• Journal of the Korea Society of Computer and Information
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• v.21 no.10
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• pp.125-133
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• 2016

In this paper, we propose a real-time fault monitoring and dual system design of the countdown time-generating system, which is the main component of the mission control system. The countdown time-generating system produces a countdown signal that is distributed to mission control system devices. The stability of the countdown signal is essential for the main launch-related devices because they perform reserved functions based on the countdown time information received from the countdown time-generating system. Therefore, a reliable and fault-tolerant design is required for the countdown time-generating system. To ensure system reliability, component devices should be redundant and faults should be monitored in real time to manage the device changeover from Active mode to Standby mode upon fault detection. In addition, designing different methods for mode changeover based on fault classification is necessary for appropriate changeover. This study presents a real-time fault monitoring and changeover system, which is based on the dual system design of countdown time-generating devices, as well as experiment on real-time fault monitoring and changeover based on fault inputs.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.10
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• pp.823-832
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• 2018

In general, the fidelity of the aircraft dynamic model is verified by comparison with the flight test results of the target aircraft. Therefore, the reference flight data for performance comparisons must be extracted. This process requires a lot of time and manpower to extract useful data from the vast quantity of flight test data containing various noise for comparing fidelity. In particular, processing of flight data is complex because rotorcraft have high non-linearity characteristics such as coupling and wake interference effect and perform various maneuvers such as hover and backward flight. This study defines flight data processing criteria for rotorcraft and provides procedures and methods for automated processing of static and dynamic flight data using data mining techniques. Finally, the methods presented are validated using flight data.

• Journal of the Korea Institute of Building Construction
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• v.17 no.6
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• pp.545-557
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• 2017

The study is about the efficient alternative to concrete surface in the field of visual inspection technology for deteriorated infrastructure. By combining industrial drones and deep learning based image analysis techniques with traditional visual inspection and research, we tried to reduce manpowers, time requirements and costs, and to overcome the height and dome structures. On board device mounted on drones is consisting of a high resolution camera for detecting cracks of more than 0.3 mm, a lidar sensor and a embeded image processor module. It was mounted on an industrial drones, took sample images of damage from the site specimen through automatic flight navigation. In addition, the damege parts of the site specimen was used to measure not only the width and length of cracks but white rust also, and tried up compare them with the final image analysis detected results. Using the image analysis techniques, the damages of 54ea sample images were analyzed by the segmentation - feature extraction - decision making process, and extracted the analysis parameters using supervised mode of the deep learning platform. The image analysis of newly added non-supervised 60ea image samples was performed based on the extracted parameters. The result presented in 90.5 % of the damage detection rate.