• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.581-586
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• 2014

In general vibration test considers both harmonic vibration and random vibration, When developing the vehicle component. But the effect of harmonic vibration is larger in the parts around the vehicle engine, sole testing the harmonic vibration is considered. In this study, the fatigue damage of the linear system fixed around the engine is analyzed when the effect of random vibration is higher, harsher than the normal road surface condition. In condition the vehicle speed and the engine RPM are similar, the higher the harshness of the road surface condition is, the larger the fatigue damage level is. Therefore both random vibration and harmonic vibration must be considered in vibration test of components around the engine. Proposing the sine on random(SOR) vibration test that can exam considering both of vibrations, harmonic and random.

• 한국도로학회:학술대회논문집
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• 2008.10a
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• pp.111-118
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• 2008

• 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.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.3
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• pp.95-105
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• 2019

In recent years, image processing techniques for detecting road surface damaged spot have been actively researched. Especially, it is mainly used to acquire images through a smart phone or a black box that can be mounted in a vehicle and recognize the road surface damaged region in the image using several algorithms. In addition, in conjunction with the GPS module, the exact damaged location can be obtained. The most important technology is image processing algorithm. Recently, algorithms based on artificial intelligence have been attracting attention as research topics. In this paper, we will also discuss artificial intelligence image processing algorithms. Among them, an object detection method based on an region-based convolution neural networks method is used. To improve the recognition performance of road surface damage objects, 600 road surface damaged images and 1500 general road driving images are added to the learning database. Also, supervised learning using background object recognition method is performed to reduce false alarm and missing rate in road surface damage detection. As a result, we introduce a new method that improves the recognition performance of the algorithm to 8.66% based on average value of mAP through the same test database.

• Journal of Power System Engineering
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• v.6 no.2
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• pp.54-59
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• 2002

The principle of the FDM(fused deposition modeling) process is based on the layer by layer manufacturing technology, like other RP(rapid prototyping) process. In the FDM process, each layer may have different shape. Therefore, the built model may have stairs shape on its surface. This stairs shape is one of the serious problems in the FDM process. Thus in this study, cube models and spherical models were fabricated by FDM process to investigate the influence of injection amount on surface roughness. Models with various road width were also built to investigate the influence of road width on surface roughness. Surface roughness of the models was measured and analyzed. The result obtained in this study are expected to help selecting the part build orientation for optimum surface roughness.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.1 no.1
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• pp.18-27
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• 1998

To investigate the restoration progression on soil physical properties and vegetation at the surface of logging road affected by timber harvesting operation. This study was carried out at logging roads constructed from 1989 to 1994 in Mt. Baekwoon, Kwangyang, Chollanam-do. Judging from the analysis of soil hardness, there were significant changes in the depth of soil between 5 and 10cm. Soil hardness was recovered from the compacted condition to the natural forest condition after 9 years passed. Soil macroporous ratio (pF2.7) of topsoil was higher than that of deep soil. Soil moisture retention of topsoil was more improved than that of deep soil. From the view of soil bulk density, the necessary time for recovering to the undisturbed condition of forest soil was about 10 years in the logging road left. Soil physical properties such as soil bulk density and porous ratio were recovered as time passed. Improved soil physical properties promoted the plant recovery on the logging road surface. The dominant species on the logging roads were Comus kousa, Prunus sargentii as overstory species, Rubus crataegifolius, Lespedeza bicolor as understory species, and Saussurea gracilis, Pteridium aquilinum var. latiusculum as herbaceous species. The plant recovery of bank-slopes was faster than that of cut-slopes and road surface. In progress of year, average plant coverage were 70 to 90% in cut- and bank-slopes and 30 to 60% on the logging road, surface which was elapsed 9 years after logging road construction. Therefore, additional planting and seeding work could be effective to the soil condition and vegetation restoration.

• 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.

• International Journal of Highway Engineering
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• v.15 no.6
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• pp.61-68
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• 2013

PURPOSES : The purpose of this study is to study the noise reducing effect of Micro Surfacing by comparing general asphalt, longitudinal tining and Slurry Seal. METHODS : This study measures vehicles' noise of each section by the NCPX method that can measure noise between the road surface and the tire at the field. Total sound pressure and sound pressure level by the 1/3 octave band frequency are calculated through the field data of each section. Total sound pressure level is compared by ANOVA test statistically. After ANOVA test, post-hoc test is conducted to know mean difference of surface type by Tukey. RESULTS : As the result of CPB analysis to confirm sound pressure levels by frequency, it was shown that sound pressure levels by frequency are totally similar except for those of frequency bands between 100Hz and 500Hz. The result of ANOVA test and post-hoc test, it was shown that sections of surface type have a difference. The result of Micro Surfacing was lower 2~5dB(A) than other surface type. CONCLUSIONS : It is found that the noise reduction performance of Micro Surfacing was better than other surface type.

• Journal of Korean Society for Geospatial Information Science
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• v.13 no.4 s.34
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• pp.19-25
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• 2005

This study suggests a method for securing road safety by extracting the expected surface freezing section in planning a route using GIS. When planning a road construction in a mountainous area it is possible to confront surface freezing especially in the wintertime. In addition, it is required assessment data of surface freezing rates in the case of turnkey inspections of newly constructed or expanded roads. Consequently, an analysis method that can quantitatively estimate the surface freezing section and sunshine influence on each section of a road is needed. We can extract the expected surface freezing section which amounted to around 29km of the Donghae highway, with such techniques as three-dimensional modeling, sunshine simulation geographical database construction and spatial analysis using the overlay function of the GIS spatial analysis. This study can be used as a method to assess advance safety which has a direct influence on planning the blueprint that should be approved by a policy maker after efficiently understanding the expected surface freezing section in accordance with hill shade.

• Structural Engineering and Mechanics
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• v.51 no.2
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• pp.315-331
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• 2014

The present study investigated the effect of the correlation of the measured road roughness profiles corresponding to the left and right wheels of a vehicle on the vibration of a vehicle-bridge coupling system. Four sets of road roughness profiles were measured by a laser road-testing vehicle. A correlation analysis was carried out on the four roughness samples, and two samples with the strongest correlation and weakest correlation were selected for the power spectral density, autocorrelation and cross-correlation analyses. The scenario of a three-axle truck moving across a rigid-frame arch bridge was used as an example. The two selected road roughness profiles were used as inputs to the vehicle-bridge coupling system. Three different input modes were adopted in the numerical analysis: (1) using the measured road roughness profile of the left wheel for the input of both wheels in the numerical simulation; (2) using the measured road roughness profile of the right wheel for both wheels; and (3) using the measured road roughness profiles corresponding to left and right wheels for the input corresponding to the vehicle's left and right wheels, respectively. The influence of the three input modes on the vibration of the vehicle-bridge system was analyzed and compared in detail. The results show that the correlation of the road roughness profiles corresponding to left and right wheels and the selected roughness input mode both have a significant influence on the vibration of the vehicle-bridge coupling system.