［1］ 王术新,姜哲.基于结构振动损伤识别技术的研究现状及进展［J］.振动与冲击,2004,23(4):99-102．
Wang Shu-xin, JIANG Zhe. Present Developing Situation and Research Advances in the Field of Structural Damage Detection ［J］. Journal of Vibration and Shock, 2004, 23(4):99-102．
［2］ 郑栋梁,李中付,华宏星.结构早期损伤识别技术的现状和发展趋势［J］.振动与冲击,2002,21(2):1-10．
ZHENG Dong-liang, LI Zhong-fu, HUA Hong-xing. A summary review of structural initial damage identification methods ［J］. Journal of Vibration and Shock, 2002, 21(2):1-10．
［3］ 张敬芬,赵德有.工程结构裂纹损伤振动诊断的发展现状和展望［J］.振动与冲击,2002,21(4):22-26．
ZHANG Jing-fen, ZHAO De-you. Summary review of vibration-based crack diagnosis technique for engingeering structures ［J］. Journal of Vibration and Shock, 2002, 21(4):22-26．
［4］ 马宏伟,杨桂通.结构损伤探测的基本方法和研究进展［J］.力学进展,1999,29(4):513-527．
MA Hong-wei, YANG Gui-tong. Basic methods for damage detection based on structural vibration ［J］. Journal of Taiyuan University of Technology, 1999,29(4):513-527．
［5］ 刘国华,吴志根.引入信息熵理论的砼结构损伤动力识别新思路［J］.振动与冲击,2011,30(6):162-171．
LIU Guo-hua, WU Zhi-gen. New thought on dynamic identification technology for damage detection of RC structures by introducing information entropy theory ［J］. Journal of Vibration and Shock, 2011, 30(6):162-171．
［6］ PINCUS S M. Approximate Entropy as a measure of system complexity［J］.Proceedings of the National Academy of Sciences of the United States of America,1991,88(6):2297-2301.
［7］ PINCUS S M, KEEFE D L. Quantification of hormone pulsatility via an approximate entropy algorithm［J］. American Journal of Physiology, 1992, 262(5):E741-E754．
［8］ PINCUS S M. Assessing serial irregularity and its Implications for health［J］. Annals of the New York Academy of Sciences, 2001, 954(1):245-267．
［9］ CANCIO L C, BATCHINSKY A I, SALINAS J, et al. Heart-rate complexity for prediction of prehospital lifesaving interventions in trauma patients［J］. Journal of Trauma-Injury Infection & Critical Care, 2008, 65(4):813819．
［10］ OCAK H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy［J］. Expert Systems with Applications, 2009, 36(2):2027-2036．
［11］ PINCUS S M, KALMAN R E. Irregularity, volatility, risk and financial market time series［J］.Proceedings of the National Academy of Sciences,  2004, 101(38):1370913714．
［12］ PINCUS S M. Approximate entropy as an irregularity measure for financial data［J］. Econometric Reviews, 2008, 27:329-362．
［13］ YAN R Q, GAO R X.Approximate entropy as a diagnostic tool for machine health monitoring［J］.Mechanical System and Signal Processing, 2007, 21(2):824-839．
［14］ PREZ-CANALES D, LVAREZ-RAMREZ J , JUREGUI-CORREA J C, et al. Identification of dynamic instabilities in machining process using the approximate entropy method［J］. International Journal of Machine Tools & Manufacture, 2011, 51(6):556-564．
［15］ 胡红英,马孝江.局域波近似熵及其在机械故障诊断中的应用［J］.振动与冲击,2006,25(4):3840．
HU Hong-ying, MA Xiao-jiang. Application of local wave approximate entropy in mechanical fault diagnosis ［J］. Journal of Vibration and Shock, 2006, 25(4):38-40．
［16］ WANG Tian-yang, CHENG Wei-dong, LI Jian-yong, et al. Anomaly detection for equipment condition via cross-correlation approximate entropy［C］∥Management Science and Industrial Engineering(MSIE),2011 International Conference on. Harbin: IEEE, 2011: 5255．
［17］ 王济,胡晓.MATLAB在振动信号处理中的应用［M］.北京:中国水利水电出版社,2006:84．
［18］ WU Zhi-gen, LIU Guo-hua, ZHANG Zi-hua. Experimental study of structural damage identification based on modal parameters and decay ratio of acceleration signals［J］. Frontiers of Architecture and Civil Engineering in China, 2001, 5(1):112-120．

(上接第421页)［16］ KLADOS M A, PAPADELIS C L, BAMIDIS P D. REG-ICA: A new hybrid method for EOG artifact rejection［C］∥Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine.Larnaca, Cyprus： IEEE, 2009:14．
［17］ TICHAVSKY P, KOLDOVSKY Z, DOWN E, et al. Blind signal separation by combining two ICA algorithms： HOS-based EFICA and time structure-based WASOBI［C］∥Proceedings of The 2006 European Signal Processing Conference (EUSIPCO’2006). Florence, Italy: EUSIPCO, 2006．
［18］ GHANDEHARION H, ERFANIAN A. A fully automatic method for ocular artifact suppression from EEG data using wavelet transform and independent component analysis［C］∥Proceedings Of the 28th IEEE EMBS Annual International Conference. New York： IEEE, 2006: 5265-5268.
［19］ SALVARIS M, SEPULVEDA F. Wavelet and ensemble of FLDs for P300 classification［C］∥ Proceedings of the 4th international IEEE EMBS Conference on Neural Engineering. Antalya, Turkey:IEEE,2009: 339-342．
［20］ GUGER C, SCHLOGL A, NEUPER C, et al. Rapid prototyping of an EEG-Based brain-computer interface［J］. IEEE Transaction on Neural Systems and Rehabilitation Engineering, 2001, 9（1）: 49-58.
［21］ LI Da, WU Jin, ZHANG Jia-cai. An eog artifacts correction method based on subspace independent component analysis［C］∥2010 International Conference on Computational Intelligence and Security (CIS).Nanning, China: IEEE Computer Society, 2010:127-131.
［22］ 王魁, 叶闯, 沈一清. 脑电信号中眼电伪迹的自动去除［J］. 计算机工程, 2011, 37（23）: 257-260.
WANG Kui, YE Chuang, SHEN Yi-qing. Automatic removal algorithm of ocular artifact in EEG signal［J］. Computer Engineering, 2011, 37（23）: 257-260.