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您的检索式：作者名="Li Jingcong"
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| 1 | Axial Micro-Strain Sensor Based on Resonance Demodulation Technology Via Dual-Mode CMECF显示文摘This paper firstly and experimentally demonstrates an in-fiber axial micro-strain sensing head,combined with a Mach-Zehnder interferometer(MZI)based on the concentric multilayer elliptical-core fiber(CMECF).This MZI with a high extinction ratio(about 15dB)is successfully achieved with a CMECF-single mode fiber-CMECF(CSC)structure.The MZI sensor theory and the resonance demodulation technology are systematically described in this paper.In this CSC structure, two sections of the CMECF have a role as the mode generator and coupler,respectively.LP01 and LPlleven,which have similar excitation coefficients,are two dominated propagating mode groups supported in the CMECF.On account of the distinct dual-mode property,a good stability of this sensor is realized.The detected resonance in the MZI shifts as the axial micro-strain variated due to the strong interaction between higher order modes.High sensitivity of^1.78pm/με is experimentally achieved within the range of 0με-1250με,meanwhile,the intensity fluctuation is below 0.38dB. | Xiao LIANG Tigang NING Jingcong LI Yang LI Zhiming LIU | 2019 | Photonic Sensors2019，9，1： | 1 |
| 2 | Recent Advances in Fatigue Detection Algorithm Based on EEG显示文摘Fatigue is a state commonly caused by overworked,which seriously affects daily work and life.How to detect mental fatigue has always been a hot spot for researchers to explore.Electroencephalogram(EEG)is considered one of the most accurate and objective indicators.This article investigated the devel-opment of classification algorithms applied in EEG-based fatigue detection in recent years.According to the different source of the data,we can divide these classification algorithms into two categories,intra-subject(within the same sub-ject)and cross-subject(across different subjects).In most studies,traditional machine learning algorithms with artificial feature extraction methods were com-monly used for fatigue detection as intra-subject algorithms.Besides,deep learn-ing algorithms have been applied to fatigue detection and could achieve effective result based on large-scale dataset.However,it is difficult to perform long-term calibration training on the subjects in practical applications.With the lack of large samples,transfer learning algorithms as a cross-subject algorithm could promote the practical application of fatigue detection methods.We found that the research based on deep learning and transfer learning has gradually increased in recent years.But as afield with increasing requirements,researchers still need to con-tinue to explore efficient decoding algorithms,design effective experimental para-digms,and collect and accumulate valid standard data,to achieve fast and accurate fatigue detection methods or systems to further widely apply. | Fei Wang Yinxing Wan Man Li Haiyun Huang Li Li Xueying Hou Jiahui Pan Zhenfu Wen Jingcong Li | 2023 | Intelligent Automation & Soft Computing2023，，3： | 1 |
| 3 | Queuing performance analysis with self-similar network traffic 显示文摘 | Li Jingcong Li Zhengbin Wu Deming | 2002 | Acta Scientiarum Naturallium Universitatis Pekinensis2002，38，5： | 1 |
| 4 | A Novel Model of Resolving Contention in Optical Burst Switched Networks显示文摘A Novel segmentation and feedback model (SFM) applied to resolve collision has been proposed. The SFM is featured with Burst Segmentation and Prioritized Feedback (BSPF) that are used to provide quality of service (QoS) and realize high throughput and faster switching in the optical burst switched networks. Simulation and performance analyses show that the SFM effectively avoid collision in optical burst switching (OBS). Long delay time of deflection routing and immature technology of wavelength converter and optical buffer are not employed in the SFM. The SFM not only realizes quick switching but also allows preemption for higher priority bursts. | 黄安鹏 Xie Linzhen Li Jingcong Li Zhengbin Xu Anshi | 2004 | High Technology Letters2004，10，4： | 0 |
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