• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.42-48
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• 2011

This paper presents a bridge road surface frost prediction and monitoring system. The node sensing hardware comprises microprocessor, temperature sensors, humidity sensors and Zigbee wireless communication. A software interface is implemented the control center to monitor and acquire the temperature and humidity data of bridge road surface. A bridge road surface frost occurs when the bridge deck temperature drops below the dew point and the freezing point. Measurement data was used for prediction of road surface frost occurrences. The actual alert is performed at least 30 minutes in advance the road surface frost. The road surface frost occurrences data are sent to nearby drivers for traffic accidents prevention purposes.

• The Journal of Bigdata
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• v.2 no.2
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• pp.129-140
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• 2017

Weather acts through low visibility, precipitation, high winds, and temperature extremes to affect driver capabilities, vehicle performance (i.e., traction, stability and maneuverability), pavement friction, roadway infrastructure, crash risk, traffic flow, and agency productivity. Recently a variety of road weather big data sources such as CCTV, road sensor/systems, car sensor have been developed to solve the weather-related problems, This study identifies and defines the types and characteristics of these sources to suggest how to utilize them for car safety and efficiency as well as road management through analyzing domestic and oversea cases of road weather big data applications.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.155-163
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• 2019

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.2
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• pp.104-113
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• 2019

The road management institute needs lots of cost to repair road surface damage. These damages are inevitable due to natural factors and aging, but maintenance technologies for efficient repair of the broken road are needed. Various technologies have been developed and applied to cope with such a demand. Recently, maintenance technology for road surface damage repair is being developed using image information collected in the form of a black box installed in a vehicle. There are various methods to extract the damaged region, however, we will discuss the image recognition technology of the deep neural network structure that is actively studied recently. In this paper, we introduce a new neural network which can estimate the road damage and its location in the image by region-based convolution neural network algorithm. In order to develop the algorithm, about 600 images were collected through actual driving. Then, learning was carried out and compared with the existing model, we developed a neural network with 10.67% accuracy.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.11a
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• pp.77-79
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• 2007

터널조명 상황을 안전하고 신속하게 측정하기 위해 고속주행에서도 노면의 조도와 휘도에 대해 다량의 데이터를 수집할 수 있는 '고성능 터널조명 측정시스템'을 개발하였다

• Transactions of the Korean Society of Automotive Engineers
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• v.10 no.5
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• pp.190-198
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• 2002

Full scale durability test in the laboratory is an essential of any fatigue life evaluation of components or structures of the automotive vehicle. Component testing is particularly important in today's highly competitive industries where the design to reduce weight and production costs must be balanced with the necessity to avoid expensive service failure. Generally, hydraulic road simulator is used to carry out the fatigue test and the vibration test. In this paper, the algorithm and software to realize the real road profile are developed. The operation software for simultaneously controlled multi-axial road simulator is developed and the input and output data are displayed window based PC controller in the real time. Futhermore, the software to generate the real road profile are developed. The validity of the software are verified by applying the belgian road, the city road, the highway, and the gravel road. The results of the above experiment show that the real road profiles are realized well after 10th iteration.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.3
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• pp.133-139
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• 2021

Currently, the process of constructing a high-definition road map has a high proportion of manual labor, so there are limitations in construction time and cost. Research to automate map production with high-definition road maps using artificial intelligence is being actively conducted, but since the construction of training data for the map construction is also done manually, there is a need to automatically build training data. Therefore, in this study, after converting to images using point clouds acquired by a mobile mapping system, the road marking areas were extracted through image reclassification and overlap analysis using thresholds. Then, a methodology was proposed to automatically construct training data for deep learning data for the high-definition road map through the classification of the polygon types in the extracted regions. As a result of training 2,764 lane data constructed through the proposed methodology on a deep learning-based PointNet model, the training accuracy was 99.977%, and as a result of predicting the lanes of three color types using the trained model, the accuracy was 99.566%. Therefore, it was found that the methodology proposed in this study can efficiently produce training data for high-definition road maps, and it is believed that the map production process of road markings can also be automated.

• Journal of KSNVE
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• v.8 no.6
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• pp.1158-1165
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• 1998

Low frequency interior noise in cars is mainly due to structure-borne excitations which are related with road excitation and component vibrations such as suspension and engine mounts. In order to analyze the annoying interior noise. a technique (Transfer Path Analysis) is introduced to find a noise source and the path of that noise. In this study, TPA is reviewed theoretically and applied to investigate the case when the low frequency interior noise at front seat due to road excitations needs to be optimized. The subjective and objective appraisal was performed under the conditions that a testing vehicle traveled on asphalt at 30 km/h. so that the low frequency to be eliminated was detected. The related vibration and noise data for TPA were measured on running and static vehicle. The results reveal that the noise contribution along the z-direction of trailing arm is prominent to low frequency interior noise.

• Journal of IKEEE
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• v.23 no.3
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• pp.883-892
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• 2019

This study is the method which is adapted to control ankle joint movement for resolving the problem of gait imbalance in intervals where gait environments are changed and slope walking, as applying terrain-adaptive technique to intelligent above-knee prosthesis. In this development of above-knee prosthesis, to classify the gait modes is essential. For distinguishing the stance phases and the swing phase depending on roads, a machine learning which combines decision tree and random forest from knee angle data and inertial sensor data, is proposed and adapted. By using this method, the ankle movement state of the prosthesis is controlled. This study verifies whether the problem is resolved through butterfly diagram.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.145-154
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• 2014

The road network becomes more complex and extensive. Therefore, the inconveniences are caused in accordance with the time delay of the restoration of damaged roads, demands for excessive costs on information collection, and limitations on acquisition of damage information of the roads. Recently, road centric spatial information is gathered using mobile multi sensor system for road inventory. But expensive MMS(Mobile Mapping System) equipments require high maintenance costs from beginning and takes a lot of time in the data processing. So research is needed for continuous maintenance by collecting and displaying the damaged information on a digital map using low cost mobile camera system. In this research we aim to develop the techniques for mosaic with a regular ground sample distance using successive image from oblique camera on a vehicle. For doing this, mosaic image is generated by estimating the homography of high resolution oblique image, and the ground sample distance and appropriate overlap are analyzed using high resolution aerial oblique images which contain resolution target. Based on this we have proposed the appropriate overlap and exposure interval for mobile road inventory system.