• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.988-993
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• 2002

The AVT under traffic-induced vibrations was carried out on Namhae suspension bridge in Korea. Mode shapes as well as natural frequencies up to the 15th mode were acquired exactly, and the effect of traffic mass and temperature on measured natural frequencies was investigated. The results from the AVT are compared with those from forced vibration test(FVT) and FE analysis. In the case of long span suspension bridges such as Namhae bridge which has relatively large mass, the results shows that the measured natural frequencies are not affected by vehicle mass. From the results of long-term variation of natural frequencies due to temperature change, it can be said that temperature effect may be predominant to structural demage effect. Therefore, if damage detection methods based on dynamic characteristics of bridges are to be used, the variation should be taken into consideration.

• Journal of Sensor Science and Technology
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• v.24 no.3
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• pp.155-158
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• 2015

In this paper, in-situ heat cooling with temperature monitoring is reported to solve thermal issues in electric vehicle (EV) batteries. The device consists of a thick graphene cooler on top of the substrate and a platinum-based resistive temperature sensor with an embedded heater above the graphene. The graphene layer is synthesized by using chemical vapor deposition directly on the Ni layer above the Si substrate. The proposed thick graphene heat cooler does not use transfer technology, which involves many process steps and does not provide a high yield. This method also reduces the mechanical damage of the graphene and uses only one photomask. Using this structure, temperature detection and cooling are conducted simultaneously using one device. The temperature coefficient of resistance (TCR) of a $1{\times}1mm^2$ temperature sensor on 1-$\grave{i}m$-thick graphene is $1.573{\times}10^3ppm/^{\circ}C$. The heat source cools down $7.3^{\circ}C$ from $54.4^{\circ}C$ to $47.1^{\circ}C$.

• Journal of the Korean Society of Propulsion Engineers
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• v.18 no.6
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• pp.50-58
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• 2014

This paper is about a short survey on the recent research activities regarding health monitoring and management for liquid rocket engines. For this, we investigate the precedent techniques developed in advanced space-industry countries which are USA, EU, Russia, Japan and China. Particularly, we focus on the technologies applied in China, a recently joined to the advanced space-industry countries in this field. Then we discuss some important points to be considered to apply to the development of the Korea Space Launch Vehicle KSLV-II and other related projects.

• Journal of Platform Technology
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• v.8 no.1
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• pp.3-9
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• 2020

In Korea, 63% of the nation's land is made up of forests, and the average temperature of the earth has been increasing. Forest service has been operating a proactive control system for preventing the spread of forest pests such as Pine wilt disease. but there were some hurdles in timely control due to weather, topography and manpower management difficulties. In this paper, we propose a model for building fast, accurate and efficient control system by categorizing the damage and dead wood automatically based on the images acquired using small unmanned aerial vehicles based on information and communication technology. In particular, the proposed model establishes an effective response system for government affairs through cooperation in the private sector. It can also create new jobs in the unmanned aerial vehicle business and service industries.

• Journal of the Korea institute for structural maintenance and inspection
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• v.25 no.6
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• pp.245-253
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• 2021

RC slab bridges account for the largest portion of deteriorated bridges in Korea. However, most RC slabs are not included in the first and second classes of bridges, which are subject to bridge safety management and maintenance. The highest damaged components in highway bridges are the subsidiary facilities including expansion joints and bearings. In particular, leakage through expansion joints causes deterioration and cracks of concrete and exposure of reinforced bars. Therefore, this study analyzed the effect of adhesion damage at expansion joints on the response of the deck in RC slab bridges. When the spacing between the expansion joints at both ends was closely adhered, cracks occurred in the concrete at both ends of the deck due to the resistance rigidity at the expansion joints. Based on the response results, the correlation analysis between displacements in the longitudinal direction of the expansion joint and concrete stress at both ends of the deck for each damage scenario was performed to investigate the effect of the occurrence of damage on the bridge behavior. When expansion joint devices at both sides were damaged, the correlation between displacement and stress showed a low correlation of 0.18 when the vehicles proceeded along all the lanes. Compared with those in the intact state, the deflections of the deck in the damaged case at both sides showed a low correlation of 0.34 to 0.53 while the vehicle passed and 0.17 to 0.43 after the vehicle passed. This means that the occurrence of cracks in the ends of concrete changed the behavior of the deck. Therefore, data-deriven damage detection could be developed to manage the damage to expansion joints that cause damage and deterioration of the deck.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.6
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• pp.626-632
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• 2017

Since natural disaster occurs increasingly and becomes complicated, it causes deaths, disappearances, and damage to property. As a result, there is a growing interest in the development of ICT-based natural disaster response technology which can minimize economic and social losses. In this letter, we introduce the main functions of the forest fire management platform by using images from an UAV. In addition, we propose a disaster image analysis technology based on the deep learning which is a key element technology for disaster detection. The proposed deep learning based disaster image analysis learns repeatedly generated images from the past, then it is possible to detect the disaster situation of forest-fire similar to a person. The validity of the proposed method is verified through the experimental performance of the proposed disaster image analysis technique.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.53-60
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• 2020

The bogies of railway vehicles are one of the most critical components for service. Fatigue defects in the bogie can be initiated for various reasons, such as material imperfection, welding defects, and unpredictable and excessive overloads during operation. To prevent the derailment of a railway vehicle, it is necessary to evaluate and detect the defect of a connection weldment between the car body and bogie accurately. The safety of the bogie weldment was checked using an ultrasonic test, and it is necessary to determine the occurrence of defects using a learning method. Recently, studies on deep learning have been performed to identify defects with a high recognition rate with respect to a fine and similar defect. In this paper, the databases of weldment specimens with artificial defects were constructed to detect the defect of a bogie weldment. The ultrasonic inspection using the wedge angle was performed to understand the detection ability of fatigue cracks. In addition, the convolutional neural network was applied to minimize human error during the inspection. The results showed that the defects of connection weldment between the car body and bogie could be classified with more than 99.98% accuracy using CNN, and the effectiveness can be verified in the case of an inspection.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.2
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• pp.89-103
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• 2020

As we face an aging society, the demand for personal mobility for disabled and aged people is increasing. In fact, as of 2017, the number of electric wheelchair in the country continues to increase to 90,000. However, people with disabilities and seniors are more likely to have accidents while driving, because their judgment and coordination are inferior to normal people. One of the causes of the accident is the interference of personal vehicle steering control due to unbalanced road surface conditions. In this paper, we introduce a encoder type semantic segmentation algorithm that can recognize road conditions at high speed to prevent such accidents. To this end, more than 1,500 training data and 150 test data including road surface damage were newly secured. With the data, we proposed a deep neural network composed of encoder stages, unlike the Auto-encoding type consisting of encoder and decoder stages. Compared to the conventional method, this deep neural network has a 4.45% increase in mean accuracy, a 59.2% decrease in parameters, and an 11.9% increase in computation speed. It is expected that safe personal transportation will be come soon by utilizing such high speed algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.4
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• pp.771-782
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• 2021

Regular or irregular situations such as traffic accidents, damage to road facilities, maintenance or repair work, and vehicle breakdowns occur frequently on highways. It is required to provide traffic services to drivers by promptly recognizing these regular or irregular situations, various techniques have been developed for rapidly collecting data and detecting abnormal traffic conditions to solve the problem. We propose a method that can be used for verification and demonstration of unexpected situation algorithms by establishing a system and developing algorithms for detecting unexpected situations on highways. For the detection of emergencies on expressways, a system was established by defining the expressway contingency and algorithm development, and a test bed was operated to suggest a method that can be used for verification and demonstration of contingency algorithms. In this study, a system was established by defining the unexpected situation and developing an algorithm to detect the unexpected situation on the highway, and a method that can be used verifying and demonstrating unexpected situations. It is expected to secure golden time for the injured by reducing the effectiveness of secondary accidents. Also predictable accidents can be reduced in case of unexpected situations and the detection time of unpredictable accidents.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.468-473
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• 2024

Excavation work in urban areas can induce ground deformation, which may damage nearby infrastructure. Such ground deformation can result in displacement of paving blocks near the construction site. Accurate measurement of these displacements can serve as an indicator for assessing the potential risks associated with ground deformation. This paper proposes a robust and efficient method for paving block displacement measurement using a 3D laser line sensor mounted on an Unmanned Ground Vehicle (UGV). The proposed method consists of two stages: 2D projection based object detection and measurement through the CPLF algorithm. Experimental results demonstrate that the CPLF algorithm is more efficient compared to the PLF algorithm, achieving an error of 1.36 mm and a processing time of 10.76 ms, confirming that the proposed method ensures robust online measurements with high accuracy in real-world environments with various types of paving blocks and environmental factors using a 3D laser line sensor on a UGV.