• 한국방재학회:학술대회논문집
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• 2010.02a
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• pp.89.2-89.2
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• 2010

고속도로에서 주행속도가 높아지게 되면, 도로의 노면 상태에 따라 차량의 안전과 쾌적한 운전자의 환경이 변화될 수 있다. 이처럼 도로의 노면 상태를 결정하는 주된 인자는 도로의 평탄성과 소성변형에 의한 노면의 요철이라고 할 수 있다. 평탄하지 못한 도로를 자동차가 고속으로 주행하게 되면, 자동차의 속도에 의한 도로와의 마찰이 발생하여 자동차에는 매우 큰 흔들림이 발생하게 된다. 또한, 도로의 경우에도 자동차의 차축과 도로면에서 발생하는 충격에 의해 미세한 진동이 발생하게 된다. 그리고 광섬유 브래그 격자(FBG)센서는 외부에서 작용하는 매우 미세한 물리량에 의한 변화의 측정이 가능한 매우 우수한 계측 센서로 사용이 가능하기 때문에 온도보상형 광섬유진동센서를 제작하였고, 이를 고속도로 포장면의 평탄성 모니터링에 활용하고자 하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.791-794
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• 2010

고속도로에서 주행속도가 높아지게 되면, 도로의 노면 상태에 따라 차량의 안전과 쾌적한 운전자의 환경이 변화될 수 있다. 이처럼 도로의 노면 상태를 결정하는 주된 인자는 도로의 평탄성과 소성변형에 의한 노면의 요철이라고 할 수 있다. 평탄하지 못한 도로를 자동차가 고속으로 주행하게 되면, 자동차의 속도에 의한 도로와의 마찰이 발생하여 자동차에는 매우 큰 흔들림이 발생하게 된다. 또한, 도로의 경우에도 자동차의 차축과 도로면에서 발생하는 충격에 의해 미세한 진동이 발생하게 된다. 그리고 광섬유 브래그 격자(FBG)센서는 외부에서 작용하는 매우 미세한 물리량에 의한 변화의 측정이 가능한 매우 우수한 계측 센서로 사용이 가능하기 때문에 온도보상형 광섬유진동센서를 제작하였고, 이를 고속도로 포장면의 평탄성 모니터링에 활용하고자 하였다.

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Journal of the Korea institute for structural maintenance and inspection
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• v.17 no.5
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• pp.113-121
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• 2013

The road icing forecast and snow detection system using state evaluation algorithm of multi sensor optimizes snow melting system to control spread time and amount of chemical spread application This system operates integrated of contact/non-contact sensor and infrared camera. The state evaluation algorithm of the system evaluates road freezing danger condition and snowfall condition (snowfall intensity also) using acquired data such as temperature/humidity, moisture detection and result of image signal processing from field video footage. In the field experiment, it proved excellent and reliable evaluated result of snowfall state detection rate of 89% and wet state detection rate of 94%.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.57-68
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• 2018

A system to prevent disasters by collecting and analyzing environmental information such as road surface sedimentation, sinkholes, collapse risk of bridges, temperature and humidity around tram station is continuously monitored by monitoring the condition of road surface when constructing tram which is one of the urban railways. In this paper, we propose a wireless network security management system for tram roads based on LPWA that can recognize risk factors of road surface, bridge and tram station of tram in advance and prevent risk. The proposed system consists of a sensor node that detects the state of the tram road surface, a gateway that collects sensor information, and a safety management system that monitors the safety and environmental conditions of the tram road surface, and applies the low power long distance communication technology. As a result of comparing the proposed system with the LTE system in the field test, it was confirmed that there is no significant difference between the sensor information value and the critical alarm level in the monitoring system.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.11 no.1
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• pp.81-89
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• 2017

In this paper, we propose a gait pattern recognition method for intelligent prosthesis that enables walking in various environments of femoral amputees. The proposed gait mode changing method is a single sensor based algorithm which can discriminate gait surface and gait phase using only strain gauges sensor, and it is designed to simplify the algorithm based on multiple sensors of existing intelligent prosthesis and to reduce cost of prosthesis system. For the recognition algorithm, we analyzed characteristics of the ground reaction force generated during gait of normal person and defined gait step segmentation and gait detection condition, A gait analyzer was constructed for the gait experiment in the environment similar to the femoral amputee. The validity of the paper was verified through the defined detection conditions and fabricated instruments. The accuracy of the algorithm based on the single sensor was 95%. Based on the proposed single sensor-based algorithm, it is considered that the intelligent prosthesis system can be made inexpensive, and the user can directly grasp the state of the walking surface and shift the walking mode. It is confirmed that it is possible to change the automatic walking mode to switch the walking mode that is suitable for the walking mode.

• Proceedings of the Korea Concrete Institute Conference
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• 2010.05a
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• pp.391-392
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• 2010

The frozen road is effected as major cause of car accident in winter. Especially, road surface freezing on the highway can lead to fatal accident. The accident by frozen road can effectively reduced by prevent road surface freezing before it frozen as evaluate road surface condition. Therefore, this study installed thermometer in each depth of concrete pavement for evaluate road surface conditions which freezing chronically. The result of this study will be used as preliminary data for predict before freezing.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.121-132
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• 2018

Weather is an important factor affecting roadway transportation in many aspects such as traffic flow, driver 's driving patterns, and crashes. This study focuses on the relationship between weather and road surface condition and develops a model to estimate the road surface condition using machine learning. A road surface sensor was attached to the probe vehicle to collect road surface condition classified into three categories as 'dry', 'moist' and 'wet'. Road geometry information (curvature, gradient), traffic information (link speed), weather information (rainfall, humidity, temperature, wind speed) are utilized as variables to estimate the road surface condition. A variety of machine learning algorithms examined for predicting the road surface condition, and a two - stage classification model based on 'Random forest' which has the highest accuracy was constructed. 14 days of data were used to train the model and 2 days of data were used to test the accuracy of the model. As a result, a road surface state prediction model with 81.74% accuracy was constructed. The result of this study shows the possibility of estimating the road surface condition using the existing weather and traffic information without installing new equipment or sensors.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.101-107
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• 2020

Road surface measurement is an essential process for quantifying the degree and displacement of roughness in road surface management. For safer road surface management and quick maintenance, it is important to accurately measure the road surface while mounted on a vehicle. In this paper, we propose a sophisticated road surface measurement system that can be measured on a moving vehicle. The proposed road surface measurement system supports more accurate measurement of the road surface by using a high-performance line laser sensor. It is also possible to measure the transverse and longitudinal profile by matching the position information acquired from the RTK, and the velocity adaptive update algorithm allows a manager to monitor in a real-time manner. In order to evaluate the proposed system, the Gocator laser sensor, MRP module, and NVIDIA Xavier processor were mounted on a test mobile and tested on the road surface. Our evaluation results demonstrate that our system measures accurate profile base on the MSE. Our proposed system can be used not only for evaluating the condition of roads but also for evaluating the impact of adjacent excavation.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.686-693
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• 2014

This study presents a model that enables to predict road surface temperature using neural network theory. Historical road surface temperature data were collected from Road Weather Information System. They used for the calibration of the model. The neural network was designed to predict surface temperature after 1-hour, 2-hour, and 3-hour from now. The developed model was performed on Cheongwon-Sangju highway to test. As a result, the standard deviation of the difference of the predicted and observed was $1.27^{\circ}C$, $0.55^{\circ}C$ and $1.43^{\circ}C$, respectively. Also, comparing the predicted surface temperature and the actual data, R2 was found to be 0.985, 0.923, and 0.903, respectively. It can be concluded that the explanatory power of the model seems to be high.