Jingchang Pan 山东大学主页平台管理系统--Home

Jingchang Pan Professor

潘景昌，博士，教授，博士生导师。分别在北京大学、哈尔滨工业大学、山东大学获得学士、硕士、博士学位。现任教于山东大学（威海），任山东大学威海计算机应用技术研究所所长。主要研究方向为大数据与数据挖掘，分布式与并行计算，软件工程与应用，机器学习与人工智能。获得国家自然基金项目4项，其中重点项目1项；山东省省级重点科技研发项目1项，山东省科技攻关1项；地市级项目3项；横向应用项目30余项。发表SCI、EI论文50余篇。出版教材1部，培养研究生40余名。荣获省级科技进步... More+

Paper Publications

Home >> Scientific Resear... >> Paper Publication...

[1] 岳丽丽. Identify M Subdwarfs from M-type Spectra using XGBoost. Optik-International Journal for Light and Electron Optics, 225, 2021.
[2] Atmospheric Parameter Measurement of Low-S/N Stellar Spectra Based on Deep Learning. Optik, 2020.
[3] Rare Object Search From Low-S/N Stellar Spectra in SDSS. IEEE ACCESS, VOLUME 8, 66475-66488, 2020.
[4] Information Dissemination Model of Microblogging with Internet Marketers. Journal of International Process Systems, Vol.15, pp853-864, 2019.
[5] A Method to Search Special Stellar Spectra from Low Signal-to-Noise Ratio Spectral Sky Survey Dat.... 光谱学与光谱分析, Vol. 39, pp618-621, 2019.
[6] A Method to Fit Low-Quality Stellar Spectrum. 光谱学与光谱分析, Vol. 38, pp963-967, 2018.
[7] The Automatic Recognition and Detection of Sky-subtraction Residual Component in The Stellar Spec.... 光谱学与光谱分析, Vol. 37, pp273-277, 2017.
[8] Outlier Data Mining and Analysis of LAMOST Stellar Spectra in Line Index Feature Space. 光谱学与光谱分析, Vol. 36, pp3364-3368, 2016.
[9] A Method of Stellar Spectral Classification Based on Map/Reduce Distributed Computing. 光谱学与光谱分析, Vol. 36, pp1521-1525, 2016.
[10] Research on M Dwarf Sub-Classification based on measurement of residual distribution. 光谱学与光谱分析, Vol. 36, pp2279-2283, 2016.
[11] Research on Clustering of Massive Stellar Spectra Based on Line Index. 光谱学与光谱分析, Vol. 33, pp2646-2650, 2016.
[12] 基于多分辨率融合距离的低质量星系光谱红移测量方法. 光谱学与光谱分析, Vol. 36, pp1521-1525, 2016.
[13] 一种新的恒星光谱间距离度量方法：残差分布距离. 光谱学与光谱分析, Vol 35, pp3524-3528, 2015.
[14] A method to estimate metal abundance from stellar spectra using Ca lineindex. 光谱学与光谱分析, Vol.35, pp2650-2653, 2015.
[15] Automated Determination of Stellar Atmospheric Parameters Based on Lick Indices. Journal of Information & Computational Science, Vol. 12, PP5339-5345, 2015.
[16] Stellar Parameter Extraction Based on Template Matching and Monte-Carlo Simulation. Journal of Information & Computational Science, Vol. 12, PP2701-2709, 2015.
[17] New cross-matching algorithm in large-scale catalogs with ThreadPool technique. SCIENCE CHINA, Vol. 57, 577–583, 2014.
[18] 基于神经网络的线指数恒星大气物理参数测量方法. 光谱学与光谱分析, Vol.33, pp1701-1705, 2013.
[19] 基于线指数线性回归的恒星光谱大气物理参数测量. 光谱学与光谱分析, Vol.33, pp1397-1400, 2013.
[20] 恒星光谱参数自动测量中不同模板匹配度量方法比较. 光谱学与光谱分析, Vol. 33, pp558-561, 2013.

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Paper Publications

[1] 岳丽丽. Identify M Subdwarfs from M-type Spectra using XGBoost. Optik-International Journal for Light and Electron Optics, 225, 2021.

[2] Atmospheric Parameter Measurement of Low-S/N Stellar Spectra Based on Deep Learning. Optik, 2020.

[3] Rare Object Search From Low-S/N Stellar Spectra in SDSS. IEEE ACCESS, VOLUME 8, 66475-66488, 2020.

[4] Information Dissemination Model of Microblogging with Internet Marketers. Journal of International Process Systems, Vol.15, pp853-864, 2019.

[5] A Method to Search Special Stellar Spectra from Low Signal-to-Noise Ratio Spectral Sky Survey Dat.... 光谱学与光谱分析, Vol. 39, pp618-621, 2019.

[6] A Method to Fit Low-Quality Stellar Spectrum. 光谱学与光谱分析, Vol. 38, pp963-967, 2018.

[7] The Automatic Recognition and Detection of Sky-subtraction Residual Component in The Stellar Spec.... 光谱学与光谱分析, Vol. 37, pp273-277, 2017.

[8] Outlier Data Mining and Analysis of LAMOST Stellar Spectra in Line Index Feature Space. 光谱学与光谱分析, Vol. 36, pp3364-3368, 2016.

[9] A Method of Stellar Spectral Classification Based on Map/Reduce Distributed Computing. 光谱学与光谱分析, Vol. 36, pp1521-1525, 2016.

[10] Research on M Dwarf Sub-Classification based on measurement of residual distribution. 光谱学与光谱分析, Vol. 36, pp2279-2283, 2016.

[11] Research on Clustering of Massive Stellar Spectra Based on Line Index. 光谱学与光谱分析, Vol. 33, pp2646-2650, 2016.

[12] 基于多分辨率融合距离的低质量星系光谱红移测量方法. 光谱学与光谱分析, Vol. 36, pp1521-1525, 2016.

[13] 一种新的恒星光谱间距离度量方法：残差分布距离. 光谱学与光谱分析, Vol 35, pp3524-3528, 2015.

[14] A method to estimate metal abundance from stellar spectra using Ca lineindex. 光谱学与光谱分析, Vol.35, pp2650-2653, 2015.

[15] Automated Determination of Stellar Atmospheric Parameters Based on Lick Indices. Journal of Information & Computational Science, Vol. 12, PP5339-5345, 2015.

[16] Stellar Parameter Extraction Based on Template Matching and Monte-Carlo Simulation. Journal of Information & Computational Science, Vol. 12, PP2701-2709, 2015.

[17] New cross-matching algorithm in large-scale catalogs with ThreadPool technique. SCIENCE CHINA, Vol. 57, 577–583, 2014.

[18] 基于神经网络的线指数恒星大气物理参数测量方法. 光谱学与光谱分析, Vol.33, pp1701-1705, 2013.

[19] 基于线指数线性回归的恒星光谱大气物理参数测量. 光谱学与光谱分析, Vol.33, pp1397-1400, 2013.

[20] 恒星光谱参数自动测量中不同模板匹配度量方法比较. 光谱学与光谱分析, Vol. 33, pp558-561, 2013.