Denoising and Trend Terms Elimination Algorithm of Accelerometer Signals

Acceleration-based displacement measurement approach is often used to measure the polish rod displacement in the oilfield pumping well. Random noises and trend terms of the accelerometer signals are the main factors that affect the measuring accuracy. In this paper, an efficient online learning algorithm is proposed to improve the measurement precision of polish rod displacement in the oilfield pumping well. To remove the random noises and eliminate the trend term of accelerometer signals, the ARIMA model and its parameters are firstly derived by using the obtained data of time series of acceleration sensor signals. Secondly, the period of the accelerometer signals is estimated through the Rife-Jane frequency estimation approach based on Fast Fourier Transform. With the obtained model and parameters, the random noises are removed by employing the Kalman filtering algorithm. The quadratic integration of the period is calculated to obtain the polish rod displacement. Moreover, the windowed recursive least squares algorithm is implemented to eliminate the trend terms. The simulation results demonstrate that the proposed online learning algorithm is able to remove the random noises and trend terms effectively and greatly improves the measurement accuracy of the displacement

Physics-Based Modeling of Nonrigid Objects for Vision and Graphics (Dissertation)

This thesis develops a physics-based framework for 3D shape and nonrigid motion modeling for computer vision and computer graphics. In computer vision it addresses the problems of complex 3D shape representation, shape reconstruction, quantitative model extraction from biomedical data for analysis and visualization, shape estimation, and motion tracking. In computer graphics it demonstrates the generative power of our framework to synthesize constrained shapes, nonrigid object motions and object interactions for the purposes of computer animation. Our framework is based on the use of a new class of dynamically deformable primitives which allow the combination of global and local deformations. It incorporates physical constraints to compose articulated models from deformable primitives and provides force-based techniques for fitting such models to sparse, noise-corrupted 2D and 3D visual data. The framework leads to shape and nonrigid motion estimators that exploit dynamically deformable models to track moving 3D objects from time-varying observations. We develop models with global deformation parameters which represent the salient shape features of natural parts, and local deformation parameters which capture shape details. In the context of computer graphics, these models represent the physics-based marriage of the parameterized and free-form modeling paradigms. An important benefit of their global/local descriptive power in the context of computer vision is that it can potentially satisfy the often conflicting requirements of shape reconstruction and shape recognition. The Lagrange equations of motion that govern our models, augmented by constraints, make them responsive to externally applied forces derived from input data or applied by the user. This system of differential equations is discretized using finite element methods and simulated through time using standard numerical techniques. We employ these equations to formulate a shape and nonrigid motion estimator. The estimator is a continuous extended Kalman filter that recursively transforms the discrepancy between the sensory data and the estimated model state into generalized forces. These adjust the translational, rotational, and deformational degrees of freedom such that the model evolves in a consistent fashion with the noisy data. We demonstrate the interactive time performance of our techniques in a series of experiments in computer vision, graphics, and visualization

ADVANCED REFLECTION SEISMIC STUDIES OF PHASE I WEYBURN CO2 SEQUESTRATION MONITORING

Three-dimensional, time-lapse (TL) reflection seismic datasets and well logs collected for Phase I CO2 sequestration project in Weyburn oilfield (southern Saskatchewan, Canada) are utilized for developing new approaches in three research areas: 1) estimation of seismic source waveforms, 2) evaluation of TL acoustic impedance (AI) variations for monitoring CO2 propagation, and 3) rigorous modeling of seismic waves propagating through finely layered rock. All three study areas are interconnected and important for accurate analysis of seismic data and TL monitoring of this and other oil reservoirs undergoing fluid injection. The first approach focuses on estimating the source waveforms from reflection seismic data, which is critical for evaluating accurate well-to-seismic ties as well as in other applications. A simple and effective method is proposed, based on iterative identification of the strongest and sparse reflections in seismic records, which allows estimation of source waveforms through an optimization approach, without well-log control and statistical hypotheses. The method allows correcting for coherent noise which seems to occur in stacked Weyburn data, consisting in (de)amplification and time shifts of the low-frequency components of the records. The method is tested on real and self-similar synthetic well-log models and applied to the Weyburn seismic data. For the second topic, a post-stack waveform-calibration processing procedure is developed in order to achieve accurate consistency of TL datasets. Time shifts between the monitor and baseline records are also measured during this procedure, and an improved method for calculating the TL reflectivity differences is proposed. Further, instead of subtraction of the baseline and monitor AIs, TL AI variations are evaluated directly from the reflectivity differences and baseline AI. AI inversion is performed by an accurate and stable method using the stacked reflection and well-log data, and also seismic velocities measured during data processing. The inverted time shifts and TL AI variations correlate with CO2 distributions within the reservoir and allow estimating parameters of the reservoir. In the third research area, a completely new approach to seismic wave modeling is proposed. Rigorous first-principle continuum mechanics is used instead of the conventional viscoelastic approximation. This modeling considers the existence of internal variables, body-force internal friction, and boundary conditions for internal variables. These factors are disregarded in the viscoelastic model, but they should cause dominant effects on seismic-wave attenuation and velocity dispersion in layered media. Numerical modeling of seismic wave propagation is performed in a model of the Weyburn Field. The resulting wavefield and seismic attenuation parameters are found to strongly depend on the internal boundary conditions between layers. Several types of quality (Q) factors are measured in the modeled synthetic waveforms

Crosswind stability of vehicles under nonstationary wind excitation

This work has studied the crosswind stability of vehicles under nonstationary wind excitation in various scenarios. Railway vehicles running on curved and straight track with varying vehicle speed are studied. Road vehicles are classified into different categories. For each vehicle class, a corresponding worst-case vehicle model has been built. As the wind excitation on the vehicle is a stochastic process, a risk analysis has to be carried out and failure probabilities are computed and analyzed

Model based estimation of image depth and displacement

Passive depth and displacement map determinations have become an important part of computer vision processing. Applications that make use of this type of information include autonomous navigation, robotic assembly, image sequence compression, structure identification, and 3-D motion estimation. With the reliance of such systems on visual image characteristics, a need to overcome image degradations, such as random image-capture noise, motion, and quantization effects, is clearly necessary. Many depth and displacement estimation algorithms also introduce additional distortions due to the gradient operations performed on the noisy intensity images. These degradations can limit the accuracy and reliability of the displacement or depth information extracted from such sequences. Recognizing the previously stated conditions, a new method to model and estimate a restored depth or displacement field is presented. Once a model has been established, the field can be filtered using currently established multidimensional algorithms. In particular, the reduced order model Kalman filter (ROMKF), which has been shown to be an effective tool in the reduction of image intensity distortions, was applied to the computed displacement fields. Results of the application of this model show significant improvements on the restored field. Previous attempts at restoring the depth or displacement fields assumed homogeneous characteristics which resulted in the smoothing of discontinuities. In these situations, edges were lost. An adaptive model parameter selection method is provided that maintains sharp edge boundaries in the restored field. This has been successfully applied to images representative of robotic scenarios. In order to accommodate image sequences, the standard 2-D ROMKF model is extended into 3-D by the incorporation of a deterministic component based on previously restored fields. The inclusion of past depth and displacement fields allows a means of incorporating the temporal information into the restoration process. A summary on the conditions that indicate which type of filtering should be applied to a field is provided

Crosswind stability of vehicles under nonstationary wind excitation

This work has studied the crosswind stability of vehicles under nonstationary wind excitation in various scenarios. Railway vehicles running on curved and straight track with varying vehicle speed are studied. Road vehicles are classified into different categories. For each vehicle class, a corresponding worst-case vehicle model has been built. As the wind excitation on the vehicle is a stochastic process, a risk analysis has to be carried out and failure probabilities are computed and analyzed

Nonlinear Time-Frequency Control Theory with Applications

Nonlinear control is an important subject drawing much attention. When a nonlinear system undergoes route-to-chaos, its response is naturally bounded in the time-domain while in the meantime becoming unstably broadband in the frequency-domain. Control scheme facilitated either in the time- or frequency-domain alone is insufficient in controlling route-to-chaos, where the corresponding response deteriorates in the time and frequency domains simultaneously. It is necessary to facilitate nonlinear control in both the time and frequency domains without obscuring or misinterpreting the true dynamics. The objective of the dissertation is to formulate a novel nonlinear control theory that addresses the fundamental characteristics inherent of all nonlinear systems undergoing route-to-chaos, one that requires no linearization or closed-form solution so that the genuine underlying features of the system being considered are preserved. The theory developed herein is able to identify the dynamic state of the system in real-time and restrain time-varying spectrum from becoming broadband. Applications of the theory are demonstrated using several engineering examples including the control of a non-stationary Duffing oscillator, a 1-DOF time-delayed milling model, a 2-DOF micro-milling system, unsynchronized chaotic circuits, and a friction-excited vibrating disk. Not subject to all the mathematical constraint conditions and assumptions upon which common nonlinear control theories are based and derived, the novel theory has its philosophical basis established in the simultaneous time-frequency control, on-line system identification, and feedforward adaptive control. It adopts multi-rate control, hence enabling control over nonstationary, nonlinear response with increasing bandwidth ? a physical condition oftentimes fails the contemporary control theories. The applicability of the theory to complex multi-input-multi-output (MIMO) systems without resorting to mathematical manipulation and extensive computation is demonstrated through the multi-variable control of a micro-milling system. The research is of a broad impact on the control of a wide range of nonlinear and chaotic systems. The implications of the nonlinear time-frequency control theory in cutting, micro-machining, communication security, and the mitigation of friction-induced vibrations are both significant and immediate

Aeronautical engineering: A continuing bibliography with indexes (supplement 275)

This bibliography lists 379 reports, articles, and other documents introduced into the NASA scientific and technical information system in Jan. 1991