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116 results on '"Zhang, Chentao"'

7. Plasmonic and bi-piezoelectric enhanced photocatalysis using PVDF/ZnO/Au nanobrush

Author

Zhao Xiaofei, Li Zhen, Yu Jing, Li Chonghui, Xu Shicai, Li Fengrui, Zhang Chentao, Man Baoyuan, and Zhang Chao

Subjects
bi-piezoelectric, nanobrush, photocatalysis, plasmonic, zno, Physics, QC1-999
Abstract

The photocatalytic degradation, as an environmental-friendly technology, has great significance for cost-effective and efficient catalysis processes, wherein piezo-photocatalysis can significantly increase the catalytic degradation rate using both solar and mechanical energy. Here, a ternary heterostructure PVDF/ZnO/Au (PZA) nanobrush photocatalyst with high piezo-photocatalytic efficiency was presented via low-temperature hydrothermal and chemical reduction methods. Under both solar and mechanical energy, the current response and degradation rate of the as-synthesized PZA nanobrush all increase significantly compared with that under solar alone and under mechanical energy alone, and the excellent recyclability is investigated. It is found that the PZA nanobrush with ultrasonic-assisted piezo-photocatalysis completely degrade MO of 20 mg/L in 60 min, which exhibits greater enhancement of photocatalytic activity than with stirring-assisted piezo-photocatalysis due to higher power. The high piezo-photocatalytic activity of PZA nanobrush is attributed to the surface plasmon resonance (SPR) coupling of Au and built-in electric field originating from the ZnO and PVDF, which can increase the absorption of visible light, promote the charge transfer and separation of photogenerated electrons/holes. This work introduces the SPR and bipiezotronic effect to improve plasmonic photocatalysis with PZA heterostructures, which offers a new solution in green technologies to design high-performance catalysts for the environmental remediation.

Published
2022
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21. Research on Digital Meter Reading Method of Inspection Robot Based on Deep Learning.

Author

Lin, Wenwei, Zhao, Ziyang, Tao, Jin, Lian, Chaoming, and Zhang, Chentao

Subjects
DEEP learning, FAST Fourier transforms, IMAGE reconstruction, ROBOTS, IMAGE recognition (Computer vision), READING
Abstract

Aiming at solving the issue of blurred images and difficult recognition of digital meters encountered by inspection robots in the inspection process, this paper proposes a deep-learning-based method for blurred image restoration and LED digital identification. Firstly, fast Fourier transform (FFT) is used to perform blur detection on the acquired images. Then, the blurred images are recovered using spatial-attention-improved adversarial neural networks. Finally, the digital meter region is extracted using the polygon-YOLOv5 model and corrected via perspective transformation. The digits in the image are extracted using the YOLOv5s model, and then recognized by the CRNN for digit recognition. It is experimentally verified that the improved adversarial neural network in this paper achieves 26.562 in the PSNR metric and 0.861 in the SSIM metric. The missing rate of the digital meter reading method proposed in the paper is only 1% and the accuracy rate is 98%. The method proposed in this paper effectively overcomes the image blurring problem caused by the detection robot during the detection process. This method solves the problems of inaccurate positioning and low digital recognition accuracy of LED digital meters in complex and changeable environments, and provides a new method for reading digital meters. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Hyperspectral Analysis of Biomass Element Content in Straw

Author

Fang Junlong, Li Heng, Zhen Jinglong, Nie Yu, and Zhang Chentao

Subjects
Chemical engineering, TP155-156, Computer engineering. Computer hardware, TK7885-7895
Abstract

In this paper, a quantitative analysis model is established with the help of hyperspectral imaging technology and the least squares method to study the biomass content in straw. The results show that the optimal selection of spectral dimensional data can be achieved through a competitive adaptive weighted sampling algorithm. In the experiment, the correlation coefficient of nitrogen in the verification set is 0.923 and the correlation coefficient of oxygen in the verification set is 0.876. Given that the prediction results of these two elements are relatively good and thus can be applied in practice. The practicality of other elements is poor. However, in the quantitative analysis model, the prediction results of these five elements are not satisfactory and the practicality is poor.

Published
2018
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31. New Robust Tensor PCA via Affine Transformations and L2,1 Norms for Exact Tubal Low-Rank Recovery from Highly Corrupted and Correlated Images in Signal Processing

Author

Liang, Peidong, Zhang, Chentao, Likassa, Habte Tadesse, and Guo, Jielong

Subjects
Article Subject
Abstract

In this latest work, the Newly Modified Robust Tensor Principal Component Analysis (New RTPCA) using affine transformation and L2,1 norms is proposed to remove the outliers and heavy sparse noises in signal processing. This process is done by decomposing the original data matrix as the low-rank heavy sparse noises. The determination of the potential variables is casted as constrained convex optimization problem, and the Alternating Direction Method of Multipliers (ADMM) method is considered to reduce the computational loads in an iterative manner. The simulation results validate the effectiveness of the new method as compared with that of the state-of-the-art methods.

Published
2022
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40. New Robust PCA for Outliers and Heavy Sparse Noises’ Detection via Affine Transformation, the L∗,w and L2,1 Norms, and Spatial Weight Matrix in High-Dimensional Images: From the Perspective of Signal Processing

Author

Liang, Peidong, Likassa, Habte Tadesse, Zhang, Chentao, and Guo, Jielong

Subjects
Article Subject, QA1-939, Mathematics
Abstract

In this paper, we propose a novel robust algorithm for image recovery via affine transformations, the weighted nuclear, L∗,w, and the L2,1 norms. The new method considers the spatial weight matrix to account the correlated samples in the data, the L2,1 norm to tackle the dilemma of extreme values in the high-dimensional images, and the L∗,w norm newly added to alleviate the potential effects of outliers and heavy sparse noises, enabling the new approach to be more resilient to outliers and large variations in the high-dimensional images in signal processing. The determination of the parameters is involved, and the affine transformations are cast as a convex optimization problem. To mitigate the computational complexity, alternating iteratively reweighted direction method of multipliers (ADMM) method is utilized to derive a new set of recursive equations to update the optimization variables and the affine transformations iteratively in a round-robin manner. The new algorithm is superior to the state-of-the-art works in terms of accuracy on various public databases.

Published
2021

42. Organohydrogel‐Based Soft SEMG Electrodes for Algorithm‐Assisted Gesture Recognition

Author

Xu, Yixin, Deng, Lianjun, Lu, Yuyao, Zhang, Jianhuan, Xu, Zhouyi, Xu, Kaichen, and Zhang, Chentao

Abstract

Epidermal electronics that can monitor physiological signals such as surface electromyogram (sEMG) signals attract widespread attentions in personalized healthcare, human–machine interfaces (HMI) and virtual/augmented reality (AR/VR). However, conventional electromyographic electrodes suffer from skin discomfort, susceptibility to motion artifact interference, and short service lifetime. Here, an organohydrogel‐based sEMG electrode endows with high conductivity, low modulus and long‐term stability is developed by doping partially reduced graphene oxide (pRGO) into highly cross‐linked organohydrogel network. The as‐fabricated polyacrylamide/sodium alginate/tannic acid/partially reduced graphene oxide (PAM/SA/TA/pRGO) organohydrogel possesses farewell conductivity (4.22 S m−1) while preserving tissue‐like compliance (Young's modulus ≈32 KPa), excellent stretchability (≈600%), high adhesion as well as superior anti‐drying properties. In addition, a stretchable sEMG electrode for long‐term reliable service is fabricated via immobilizing the organohydrogel electrodes onto a flexible very high bond (VHB) substrate. As a result, the integrated electrodes show high signal‐to‐noise ratio (SNR) (35.15 db) comparable to that of the commercial electrodes. Furthermore, with assistance of deep learning, the proposed sEMG electrodes obtain high identification accuracy of 97.11% in distinguishing sophisticated gestures. This system can be further exploited for real‐time tele‐operations and offers broad prospects in human–machine immersive interactive application. This work studies high conductive and adhesive organohydrogel as surface electromyogram electrodes for the application of gesture recognition. The organohydrogel‐based sEMG electrodes perform good in the system, which offers broad prospects in human–machine immersive interactive application.

Published
2024
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