報(bào)告承辦單位：土木工程學(xué)院

報(bào)告內(nèi)容： Power spectrum estimation of stochastic processes from bounded and gappy sensor data (基于有限與缺失傳感器數(shù)據(jù)的隨機(jī)過(guò)程功率譜估計(jì))

報(bào)告人姓名：Dr. Liam Comerford

報(bào)告人所在單位：Leibniz University Hannover（萊布尼茨漢諾威大學(xué)，德國(guó)）

報(bào)告人職稱/職務(wù)及學(xué)術(shù)頭銜：PhD, Lecturer (博士、講師)

報(bào)告時(shí)間：2018年11月28號(hào)10:00（周三上午）

報(bào)告地點(diǎn)：工科二號(hào)樓A502學(xué)術(shù)活動(dòng)中心

報(bào)告人簡(jiǎn)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.