Modern civil structures as well as loads on them are still too complex to be accurately modeled or simulated. Therefore, structural failures and structural defects are NOT uncommon! More and more full-scale structural monitoring systems have been deployed in order to monitor how structures behave under various loading conditions. This research focuses on how to maximise benefits from such full-scale measurements by employing advanced digital signal processing techniques. This study is based on accelerometer and GPS data collected on three very different structures, namely, the steel tower in Tokyo, the long and slender suspension bridge in Hong Kong, and the tall office tower in Sydney, under a range of loading conditions, i.e., typhoon, earthquake, heavy traffic, and small scale wind. Systematic analysis of accelerometer and GPS data has demonstrated that the two sensors complement each other in monitoring the static, quasi-static and dynamic movements of the structures. It has also been confirmed that the Finite Element Model could under-estimate the natural frequencies of structures by more than 40% in some case. The effectiveness of using wavelet to de-noise GPS measurement has been demonstrated. The weakness and strengths of accelerometer and GPS have been identified and framework has been developed on how to integrate the two as well as how to optimize the integration. The three-dimensional spectral analysis framework has been developed which can track the temporal evolution of all the frequency components and effectively represents the result in the 3D spectrogram of frequency, time and magnitude. The dominant frequency can also be tracked on the 3D mesh to vividly illustrate the damping signature of the structure. The frequency domain coherent analysis based on this 3D analysis framework can further enhance the detection of common signals between sensors. The developed framework can significantly improve the visualized performance of the integrated system without increasing hardware costs. Indoor experiments have shown the excellent characteristics of the optical fibre Bragg gratings (FBGs) for deformation monitoring. Innovative and low-cost approach has been developed to measure the shift of FBG???s central wavelength. Furthermore, a schematic design has been completed to multiplex FBGs in order to enable distributed monitoring. In collaboration with the University of Sydney, the first Australian full-scale structural monitoring system of GPS and accelerometer has been deployed on the Latitude Tower in Sydney to support current and future research.

Thesis (M.Eng.)--Memorial University of Newfoundland, 2005. / Bibliography: leaves 110-113.

With the recent development of smart-well technology, the reservoir community now faces the challenge of developing robust and efficient techniques for reservoir characterization by means of data inversion. Unfortunately, classical history-matching methodologies do not possess computational efficiency and robustness needed to assimilate data measured almost in real time. Therefore, the reservoir community has started to explore techniques previously applied in other disciplines. Such is the case of the representer method, a variational data assimilation technique that was first applied in physical oceanography. The representer method is an efficient technique for solving linear inverse problems when a finite number of measurements are available. To the best of our knowledge, a general representer-based methodology for nonlinear inverse problems has not been fully developed. We fill this gap by presenting a novel implementation of the representer method applied to the nonlinear inverse problem of identifying petrophysical properties in reservoir models. Given production data from wells and prior knowledge of the petrophysical properties, the goal of our formulation is to find improved parameters so that the reservoir model prediction fits the data within some error given a priori. We first define an abstract framework for parameter identification in nonlinear reservoir models. Then, we propose an iterative representer-based scheme (IRBS) to find a solution of the inverse problem. Sufficient conditions for convergence of the proposed algorithm are established. We apply the IRBS to the estimation of absolute permeability in single-phase Darcy flow through porous media. Additionally, we study an extension of the IRBS with Karhunen-Loeve (IRBS-KL) expansions to address the identification of petrophysical properties subject to linear geological constraints. The IRBS-KL approach is compared with a standard variational technique for history matching. Furthermore, we apply the IRBS-KL to the identification of porosity, absolute and relative permeabilities given production data from an oil-water reservoir. The general derivation of the IRBS-KL is provided for a reservoir whose dynamics are modeled by slightly compressible immiscible displacement of two-phase flow through porous media. Finally, we present an ad-hoc sequential implementation of the IRBS-KL and compare its performance with the ensemble Kalman filter. / text

No description available.

Behavioral research involves the study of the behaviors of one or more agents (often animals) in order to better understand the agents' thoughts and actions. Identifying subject movements and behaviors based upon those movements is a critical, time-consuming step in behavioral research. To successfully perform behavior analysis, three goals must be met. First, the agents of interest are observed, and their movements recorded. Second, each individual must be uniquely identified. Finally, behaviors must be identified and recognized. I explore a system that can uniquely identify and track agents, then use these tracks to automatically build behavioral models and recognize similar behaviors in the future. I address the tracking and identification problems using a combination of laser range finders, active RFID sensors, and probabilistic models for real-time tracking. The laser range component adds environmental flexibility over vision based systems, while the RFID tags help disambiguate individual agents. The probabilistic models are important to target identification during the complex interactions with other agents of similar appearance. In addition to tracking, I present work on automatic methods for generating behavioral models based on supervised learning techniques using the agents' tracked data. These models can be used to classify new tracked data and identify the behavior exhibited by the agent, which can then be used to help automate behavior analysis.

Modern civil structures as well as loads on them are still too complex to be accurately modeled or simulated. Therefore, structural failures and structural defects are NOT uncommon! More and more full-scale structural monitoring systems have been deployed in order to monitor how structures behave under various loading conditions. This research focuses on how to maximise benefits from such full-scale measurements by employing advanced digital signal processing techniques. This study is based on accelerometer and GPS data collected on three very different structures, namely, the steel tower in Tokyo, the long and slender suspension bridge in Hong Kong, and the tall office tower in Sydney, under a range of loading conditions, i.e., typhoon, earthquake, heavy traffic, and small scale wind. Systematic analysis of accelerometer and GPS data has demonstrated that the two sensors complement each other in monitoring the static, quasi-static and dynamic movements of the structures. It has also been confirmed that the Finite Element Model could under-estimate the natural frequencies of structures by more than 40% in some case. The effectiveness of using wavelet to de-noise GPS measurement has been demonstrated. The weakness and strengths of accelerometer and GPS have been identified and framework has been developed on how to integrate the two as well as how to optimize the integration. The three-dimensional spectral analysis framework has been developed which can track the temporal evolution of all the frequency components and effectively represents the result in the 3D spectrogram of frequency, time and magnitude. The dominant frequency can also be tracked on the 3D mesh to vividly illustrate the damping signature of the structure. The frequency domain coherent analysis based on this 3D analysis framework can further enhance the detection of common signals between sensors. The developed framework can significantly improve the visualized performance of the integrated system without increasing hardware costs. Indoor experiments have shown the excellent characteristics of the optical fibre Bragg gratings (FBGs) for deformation monitoring. Innovative and low-cost approach has been developed to measure the shift of FBG???s central wavelength. Furthermore, a schematic design has been completed to multiplex FBGs in order to enable distributed monitoring. In collaboration with the University of Sydney, the first Australian full-scale structural monitoring system of GPS and accelerometer has been deployed on the Latitude Tower in Sydney to support current and future research.

Thesis (Ph. D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008. / Committee Chair: Michaels, Jennifer; Committee Member: Habetler, Thomas; Committee Member: Jacobs, Laurence; Committee Member: Michaels, Thomas; Committee Member: Vachtsevanos, George.

Thesis (M.S.)--University of North Texas, Dec., 2009. / Title from title page display. Includes bibliographical references.

Thesis (M.S.)--Ohio University, August, 2004. / Includes bibliographical references (p. 110-111).

Thesis (M.S.)--Ohio University, August, 2004. / Title from PDF t.p. Includes bibliographical references (p. 110-111)