OPT OpenIR
(本次检索基于用户作品认领结果)

浏览/检索结果: 共33条,第1-10条 帮助

限定条件        
已选(0)清除 条数/页:   排序方式:
Spectral encoder to extract the efficient features of Raman spectra for reliable and precise quantitative analysis 期刊论文
Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy, 2024, 卷号: 312
作者:  Gao, Chi;  Fan, Qi;  Zhao, Peng;  Sun, Chao;  Dang, Ruochen;  Feng, Yutao;  Hu, Bingliang;  Wang, Quan
Adobe PDF(2752Kb)  |  收藏  |  浏览/下载:24/0  |  提交时间:2024/04/08
Raman spectroscopy  Quantitative analysis  Deep learning  Spectral encoder  Feature extraction  
精细光谱探测与计量分析技术在长江干流水质水环境监测中的应用 期刊论文
地理学报, 2024, 卷号: 79, 期号: 1, 页码: 45-57
作者:  赵宇博;  王雪霁;  刘骁;  巩凯杰;  邹磊;  林忠辉;  于涛;  鱼卫星;  胡炳樑
Adobe PDF(2469Kb)  |  收藏  |  浏览/下载:24/0  |  提交时间:2024/04/10
水质监测  长江水系统  精细光谱探测与计量分析技术  空—地立体监测  
Deep neural network: As the novel pipelines in multiple preprocessing for Raman spectroscopy 期刊论文
SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2023, 卷号: 302
作者:  Gao, Chi;  Zhao, Peng;  Fan, Qi;  Jing, Haonan;  Dang, Ruochen;  Sun, Weifeng;  Feng, Yutao;  Hu, Bingliang;  Wang, Quan
Adobe PDF(2962Kb)  |  收藏  |  浏览/下载:80/0  |  提交时间:2023/09/25
Raman spectroscopy  Deep learning  Baseline correction  Spectroscopy denoising  
Extraction and analysis algorithms for Sanxingdui cultural relics based on hyperspectral imaging 期刊论文
Computers and Electrical Engineering, 2023, 卷号: 111
作者:  Qiu, Shi;  Zhang, Pengchang;  Li, Siyuan;  Hu, Bingliang
Adobe PDF(8586Kb)  |  收藏  |  浏览/下载:52/0  |  提交时间:2023/10/30
Sanxingdui Ruins  Hyperspectral  Unet  FCM  
Stability research of fore-Telescope system with mechanical passive athermalization design 会议论文
AOPC 2022: Optical Spectroscopy and Imaging, Virtual, Online, China, 2022-12-18
作者:  Sun, Jian;  Wang, Wei;  Hu, Bing-Liang;  Li, Si-Yuan;  Zou, Chun-Bo;  Feng, Yu-Tao
Adobe PDF(512Kb)  |  收藏  |  浏览/下载:106/0  |  提交时间:2023/03/13
R-C  Optimal structure  Mechanical passive athermalization design  Stability  VNIR  
Quantifying the Impact of Myopia on Visual Perception in Children Through Eye Tracking 会议论文
2023 8th International Conference on Image, Vision and Computing, ICIVC 2023, Dalian, China, 2023 - 7 - 27
作者:  Liu, Xi;  Yan, Xiangyu;  You, Jialu;  Wang, Yuqi;  Dang, Ruochen;  Hu, Bingliang;  Zhang, Le;  Wang, Quan
Adobe PDF(1347Kb)  |  收藏  |  浏览/下载:66/0  |  提交时间:2023/11/23
myopia  eye tracking  cognitive function  memory  visual recognition  
Optomechanical design analysis and development of space-based visible coded spectrometer based on curved prism dispersion 期刊论文
Journal of Optics (India), 2023
作者:  Jia, Xinyin;  Wang, Feicheng;  Liu, Jia;  Zhang, Zhanghui;  Li, Siyuan;  Yang, Ying;  Hu, Bingliang;  He, Xianqiang;  Liu, Yupeng
Adobe PDF(4321Kb)  |  收藏  |  浏览/下载:67/0  |  提交时间:2024/01/19
Dispersion spectrometer  Curved prism  Optomechanical design  
Sanxingdui Cultural Relics Recognition Algorithm Based on Hyperspectral Multi-Network Fusion 期刊论文
CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 卷号: 77, 期号: 3, 页码: 3783-3800
作者:  Qiu, Shi;  Zhang, Pengchang;  Tang, Xingjia;  Zeng, Zimu;  Zhang, Miao;  Hu, Bingliang
Adobe PDF(2336Kb)  |  收藏  |  浏览/下载:57/0  |  提交时间:2024/03/26
Sanxingdui  cultural relic  spatial features  hyperspectral  integration  
Image blurring and spectral drift in imaging spectrometer system with an acousto-optic tunable filter and its application in UAV remote sensing 期刊论文
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2022, 卷号: 43, 期号: 19-24, 页码: 6957-6978
作者:  Liu, Hong;  Hou, Xingsong;  Hu, Bingliang;  Yu, Tao;  Zhang, Zhoufeng;  Liu, Xiao;  Liu, Jiacheng;  Wang, Xueji;  Zhong, Jingjing;  Tan, Zhengxuan
Adobe PDF(5386Kb)  |  收藏  |  浏览/下载:96/0  |  提交时间:2023/02/06
Retrieval of Water Quality Parameters Based on Near-Surface Remote Sensing and Machine Learning Algorithm 期刊论文
REMOTE SENSING, 2022, 卷号: 14, 期号: 21
作者:  Zhao, Yubo;  Yu, Tao;  Hu, Bingliang;  Zhang, Zhoufeng;  Liu, Yuyang;  Liu, Xiao;  Liu, Hong;  Liu, Jiacheng;  Wang, Xueji;  Song, Shuyao
Adobe PDF(6255Kb)  |  收藏  |  浏览/下载:102/0  |  提交时间:2022/12/02
water quality monitoring  near-surface remote sensing  machine learning algorithm  ensemble learning model