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Small-radius wheels are gradually introduced on special rail vehicles, such as double-decker coaches and piggyback wagons. It allows for a low-floor design and further increases the loading capacity of vehicles. From the perspective of vehicle dynamics, small-radius wheels have lower unsprung mass than the nominal wheels with a radius of 460 mm, and it could mitigate the wheel-rail dynamic interaction. Also, the contact patch size is reduced due to the increased curvature of the rolling contact circle, and they are more sensitive to changes in the track geometry than nominal wheels, especially in turnouts with discontinuous rail profiles. To investigate the dynamic response of small-radius wheels in a turnout, the Manchester Benchmark passenger coach with variable radius wheels and a left-hand turnout with a radius of 245 m are introduced in this work. The influence of wheel radii on the running performance, including the wheel-rail interaction and wheel damage behaviour, is investigated by numerical simulations. In addition, a sensitivity study is executed to evaluate the influence of unsprung mass and wheel flange friction on the performance of a vehicle whose wheel radius is 275 mm, and the potential remedy to mitigate high wheel wear is proposed. [ABSTRACT FROM AUTHOR]

Recently, composite brake blocks have been widely adopted in European railway systems to replace cast iron brake blocks in order to reduce braking noise. However, the poor heat dissipation of composite materials has caused more wheel tread damage, including wheel flats (WFs). The WF, as an undesirable tread defect in railway vehicles, can cause severe wheel/rail impact and increase the damage probability of wheelset components and track structures. Early detection of WFs is vital to ensure operational safety and reduced maintenance costs. In this work, to help understand the dynamic impact caused by WFs, a single WF with a length of 20 mm is artifically produced on a wheel of a tank wagon, and field experiments are performed. The influence of the WF on the axle box accelerations (ABAs), vertical accelerations of the bogie frame and vertical accelerations of the car body is discussed in the time and time-frequency domains. On the other hand, the angular domain synchronous averaging (ADSA) method is used to detect WFs by averaging the ABA signals in the angular domain over a fixed time interval, and it requires ABA data and the axle rotational speed as inputs. This method can greatly reduce the amount of measured data by averaging it in the angle domain and can eliminate the short-time influence of interference signals caused by switches and crossings, rail joints, and so on. The results show that the ADSA of a defective wheel with the WF has a clear peak with an amplitude of 21–104 m/s2 while that of a healthy wheel fluctuates around zero in the polar coordinate. Also, the waveform of defective signals can be well presented in polar coordinates. The effectiveness of this method is compared with the envelope spectrum technology in three scenarios and its application to three journeys is presented. [ABSTRACT FROM AUTHOR]

Multimodality Representation Learning, as a technique of learning to embed information from different modalities and their correlations, has achieved remarkable success on a variety of applications, such as Visual Question Answering (VQA), Natural Language for Visual Reasoning (NLVR), and Vision Language Retrieval (VLR). Among these applications, cross-modal interaction and complementary information from different modalities are crucial for advanced models to perform any multimodal task, e.g., understand, recognize, retrieve, or generate optimally. Researchers have proposed diverse methods to address these tasks. The different variants of transformer-based architectures performed extraordinarily on multiple modalities. This survey presents the comprehensive literature on the evolution and enhancement of deep learning multimodal architectures to deal with textual, visual and audio features for diverse cross-modal and modern multimodal tasks. This study summarizes the (i) recent task-specific deep learning methodologies, (ii) the pretraining types and multimodal pretraining objectives, (iii) from state-of-the-art pretrained multimodal approaches to unifying architectures, and (iv)multimodal task categories and possible future improvements that can be devised for better multimodal learning. Moreover, we prepare a dataset section for new researchers that covers most of the benchmarks for pretraining and finetuning. Finally, major challenges, gaps, and potential research topics are explored. [ABSTRACT FROM AUTHOR]