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Transportation Safety and Environment
Number of Followers: 2

This is an Open Access Journal

Open Access journal
ISSN (Online) 2631-4428
Published by Oxford University Press

[413 journals]

Deep Learning for Visual SLAM in Transportation Robotics: A review
- Authors: Duan C; Junginger S, Huang J, et al.
  Pages: 177 - 184
  Abstract: AbstractVisual SLAM (Simultaneously Localization and Mapping) is a solution to achieve localization and mapping of robots simultaneously. Significant achievements have been made during the past decades, geography-based methods are becoming more and more successful in dealing with static environments. However, they still cannot handle a challenging environment. With the great achievements of deep learning methods in the field of computer vision, there is a trend of applying deep learning methods to visual SLAM. In this paper, the latest research progress of deep learning applied to the field of visual SLAM is reviewed. The outstanding research results of deep learning visual odometry and deep learning loop closure detect are summarized. Finally, future development directions of visual SLAM based on deep learning is prospected.
  PubDate: Thu, 30 Jan 2020 00:00:00 GMT
  DOI: 10.1093/tse/tdz019
  Issue No: Vol. 1, No. 3 (2020)
A review of applications of visual inspection technology based on image
processing in the railway industry
- Authors: Liu S; Wang Q, Luo Y.
  Pages: 185 - 204
  Abstract: AbstractIn order to ensure the safety of railway transportation, it is necessary to regularly check for faults and defects in the railway system. Visual inspection technology is conducive to improving the low efficiency, poor economy and inaccurate detection results of traditional detection methods. This paper introduces the research and contribution of various scholars in the field of visual inspection, summarizes the application and development of visual inspection technology in the railway industry, and finally forecasts the future research direction of visual inspection technology.
  PubDate: Wed, 05 Feb 2020 00:00:00 GMT
  DOI: 10.1093/tse/tdz007
  Issue No: Vol. 1, No. 3 (2020)
Transportation robot battery power forecasting based on bidirectional
deep-learning method
- Authors: Thurow K; Chen C, Junginger S, et al.
  Pages: 205 - 211
  Abstract: AbstractThis paper proposes a data-driven hybrid model for forecasting the battery power voltage of transportation robots by combining a wavelet method and a bidirectional deep-learning technique. In the proposed model, the on-board battery power data is measured and transmitted. A WPD (wavelet packet decomposition) algorithm is employed to decompose the original collected non-stationary series into several relatively more stable subseries. For each subseries, a deep learning–based predictor – bidirectional long short-term memory (BiLSTM) – is constructed to forecast the battery power voltage from one step to three steps ahead. Two experiments verify the effectiveness and generalization ability of the proposed hybrid forecasting model, which shows the highest forecasting accuracy. The obtained forecasting results can be used to decide whether the robot can complete the given task or needs to be recharged, providing effective support for the safe use of transportation robots.
  PubDate: Sat, 22 Feb 2020 00:00:00 GMT
  DOI: 10.1093/tse/tdz016
  Issue No: Vol. 1, No. 3 (2020)
Hybrid TaON/LaTiO2N photoelectrode for water oxidation
- Authors: An P; Yang B, Zhang N, et al.
  Pages: 212 - 219
  Abstract: AbstractEfficient and stable photoelectrodes for water oxidation are highly desirable in the field of photoelectrochemical (PEC) water splitting. However, photoelectrodes with low externally applied bias usually exhibit weak photocurrent and vice versa. Herein, novel and efficient CoOx-TaON/LTON composite photoanodes have been successfully prepared by a microwave assisted method followed with a particle transfer procedure. The obtained photoanode generated an anodic photocurrent of ~7.2 mA cm−2 at 1.2 VRHE and initiated the anodic photourrent at ~0.5 VRHE. The HC-STH of the composite photoelectrode reached 1.0% at 1.2 VRHE. Further, stoichiometric oxygen and hydrogen are stably produced on the photoanode and the counter electrode with a Faraday efficiency of unity for 2 h.
  PubDate: Sat, 22 Feb 2020 00:00:00 GMT
  DOI: 10.1093/tse/tdz020
  Issue No: Vol. 1, No. 3 (2020)
Evaluating the environmental impact of selected chemical de-icers
- Authors: Li X; Van Aken B, Mckenzie E, et al.
  Pages: 220 - 229
  Abstract: AbstractIn this study, the environmental impacts of six commercial chloride-based de-icer products, including Rock Salt and Salt Brine, were evaluated. The nutrients, oxygen demand, heavy metal concentration, corrosion and toxicity of all six de-icers were analysed. With an assumed 500-fold dilution, the test results were compared to the public water-quality criteria. The results show that the selected chemical de-icers have little chance of severely damaging the environment and ecosystem under a normal dilution scenario. However, attention needs to be paid to small water bodies and sensitive receiving water.
  PubDate: Sat, 22 Feb 2020 00:00:00 GMT
  DOI: 10.1093/tse/tdz015
  Issue No: Vol. 1, No. 3 (2020)
Effect of initial gravimetric water content and cyclic wetting-drying on
soil-water characteristic curves of disintegrated carbonaceous mudstone
- Authors: Zeng L; Li F, Liu J, et al.
  Pages: 230 - 240
  Abstract: AbstractThe soil-water characteristic curve (SWCC) is often used to estimate unsaturated soil properties (e.g. strength, permeability, volume change, solute and thermal diffusivity). The SWCC of soil samples is significantly affected by cyclic wetting-drying. To examine how water content and cyclic wetting-drying affect the SWCC of disintegrated carbonaceous mudstone (DCM), SWCC tests were implemented using a pressure-plate apparatus. In addition, SWCC models for DCM considering the initial gravimetric water content and cyclic wetting-drying were developed. The test results showed that the volumetric water content (θ) of the DCM first decreased rapidly and then became stable as matric suction (s) increased. The initial water content affected the SWCC by altering the pore structure of the DCM. For a given number of wetting-drying cycles, the higher the initial water content, the higher the stabilized θ. At a given s value, θ decreased as the number of wetting-drying cycles increased, which suggests that cyclic wetting-drying reduces the water-holding capacity of DCM. The Gardner model for DCM was constructed considering initial water content and cyclic wetting-drying, and was effective at describing and predicting the SWCC model for DCM.
  PubDate: Sat, 01 Feb 2020 00:00:00 GMT
  DOI: 10.1093/tse/tdz018
  Issue No: Vol. 1, No. 3 (2020)

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