Xuzhou Institute of Technology

About: Xuzhou Institute of Technology is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 1696 authors who have published 1521 publications receiving 13541 citations.

Online Digital Holographic Method for Interface Reaction Monitoring in Lithium-Ion Batteries

Abstract: Understanding the reaction mechanisms at the interface of electrode and electrolyte is both of fundamental interest and essential to improve lithium-ion battery (LIB) performance. Herein, we report an online digital holographic method to in situ observe the entire interface change between electrode and electrolyte in lithium-ions batteries. The accuracy of this technology is well verified in LiFePO4/graphite full-cell systems, graphite/Li half-cell systems in EC-based and PC-based electrolyte, respectively, and supported by the characterized results of conventional instruments, including scanning electron microscopy and X-ray photoelectron spectroscopy. In particular, the time resolution of the digital holographic method is 0.04 s and fast enough to distinguish detail reduction process of ethylene carbonate (EC), for which EC will be first reduced to generate lithium alkyl carbonates, and then the reduction product is Li2CO3 to form a stable SEI films. To our best of knowledge, this is the first report on...

Low-speed direct-driven sensorless control including zero-speed for switched reluctance motor based on dynamic inductance model

Abstract: Accurate and reliable rotor position estimation is extremely required in low-speed direct-driven applications for switched reluctance motor/generator. Due to seriously nonlinearity of phase inductance, phase inductance changes obviously under saturation. On the basis of Fourier analysis, the relationship between dynamic inductance model coefficients and phase current is given. At zero-speed condition, excitation pulse method is proposed for estimate initial rotor position. Dynamic inductance model is proposed for low-speed operation. Sensorless control algorithm is achieved by DSP and FPGA chips. Experimental results indicate that motor start up without inversion at zero speed, sensorless operation error is less than 0.5 degree. The overall sensorless control scheme is easy to implement.

Adaptive Kernel Correlation Filter Tracking Algorithm in Complex Scenes

Abstract: The traditional kernel correlation filter (KCF) algorithm has poor tracking results in complex scenes with severe occlusion, deformation, and low resolution and cannot achieve long-term tracking. To improve the accuracy of the tracking algorithm in complex scenes, an adaptive kernel correlation filter algorithm is proposed. First, a multifeature complementary scheme is proposed that linearly weights the responses of the histogram of oriented gradient (HOG) features and color features and learns a target position estimation model to realize target position estimation. Then, an adaptive scale model for estimating the scale transformation of the target is learned by extracting the HOG features of the object. Finally, according to occlusion judgment criteria, the Kalman filter is introduced to correct the position of the tracking target. The accuracy and success rate of the proposed algorithm are verified by simulation analysis on TC-128/OTB2015 benchmarks. Extensive experimental results illustrate that the proposed tracker achieves competitive performance compared with state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.899 and 0.635, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes. This study provides references for computer vision processing, such as image retrieval, behavior analysis, and intelligent driving.