arXiv subjects

Xiaojun Li

Publications and source records attributed to Xiaojun Li.

25 records · Page 2Linked to original sources

arXiv2021

Time-Domain Doppler Biomotion Detections Immune to Unavoidable DC Offsets

In the past decades, continuous Doppler radar sensor-based bio-signal detections have attracted many research interests. A typical example is the Doppler heartbeat detection. While significant progresses have been achieved, reliable, time-domain accurate demodulation of bio-signals in the presence of unavoidable DC offsets remains a technical challenge. Aiming to overcome this difficulty, we propose in this paper a novel demodulation algorithm that does not need to trace and eliminate dynamic DC offsets based on approximating segmented arcs in a quadrature constellation of sampling data to directional chords. Assisted by the principal component analysis, such chords and their directions can be deterministically determined. Simulations and experimental validations showed fully recovery of micron-level pendulum movements and strongly noised human heartbeats, verifying the effectiveness and accuracy of the proposed approach.

eess.SY↗

arXiv2021

Data-driven Thermal Anomaly Detection for Batteries using Unsupervised Shape Clustering

For electric vehicles (EV) and energy storage (ES) batteries, thermal runaway is a critical issue as it can lead to uncontrollable fires or even explosions. Thermal anomaly detection can identify problematic battery packs that may eventually undergo thermal runaway. However, there are common challenges like data unavailability, environment and configuration variations, and battery aging. We propose a data-driven method to detect battery thermal anomaly based on comparing shape-similarity between thermal measurements. Based on their shapes, the measurements are continuously being grouped into different clusters. Anomaly is detected by monitoring deviations within the clusters. Unlike model-based or other data-driven methods, the proposed method is robust to data loss and requires minimal reference data for different pack configurations. As the initial experimental results show, the method not only can be more accurate than the onboard BMS and but also can detect unforeseen anomalies at the early stage.

eess.SY↗

arXiv2021

A Combination 5-DOF Active Magnetic Bearing For Energy Storage Flywheel

Conventional active magnetic bearing (AMB) systems use several separate radial and thrust bearings to provide a 5 degree of freedom (DOF) levitation control. This paper presents a novel combination 5-DOF active magnetic bearing (C5AMB) designed for a shaft-less, hub-less, high-strength steel energy storage flywheel (SHFES), which achieves doubled energy density compared to prior technologies. As a single device, the C5AMB provides radial, axial, and tilting levitations simultaneously. In addition, it utilizes low-cost and more available materials to replace silicon steels and laminations, which results in reduced costs and more convenient assemblies. Apart from the unique structure and the use of low magnetic grade material, other design challenges include shared flux paths, large dimensions, and relatively small air gaps. The finite element method (FEM) is too computationally intensive for early-stage analysis. An equivalent magnetic circuit method (EMCM) is developed for modeling and analysis. Nonlinear FEM is then used for detailed simulations. Both permanent magnets (PM) and electromagnetic control currents provide the weight-balancing lifting force. During the full-scale prototype testing, the C5AMB successfully levitates a 5440 kg and 2 m diameter flywheel at an air gap of 1.14 mm. Its current and position stiffnesses are verified experimentally.

eess.SY↗

arXiv2021

Fano resonance enabled infrared nano-imaging of local strain in bilayer graphene

Detection of local strain at the nanometer scale with high sensitivity remains challenging. Here we report near-field infrared nano-imaging of local strains in bilayer graphene through probing strain-induced shifts of phonon frequency. As a non-polar crystal, intrinsic bilayer graphene possesses little infrared response at its transverse optical (TO) phonon frequency. The reported optical detection of local strain is enabled by applying a vertical electrical field that breaks the symmetry of the two graphene layers and introduces finite electrical dipole moment to graphene phonon. The activated phonon further interacts with continuum electronic transitions, and generates a strong Fano resonance. The resulted Fano resonance features a very sharp near-field infrared scattering peak, which leads to an extraordinary sensitivity of ~0.002% for the strain detection. Our studies demonstrate the first nano-scale near-field Fano resonance, provide a new way to probe local strains with high sensitivity in non-polar crystals, and open exciting possibilities for studying strain-induced rich phenomena.

cond-mat.mes-hall↗

arXiv2020

Single Image Super-resolution via Dense Blended Attention Generative Adversarial Network for Clinical Diagnosis

During training phase, more connections (e.g. channel concatenation in last layer of DenseNet) means more occupied GPU memory and lower GPU utilization, requiring more training time. The increase of training time is also not conducive to launch application of SR algorithms. This's why we abandoned DenseNet as basic network. Futhermore, we abandoned this paper due to its limitation only applied on medical images. Please view our lastest work applied on general images at arXiv:1911.03464.

eess.IV↗

arXiv2020

Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks

The presence of residual and dense neural networks which greatly promotes the development of image Super-Resolution(SR) have witnessed a lot of impressive results. Depending on our observation, although more layers and connections could always improve performance, the increase of model parameters is not conducive to launch application of SR algorithms. Furthermore, algorithms supervised by L1/L2 loss can achieve considerable performance on traditional metrics such as PSNR and SSIM, yet resulting in blurry and over-smoothed outputs without sufficient high-frequency details, namely low perceptual index(PI). Regarding the issues, this paper develops a perception-oriented single image SR algorithm via dual relativistic average generative adversarial networks. In the generator part, a novel residual channel attention block is proposed to recalibrate significance of specific channels, further increasing feature expression capabilities. Parameters of convolutional layers within each block are shared to expand receptive fields while maintain the amount of tunable parameters unchanged. The feature maps are subsampled using sub-pixel convolution to obtain reconstructed high-resolution images. The discriminator part consists of two relativistic average discriminators that work in pixel domain and feature domain, respectively, fully exploiting the prior that half of data in a mini-batch are fake. Different weighted combinations of perceptual loss and adversarial loss are utilized to supervise the generator to equilibrate perceptual quality and objective results. Experimental results and ablation studies show that our proposed algorithm can rival state-of-the-art SR algorithms, both perceptually(PI-minimization) and objectively(PSNR-maximization) with fewer parameters.

eess.IV↗

arXiv2015

High thermal conductivity of hexagonal boron nitride laminates

Two-dimensional materials are characterised by a number of unique physical properties which can potentially make them useful to a wide diversity of applications. In particular, the large thermal conductivity of graphene and hexagonal boron nitride has already been acknowledged and these materials have been suggested as novel core materials for thermal management in electronics. However, it was not clear if mass produced flakes of hexagonal boron nitride would allow one to achieve an industrially-relevant value of thermal conductivity. Here we demonstrate that laminates of hexagonal boron nitride exhibit thermal conductivity of up to 20 W/mK, which is significantly larger than that currently used in thermal management. We also show that the thermal conductivity of laminates increases with the increasing volumetric mass density, which creates a way of fine-tuning its thermal properties.

cond-mat.mtrl-sci↗