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- 저자 : Xinyang Wu (검색결과 3 건)
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Xinyang Wu,Shuting Chen,Zixin Zhang,Yihan Zhang,Pingmei Li,Xinyi Chen,Miaomiao Liu,Qian Lu,Zhongyi Li,Zhongyan Wei,Pei Xu 한국식물병리학회 2023 Plant Pathology Journal Vol.39 No.5
Cowpea mild mottle virus (CPMMV) is a global plant virus that poses a threat to the production and quality of legume crops. Early and accurate diagnosis is essential for effective managing CPMMV outbreaks. With the advancement in isothermal recombinase polymerase amplification and lateral flow strips technologies, more rapid and sensitive methods have become available for detecting this pathogen. In this study, we have developed a reverse transcription recombinase polymerase amplification combined with lateral flow strips (RT-RPA-LFS) method for the detection of CPMMV, specifically targeting the CPMMV coat protein (CP) gene. The RT-RPA-LFS assay only requires 20 min at 40°C and demonstrates high specificity. Its detection limit was 10 copies/μl, which is approximately up to 100 times more sensitive than RT-PCR on agarose gel electrophoresis. The developed RT-RPA-LFS method offers a rapid, convenient, and sensitive approach for field detection of CPMMV, which contribute to controlling the spread of the virus.
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Loss Prediction of Vehicle Permanent Magnet Synchronous Motor Based on Deep Learning
He Liange,Wu Xinyang,Nie Yuanhang,Shi Wenjun 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.2
Based on the deep learning BP neural network algorithm, we establish the electromagnetic torque and loss prediction analysis model of permanent magnet synchronous motor to provide new design ideas and methods for optimizing motor structure design. In this paper, four-rotor structure parameters which are Rib, Air Gap, Magnet Thickness and Magnet Width, motor electromagnetic torque, and motor losses of the “V” type Interior Permanent Magnet Synchronous Motor are selected as the research object. The BP neural network structure prediction model with 2 visible layers and 2 hidden layers was built by 256 groups of sample data calculated by Maxwell transient electromagnetic simulation. 226 out of 256 randomly selected data samples were used to train the prediction model, and 30 groups were used to test the accuracy and generalization ability of the prediction model. and the prediction results data were compared with the deep learning prediction model through finite element simulation data. The results show that the BP neural network small-sample data prediction model has high prediction accuracy in the loss prediction of the vehicle permanent magnet synchronous motor, and verifies the feasibility of the motor torque and loss prediction model based on the deep learning algorithm.
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Research on Temperature Rise Characteristics of Vehicle Motors Under Bench Working Condition
He Liange,Shi Wenjun,Xia Xiaohua,Wu Xinyang,Chen Hongling,Yan Xin 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.6
The specifi c bench test specifi ed by the product design standard is an important basis for judging whether the vehicle motor meets the requirements. To study the temperature rise characteristics of automotive permanent magnet synchronous motors under bench test conditions. Firstly, the bench condition was taken as the target we need to study, and the fi nite element method was used to calculate the loss of each part during the bench test condition. Secondly, use this loss as the heat source for temperature fi eld calculation to simulate the temperature fi eld of the motor under bench test conditions. Finally, a bench test platform was built for testing, and the test results and simulation results were compared and analyzed. Studies have shown that in the entire process of changing conditions, the temperature of each component is not the same as the sensitivity to changes in operating conditions. The maximum relative error between simulation and experiment was 9.4%, which verifi es the eff ectiveness of this research method and process, which has certain guiding signifi cance for the design and optimization of vehicle motors.
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