Yanqi Wu

Yanqi Wu


Research Focus & Metrics

Academic Career Length

14Years (2013-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 July 2013 in Journal of Applied Physics
Verified
Journal Article
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YWYanqi Wu
Yanqi Wu
PHP. M. Hermkens
P. M. Hermkens
BLB. W. H. van de Loo
B. W. H. van de Loo
HKH. C. M. Knoops
H. C. M. Knoops
SPS. E. Potts
S. E. Potts
WKW. M. M. Kessels
W. M. M. Kessels
In this work, the structural, electrical, and optical properties as well as chemical bonding state of Al-doped ZnO films deposited by atomic layer deposition have been investigated to obtain insight into the doping and electrical transport mechanisms in the films. The range in doping levels from 0% to 16.4% Al was accomplished by tuning the ratio of ZnO and Al2O3 ALD cycles. With X-ray photoelectron spectroscopy depth profiling and transmission electron microscopy, we could distinguish the individual ZnO and AlOx layers in the films. For films with a thickness of 40 nm, the resistivity improved from 9.8 mΩ cm for intrinsic ZnO to an optimum of 2.4 mΩ cm at 6.9 at. % Al. The binding energy of Zn 2p3/2 increased by 0.44 eV from the intrinsic ZnO to the highest Al-doped ZnO. This shift can be ascribed to an increase of the Fermi level. Ex-situ spectroscopic ellipsometry and Fourier transform infrared spectroscopy were used to measure the optical properties from which the carrier concentration and intra-grain mobility were extracted. The results showed that with increasing Al content, the grain boundary mobility increased at first due to an increased Fermi level, and then decreased mainly due to the scattering at AlOx/ZnO interfaces. For the same reasons, the doping efficiency of Al for highly Al-doped ZnO dropped monotonically with increasing Al. Furthermore, a blue shift of the optical band-gap ΔEg up to 0.48 eV was observed, consistent with the shifts of the Fermi level and the binding energy of the Zn 2p3/2 state.

1 July 2024 in Applied Sciences
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Journal Article
Open access
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Luofa WuLuofa Wu
Luofa Wu
HLHelai Liu
Helai Liu
CYChun Ye
Chun Ye
YWYanqi Wu
Yanqi Wu
Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot’s performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well.
1

21 April 2023 in Preprints.org
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Luofa WuLuofa Wu
Luofa Wu
DCDaogen Chen
Daogen Chen
XXXieqing Xu
Xieqing Xu
YWYanqi Wu
Yanqi Wu
Threshing device is the core component of grain combine harvester, and its working performance determines the working quality and production efficiency of the harvester to a great extent. The straw guide plate plays an important role in the threshing device to dredge the threshing matter and control the threshing time of the combine harvester matter in the threshing chamber. In the past, the guiding structure of threshing device could not optimize the working performance of the machine by adjusting the spiral angle, that is, most cereal combine harvester may not be fed smoothly or even clogged when operating under different crop types, yield differences and operating environments, a kind of threshing device with adjustable guide straw board was studied. In this study, the movement model of the straw on the straw guide plate was analyzed, and it was found that the spiral angle “α”of the straw guide was 0<α<58°.Three-factor and three-level response surface method was used to carry out field experiment on drum speed, working speed and guide grass plate spiral angle, and the experimental data were analyzed by quadratic polynomial regression.The results show that the drum speed, working speed and the spiral angle of the guide grass plate have significant effects on the loss rate (PLR) , impurity content (PIR) and breakage rate (PBR) , the drum speed and working speed have an interactive effect on the impurity ratio (PIR) , and the working speed and the helical angle of the guide grass plate have an interactive effect on the loss ratio (PLR) and breakage ratio (PBR) , there is an interaction between the rotary speed of the roller and the helical angle of the guide grass plate on the crushing rate (PBR) .Further optimization analysis showed that the predicted value was PLR = 1.18% , PIR = 0.72% , PBR = 0.53% , the experimental verification value was PLR = 1.26% , PIR = 0.73% , PBR = 0.61% . The absolute error between the experimental value and the predicted value was very small, however, the optimized field test verified value decreased by 8.31% , 50.04% and 60.30% respectively, which indicated that the optimized harvester had better operation quality, in particular, PIR, PBR and other two indicators have a greater increase in the effect.

22 July 2023 in Applied Sciences
Verified
Journal Article
Open access
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Luofa WuLuofa Wu
Luofa Wu
DCDaogen Chen
Daogen Chen
XXXieqing Xu
Xieqing Xu
YWYanqi Wu
Yanqi Wu
The threshing device is the core component of the grain combine harvester, and the straw guide board plays an important role in the threshing device. In the past, the guiding structure of the threshing device could not optimize the working performance of the machine by adjusting the spiral angle. In this study, an adjustable straw guide board was designed, and the movement model of the straw on the straw guide board was analyzed. The response surface method was used to perform field experiments, and the experimental data were analyzed using quadratic polynomial regression. The results show that the drum rotation speed, operating speed, and spiral angle of the straw guide board have significant effects on the percentage of loss rate (PLR), percentage of impurities rate (PIR), and percentage of broken rate (PBR). Further optimization analysis showed that the predicted values of the PLR, PIR, and PBR were 1.18%, 0.72%, and 0.54%, respectively, whereas the experimental verification values were 1.26%, 0.73%, and 0.61%, respectively. The absolute errors between the experimental and predicted values were very small; however, the optimized field test verified values decreased by 8.31%, 50.04%, and 60.30%, respectively, which indicates that the optimized harvester had better operation quality.
1

15 March 2024 in Preprints.org
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Luofa WuLuofa Wu
Luofa Wu
DCDaogen Chen
Daogen Chen
YWYanqi Wu
Yanqi Wu
A self-propelled electric tea seed-planter is designed to address the lack of tea seed seeding equipment, low seeding efficiency, and poor seeding uniformity. Based on the physical characteristics of the tea seeds and the agronomic requirements of tea planting, the key structure of the planter is designed, and the structural parameters are determined. The motor and battery configurations are determined by calculating the power consumption of the entire machine. In a field experiment, the driving gear (A), ditching depth (B), and planting gear position (C) are used as test factors, and the acceptance index (AI), replay index (RI) and leakage index (LI) are used as test indexes. The test results showed that the driving speed and planting gear position significantly affect the acceptance index, replay, and leakage index, whereas the ditching depth has little effect on the performance indexes. When the driving speed was medium, ditching depth was 30 mm, and planting gear position was in the third gear, the corresponding acceptance index was 87.22%, the replay index was 8.89%, and the leakage index was 3.89%. The test prototype has a good working quality and can provide theoretical guidance for production practices.