請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/10138
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 羅俊雄(Chin-Hsiung Loh) | |
dc.contributor.author | Yi-Cheng Liu | en |
dc.contributor.author | 劉奕成 | zh_TW |
dc.date.accessioned | 2021-05-20T21:04:47Z | - |
dc.date.available | 2011-07-18 | |
dc.date.available | 2021-05-20T21:04:47Z | - |
dc.date.copyright | 2011-07-18 | |
dc.date.issued | 2011 | |
dc.date.submitted | 2011-07-06 | |
dc.identifier.citation | [1] Abdelghani M., Basseville M. and Benveniste A. “In-operation damage monitoring and diagnostics of vibrating structures, with applications to offshore structures and rotating machinery”. Proceedings of IMAC 15, Orlando FL,USA, 1815~182,1997.
[2] Abdelghani M., Goursat M., Biolchini T., Hermans L.and Van Der Auweraer H. “Performance of output-only identification algorithms for modal analysis of aircraft structures”. Proceedings of IMAC XVII : 17th international modal analysis conference. Kissimmee FL, 8-11 February 1999. [3] Alonso, F.J., Del Castillo J.M., and Pintado P. “Application of singular spectrum analysis to the smoothing of raw kinematic signals”. Journal of Biomechanics, 2005. 38(5): p. 1085-1092. [4] Asmussen, J.C. and Brincker R. “Statistical Theory of the vector random decrement technique”. Journal of Sound and vibration (1999) 226(2), 329-344. [5] Basseville M., Benveniste A., Goursat M. “Output-Only Subspace-Based Structural Identification: From Theory to Industrial Testing Practice”. Journal of Dynamic Systems, Measurement, and Control, Vol. 123, December, 2001. [6] Bassville M., Benveniste A., Goursat M. and Mevel L. “In-flight vibration monitoring of aeronautical structures”. IEEE Control System Magazine, October 2007, 27-41. [7] Basseville M. et al. “In situ damage monitoring in vibration mechanics: diagnostics and predictive maintenance”. Mechanical systems and signal processing (1993) 7(5), 401~423. [8] Bendat J. S., Palo P. A., Coppolino R. N. “A general identification technique for nonlinear differential equations of motion”. Probabilistic Engineering Mechanics 7: 43-61, 1992. [9] Bodeux J.B. and Golinval J.C. “Modal identification and damage detection using the data-driven stochastic subspace and ARMAv methods”. Mechanical Systems and Signal Processing (2003) 17(1), 83–89 [10] Caicedo, Juan Martin; Dyke, Shirley J.; Johnson, Erik A. “Natural Excitation Technique and Eigensystem Realization Algorithm for Phase I of the IASC-ASCE Benchmark Problem: Simulated Data”. Journal of Engineering Mechanics, ASCE, pp. 49-60, January, 2004. [11] Chang, Peter C.; Alison, Flatau and Liu, S. C.. “Review Paper: Health Monitoring of Civil Infrastructure”. Structural Health Monitoring 2003 2: 257. [12] Chang, C. C., and Li, Z. “Recursive Stochastic Subspace Identification for Structural Parameter Estimation”. SPIE Proceedings Vol. 7292 729235-9. 2009. [13] De Cock K., Mercère G., De Moor B. “Recursive subspace identification for in-flight modal analysis of airplanes”. Proceedings of the International Conference on Noise and Vibration Engineering (ISMA 2006), Leuven, Belgium, Sept. 2006, pp. 1563-1577. [14] Doebling, Scott; Farrar, Charles and Prime, Michael. “A summary review of vibration-based damage identification methods”. Shock and Vibration Digest, 30(2), 91–105, 1998. [15] Ewins, D.J. “Modal Testing: Theory and Practice”. Research Studies Press Ltd., Letchworth, Hertfordshire, UK, 1984. [16] Farrar, Charles R.; Doebling, S. W. and Nix, David A. “Vibration-based structural damage identification”. Phil. Trans. R. Soc. Lond. A 2001 359, 131-149 doi: 10.1098/ rsta. 2000.0717 [17] Goethals, I.; Mevel, L.; Benveniste, A.; and De Moor, B. “Recursive output only subspace identification for in-flight flutter monitoring”. Proc. of the 22nd International Modal Analysis Conference, Dearborn, Michigan, 2004. [18] Golub, Gene H.; Van Loan, Charles F. “Matrix computations”. Johns Hopkins University Press, 1996. [19] Gustafsson, T. “Recursive system identification using instrumental variable subspace tracking”. Proc. of the 11th IFAC Symposium on System Identification, Fukuoka, Japan, 1997. [20] Gustafsson T. “Instrumental Variable Subspace Tracking Using Projection Approximation”. IEEE transactions on signal processing, Vol. 46, No. 3, March 1998. [21] Guyader A. and Mevel L., “Covariance driven subspace methods: Input/output vs. output-only”. Proc. Int. Modal Anal. Conf., Orlando, FL, Feb. 2003, Paper no. 136. [22] Hart, Gary C. and Wong, Kevin. “Structural Dynamics for Structural Engineers”. John Wiley&Sons, Inc. 2000. [23] James, G. H.; Carne, T. G.; Lauffer, J. P.; and Nord, A. R. ‘‘Modal testing using natural excitation”. Proc., 10th Int. Modal Analysis Conference, San Diego, 1992. [24] Juang, J.N. “Applied system identification”. Englewood Cliffs, NJ, USA: Prentice Hall, 1994. [25] Juang, J.N. and Pappa R.S. “Eigensystem realization algorithm for modal parameter identification and model reduction”. Journal of Guidance, Control, and Dynamics. Vol. 8, no. 5, pp. 620-627. 1985 [26] Juang J.N., Cooper J.E., Wright J.R. “An eigensystem realization algorithm using data correlations (ERA/DC) for modal parameter identification”. Journal of Guidance Control and Dynamics (1987) Volume: 3, Pages: 620-627. [27] Larimore, W. E. “The Optimality of Canonical Variate Identification by Example”. Proc. of SYSID, 4-6 July, Copenhagen, Denmark, 2, 151-156, 1994. [28] Lin, Ping; Zhang, Nanxiong; and Ni, Bin. “On-line Modal Parameter Monitoring of Bridges Exploiting Multi-core Capacity by Recursive Stochastic Subspace Identification Method”. 2008 American Control Conference, Seattle, Washington, USA, June 11-13, 2008. Page 632 – 637. [29] Ljung, Lennart. “System Identification: Theory for the User”. Second edition, Prentice Hall, Upper Saddle River, NJ, USA, 1999. [30] Loh, C.H. and Weng, J.H. “Damage Detection Using Stochastic Subspace Identification with Partial Measurements”. Proc. of the 7th international workshop on structural health monitoring, Stanford, California, 2009. [31] Loh, C.H.; Chao, S.H.; Weng, J.H.; and Liao, W.I. “Identification of Damage in Reinforced Concrete Structures from Different Levels of Earthquake Excitations”. Proc. of 12th International Conference on Fracture, Ottawa, Canada, 2009. [32] Loh; C.H.; Mao, C.H.; Chao, S.H.; Weng, J.H. “System identification and damage evaluation of degrading hysteresis of reinforced concrete frames”. Earthquake Engng Struct. Dyn. 2011; 40:623–640 Published online 25 August 2010 in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/eqe.1051 [33] Ku, Chiu Jen; Cermak, Jack Edward; Chou, Li-Shu. “Random decrement based method for modal parameter identification of a dynamic system using acceleration responses”. Journal of Wind Engineering and Industrial Aerodynamics 95 (2007) 389–410. [34] Mercère, G., Lecoeuche, S., and Lovera, M. “Recursive subspace identification based on instrumental variable unconstrained quadratic optimization”. International J. of Adaptive Control and Signal Processing, 18, 771-797, 2004. [35] Ni, Y. Q.; Xia, Y.; Liao, W. Y.; Ko, J. M.. “Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower”. Journal of Structural Control and Health Monitoring, 2009; 16: 73-98. [36] Peeters, Bart and De Roeck, Guido, “Reference-based stochastic subspace identification for output-only modal analysis”. Mechanical Systems and Signal Processing 13(6) 855-878, 1999. [37] Peeters, Bart. “System Identification and Damage Detection in Civil Engineering”. Ph.D. Dissertation, Katholieke Universiteit, Leuven, December 2000. [38] Peeters, Bart and De Roeck, Guido, “Stochastic system identification for operational modal analysis: a review”. J. Dyn. Syst. Meas. Control 123, 2001, 659-666. [39] Pridham, Brad A. and Wilson, John C. “Identification of base-excited structures using output-only parameter estimation”. Earthquake Engng. Struct. Dyn. 33: 133-155, 2004. [40] Qin, Q.; Li, H.B. and Qian, L.Z. “Modal identification of Tsing Ma Bridge by using improved eigensystem realization algorithm”. Journal of Sound and vibration (2001) 247(2), 325-341. [41] Reynders, Edwin et al. “Uncertainty bounds on modal parameters obtained from stochastic subspace identification”. Mechanical Systems and Signal Processing 22, 948-969, 2008. [42] Smith, GA; Robinson, AJ. “A comparison between the EM and subspace identification algorithms for time-invariant linear dynamical systems”. November7, 2000. Cambridge University. [43] Söderström, Torsten and Stoica, Petre. “System identification”. Prentice-Hall International, 1989. [44] Sohn H., Farrar, C.R., Hemez F.M., Shunk D.D., Stinemates D.W., Nadler B.R. “A review of structural health monitoring literature: 1996–2001”. Los Alamos National Laboratory report No. LA-13976-MS. Los Alamos, New Mexico; 2004. [45] Trefethen, Lloyd N. and Bau, David. “Numerical linear algebra”. Society for Industrial and Applied Mathematics, 1997. [46] Van Overschee, P. and De Moor, B. “N4SID: Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems”. Automatica, 30(1), 75-93, 1994. [47] Van Overschee, P. and De Moor, B. “Subspace Identification for Linear Systems: Theory - Implementation - Applications”. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1996. [48] Verhaegen, M. and Dewilde, P. “Subspace model identification, Part Ⅰ: The output-error state space model identification class of algorithms”. International J. of Control, 56, 1187-1210, 1992. [49] Weng, J.H.; Loh, C.H.; Lynch, J.P.; Lu, K.C.; Linn, P.Y.; Wang, Y. “Output-Only Modal Identification of a Cable-Stayed Bridge Using Wireless Monitoring Systems”. J. of Engineering Structure, 30 (2), 2008, 1802-1830. [50] Weng, J.H. “Application of Subspace Identification in System Identification and Structural Damage Detection”. Ph.D. Dissertation, National Taiwan University, Taiwan, June 2010. [51] Wu, Ai-Lun; Yang, J.N and Loh, C.H. “Detection of damages in nonlinear reinforced concrete frames”. Proc. SPIE 7981, 79812O (2011); doi:10.1117/12.877203. San Diego, California. [52] Yu, Dan-Jiang and Ren, Wei-Xin. “EMD-based stochastic subspace identification of structures from operational vibration measurements”. Engineering Structures Volume 27, Issue 12, October 2005, Pages 1741-1751. [53] Zhang, Y.; Zhang, Z.; Xu, X.; Hua, H. “Modal parameter identification using response data only”. Journal of Sound and Vibration 282: 367-380, 2005. [54] Welch, Peter D. “The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms”. IEEE Transactions on Audio Electroacoustics, Volume AU-15 (June 1967), pp. 70–73. [55] Ren, Wei-Xin and Zong, Zhou-Hong. “Output-only modal parameter identification of civil engineering structures”. Structural Engineering and Mechanics, Vol. 17, No. 3-4 (2004) [56] Yang, Bin. “Projection Approximation Subspace Tracking”. IEEE transactions on signal processing, Vol. 43, No. I, January 1995. [57] Chung, L.L. “Course note on structural control (II)”. National Taiwan University, Civil Engineering department. Structure Division. 2010. [58] Loh, C.H.; Weng, J.H.; Liu, Y.C.; Lin, P.Y. and Huang, S.K. “Structural damage diagnosis based on on-line recursive stochastic subspace identification”. Smart Mater. Struct. 20 (2011) 055004 (10pp). [59] Giraldo, Diego F.; Song, Wei; Dyke, Shirley J.; and Caicedo, Juan M.. “Modal Identification through Ambient Vibration: Comparative Study”. J. Engrg. Mech. 135, 759 (2009). [60] Jazwinski, A.H. “Stochastic processes and filtering theory”. Academic Press, Inc, New York, 1970. [61] Andersen, P. “Identification of civil engineering structures using ARMA models.” Ph.D. dissertation, Univ. of Aalborg, Aalborg, Denmark, 1997. [62] Yan, Ai-Min; De Boe, Pascal and Golinval, Jean-Claude. “Structural Damage Diagnosis by Kalman Model Based on Stochastic Subspace Identification”. Structural Health Monitoring, June 2004, vol. 3 no. 2 103-119. [63] A Benchmark Problem on Structural Health Monitoring of High-Rise Slender Structures. Phase I: Field vibration measurement and model updating. “Description of the measurement”. http://www.cse.polyu.edu.hk/benchmark/index.htm [64] A Benchmark Problem on Structural Health Monitoring of High-Rise Slender Structures. Phase I: Field vibration measurement and model updating. “Description of the FE model and model reduction”. http://www.cse.polyu.edu.hk/benchmark/index.htm [65] Golyandina, Nina; Nekrutkin, Vladimir; Zhigljavsky, Anatoly. “Analysis of time series structure: SSA and related techniques”. Boca Raton, Fla. : Chapman & Hall/CRC, c2001 [66] Xin J., Tsuji H., Sano A. “Optimal sampling interval for system identification based on decimation and interpolation”. IEE Proc.-Control Theory Appl., Vol. 142, No. I , January I995. [67] Sinha N.K., Puthenpura S. “Choice of the sampling interval for the identification of continuous-time systems from samples of input/output data”. IEE Proc. Vol. 132, Pt. D, No. 6, November 1985. [68] Middleton, R.H., and Goodwin, G.C. “Digital estimation and control”. Prentice-Hall, 1990. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/10138 | - |
dc.description.abstract | 本研究目的是探討隨機子空間識別法(Stochastic Subspace Identification, SSI)在只有結構微震反應的量測下,於土木結構系統識別及損壞診斷上的應用範疇。在離線分析的應用上,可將於不同矩陣維度識別出之系統極點(system poles)繪製成穩定圖,以達正確識別結構震態的目的。在此研究的前半段,首先將對隨機子空間識別法搭配穩定圖的識別效果做研究,在不同情況諸如:訊號之雜訊、非線性、時變性與間隔緊密頻率等因素之甘擾下,比較各種隨機子空間識別法對此等甘擾因素之敏感度。接下來,協方差型隨機子空間識別法(Covariance driven Stochastic Subspace Identification, SSI-COV)將應用在廣州電視塔(Canton Tower)的系統識別工作,其為一座大型挑高細長結構,並為結構健康監測之標杆問題。除此之外,奇異譜分析法(Singular Spectrum Analysis, SSA)將以「前置子空間濾波器」的概念與協方差型隨機子空間識別法結合,名為「SSA-SSI-COV」識別法,除了能有效提昇資料解析能力,更提供一個能決定系統識別之最佳系統維度的做法。
研究的第二部份是針對系統震態參數之線上識別與損壞診斷技巧的開發,以遞迴式協方差型隨機子空間識別法(Recursive Covariance-driven Stochastic Subspace identification, RSSI-COV)為主體,並搭配延伸工具變項─投影近似子空間追蹤演算法(Extended Instrumental Variable – Projection Approximation Subspace Tracking algorithm, EIV-PAST)達成線上更新子空間的目地。另外,一個可供線上作業之子空間前置濾波器─「遞迴式奇異譜分析法(recursive Singular Spectrum, rSSA)」的開發與搭配,可有效減低雜訊對實地結構識別品質之影響,提昇線上分析的穩定性。此兩種子空間技術將透過時變性系統之數值模擬與實地試驗數據得到驗証,並從中取得可靠的識別模型控制參數。最後,它們將被應用在三個結構震態追縱的實驗上:(1)三層樓鋼構試體瞬時勁度折減之震動台實驗,(2)單層雙跨鋼筋混凝土結構之震動台試驗,此兩者皆以結構受到地震作用下之輸出反應做線上震態識別。最後,(3)橋樑沖刷實驗之損壞診斷與預警之應用。 | zh_TW |
dc.description.abstract | In this research the application of output-only system identification technique known as Stochastic Subspace Identification (SSI) algorithms in civil structures is carried out. With the aim of finding accurate modal parameters of the structure in off-line analysis, a stabilization diagram is constructed by plotting the identified poles of the system with increasing the size of data matrix. A sensitivity study of the implementation of SSI through stabilization diagram is firstly carried out, different scenarios such as noise effect, nonlinearity, time-varying systems and closely-spaced frequencies are considered. Comparison between different SSI approaches was also discussed. In the following, the identification task of a real large scale structure: Canton Tower, a benchmark problem for structural health monitoring of high-rise slender structures is carried out, for which the capacity of Covariance-driven SSI algorithm (SSI-COV) will be demonstrated. The introduction of a subspace preprocessing algorithm known as Singular Spectrum Analysis (SSA) can greatly enhance the identification capacity, which in conjunction with SSI-COV is called the SSA-SSI-COV method, it also allows the determination of the best system order.
The objective of the second part of this research is to develop on-line system parameter estimation and damage detection technique through Recursive Covariance-driven Stochastic Subspace identification (RSSI-COV) approach. The Extended Instrumental Variable version of Projection Approximation Subspace Tracking algorithm (EIV-PAST) is taking charge of the system-related subspace updating task. To further reduce the noise corruption in field experiments, the data pre-processing technique called recursive Singular Spectrum Analysis technique (rSSA) is developed to remove the noise contaminant measurements, so as to enhance the stability of data analysis. Through simulation study as well as the experimental research, both RSSI-COV and rSSA-SSI-COV method are applied to identify the dynamic behavior of systems with time-varying characteristics, the reliable control parameters for the model are examined. Finally, these algorithms are applied to track the evolution of modal parameters for: (1) shaking table test of a 3-story steel frame with instantaneous stiffness reduction. (2) Shaking table test of a 1-story 2-bay reinforced concrete frame, both under earthquake excitation, and at last, (3) damage detection and early warning of an experimental steel bridge under continuous scour. | en |
dc.description.provenance | Made available in DSpace on 2021-05-20T21:04:47Z (GMT). No. of bitstreams: 1 ntu-100-R98521256-1.pdf: 6397982 bytes, checksum: 5f1a1b47189384e83647328f507792ea (MD5) Previous issue date: 2011 | en |
dc.description.tableofcontents | 口試委員審定書………………………………………………………i
Acknowledgement ……………………………………………………ii Abstract (in Chinese) ……………………………………………iii Abstract (in English) ……………………………………………iv Contents ………………………………………………………………vi Table List ……………………………………………………………x Figure List …………………………………………………………xi Chapter 1 Introduction …1 1.1 Background …1 1.2 Research Objectives …6 Chapter 2 Stochastic Subspace Identification (SSI) Methods …9 2.1 Introduction …9 2.2 Models of vibrating structures …9 2.2.1 Continuous-time state-space model …9 2.2.2 Discrete-time state-space model …14 2.2.3 Stochastic state-space model …15 2.3 Covariance-driven Stochastic Subspace Identification (SSI-COV) …17 2.4 Data-driven Stochastic Subspace Identification (SSI-DATA) …19 2.5 Singular Spectrum Analysis (SSA) …23 2.6 Pole discrimination: the stabilization diagram …24 2.6.1 Alternatives to build the stabilization diagram …24 2.6.2 Comparison of stabilization diagram alternatives and influence of the model order determination …29 2.6.2.1 Simulation example: 6-DOF simulation study …29 2.6.2.2 Experimental example: identification of a 6-story steel frame from shaking table test …32 Chapter 3 Simulation study of SSI-based algorithms …34 3.1 Noise effect in the identification of modal parameters …34 3.1.1 Addition of a spatially white noise (from 50% to 200%) …34 3.1.2 Addition of a white noise correlated with output (violation to SSI assumption) …35 3.2 Nonlinearity in the signal …36 3.3 Closely-spaced frequencies blended with signals of a time-varying system …38 3.4 Preprocessing with SSA and noise effect in closely-spaced frequencies …41 3.4.1 Sinusoidal waves …41 3.4.2 Response of a 2-DOF system subjected to white noise excitation …42 Chapter 4 Application of SSI to the identification of Canton Tower …46 4.1 Frequency Domain Decomposition (FDD) …47 4.2 SSI-COV and SSI-DATA …47 4.3 SSA-SSI-COV …49 4.3.1 Implementation …49 4.3.2 Canto Tower identification through SSA-SSI-COV …51 4.3.3 Canto Tower identification through SSA-SSI-DATA …53 4.4 Low pass filter with SSI-COV …54 4.5 Improve the identification convergence speed with decimation …55 Chapter 5 Recursive Stochastic Subspace Identification algorithms …58 5.1 Recursive Covariance-driven Stochastic Subspace Identification algorithm (RSSI-COV) …58 5.1.1 Projection Approximation Subspace Tracking (PAST) …61 5.1.2 Instrumental Variable Projection Approximation Subspace Tracking (IV-PAST) …64 5.1.3 Extended Instrumental Variable Projection Approximation Subspace Tracking (EIV-PAST) …65 5.1.4 Adaptation of EIV-PAST to RSSI-COV …66 5.2 Recursive Singular Spectrum Analysis (RSSA) …71 Chapter 6 Simulation study of RSSI-COV and rSSA-SSI-COV…77 6.1 Implementation of the RSSI-COV and rSSA-SSI-COV algorithm …77 6.2 Simulation study 1: time invariant 6-DOF system …79 6.3 Simulation study 2: time varying 6-DOF system with sudden stiffness reduction …80 6.4 Simulation study 3: time-varying 6-DOF system with gradual stiffness reduction …82 Chapter 7 Application of recursive SSI algorithms in damage detection and early warning …87 7.1 Application 1: shaking table test of a 3-story steel structure with instantaneous stiffness reduction…87 7.1.1 White noise base excitation …88 7.1.2 El Centro 100 gal …89 7.1.3 TCU082 100 gal …93 7.2 Application 2: shaking table test of a 1-story 2-bay RC frame …95 7.3 Application 3: bridge pier scouring experiment…99 7.3.1 Bridge pier imminent settlement indicator: modal frequency drop …100 7.3.1.1 Test conducted in 2011/01/19 with full measurements …100 7.3.1.2 Test conducted in 2011/01/24 with full measurements …104 7.3.1.3 Test conducted in 2011/01/26 with full measurements …105 7.3.1.4 Test conducted in 2011/03/29 with full measurements …106 7.3.2 Damage location indicator: mode shape slope ratio …108 7.3.2.1 Mode shape slope ratio for test conducted in 2011/01/19 …109 7.3.2.2 Mode shape slope ratio for test conducted in 2011/01/26 …111 7.3.2.3 Mode shape slope ratio for test conducted in 2011/03/29 …112 7.3.3 Novelty Index …112 Chapter 8 Conclusions …115 8.1 Research conclusions …115 8.2 Recommendations for future work …116 References …121 Appendix A: Frequency Domain Decomposition (FDD) …128 Appendix B: Prediction Error Method through Stochastic Subspace Identification (PEM/SSI) …130 Appendix C: Novelty index through Kalman-Filter-based prediction error …135 | |
dc.language.iso | en | |
dc.title | 協方差型隨機子空間識別法之應用 | zh_TW |
dc.title | Application of Covariance Driven Stochastic Subspace Identification Method | en |
dc.type | Thesis | |
dc.date.schoolyear | 99-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 莊哲男(Jer-Nan Juang),田堯彰(R. Y. Tan),鍾立來(Lap-Loi Chung) | |
dc.subject.keyword | 隨機子空間識別,協方差型,系統識別,結構健康監測,遞歸式隨機子空間識別,遞歸式奇異譜分析,廣州電視塔, | zh_TW |
dc.subject.keyword | Stochastic Subspace Identification,Covariance Driven,System Identification,Structural Health Monitoring,Recursive Stochastic Subspace Identification,Recursive Singular Spectrum Analysis,Canton Tower, | en |
dc.relation.page | 221 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2011-07-07 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 土木工程學研究所 | zh_TW |
顯示於系所單位: | 土木工程學系 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-100-1.pdf | 6.25 MB | Adobe PDF | 檢視/開啟 |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。