Automated traffic route identification through the shared nearest neighbour algorithm

Many organisations need to extract useful information from huge amounts of movement data. One example is found in maritime transportation, where the automated identification of a diverse range of traffic routes is a key management issue for improving the maintenance of ports and ocean routes, and accelerating ship traffic. This paper addresses, in a first stage, the research challenge of developing an approach for the automated identification of traffic routes based on clustering motion vectors rather than reconstructed trajectories. The immediate benefit of the proposed approach is to avoid the reconstruction of trajectories in terms of their geometric shape of the path, their position in space, their life span, and changes of speed, direction and other attributes over time. For clustering the moving objects, an adapted version of the Shared Nearest Neighbour algorithm is used. The motion vectors, with a position and a direction, are analysed in order to identify clusters of vectors that are moving towards the same direction. These clusters represent traffic routes and the preliminary results have shown to be promising for the automated identification of traffic routes with different shapes and densities, as well as for handling noise data.

Mechanical, electrical and electro-mechanical properties of thermoplastic elastomer styrene–butadiene–styrene/multiwal carbon nanotubes composites

Composites of styrene–butadiene–styrene (SBS) block copolymer with multiwall carbon nanotubes were processed by solution casting to investigate the influence of filler content, the different ratios of styrene/butadiene in the copolymer and the architecture of the SBS matrix on the electrical, mechanical and electro-mechanical properties of the composites. It was found that filler content and elastomer matrix architecture influence the percolation threshold and consequently the overall composite electrical conductivity. Themechanical properties aremainly affected by the styrene and filler content. Hopping between nearest fillers is proposed as the main mechanism for the composite conduction. The variation of the electrical resistivity is linear with the deformation. This fact, together with the gauge factor values in the range of 2–18, results in appropriate composites to be used as (large) deformation sensors.

Mechanical, electrical and electro-mechanical properties of thermoplastic elastomer styrene–butadiene–styrene/multiwall carbon nanotubes composites

Composites of styrene–butadiene–styrene (SBS) block copolymer with multiwall carbon nanotubes were processed by solution casting to investigate the influence of filler content, the different ratios of styrene/butadiene in the copolymer and the architecture of the SBS matrix on the electrical, mechanical and electro-mechanical properties of the composites. It was found that filler content and elastomer matrix architecture influence the percolation threshold and consequently the overall composite electrical conductivity. The mechanical properties are mainly affected by the styrene and filler content. Hopping between nearest fillers is proposed as the main mechanism for the composite conduction. The variation of the electrical resistivity is linear with the deformation. This fact, together with the gauge factor values in the range of 2–18, results in appropriate composites to be used as (large) deformation sensors.

Modeling Carbon Nanotube Electrical Properties in CNT/Polymer Composites

In this work it is demonstrated that the capacitance between two cylinders increases with the rotation angle and it has a fundamental influence on the composite dielectric constant. The dielectric constant is lower for nematic materials than for isotropic ones and this can be attributed to the effect of the filler alignment in the capacitance. The effect of aspect ratio in the conductivity is also studied in this work. Finally, based on previous work and by comparing to results from the literature it is found that the electrical conductivity in this type of composites is due to hopping between nearest fillers resulting in a weak disorder regime that is similar to the single junction expression.

Estimating Local Part Thickness in Midplane Meshes for Finite Element Analysis

Within the development of motor vehicles, crash safety (e.g. occupant protection, pedestrian protection, low speed damageability), is one of the most important attributes. In order to be able to fulfill the increased requirements in the framework of shorter cycle times and rising pressure to reduce costs, car manufacturers keep intensifying the use of virtual development tools such as those in the domain of Computer Aided Engineering (CAE). For crash simulations, the explicit finite element method (FEM) is applied. The accuracy of the simulation process is highly dependent on the accuracy of the simulation model, including the midplane mesh. One of the roughest approximations typically made is the actual part thickness which, in reality, can vary locally. However, almost always a constant thickness value is defined throughout the entire part due to complexity reasons. On the other hand, for precise fracture analysis within FEM, the correct thickness consideration is one key enabler. Thus, availability of per element thickness information, which does not exist explicitly in the FEM model, can significantly contribute to an improved crash simulation quality, especially regarding fracture prediction. Even though the thickness is not explicitly available from the FEM model, it can be inferred from the original CAD geometric model through geometric calculations. This paper proposes and compares two thickness estimation algorithms based on ray tracing and nearest neighbour 3D range searches. A systematic quantitative analysis of the accuracy of both algorithms is presented, as well as a thorough identification of particular geometric arrangements under which their accuracy can be compared. These results enable the identification of each technique’s weaknesses and hint towards a new, integrated, approach to the problem that linearly combines the estimates produced by each algorithm.