arXiv subjects

Risheng Wang

Publications and source records attributed to Risheng Wang.

3 recordsLinked to original sources

arXiv2026

Compositional Complexity-Induced Ultralow Friction in Medium-Entropy MXenes

Two-dimensional MXenes are promising solid lubricants, but the roles of compositional complexity and surface chemistry in governing interfacial friction remain unclear. Here, we systematically investigate the adhesion and friction behaviors of medium-entropy (ME) MXenes, TiVNbMoC3 and TiVCrMoC3, and compare them with conventional titanium carbide MXenes, Ti2C and Ti3C2, using a SiO2 colloidal atomic force microscopy probe. Thermal annealing at 200 C converts OH surface terminations to O terminations, leading to pronounced reductions in adhesion energy and friction force across all MXenes studied. ME MXenes exhibit larger adhesion reductions because of their higher initial OH contents and more extensive OH-to-O conversion. In addition, their intrinsically higher out-of-plane bending stiffness suppresses energy dissipation during sliding, enabling ultralow friction. Notably, superlubricity is achieved in ME MXenes, with annealed TiVCrMoC3 exhibiting a coefficient of friction as low as 0.0022, outperforming graphene, MoSe2, and other MXenes evaluated using the same experimental approach. These findings identify compositional complexity as a powerful strategy for engineering MXenes with exceptional tribological performance and establish ME MXenes as a new class of solid lubricants.

cond-mat.mtrl-sci↗

arXiv2021

Medical Image Segmentation Using Deep Learning: A Survey

Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches.

eess.IV↗

arXiv2015

Large-scale Artificial Neural Network: MapReduce-based Deep Learning

Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundant data aggravates the system workload. This project is mainly focused on the solution to the issues above, combining deep learning algorithm with cloud computing platform to deal with large-scale data. A MapReduce-based handwriting character recognizer will be designed in this project to verify the efficiency improvement this mechanism will achieve on training and practical large-scale data. Careful discussion and experiment will be developed to illustrate how deep learning algorithm works to train handwritten digits data, how MapReduce is implemented on deep learning neural network, and why this combination accelerates computation. Besides performance, the scalability and robustness will be mentioned in this report as well. Our system comes with two demonstration software that visually illustrates our handwritten digit recognition/encoding application.

cs.DC↗