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Journal Article

The Placement of Digitized Objects in a Point Cloud as a Photogrammetric Technique

Abstract The frequency of video-capturing collision events from surveillance systems are increasing in reconstruction analyses. The video that has been provided to the investigator may not always include a clear perspective of the relevant area of interest. For example, surveillance video of an incident may have captured a pre- or post-incident perspective that, while failing to capture the precise moment when the pedestrian was struck by a vehicle, still contains valuable information that can be used to assist in reconstructing the incident. When surveillance video is received, a quick and efficient technique to place the subject object or objects into a three-dimensional environment with a known rate of error would add value to the investigation.
Journal Article

Railway Fastener Positioning Method Based on Improved Census Transform

Abstract In view of the fact that the current positioning methods of railway fasteners are easily affected by illumination intensity, bright spots, and shadows, a positioning method with relative grayscale invariance is proposed. The median filter is used to remove the noise in order to reduce the adverse effects on the subsequent processing results, and the baffle seat edge features are enhanced by improved Census transform. The mean-shift clustering algorithm is used to classify the edges to weaken the interference by short lines. Finally, the Hough transform is used to quickly extract the linear feature of the baffle seat edge and achieve the exact position of the fastener with the prior knowledge. Experimental results show that the proposed method can accurately locate and have good adaptability under different illumination conditions, and the position accuracy is increased by 4.3% and 8%, respectively, in sunny and rainy days.
Journal Article

Investigations on Spark and Corona Ignition of Oxymethylene Ether-1 and Dimethyl Carbonate Blends with Gasoline by High-Speed Evaluation of OH* Chemiluminescence

Abstract Bio-fuels of the 2nd generation constitute a key approach to tackle both Greenhouse Gas (GHG) and air quality challenges associated with combustion emissions of the transport sector. Since these fuels are obtained of residual materials of the agricultural industry, well-to-tank CO2 emissions can be significantly lowered by a closed-cycle of formation and absorption of CO2. Furthermore, studies of bio-fuels have shown reduced formation of particulate matter on account of the fuels’ high oxygen content therefore addressing air quality issues. However, due to the high oxygen content and other physical parameters these fuels are expected to exhibit different ignition behaviour. Moreover, the question is whether there is a positive superimposition of the fuels ignition behaviour with the benefits of an alternative ignition system, such as a corona ignition.
Journal Article

Interference between Tin Sulfides, Graphite and Novolak Oxidation

Abstract Tin sulfides (SnS and SnS2), represent a safer and greener alternative to other metal sulfides such as copper sulfides, and MoS2 etc. Their behavior is usually associated to that of solid lubricants such as graphite. A mixture of tin sulfides, with the 65 wt% of SnS2, has been characterized by scanning electron microscopy and by thermal gravimetric analysis (TGA). In order to investigate the effect of tin sulfides upon two crucial friction material ingredients, two mixtures were prepared: the former was made by mixing tin sulfides with a natural flake graphite and the latter was made mixing tin sulfides with a straight novolak. They were analyzed by TGA and differential thermal analysis (DTA) in both nitrogen and air. Some interferences were detected between tin sulfides and graphite in air.
Journal Article

Efficient Lane Detection Using Deep Lane Feature Extraction Method

Abstract In this paper, an efficient lane detection using deep feature extraction method is proposed to achieve real-time lane detection in diverse road environment. The method contains three main stages: 1) pre-processing, 2) deep lane feature extraction and 3) lane fitting. In pre-processing stage, the inverse perspective mapping (IPM) is used to obtain a bird's eye view of the road image, and then an edge image is generated using the canny operator. In deep lane feature extraction stage, an advanced lane extraction method is proposed. Firstly, line segment detector (LSD) is applied to achieve the fast line segment detection in the IPM image. After that, a proposed adaptive lane clustering algorithm is employed to gather the adjacent line segments generated by the LSD method. Finally, a proposed local gray value maximum cascaded spatial correlation filter (GMSF) algorithm is used to extract the target lane lines among the multiple lines.
Journal Article

Effects of Reflux Temperature and Molarity of Acidic Solution on Chemical Functionalization of Helical Carbon Nanotubes

Abstract The use of nanomaterials and nanostructures have been revolutionizing the advancements of science and technology in various engineering and medical fields. As an example, Carbon Nanotubes (CNTs) have been extensively used for the improvement of mechanical, thermal, electrical, magnetic, and deteriorative properties of traditional composite materials for applications in high-performance structures. The exceptional materials properties of CNTs (i.e., mechanical, magnetic, thermal, and electrical) have introduced them as promising candidates for reinforcement of traditional composites. Most structural configurations of CNTs provide superior material properties; however, their geometrical shapes can deliver different features and characteristics. As one of the unique geometrical configurations, helical CNTs have a great potential for improvement of mechanical, thermal, and electrical properties of polymeric resin composites.
Journal Article

3D Scene Reconstruction with Sparse LiDAR Data and Monocular Image in Single Frame

Abstract Real-time reconstruction of 3D environment attributed with semantic information is significant for a variety of applications, such as obstacle detection, traffic scene comprehension and autonomous navigation. The current approaches to achieve it are mainly using stereo vision, Structure from Motion (SfM) or mobile LiDAR sensors. Each of these approaches has its own limitation, stereo vision has high computational cost, SfM needs accurate calibration between a sequences of images, and the onboard LiDAR sensor can only provide sparse points without color information. This paper describes a novel method for traffic scene semantic segmentation by combining sparse LiDAR point cloud (e.g. from Velodyne scans), with monocular color image. The key novelty of the method is the semantic coupling of stereoscopic point cloud with color lattice from camera image labelled through a Convolutional Neural Network (CNN).