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您的检索式：作者名="Keyin Chen"
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| 1 | Inductance calculation for 3D microsolenoids with single-layer coils显示文摘Three-dimensional(3D) single-layer microcoils have always been a key element for electromagnetic systems;but they lack an easy and accurate method to calculate the inductance value for their complex 3D micro-structures. This paper employed a curve-fitting process to obtain the associated equation for the inductance value and geometric parameters based on the simulation results. The correction factors regarding helical pitch and wire diameter were reviewed,which are used for compensation in the Nagaoka formula. The simulation process numerically simulated the performance of the 3D microcoils using a FEM electro-magnetic-coupled analysis method. Comparison of the simulated inductance value and the Nagaoka formula was undertaken,which shows that the helical pitch and wire diameter contribute a main role in the calculation error. The derived formula was expressed in a concise form to precisely calculate the inductance value of 3D microsolenoids with single-layer coils. | LIU Keyin YANG Qing CHEN Feng ZHAO Yulong MENG Xiangwei SHAN Chao LI Yanyang | 2014 | Instrumentation2014，1，2： | 6 |
| 2 | Sculpturing spatiotemporal wavepackets with chirped pulses显示文摘Pulse shaping has become a powerful tool in generating complicated ultrafast optical waveforms to meet specific application needs.Traditionally,pulse shaping focuses on the temporal waveform synthesis.Recent interests in structuring light in the spatiotemporal domain rely on Fourier analysis.A space-to-time mapping technique allows us to directly imprint complex spatiotemporal modulation through taking advantage of the relationship between frequency and time of chirped pulses.The concept is experimentally verified through the generation of spatiotemporal optical vortex(STOV)and STOV lattice.The power of this method is further demonstrated by STOV polarity reversal,vortex collision,and vortex annihilation.Such a direct mapping technique opens tremendous potential opportunities for sculpturing complex spatiotemporal waveforms. | QIAN CAO JIAN CHEN KEYIN LU CHENHAO WAN ANDY CHONG QIWEN ZHAN | 2021 | Photonics Research2021，9，11： | 2 |
| 3 | Litchi detection in the field using an improved YOLOv3 model显示文摘Due to the illumination,complex background,and occlusion of the litchi fruits,the accurate detection of litchi in the field is extremely challenging.In order to solve the problem of the low recognition rate of litchi-picking robots in field conditions,this study was inspired by the ideas of ResNet and dense convolution and proposed an improved feature-extraction network model named“YOLOv3_Litchi”,combining dense connections and residuals for the detection of litchis.Firstly,based on the traditional YOLOv3 deep convolution neural network and regression detection,the idea of residuals was to be put into the feature-extraction network to effectively avoid the problem of decreasing detection accuracy due to the excessive depths of the network layers.Secondly,under the premise of a good receptive field and high detection accuracy,the large convolution kernel was replaced by a small convolution kernel in the shallow layer of the network,thereby effectively reducing the model parameters.Finally,the idea of feature pyramid was used to design the network to identify the small target litchi to ensure that the shallow features were not lost and simultaneously reduced the model parameters.Experimental results show that the improved YOLOv3_Litchi model achieved better results than the classic YOLOv3_DarkNet-53 model and the YOLOv3_Tiny model.The mean average precision(mAP)score was 97.07%,which was higher than the 95.18%mAP of the YOLOv3_DarkNet-53 model and the 94.48%mAP of the YOLOv3_Tiny model.The frame frequency was 58 fps,which was higher than 29 fps of the YOLOv3_DarkNet-53 model.Compared with the classic Faster R-CNN model with the feature-extraction network VGG16,the mAP was increased by 1%,and the FPS advantage was obvious.Compared with the classic single shot multibox detector(SSD)model,both the accuracy and the running efficiency were improved.The results show that the improved YOLOv3_Litchi model had stronger robustness,higher detection accuracy,and less computational complexity for the identification of litchi in the field conditions,which should be helpful for litchi orchard precision management. | Hongxing Peng Chao Xue Yuanyuan Shao Keyin Chen Huanai Liu Juntao Xiong Hu Chen Zongmei Gao Zhengang Yang | 2022 | International Journal of Agricultural and Biological Engineering2022，15，2： | 0 |
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