胡慶培

胡慶培，男，中國科學院大學專任教師。

基本介紹

  • 中文名：胡慶培
  • 國籍：中國
  • 職業：教師
  • 性別：男
人物經歷,研究領域,專利與獎勵,專利成果,獎勵榮譽,論文發表,

人物經歷

2020.5-至 今 中科院數學院系統所統計科學研究室，副主任
2017.1-至 今 中科院數學院與航天五院可靠性技術聯合實驗室，副主任
2018.1-至 今 中科院數學院質量與數據科學研究中心，常務副主任
2015.3-至 今 中國科學院數學與系統科學研究院，副研究員；
2013.4-2015.3 中國科學院數學與系統科學研究院，所聘副研究員；
2008.8-2013.3 中國科學院數學與系統科學研究院，助理研究員；
2008.5-2008.8 香港城市大學，Research Fellow；
2007.7-2008.4 韓國高級科學技術學院，博士後。

研究領域

統計學，工業統計，系統可靠性

專利與獎勵

專利成果

  1. 于丹,徐建宇,胡慶培. 一種部件級信息多樣性條件下的系統可靠性評估方法[P]. 北京：CN107862126A, 2018-03-30. （已授權）
  2. 于丹,李趙輝,胡慶培. 系統級產品可靠性綜合評估置信推斷方法[P]. 北京：CN106169124A, 2016-11-30. （已授權）
  3. 孫麗,武志忠,胡慶培,于丹,馮士偉,張金保,陳小娟,付明睿,張辰, 忽敏學, 張俊. 一種半球軸承動壓馬達的可靠性驗證方法[P]. 北京：CN104062585A, 2014-09-24. （已授權）

獎勵榮譽

  1. 中國科學院系統科學研究所關肇直青年研究獎，2014
  2. 美國工業系統工程學會旗艦期刊IISE Transactions 2020年度最佳論文提名獎： Li, Z. H., Yu, D., Liu, J. and Hu, Q.P. (2020), “Higher-order normal approximation approach for highly reliable system assessment.” IISE Transactions, 52 (5), 555-567
  3. 美國工業系統工程學會旗艦期刊IISE Transactions 2020年第五期 Featured Article： Li, Z. H., Yu, D., Liu, J. and Hu, Q.P. (2020), “Higher-order normal approximation approach for highly reliable system assessment.” IISE Transactions, 52 (5), 555-567
  4. IRC-SEMS2019最佳學生論文獎：Wang, C. J., Hu, Q. P., and Yu, D. (2019). "Assessment of Reliability for Accelerated Degradation Testing with Random Initial Degradation." The 7th International Research Conference on Systems Engineering and Management Science. Nanjing, pp. 1-6. (導師)
  5. 全國可靠性物理年會優秀論文獎：李贊，王奉明，王成傑，胡慶培，于丹，"基於壽命延遲退化過程的可靠性評估及檢測最佳化設計"，全國可靠性物理年會 NRPS2019，桂林
  6. QR2MSE2018最佳論文獎：Li, D.M., Q. P. Hu, and D. Yu, Likelihood based Statistical Inference Approach for Mt/G/Inf Queueing Systems Under Incomplete Observations, in 2018 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering, Qingdao.
  7. IRC-SEMS2018最佳學生論文獎：Li, D.M., Q. P. Hu, and D. Yu, Likelihood based Statistical Inference Approach for Mt/G/Inf Queueing Systems Under Incomplete Observations, in the 6th International Research Conference on Systems Engineering and Management Science. 2018. (導師)
  8. ICRMS2016最佳論文獎：Li, Z., Q.P. Hu and D. Yu (2016). A Normal Approximation Approach with Higher Order for System Reliability Assessment. 11th International Conference on Reliability, Maintainability and Safety (ICRMS) Hangzhou, China.
  9. 美國工業系統工程學會旗艦期刊IISE Transactions2016年第3期Featured Article：Wang, L., Q.P. Hu and J. Liu (2016). Software reliability growth modeling and analysis with dual fault detection and correction processes. IIE Transactions 48(4): 359-370. (SCI)
  10. PRDC2006最佳學生論文獎：Hu, Q. P, Xie, M., and Ng, S. h. (2006). "Early Software Reliability Prediction with ANN Models" 12th Pacific Rim International Symposium on Dependable Computing (PRDC'06). Riverside, 210-220.

論文發表

  1. Li, Z., Wang, F.M., Wang, C.J., Hu, Q.P.*, Yu, D. (2020), “Reliability Modeling and Evaluation of Lifetime Delayed Degradation Process with Nondestructive Testing,” Reliability Engineering & System Safety (Online)
  2. Li, Z. H., Yu, D., Liu, J. and Hu, Q.P.* (2020), “Higher-order normal approximation approach for highly reliable system assessment.” IISE Transactions, 52 (5), 555-567. (Featured Article)
  3. Li, D. M., Hu, Q.P.*, and Yu, D. (2019), “Statistical Inference for Mt/G/Infinity Queueing Systems Under Incomplete Observations.”, European Journal of Operational Research, 279, 882-901
  4. Ye, Z. S., Hu, Q. P.*, and Yu, D. (2019). "Strategic allocation of test units in an accelerated degradation test plan." Journal of Quality Technology, 51(1), 64-80.
  5. Sheng, Z., Hu, Q. P.*, Liu, J., and Yu, D. (2019). "Residual life prediction for complex systems with multi-phase degradation by ARMA-filtered hidden Markov model." Quality Technology & Quantitative Management, 16(1), 19-35.
  6. Wang, L., Hu, Q. P.*, and Liu, J. (2016). "Software reliability growth modeling and analysis with dual fault detection and correction processes." IIE Transactions, 48(4), 359-370. (Featured Article)
  7. Zhang, M., Hu, Q. P., Xie, M., and Yu, D. (2014). "Lower confidence limit for reliability based on grouped data using a quantile-filling algorithm." Computational Statistics & Data Analysis, 75, 96-111.
  8. Hu, Q. P., Xie, M., Ng, S. H., and Levitin, G. (2007). "Robust recurrent neural network modeling for software fault detection and correction prediction." Reliability Engineering & System Safety, 92(3), 332-340.

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