Train post‑derailment behaviours and containment methods:a review

Railway accidents,particularly serious derailments,can lead to catastrophic consequences.Therefore,it is essential to prevent derailment escalation to reduce the likelihood of severe derailments.Train post-derailment behaviours and containment methods play a critical role in preventing derailment escalation and providing passive safety protection and accident prevention in the event of a derailment.However,despite the increasing attention on this field from academia and industry in recent years,there is a lack of systematic exploration and summarization of emerging applications and containment methods in train post-derailment research.For this reason,this paper presents a comprehensive review of existing studies on train post-derailment behaviours,encompassing various topics such as post-derailment contact-impact models,dynamic modelling and simulation techniques,and the primary factors influencing post-derailment behaviours.Significantly,this review introduces and elucidates substitute guidance mechanisms(SGMs),which serve as railway-specific passive safety protection and accident prevention measures.The various types of SGMs are depicted,and their ongoing developments and applications are explored in depth.The review additionally points out several unresolved challenges including the adverse effects of SGMs,and proposes future research directions to advance the theoretical understanding and practical application of train post-derailment behaviours and containment methods.This review seeks to be a valuable reference for railway industry professionals in preventing catastrophic derailment consequences through post-derailment containment methods.

Stochastic dynamic simulation of railway vehicles collision using data-driven modelling approach

Using stochastic dynamic simulation for railway vehicle collision still faces many challenges,such as high modelling complexity and time-consuming.To address the challenges,we introduce a novel data-driven stochastic process modelling(DSPM)approach into dynamic simulation of the railway vehicle collision.This DSPM approach consists of two steps:(i)process description,four kinds of kernels are used to describe the uncertainty inherent in collision processes;(ii)solving,stochastic variational inferences and mini-batch algorithms can then be used to accelerate computations of stochastic processes.By applying DSPM,Gaussian process regression(GPR)and finite element(FE)methods to two collision scenarios(i.e.lead car colliding with a rigid wall,and the lead car colliding with another lead car),we are able to achieve a comprehensive analysis.The comparison between the DSPM approach and the FE method revealed that the DSPM approach is capable of calculating the corresponding confidence interval,simultaneously improving the overall computational efficiency.Comparing the DSPM approach with the GPR method indicates that the DSPM approach has the ability to accurately describe the dynamic response under unknown conditions.Overall,this research demonstrates the feasibility and usability of the proposed DSPM approach for stochastic dynamics simulation of the railway vehicle collision.

A Linear Approach for Depth and Colour Camera Calibration Using Hybrid Parameters

Many recent applications of computer graphics and human computer interaction have adopted both colour cameras and depth cameras as input devices. Therefore, an effective calibration of both types of hardware taking different colour and depth inputs is required. Our approach removes the numerical difficulties of using non-linear optimization in previous methods which explicitly resolve camera intrinsics as well as the transformation between depth and colour cameras. A matrix of hybrid parameters is introduced to linearize our optimization. The hybrid parameters offer a transformation from a depth parametric space （depth camera image） to a colour parametric space （colour camera image） by combining the intrinsic parameters of depth camera and a rotation transformation from depth camera to colour camera. Both the rotation transformation and intrinsic parameters can be explicitly calculated from our hybrid parameters with the help of a standard QR factorisation. We test our algorithm with both synthesized data and real-world data where ground-truth depth information is captured by Microsoft Kinect. The experiments show that our approach can provide comparable accuracy of calibration with the state-of-the-art algorithms while taking much less computation time （1/50 of Herrera＇s method and 1/10 of Raposo＇s method） due to the advantage of using hybrid parameters.

Stretch-Minimizing Volumetric Parameterization

Not many methods for parameterization guarantee bijectivity or local injectivity, which is essential for foldover- free mappings. Stretch-minimizing parameterization which is widely used for surface parameterization, provides foldover-free mappings and is capable of trading off between angle and area distortions. We extend its usage to volumetric parameterization in this paper by deriving a 3D version of stretch-distortion energy and incorporating fixed boundary conditions. Our energy definition includes a naturM barrier term which effectively prevents elements from collapsing and folding over. It saves the effort in other methods of formulating additional energy or constrains to ensure the local injectivity. We propose to minimize the overall energy integrated over the whole mesh with a relaxation-enhanced solver and optimize the energy globally. This is different from the conventional approach of surface parameterization where mesh nodes are optimized individually. Compared with other volumetric parameterizations, method, being foldover-free and offering a good trade-off between our approach bears the advantages of stretch-minimizing angle and volume distortions.

GPU based real-time simulation of massive falling leaves

As an important autumn feature,scenes with large numbers of falling leaves are common in movies and games. However,it is a challenge for computer graphics to simulate such scenes in an authentic and efficient manner. This paper proposes a GPU based approach for simulating the falling motion of many leaves in real time. Firstly,we use a motionsynthesis based method to analyze the falling motion of the leaves,which enables us to describe complex falling trajectories using low-dimensional features. Secondly,we transmit a primitive-motion trajectory dataset together with the low-dimensional features of the falling leaves to video memory,allowing us to execute the appropriate calculations on the GPU.