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土木工程学院:Power spectrum estimation of stochastic processes from bounded and gappy sensor data

发布日期: 2018年11月27日 20:31 来源:

报告承办单位:土木工程学院

报告内容:Power spectrum estimation of stochastic processes from bounded and gappy sensor data (基于有限与缺失传感器数据的随机过程功率谱估计)

报告人姓名:Dr. Liam Comerford

报告人所在单位:Leibniz University Hannover(莱布尼茨汉诺威大学,德国)

报告人职称/职务及学术头衔:PhD, Lecturer (博士、讲师)

报告时间:2018112810:00(周三上午)

报告地点:工科二号楼A502学术活动中心

报告人简介:

       Sensors used to capture time-history data will never provide perfect digital reconstructions of the processes they originally recorded. At best, a sensor will have an ideal working tolerance and defined accuracy bounds, and at worst will fail, leaving gaps in the data. When estimating power spectra from these data, it is important to consider the effect that such uncertainties could have on the output model. In this talk, some common missing data reconstruction techniques and their shortfalls will be presented in the context of power spectrum estimation, as well as methods to quantify power spectrum uncertainties under incomplete data.

Bio:

      Liam Comerford graduated with a Bachelor in Aerospace Engineering from the University of Liverpool in 2009. He received his PhD in 2015 from the Institute for Risk and Uncertainty at the University of Liverpool. He then began his academic career as a Research Associate in Leibniz University Hannover, Germany, within the Institute Risk and Reliability. He currently maintains academic links through two European funded research projects in the areas of Stochastic Process Simulation and Compressive Sensing.

Publications:

[1]   Comerford L, Kougioumtzoglou I A, Beer M. Compressive sensing based stochastic process power spectrum estimation subject to missing data[J]. Probabilistic Engineering Mechanics, 2016, 44: 66-76.

[2]   Comerford L, Kougioumtzoglou I A, Beer M. An artificial neural network approach for stochastic process power spectrum estimation subject to missing data[J]. Structural Safety, 2015, 52: 150-160.

[3]   Comerford L, Jensen H A, Mayorga F, et al. Compressive sensing with an adaptive wavelet basis for structural system response and reliability analysis under missing data[J]. Computers & Structures, 2017, 182: 26-40.

[4]   Comerford L, Mannis A, DeAngelis M, et al. Utilising database-driven interactive software to enhance independent home-study in a flipped classroom setting: going beyond visualising engineering concepts to ensuring formative assessment[J]. European Journal of Engineering Education, 2018, 43(4): 522-537.

[5]   Comerford L, Kougioumtzoglou I A, Beer M. On quantifying the uncertainty of stochastic process power spectrum estimates subject to missing data[J]. International Journal of Sustainable Materials and Structural Systems, 2015, 2(1-2): 185-206.

[6]   Zhang Y, Comerford L, Kougioumtzoglou I A, et al. Lp-norm minimization for stochastic process power spectrum estimation subject to incomplete data[J]. Mechanical Systems and Signal Processing, 2018, 101: 361-376.


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