arXiv subjects

Yong Cui

Publications and source records attributed to Yong Cui.

42 records · Page 3Linked to original sources

arXiv2020

Defect Prediction of Railway Wheel Flats based on Hilbert Transform and Wavelet Packet Decomposition

For efficient railway operation and maintenance, the demand for onboard monitoring systems is increasing with technological advances in high-speed trains. Wheel flats, one of the common defects, can be monitored in real-time through accelerometers mounted on each axle box so that the criteria of relevant standards are not exceeded. This study aims to identify the location and height of a single wheel flat based on non-stationary axle box acceleration (ABA) signals, which are generated through a train dynamics model with flexible wheelsets. The proposed feature extraction method is applied to extract the root mean square distribution of decomposed ABA signals on a balanced binary tree as orthogonal energy features using the Hilbert transform and wavelet packet decomposition. The neural network-based defect prediction model is created to define the relationship between input features and output labels. For insufficient input features, data augmentation is performed by the linear interpolation of existing features. The performance of defect prediction is evaluated in terms of the accuracy of detection and localization and improved by augmented input features and highly decomposed ABA signals. The results show that the trained neural network can predict the height and location of a single wheel flat from orthogonal energy features with high accuracy.

eess.SP↗

arXiv2019

High-Efficiency Second Harmonic Generation of Low-Temporal-Coherent Light Pulse

The nonlinear frequency conversion of low-temporal-coherent light holds a variety of applications and has attracted considerable interest. However, its physical mechanism remains relatively unexplored, and the conversion efficiency and bandwidth are extremely insufficient. Here, considering the instantaneous broadband characteristic, we establish a model of second harmonic generation (SHG) of low-temporal-coherent pulse, and reveal its differences from the coherent conditions. It is found that the second harmonic (SH) of low-temporal-coherent light is produced by not only the degenerate SH processes but also crossed sum-frequency processes. On the basis of this, we propose a method for realizing low-temporal-coherent SHG with high efficiency and broad bandwidth, and experimentally demonstrate a conversion efficiency up to 70% with a bandwidth of 3.1 THz (2.9 nm centered at 528 nm). To the best of our knowledge, this is the highest efficiency and broadest bandwidth of low-temporal-coherent SHG, and its efficiency is almost the same with that of the narrowband coherent condition. Furthermore, the spectral evolution characteristics of the broadband low-temporal-coherent pulse in SHG process are revealed in experiments, that the SH power spectral density (PSD) is proportional to the self-convolution of the fundamental wave PSD, which is greatly different from that of the coherent process. Our research opens a door for the study of the low-coherent nonlinear optical processes.

physics.optics↗

arXiv2018

A First Look at Mobile Intelligence: Architecture, Experimentation and Challenges

Artificial intelligence (AI) technology makes mobile devices become intelligent objects which can learn and act automatically. Although AI will bring great opportunities for mobile applications, little work has focused on the architecture and the interaction with the cloud. In this article, we present three existing architectures of mobile intelligence in detail and introduce its broad application prospects. Furthermore, we conduct a series of experiments to evaluate the performance of the prevalent commercial applications and intelligent frameworks. Our results show that there is a big gap between Quality of Experience (QoE) requirements and the status quo. So far, we have seen only the tip of the iceberg. We pose issues and challenges to advance the area of mobile intelligence and hope to pave the way for the forthcoming.

cs.CY↗

arXiv2018

Immersion on the Edge: A Cooperative Framework for Mobile Immersive Computing

Immersive computing (IC) technologies such as virtual reality and augmented reality are gaining tremendous popularity. In this poster, we present CoIC, a Cooperative framework for mobile Immersive Computing. The design of CoIC is based on a key insight that IC tasks among different applications or users might be similar or redundant. CoIC enhances the performance of mobile IC applications by caching and sharing computation-intensive IC results on the edge. Our preliminary evaluation results on an AR application show that CoIC can reduce the recognition and rendering latency by up to 52.28% and 75.86% respectively on current mobile devices.

cs.NI↗

arXiv2017

Machine Learning for Networking: Workflow, Advances and Opportunities

Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of Machine Learning techniques for Networking (MLN), which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply the machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations on their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities on networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help and motivate researchers to develop innovative algorithms, standards and frameworks.

cs.NI↗

arXiv2013

Towards a System Theoretic Approach to Wireless Network Capacity in Finite Time and Space

In asymptotic regimes, both in time and space (network size), the derivation of network capacity results is grossly simplified by brushing aside queueing behavior in non-Jackson networks. This simplifying double-limit model, however, lends itself to conservative numerical results in finite regimes. To properly account for queueing behavior beyond a simple calculus based on average rates, we advocate a system theoretic methodology for the capacity problem in finite time and space regimes. This methodology also accounts for spatial correlations arising in networks with CSMA/CA scheduling and it delivers rigorous closed-form capacity results in terms of probability distributions. Unlike numerous existing asymptotic results, subject to anecdotal practical concerns, our transient one can be used in practical settings: for example, to compute the time scales at which multi-hop routing is more advantageous than single-hop routing.

cs.PF↗