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您的检索式：作者名="LAN Chaofeng"
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| 1 | The Sunway TaihuLight supercomputer: system and applications显示文摘The Sunway Taihu Light supercomputer is the world's first system with a peak performance greater than 100 PFlops. In this paper, we provide a detailed introduction to the Taihu Light system. In contrast with other existing heterogeneous supercomputers, which include both CPU processors and PCIe-connected many-core accelerators(NVIDIA GPU or Intel Xeon Phi), the computing power of Taihu Light is provided by a homegrown many-core SW26010 CPU that includes both the management processing elements(MPEs)and computing processing elements(CPEs) in one chip. With 260 processing elements in one CPU, a single SW26010 provides a peak performance of over three TFlops. To alleviate the memory bandwidth bottleneck in most applications, each CPE comes with a scratch pad memory, which serves as a user-controlled cache. To support the parallelization of programs on the new many-core architecture, in addition to the basic C/C++and Fortran compilers, the system provides a customized Sunway Open ACC tool that supports the Open ACC2.0 syntax. This paper also reports our preliminary efforts on developing and optimizing applications on the Taihu Light system, focusing on key application domains, such as earth system modeling, ocean surface wave modeling, atomistic simulation, and phase-field simulation. | Haohuan FU Junfeng LIAO Jinzhe YANG Lanning WANG Zhenya SONG Xiaomeng HUANG Chao YANG Wei XUE Fangfang LIU Fangli QIAO Wei ZHAO Xunqiang YIN Chaofeng HOU Chenglong ZHANG Wei GE Jian ZHANG Yangang WANG Chunbo ZHOU Guangwen YANG | 2016 | Science China(Information Sciences)2016，59，7： | 45 |
| 2 | The Theory and Experiment of Parametric Amplification of Three-wave Nonlinear Interaction in Water显示文摘 | LAN Chaofeng YANG Desen LU Di GUO Xiaoxia ZHOU Zhonghai | 2013 | Chinese Journal of Electronics2013，22，2： | 0 |
| 3 | Detection of the foreign object positions in agricultural soils using Mask-RCNN显示文摘Objects in agricultural soils will seriously affect the farming operations of agricultural machinery.At present,it still relies on human experience to judge abnormal Gounrd-penetrting Radar(GPR)signals.It is difficult for traditional image processing technology to form a general positioning method for the randomness and diversity characteristics of GPR signals in soil.Although many scholars had researched a variety of image-processing techniques,most methods lack robustness.In this study,the deep learning algorithm Mask Region-based Convolutional Neural Network(Mask-RCNN)and a geometric model were combined to improve the GPR positioning accuracy.First,a soil stratification experiment was set to classify the physical parameters of the soil and study the attenuation law of electromagnetic waves.Secondly,a SOIL-GPR geometric model was proposed,which can be combined with Mask-RCNN's MASK geometric size to predict object sizes.The results proved the effectiveness and accuracy of the model for position detection and evaluation of objects in soils;then,the improved Mask RCNN method was used to compare the feature extraction accuracy of U-Net and Fully Convolutional Networks(FCN);Finally,the operating speed of agricultural machinery was simulated and designed the A-B survey line experiment.The detection accuracy was evaluated by several indicators,such as the survey line direction,soil depth false alarm rate,Mean Average Precision(mAP),and Intersection over Union(IoU).The results showed that pixel-level segmentation and positioning based on Mask RCNN can improve the accuracy of the position detection of objects in agricultural soil effectively,and the average error of depth prediction is 2.87 cm.The results showed that the detection technology proposed in this study integrates the advantage of soil environmental parameters,geometric models,and artificial intelligence algorithms to provide a high-precision and technical solution for the GPR non-destructive detection of soils. | Yuanhong Li Chaofeng Wang Congyue Wang Xiaoling Deng Zuoxi Zhao Shengde Chen Yubin Lan | 2023 | International Journal of Agricultural and Biological Engineering2023，16，1： | 0 |
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