Study on the symmetrical laser displacement sensor for the road surface measurement

Abstract This is a summary of work by the author and his colleagues, as well as by others reported in the literature, that demonstrate a need for considering a vehicle, its tires, and the road surface as a system. The central theme is interaction at the footprint, especially that of truck tires. Individual and interactive effects of road and tires are considered under the major topics of road aggregate (macroscopic and microscopic properties), development of a novel road surface, safety, noise, rolling resistance, riding comfort, water drainage by both road and tire, development of tire tread compounds and a proving ground, and influence of tire wear on wet traction. A general conclusion is that road surfaces have both the major effect and the greater potential for improvement.

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Method of signal singularity identification and correction in road surface measurement data

JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT ◽

10.3724/sp.j.1187.2010.00878 ◽

2010 ◽

Vol 24 (9) ◽

pp. 878-884

Author(s):

Ying Ma ◽

Huming Duan ◽

Fei Xie ◽

Kaibin Zhang

Keyword(s):

Measurement Data ◽

Road Surface ◽

Surface Measurement

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Turning Dynamics: Part 1 — Experimental Analysis

Volume 4: Dynamics, Control and Uncertainty, Parts A and B ◽

10.1115/imece2012-87933 ◽

2012 ◽

Author(s):

Eric B. Halfmann ◽

C. Steve Suh ◽

N. P. Hung

Keyword(s):

Instantaneous Frequency ◽

Period Doubling ◽

Displacement Sensor ◽

Laser Displacement Sensor ◽

Turning Process ◽

Time Frequency ◽

Spectral Components ◽

Tool Vibrations ◽

Exact Moment ◽

The Stability

The workpiece and tool vibrations in a lathe are experimentally studied to establish improved understanding of cutting dynamics that would support efforts in exceeding the current limits of the turning process. A Keyence laser displacement sensor is employed to monitor the workpiece and tool vibrations during chatter-free and chatter cutting. A procedure is developed that utilizes instantaneous frequency (IF) to identify the modes related to measurement noise and those innate of the cutting process. Instantaneous frequency is shown to thoroughly characterize the underlying turning dynamics and identify the exact moment in time when chatter fully developed. That IF provides the needed resolution for identifying the onset of chatter suggests that the stability of the process should be monitored in the time-frequency domain to effectively detect and characterize machining instability. It is determined that for the cutting tests performed chatters of the workpiece and tool are associated with the changing of the spectral components and more specifically period-doubling bifurcation. The analysis presented provides a view of the underlying dynamics of the lathe process which has not been experimentally observed before.

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CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

Electronics ◽

10.3390/electronics10121402 ◽

2021 ◽

Vol 10 (12) ◽

pp. 1402

Author(s):

Taehee Lee ◽

Yeohwan Yoon ◽

Chanjun Chun ◽

Seungki Ryu

Keyword(s):

Surface Crack ◽

Autonomous Vehicles ◽

Crack Detection ◽

Model Performance ◽

Semantic Segmentation ◽

Road Surface ◽

Image Brightness ◽

The Road ◽

Detection Model ◽

Artificial Intelligence Models

Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values.

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Laboratory experiment on the nano-TiO2 photocatalytic degradation effect of road surface oil pollution

Nanotechnology Reviews ◽

10.1515/ntrev-2020-0072 ◽

2020 ◽

Vol 9 (1) ◽

pp. 922-933

Author(s):

Qing’e Wang ◽

Kai Zheng ◽

Huanan Yu ◽

Luwei Zhao ◽

Xuan Zhu ◽

...

Keyword(s):

Photocatalytic Degradation ◽

Oil Pollution ◽

Road Surface ◽

Test Method ◽

Driving Safety ◽

The Road ◽

Road Surfaces ◽

The Many ◽

The Impact ◽

Degradation Effect

AbstractOil leak from vehicles is one of the most common pollution types of the road. The spilled oil could be retained on the surface and spread in the air voids of the road, which results in a decrease in the friction coefficient of the road, affects driving safety, and causes damage to pavement materials over time. Photocatalytic degradation through nano-TiO2 is a safe, long-lasting, and sustainable technology among the many methods for treating oil contamination on road surfaces. In this study, the nano-TiO2 photocatalytic degradation effect of road surface oil pollution was evaluated through the lab experiment. First, a glass dish was used as a substrate to determine the basic working condition of the test; then, a test method considering the impact of different oil erosion degrees was proposed to eliminate the effect of oil erosion on asphalt pavement and leakage on cement pavement, which led to the development of a lab test method for the nano-TiO2 photocatalytic degradation effect of oil pollution on different road surfaces.

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