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A seasonality of δD of water vapor (850–500 hPa) observed from space over Jeju Island, Korea
이정훈,John Worden,고동찬,Kei Yoshimura,Jung-Eun Lee 한국지질과학협의회 2013 Geosciences Journal Vol.17 No.1
We examined the seasonal variations of isotopic composition of water vapor in the lower troposphere (850–500 hPa) to relate those of precipitation and groundwater using satellite observations from the Aura Tropospheric Emission Spectrometer (TES) over the volcanic Island of Jeju, Korea. We ran an isotope-enabled general circulation model (IsoGSM) and calculated 120-hr reverse-calculated trajectories for air parcels corresponding to the TES observations to better understand the seasonal variations of D of water vapor in the lower troposphere. D of precipitation by previous studies and the model results show winter-enriched, while summer-enriched water vapor isotope is observed by the TES observations, which may require a validation campaign using in-situ measurements or continuous monitoring of water vapor isotopes around Jeju Island.
Shahruddin Mahzan,Wieslaw J. Staszewski,Keith Worden 국제구조공학회 2010 Smart Structures and Systems, An International Jou Vol.6 No.2
Impact damage detection in composite structures has gained a considerable interest in many engineering areas. The capability to detect damage at the early stages reduces any risk of catastrophic failure. This paper compares two advanced signal processing methods for impact location in composite aircraft structures. The first method is based on a modified triangulation procedure and Genetic Algorithms whereas the second technique applies Artificial Neural Networks. A series of impacts is performed experimentally on a composite aircraft wing-box structure instrumented with low-profile, bonded piezoceramic sensors. The strain data are used for learning in the Neural Network approach. The triangulation procedure utilises the same data to establish impact velocities for various angles of strain wave propagation. The study demonstrates that both approaches are capable of good impact location estimates in this complex structure.
Mahzan, Shahruddin,Staszewski, Wieslaw J.,Worden, Keith Techno-Press 2010 Smart Structures and Systems, An International Jou Vol.6 No.2
Impact damage detection in composite structures has gained a considerable interest in many engineering areas. The capability to detect damage at the early stages reduces any risk of catastrophic failure. This paper compares two advanced signal processing methods for impact location in composite aircraft structures. The first method is based on a modified triangulation procedure and Genetic Algorithms whereas the second technique applies Artificial Neural Networks. A series of impacts is performed experimentally on a composite aircraft wing-box structure instrumented with low-profile, bonded piezoceramic sensors. The strain data are used for learning in the Neural Network approach. The triangulation procedure utilises the same data to establish impact velocities for various angles of strain wave propagation. The study demonstrates that both approaches are capable of good impact location estimates in this complex structure.
Vibration-based structural health monitoring using large sensor networks
A. Deraemaeker,A. Preumont,E. Reynders,G. De Roeck,J. Kullaa,V. Lämsä,K. Worden,G. Manson,R. Barthorpe,E. Papatheou,P. Kudela,P. Malinowski,W. Ostachowicz,T. Wandowski 국제구조공학회 2010 Smart Structures and Systems, An International Jou Vol.6 No.3
Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project Smart Sensing For Structural Health Monitoring(S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.
Zhao, Xueyan Y.,Zi-Qiang Lang,Gyuhae Park,Farrar, Charles R.,Todd, Michael D.,Zhu Mao,Worden, Keith IEEE 2015 IEEE/ASME transactions on mechatronics Vol.20 No.4
<P>In this paper, a new transmissibility analysis method is proposed for the detection and location of damage via nonlinear features in multidegree-of-freedom (MDOF) structural systems. The method is derived based on the transmissibility of nonlinear output frequency response functions (NOFRFs), a concept recently proposed to extend the traditional transmissibility concept to the nonlinear case. The implementation of the method is only based on measured system output responses and by evaluating and analyzing the transmissibility of these system responses at super-harmonics. This overcomes the problems with available techniques, which assume there is one damaged component with nonlinear features in the system and/or require loading on inspected structural systems is measurable. Both numerical simulation studies and experimental data analysis have been conducted to verify the effectiveness and demonstrate the potential practical applications of the new method.</P>
Vibration-based structural health monitoring using large sensor networks
Deraemaeker, A.,Preumont, A.,Reynders, E.,De Roeck, G.,Kullaa, J.,Lamsa, V.,Worden, K.,Manson, G.,Barthorpe, R.,Papatheou, E.,Kudela, P.,Malinowski, P.,Ostachowicz, W.,Wandowski, T. Techno-Press 2010 Smart Structures and Systems, An International Jou Vol.6 No.3
Recent advances in hardware and instrumentation technology have allowed the possibility of deploying very large sensor arrays on structures. Exploiting the huge amount of data that can result in order to perform vibration-based structural health monitoring (SHM) is not a trivial task and requires research into a number of specific problems. In terms of pressing problems of interest, this paper discusses: the design and optimisation of appropriate sensor networks, efficient data reduction techniques, efficient and automated feature extraction methods, reliable methods to deal with environmental and operational variability, efficient training of machine learning techniques and multi-scale approaches for dealing with very local damage. The paper is a result of the ESF-S3T Eurocores project "Smart Sensing For Structural Health Monitoring" (S3HM) in which a consortium of academic partners from across Europe are attempting to address issues in the design of automated vibration-based SHM systems for structures.