid growth recently, began to be applied to the marine environment such as ships, there have been active researches on the application of CNN-based models specialized for digital videos. In E-Navigation service, which is combined with various technologies to detect floating objects of clash risk to reduce human errors and prevent fires inside ships, real-time processing is of huge importance. More functions added, however, mean a need for high-performance processes, which raises prices and poses a cost burden on shipowners. This study thus set out to propose a method capable of processing information at a high rate while maintaining the accuracy by applying Quantization techniques of a deep learning model. First, videos were pre-processed fit for the detection of floating matters in the sea to ensure the efficient transmission of video data to the deep learning entry. Secondly, the quantization technique, one of lightweight techniques for a deep learning model, was applied to reduce the usage rate of memory and increase the processing speed. Finally, the proposed deep learning model to which video pre-processing and quantization were applied was applied to various embedded boards to measure its accuracy and processing speed and test its performance. The proposed method was able to reduce the usage of memory capacity four times and improve the processing speed about four to five times while maintaining the old accuracy of recognition.