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1
Chen, Yi. "Second Generation Tactile Imaging System for Mechanical Properties Estimation." Master's thesis, Temple University Libraries, 2014. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/284711.
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Electrical EngineeringM.S.E.E.
The Tactile Imaging System (TIS), developed in the Control, Sensor, Network, and Perception (CSNAP) Laboratory at Temple University, is a novel non-invasive method to measure the size and elastic modulus of inclusions. In this thesis, we revamped TIS to enlarge the contact area and replaced the lens to provide clearer shape information. We investigated different lighting sources and configurations in order to obtain clearer images of the target. We also improved the illuminating circuit using a constant current driver and a Pulse Width Modulation (PWM) dimming to stabilize the light brightness. After confirming the ability to obtain clearer images of the target, we compared the newly revamped TIS with the older version. The results showed that the images are clearer with a 214% larger interrogation area. We compared the accuracies of two tactile imaging systems using the depth, size and elastic modulus estimation algorithms. The results indicated that the second generation TIS (TIS G2) has better performance in all three parameters. TIS G2's error for depth estimation was 1.4%, which is 5.78% better than first generation TIS (TIS G1). TIS G2's error for size estimation was 5.28%, which is 0.96% better than first TIS G1. TIS can only calculate relative elasticity of the phantoms. We made five phantoms with varying stiffnesses and TIS G1 could not differentiate different stiffnesses, while TIS G2 recognized four out of five phantoms' relative stiffness. Finally, we performed a t-test to see whether the differences in G1 and G2 are statistically significant. The results showed that there are significant differences between the two systems.
Temple University--Theses
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Ghassempour, Aghamolki Hossein. "Phasor Measurement Unit Data-based States and Parameters Estimation in Power System." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6505.
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The dissertation research investigates estimating of power system static and dynamic states (e.g. rotor angle, rotor speed, mechanical power, voltage magnitude, voltage phase angle, mechanical reference point) as well as identification of synchronous generator parameters. The research has two focuses:
i. Synchronous generator dynamic model states and parameters estimation using real-time PMU data.
ii.Integrate PMU data and conventional measurements to carry out static state estimation.
The first part of the work focuses on Phasor Measurement Unit (PMU) data-based synchronous generator states and parameters estimation. In completed work, PMU data-based synchronous generator model identification is carried out using Unscented Kalman Filter (UKF). The identification not only gives the states and parameters related to a synchronous generator swing dynamics but also gives the states and parameters related to turbine-governor and primary and secondary frequency control. PMU measurements of active power and voltage magnitude, are treated as the inputs to the system while voltage phasor angle, reactive power, and frequency measurements are treated as the outputs. UKF-based estimation can be carried out at real-time. Validation is achieved through event play back to compare the outputs of the simplified simulation model and the PMU measurements, given the same input data. Case studies are conducted not only for measurements collected from a simulation model, but also for a set of real-world PMU data. The research results have been disseminated in one published article.
In the second part of the research, new state estimation algorithm is designed for static state estimation. The algorithm contains a new solving strategy together with simultaneous bad data detection. The primary challenge in state estimation solvers relates to the inherent non-linearity and non-convexity of measurement functions which requires using of Interior Point algorithm with no guarantee for a global optimum solution and higher computational time. Such inherent non-linearity and non-convexity of measurement functions come from the nature of power flow equations in power systems.
The second major challenge in static state estimation relates to the bad data detection algorithm. In traditional algorithms, Largest Normalized Residue Test (LNRT) has been used to identify bad data in static state estimation. Traditional bad data detection algorithm only can be applied to state estimation. Therefore, in a case of finding any bad datum, the SE algorithm have to rerun again with eliminating found bad data. Therefore, new simultaneous and robust algorithm is designed for static state estimation and bad data identification.
In the second part of the research, Second Order Cone Programming (SOCP) is used to improve solving technique for power system state estimator. However, the non-convex feasible constraints in SOCP based estimator forces the use of local solver such as IPM (interior point method) with no guarantee for quality answers. Therefore, cycle based SOCP relaxation is applied to the state estimator and a least square estimation (LSE) based method is implemented to generate positive semi-definite programming (SDP) cuts. With this approach, we are able to strengthen the state estimator (SE) with SOCP relaxation. Since SDP relaxation leads the power flow problem to the solution of higher quality, adding SDP cuts to the SOCP relaxation makes Problem’s feasible region close to the SDP feasible region while saving us from computational difficulty associated with SDP solvers. The improved solver is effective to reduce the feasible region and get rid of unwanted solutions violate cycle constraints. Different Case studies are carried out to demonstrate the effectiveness and robustness of the method.
After introducing the new solving technique, a novel co-optimization algorithm for simultaneous nonlinear state estimation and bad data detection is introduced in this dissertation. ${\ell}_1$-Norm optimization of the sparse residuals is used as a constraint for the state estimation problem to make the co-optimization algorithm possible. Numerical case studies demonstrate more accurate results in SOCP relaxed state estimation, successful implementation of the algorithm for the simultaneous state estimation and bad data detection, and better state estimation recovery against single and multiple Gaussian bad data compare to the traditional LNRT algorithm.
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Kim, Jae-Hak, and Jae-Hak Kim@anu edu au. "Camera Motion Estimation for Multi-Camera Systems." The Australian National University. Research School of Information Sciences and Engineering, 2008. http://thesis.anu.edu.au./public/adt-ANU20081211.011120.
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The estimation of motion of multi-camera systems is one of the most important tasks in computer vision research. Recently, some issues have been raised about general camera models and multi-camera systems. Using many cameras as a single camera is studied [60], and the epipolar geometry constraints of general camera models is theoretically derived. Methods for calibration, including a self-calibration method for general camera models, are studied [78, 62]. Multi-camera systems are an example of practically implementable general camera models and they are widely used in many applications nowadays because of both the low cost of digital charge-coupled device (CCD) cameras and the high resolution of multiple images from the wide field of views. To our knowledge, no research has been conducted on the relative motion of multi-camera systems with non-overlapping views to obtain a geometrically optimal solution. ¶
In this thesis, we solve the camera motion problem for multi-camera systems by using linear methods and convex optimization techniques, and we make five substantial and original contributions to the field of computer vision. First, we focus on the problem of translational motion of omnidirectional cameras, which are multi-camera systems, and present a constrained minimization method to obtain robust estimation results. Given known rotation, we show that bilinear and trilinear relations can be used to build a system of linear equations, and singular value decomposition (SVD) is used to solve the equations. Second, we present a linear method that estimates the relative motion of generalized cameras, in particular, in the case of non-overlapping views. We also present four types of generalized cameras, which can be solvable using our proposed, modified SVD method. This is the first study finding linear relations for certain types of generalized cameras and performing experiments using our proposed linear method. Third, we present a linear 6-point method (5 points from the same camera and 1 point from another camera) that estimates the relative motion of multi-camera systems, where cameras have no overlapping views. In addition, we discuss the theoretical and geometric analyses of multi-camera systems as well as certain critical configurations where the scale of translation cannot be determined. Fourth, we develop a global solution under an L∞ norm error for the relative motion problem of multi-camera systems using second-order cone programming. Finally, we present a fast searching method to obtain a global solution under an L∞ norm error for the relative motion problem of multi-camera systems, with non-overlapping views, using a branch-and-bound algorithm and linear programming (LP). By testing the feasibility of LP at the earlier stage, we reduced the time of computation of solving LP.¶
We tested our proposed methods by performing experiments with synthetic and real data. The Ladybug2 camera, for example, was used in the experiment on estimation of the translation of omnidirectional cameras and in the estimation of the relative motion of non-overlapping multi-camera systems. These experiments showed that a global solution using L∞ to estimate the relative motion of multi-camera systems could be achieved.
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Vignoni, Alejandro. "Invariance and Sliding Modes. Application to coordination of multi-agent systems, bioprocesses estimation, and control in living cells." Doctoral thesis, Editorial Universitat Politècnica de València, 2014. http://hdl.handle.net/10251/37743.
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The present thesis employs ideas of set invariance and sliding modes
in order to deal with different relevant problems control of nonlinear
systems. Initially, it reviews the techniques of set invariance as well as
the more relevant results about sliding modes control. Then the main
methodologies used are presented: sliding mode reference conditioning,
second order sliding modes and continuous approximation of sliding
modes. Finally, the methodologies are applied to different problems in
control theory and to a variety of biologically inspired applications.
The contributions of the thesis are:
The development of a method to coordinate dynamical systems
with different dynamic properties by means of a sliding mode auxiliary
loop shaping the references given to the systems as function
of the local and global goals, the achievable performance of each
system and the available information of each system.
Design methods for second order sliding mode algorithms. The
methods decouple the problem of stability analysis from that of
finite-time convergence of the super-twisting sliding mode algorithm.
A nonlinear change of coordinates and a time-scaling are
used to provide simple, yet flexible design methods and stability
proofs. Application of the method to the design of finite-time convergence
estimators of bioprocess kinetic rates and specific biomass
growth rate, from biomass measurements. Also the estimators are
validated with experimental data.
The proposal of a strategy to reduce the variability of a cell-to-cell
communication signal in synthetic genetic circuits. The method
uses set invariance and sliding mode ideas applied to gene expression
networks to obtain a reduction in the variance of the communication signal. Experimental approaches available to modify the
characteristics of the gene regulation function are described.Vignoni, A. (2014). Invariance and Sliding Modes. Application to coordination of multi-agent systems, bioprocesses estimation, and control in living cells [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/37743
Alfresco
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常春起 and Chunqi Chang. "Blind signal estimation using second order statistics." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31241487.
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Chang, Chunqi. "Blind signal estimation using second order statistics /." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B23272806.
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Ghosh, Roy Gourab. "A Simple Second Derivative Based Blur Estimation Technique." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366890068.
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Fedorov, Valery V., Peter Hackl, and Werner Müller. "Estimation and Experimental Design for Second Kind Regression Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 1990. http://epub.wu.ac.at/86/1/document.pdf.
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Estimation procedures and optimal designs for estimation of the individual parameters and of the global parameters are discussed under various conditions of prior knowledge. The extension to nonlinear parametrization of the response function ís based on the asymptotical validity of the results for the linear parametrization. For the case where the error variance and the dispersion matrix are unknown, an iterative estimation procedure is suggested. An example based on dental plaque pH profiles demonstrates the improvement that is achieved (a) through using the optimal design or a design that ís close to the optimal, and (b) through taking into account prior information. (author's abstract)Series: Forschungsberichte / Institut für Statistik
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Kim, Kyong Ryun. "Second order accurate variance estimation in poststratified two-stage sampling." Texas A&M University, 2003. http://hdl.handle.net/1969.1/5895.
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We proposed new variance estimators for the poststratified estimator of the population
total in two-stage sampling. The linearization or Taylor series variance estimator
and the jackknife linearization variance estimator are popular for the poststratified estimator. The jackknife linearization variance estimator utilizes the ratio, ^Rc, which
balances the weights for the poststrata while the linearization or Taylor series estimator
does not. The jackknife linearization variance estimator is equivalent to Rao's
(1985) adjusted variance estimator. Our proposed estimator makes use of the ratio,
^R c, in a different shape which is naturally derived from the process of expanding
to the second-order Taylor series linearization, while the standard linearization variance
estimator is only expanded to the first-order. We investigated the properties
and performance of the linearization variance estimator, the jackknife linearization
estimator, the proposed variance estimator and its modified version analytically and
through simulation study. The simulation study was carried out on both artificially
generated data and real data. The result showed that the second order accurate
variance estimator and its modified version could be very good candidates for the
variance estimation of poststratified estimator of population total.
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Woo, Wai Lok. "Blind inverse channel estimation using second and higher order statistics." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.421174.
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Biron, Matthieu Etienne. "Prediction and estimation for multivariate stationary time series models." Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.341888.
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Zhang, Fan. "Power system harmonic state estimation." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/13019.
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Chadwick, Robin. "Multi-spectral satellite rainfall estimation over Africa using meteosat second generation data." Thesis, University of Reading, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542062.
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Li, Dong. "Mixed Signal Detection and Parameter Estimation based on Second-Order Cyclostationary Features." Wright State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1448386709.
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Meyer, Folker. "GENDB a second generation genome annotation system /." [S.l.] : [s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=964442671.
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Majumdar, Ankur. "Security in power system state estimation." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/33744.
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With the power system evolving from passive to a more active system there is an incorporation of information and communication infrastructures in the system. The measurement data are more prone to tampering from attackers for mala fide intentions. Therefore, security and reliability of distribution have become major concerns. State estimation (SE), being the core function of the energy/distribution management system (EMS/DMS), has become necessary in order to operate the system efficiently and in a controlled manner. Although SE is a well-known task in transmission systems, it is usually not a common task in unbalanced distribution systems due to the difference in design and operation philosophy. This thesis addresses these issues and investigates the distribution system state estimation with unbalanced full three-phase modelling. The formulation, based on weighted least squares estimation, is extended to include the open/closed switches as equality constraints. This research then explores the vulnerabilities of the state estimation problem against attacks associated with leverage measurements. Detecting gross error particularly for leverage measurements have been found to be difficult due to low residuals. The thesis presents and discusses the suitability of externally studentized residuals compared to traditional residual techniques. Additionally, the masking/swamping phenomenon associated with multiple leverages makes the identification of gross error even more difficult. This thesis proposes a robust method of identifying the high leverages and then detecting gross error when the leverage measurements are compromised. All algorithms are validated in different IEEE test systems.
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Rodríguez, Cortés Francisco Javier. "Modelling, Estimation and Applications of Second-Order Spatio-Temporal Characteristics of Point Processes." Doctoral thesis, Universitat Jaume I, 2014. http://hdl.handle.net/10803/394025.
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Esta tesis se centra principalmente en el desarrollo de propiedades y características de los estimadores de segundo orden de procesos puntuales y espacio-temporales. En primer lugar, se presenta un marco teórico acerca de procesos puntuales espaciales y espacio-temporales. El resto de la tesis se organiza como sigue. En el capítulo 2, se presenta una nueva familia de kernel positivos y óptimos, además se propone un estimador insensgado alternativo para la función de la densidad del producto. Su rendimiento se compara para varios kernel mediante MISE. En el capítulo 3, se dada un nuevo estimador kernel de la función de la densidad producto espacio-temporal y también se desarrollan expresiones cerradas para la varianza en el caso de Poisson. En el Capítulo 4, nos centramos en los métodos de orientación de segundo orden los cuales proporcionan una herramienta para el análisis natural para los datos espaciales anisótropicos. Finalmente, se proporciona una descripción general de los proyectos de investigación actualmente en curso que han surgido motivadas por la estrecha relación con las propiedades de segundo orden de los procesos puntuales espaciales y espacio-temporales.This thesis is mainly focused on developing properties and estimators for second-order characteristics of spatio-temporal point processes. First, we present a theoretical framework of spatial and spatio-temporal point processes. The rest of the thesis is organized as follows. In Chapter 2 we present a new family of optimal and positive kernels an alternative unbiased estimator for the product density function. Its performance is compare under several kernel through MISE. In Chapter 3 a new kernel estimator of spatio-temporal product density function are given and also are developed close expressions for the variance under the Poisson case. En el capítulo 4 nos centramos en los métodos de orientación de segundo orden que proporcionan una herramienta natural para el análisis de los datos de proceso Punto espaciales anisótropas. Finally, we provide a general description of the currently ongoing research projects which have emerged motivated by the close relationship with the second-order properties.
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RAPELLO, DEMETRIUS COSTA. "SECOND LEVEL RECOMMENDATION SYSTEM TO SUPPORT NEWS EDITING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2012. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=22832@1.
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Sistemas de recomendação têm sido amplamente utilizados pelos grandes
portais na Web, em decorrência do aumento do volume de dados disponíveis na
Web. Tais sistemas são basicamente utilizados para sugerir informações
relevantes para os seus usuários. Esta dissertação apresenta um sistema de
recomendação de segundo nível para auxiliar equipes de jornalistas de portais de
notícias no processo de recomendação de notícias relacionadas para os usuários do
portal. O sistema é chamado de segundo nível pois apresenta recomendações aos
jornalistas para que, por sua vez, geram recomendações aos usuários do portal. O
modelo seguido pelo sistema consiste na recomendação de notícias relacionadas
com base em características extraídas do próprio texto da notícia original. As
características extraídas permitem a criação de consultas contra um banco de
dados de notícias anteriormente publicadas. O resultado de uma consulta é uma
lista de notícias candidatas à recomendação, ordenada pela similaridade com a
notícia original e pela data de publicação, que o editor da notícia original
manualmente processa para gerar a lista final de notícias relacionadas.Recommendation systems are widely used by major Web portals due to the increase in the volume of data available on the Web. Such systems are basically used to suggest information relevant to their users. This dissertation presents a second-level recommendation system, which aims at assisting the team of journalists of a news Web portal in the process of recommending related news for the users of the Web portal. The system is called second level since it creates recommendations to the journalists Who, in turn, generate recommendations to the users. The system follows a model based on features extracted from the text itself. The extracted features permit creating queries against a news database. The query result is a list of candidate news, sorted by score and date of publication, which the news editor manually processes to generate the final list of related news.
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Ghofranih, Jahangir. "Control and estimation of a chaotic system." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/29601.
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A class of deterministic nonlinear systems known as ”chaotic” behaves similar to noise-corrupted systems. As a specific example, Duffing equation, a nonlinear oscillator representing the roll dynamics of a vessel, was chosen for the study. State estimation and control of such systems in the presence of measurement noise is the prime goal of this research. A nonlinear estimation suitable for chaotic systems was evaluated against conventional methods based on linear equivalent model, and proved to be very efficient. A state feedback controller and a sliding mode controller were applied to the chaotic system and both techniques provided satisfactory results. Investigating the persistence of chaotic behavior of the controlled system is a secondary goal. Simulation results showed that the chaotic behavior persisted in case of the linear feedback controller, while in case of the sliding mode controller the system did not exhibit any chaotic behavior.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Elnaggar, Ashraf. "Variable regression estimation of unknown system delay." Thesis, University of British Columbia, 1990. http://hdl.handle.net/2429/30804.
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This thesis describes a novel approach to model and estimate systems of unknown delay. The a-priori knowledge available about the systems is fully utilized so that the number of parameters to be estimated equals the number of unknowns in the systems. Existing methods represent the single unknown system delay by a large number of unknown parameters in the system model.
The purpose of this thesis is to develop new methods of modelling the systems so that the unknowns are directly estimated. The Variable Regression Estimation technique is developed to provide direct delay estimation. The delay estimation requires minimum excitation and is robust, bounded, and it converges to the true value for first-order and second-order systems. The delay estimation provides a good model approximation for high-order systems and the model is always stable and matches the frequency response of the system at any given frequency. The new delay estimation method is coupled with the Pole Placement, Dahlin and the Generalized Predictive Controller (GPC) design and adaptive versions of these controllers result. The new adaptive GPC has the same closed-loop performance for different values of system delay. This was not achievable in the original adaptive GPC. The adaptive controllers with direct delay estimation can regulate systems with dominant time delay with minimum parameters in the controller and the system model. The delay does not lose identifiability in closed-loop estimation. Experiments on the delay estimation show excellent agreement with the theoretical analysis of the proposed methods.Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Loh, A. P. "Uncertainty estimation and multivariable control system design." Thesis, University of Oxford, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.375267.
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Michiorri, Andrea. "Power system real-time thermal rating estimation." Thesis, Durham University, 2010. http://etheses.dur.ac.uk/469/.
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This Thesis describes the development and testing of a real-time rating estimation algorithm developed at Durham University within the framework of the partially Government-funded research and development project “Active network management based on component thermal properties”, involving Durham University, ScottishPower EnergyNetworks, AREVA-T&D, PB Power and Imass. The concept of real time ratings is based on the observation that power system component current carrying capacity is strongly influenced by variable environmental parameters such as air temperature or wind speed. On the contrary, the current operating practice consists of using static component ratings based on conservative assumptions. Therefore, the adoption of real-time ratings would allow latent network capacity to be unlocked with positive outcomes in a number of aspects of distribution network operation. This research is mainly focused on facilitating renewable energy connection to the distribution level, since thermal overloads are the main cause of constraints for connections at the medium and high voltage levels. Additionally its application is expected to facilitate network operation in case of thermal problems created by load growth, delaying and optimizing network reinforcements. The work aims at providing a solution to part of the problems inherent in the development of a real-time rating system, such as reducing measurements points, data uncertainty and communication failure. An extensive validation allowed a quantification of the performance of the algorithm developed, building the necessary confidence for a practical application of the system developed.
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Al-Othman, Abdul Rahman K. "Uncertainty modelling in power system state estimation." Thesis, Brunel University, 2004. http://bura.brunel.ac.uk/handle/2438/7889.
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As a special case of the static state estimation problem, the load-flow problem is studied in this thesis. It is demonstrated that the non-linear load-flow formulation may be solved by real-coded genetic algorithms. Due to its global optimisation ability, the proposed method can be useful for off-line studies where multiple solutions are suspected. This thesis presents two methods for estimating the uncertainty interval in power system state estimation due to uncertainty in the measurements. The proposed formulations are based on a parametric approach which takes in account the meter inaccuracies. A nonlinear and a linear formulation are proposed to estimate the tightest possible upper and lower bounds on the states. The uncertainty analysis, in power system state estimation, is also extended to other physical quantities such as the network parameters. The uncertainty is then assumed to be present in both measurements and network parameters. To find the tightest possible upper and lower bounds of any state variable, the problem is solved by a Sequential Quadratic Programming (SQP) technique. A new robust estimator based on the concept of uncertainty in the measurements is developed here. This estimator is known as Maximum Constraints Satisfaction (MCS). Robustness and performance of the proposed estimator is analysed via simulation of simple regression examples, D.C. and A.C. power system models.
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Babuscia, Alessandra. "Statistical risk estimation for communication system design." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/76087.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2012.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 281-295).
Spacecraft are complex systems that involve many subsystems and multiple relationships among them. The design of a spacecraft is an evolutionary process that starts from requirements and evolves over time. During this process, changes can affect mass and power at component, subsystem, and system level. Each spacecraft has to respect overall constraints in terms of mass and power. The current practice in system design deals with this problem by allocating margins to individual components and to individual subsystems. However, a statistical characterization of the fluctuations in mass and power of the overall system (i.e. the spacecraft) is missing. This lack can result in a risky spacecraft design that might not fit the mission constraints and requirements, or in a conservative design that might not fully utilize the available resources. This problem is especially challenging at the initial stage of the design, when high levels of uncertainty due to lack of knowledge are unavoidable. This research proposes a statistical approach to quantify the likelihood that the design of a spacecraft would meet the mission constraints in mass and power consumption, focusing on the initial stage of the design. Due to the complexity of the problem and the different expertise required to develop a complete risk model for a spacecraft design, the scope of this research is focused on risk estimation for a specific spacecraft subsystem: the communication subsystem. The current research aims to be a "proof of concept" of a risk-based design approach, which can then be further expanded to the design of other subsystems as well as to the whole spacecraft. The approach presented in this thesis includes a baseline communication system design tool, and a statistical characterization of the design risks through a combination of historical mission data and expert opinion. Different statistical techniques are explored to ensure that the amount of information extracted from data and expert opinion is maximized. Specifically, for statistics based on data, Kernel Density Estimator is selected as the preferred technique to extract probability densities from a database of previous space missions' components. Expert elicitation is generated through a four-part model which quantifies experts' sensitivity to biases, and uses this measurement to compose properly the assessments from different experts. Finally, an optimization framework is developed to compare multiple possible design architectures, and to select the one that minimizes design objectives, like mass and power consumption, while minimizing the risk associated with the same metrics. Examples of missions are applied to validate the model. Results show that the statistical approach recognizes whether the initial estimate of the system is an overestimation or an underestimation, providing a valuable tool to measure the risk of a communication system at the initial state of the design. Specifically, statistics based on historical data and on expert elicitation allow the designer to size contingency properly, providing a reliable estimation of mass and power in the initial stage of the design. Thanks to this method, the communication system designers will be able to evaluate and compare different communication architectures in a risk trade-off prospective across the evolution of the design. Extensions to different subsystems and to additional metrics (like cost) make this model applicable to a wider range of problems.
by Alessandra Babuscia.
Ph.D.
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Wang, Fei. "Pilot-Based Channel Estimation in OFDM System." University of Toledo / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1302129482.
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Ebrahimian, Mohammad Reza. "Power system operations : state estimation distributed processing /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.
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Holmqvist, Niclas. "HANDHELD LIDAR ODOMETRY ESTIMATION AND MAPPING SYSTEM." Thesis, Mälardalens högskola, Inbyggda system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-41137.
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Ego-motion sensors are commonly used for pose estimation in Simultaneous Localization And Mapping (SLAM) algorithms. Inertial Measurement Units (IMUs) are popular sensors but suffer from integration drift over longer time scales. To remedy the drift they are often used in combination with additional sensors, such as a LiDAR. Pose estimation is used when scans, produced by these additional sensors, are being matched. The matching of scans can be computationally heavy as one scan can contain millions of data points. Methods exist to simplify the problem of finding the relative pose between sensor data, such as the Normal Distribution Transform SLAM algorithm. The algorithm separates the point cloud data into a voxelgrid and represent each voxel as a normal distribution, effectively decreasing the amount of data points. Registration is based on a function which converges to a minimum. Sub-optimal conditions can cause the function to converge at a local minimum. To remedy this problem this thesis explores the benefits of combining IMU sensor data to estimate the pose to be used in the NDT SLAM algorithm.
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Dolne, Jean J. "Estimation theoretical image restoration." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/47859.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2008.Includes bibliographical references.
In this thesis, we have developed an extensive study to evaluate image restoration from a single image, colored or monochromatic. Using a mixture of Gaussian and Poisson noise process, we derived an objective function to estimate the unknown object and point spread function (psf) parameters. We have found that, without constraint enforcement, this blind deconvolution algorithm tended to converge to the trivial solution: delta function as the estimated psf and the detected image as the estimated object. We were able to avoid this solution set by enforcing a priori knowledge about the characteristics of the solution, which included the constraints on object sharpness, energy conservation, impulse response point spread function solution, and object gradient statistics. Applying theses constraints resulted in significantly improved solutions, as evaluated visually and quantitatively using the distance of the estimated to the true function. We have found that the distance of the estimated psf was correlated better with visual observation than the distance metric using the estimated object. Further research needs to be done in this area. To better pose the problem, we expressed the point spread function as a series of Gaussian basis functions, instead of the pixel basis function formalism used above. This procedure has reduced the dimensionality of the parameter space and has resulted in improved results, as expected. We determined a set of weights that yielded optimum algorithm performance.
(cont.) Additional research needs to be done to include the weight set as optimization parameters. This will free the user from having to adjust the weights manually. Of course, if certain knowledge of a weight is available, then it may be better to start with that as an initial guess and optimize from there. With the knowledge that the gradient of the object obeys long-tailed distribution, we have incorporated a constraint using the first two moments, mean and variance, of the gradient of the object in the objective function. Additional research should be done to incorporate the entire distribution in the objective and gradient functions and evaluate the performance.
by Jean J. Dolne.
S.M.
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Haumesser, Lauren Nicole. "Democratic women in the second party system: 1824-1856." Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/11852.
Full textAbstract:
Newly enfranchised voters, rowdy political campaigns, and the emergence of two major national political parties characterized American politics from 1824 to 1856, an era known as the Second Party System. During this same time, however, women remained disenfranchised, and they were increasingly subject to gender prescriptions that told them that their place was in the home. As the nation’s political culture became its popular culture, then, how did women respond? Political histories and women’s histories alike long overlooked this question. When historians finally began to study women’s political history, many still minimized women’s roles in partisan politics, arguing that women were involved only in benevolent reform associations or that they were much more involved with the Whigs than with the Democrats. I challenge this view. This thesis argues that women were indeed Democrats, and that they played an important and continuous role in the Democratic Party throughout the Second Party System. To do so, women had to negotiate between gender prescriptions, which varied wildly, and local Democratic leaders, who allowed or denied access to party events based on their own beliefs about women’s place in politics. Though these conditions certainly discouraged many Democratic women from participating in politics, many others sewed banners, baked cakes, marched in parades, wrote, and even spoke on behalf of the Democrats, despite this complex social and political landscape. This speaks to the depth of Democratic women’s partisan loyalties and their continuous role in politics throughout the Second Party System.
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Horbelt, Werner. "Maximum likelihood estimation in dynamical systems." [S.l. : s.n.], 2001. http://deposit.ddb.de/cgi-bin/dokserv?idn=963810812.
Full text
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KAWAGUCHI, Nobuo, and Seigo ITO. "Bayesian Based Location Estimation System Using Wireless LAN." IEEE, 2005. http://hdl.handle.net/2237/15455.
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Fayegh, A. David. "Flood advisor : an expert system for flood estimation." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25069.
Full textAbstract:
Expert computer programs have recently emerged from research on artificial intelligence as a practical problem-solving tool. An expert system is a knowledge-based program that imitates the problem-solving behaviour of a human expert to solve complex real-world problems. While conventional programs organize knowledge on two levels: data and program, most expert programs organize knowledge on three levels: data, knowledge base, and control. Thus, what distinguishes such a system from conventional programs is that in most expert systems the problem solving model is treated as a separate entity rather than appearing only implicitly as part of the coding of the program.
The purpose of this thesis is twofold. First, it is intended to demonstrate how domain-specific problem-solving knowledge may be represented in computer memory by using the frame representation technique. Secondly, it is intended to simulate a typical flood estimation situation, from the point-of-view of an expert engineer. A frame network was developed to represent, in data structures, the declarative, procedural, and heuristic knowledge necessary for solving a typical flow estimation problem. The control strategy of this computer-based consultant (FLOOD ADVISOR) relies on the concept that reasoning is dominated by a recognition process which is used to compare new instances of a given phenomena to the stereotyped conceptual framework used in understanding that phenomena. The primary purpose of the FLOOD ADVISOR is to provide interactive advice about the flow estimation technique most suitable to one of five generalized real-world situations. These generalizations are based primarily on the type and quantity of the data and resources available to the engineer. They are used to demonstrate how problem solving knowledge may be used to interactively assist the engineer in making difficult decisions. The expertise represented in this prototype system is far from complete and the recommended solution procedures for each generalized case are in their infancy. However, modifications may be easily implemented as the domain-specific expert knowledge becomes available. It is concluded that over the long term, this type of approach for building problem-solving models of the real world are computationally cheaper and easier to develop and maintain than conventional computer programs.Applied Science, Faculty of
Civil Engineering, Department of
Graduate
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Vukovic, Divna, and Cecilia Wester. "Staff Prediction Analysis : Effort Estimation In System Test." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-1739.
Full textAbstract:
This master thesis is made in 2001 at Blekinge Institute of Technology and Symbian, which is a software company in Ronneby, Sweden. The purpose of the thesis is to find a suitable prediction and estimation model for the test effort. To do this, we have studied the State of the Art in cost/effort estimation and fault prediction. The conclusion of this thesis is that it is hard to make a general proposal, which is applicable for all organisations. For Symbian we have proposed a model based on use and test cases to predict the test effort.
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Rababy, Nada. "Estimation of EMG conduction velocity using system identification." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=63819.
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Wang, Jing. "Marine power system protection using active impedance estimation." Thesis, University of Nottingham, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.537689.
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Chen, Jiaxiong. "Power System State Estimation Using Phasor Measurement Units." UKnowledge, 2013. http://uknowledge.uky.edu/ece_etds/35.
Full textAbstract:
State estimation is widely used as a tool to evaluate the real time power system prevailing conditions. State estimation algorithms could suffer divergence under stressed system conditions. This dissertation first investigates impacts of variations of load levels and topology errors on the convergence property of the commonly used weighted least square (WLS) state estimator. The influence of topology errors on the condition number of the gain matrix in the state estimator is also analyzed. The minimum singular value of gain matrix is proposed to measure the distance between the operating point and state estimation divergence.
To study the impact of the load increment on the convergence property of WLS state estimator, two types of load increment are utilized: one is the load increment of all load buses, and the other is a single load increment. In addition, phasor measurement unit (PMU) measurements are applied in state estimation to verify if they could solve the divergence problem and improve state estimation accuracy.
The dissertation investigates the impacts of variations of line power flow increment and topology errors on convergence property of the WLS state estimator. A simple 3-bus system and the IEEE 118-bus system are used as the test cases to verify the common rule. Furthermore, the simulation results show that adding PMU measurements could generally improve the robustness of state estimation.
Two new approaches for improving the robustness of the state estimation with PMU measurements are proposed. One is the equality-constrained state estimation with PMU measurements, and the other is Hachtel's matrix state estimation with PMU measurements approach. The dissertation also proposed a new heuristic approach for optimal placement of phasor measurement units (PMUs) in power system for improving state estimation accuracy. In the problem of adding PMU measurements into the estimator, two methods are investigated. Method I is to mix PMU measurements with conventional measurements in the estimator, and method II is to add PMU measurements through a post-processing step. These two methods can achieve very similar state estimation results, but method II is a more time-efficient approach which does not modify the existing state estimation software.
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Harry, Cyril Massey. "A thermodynamics-system approach to software resource estimation." Thesis, Imperial College London, 1991. http://hdl.handle.net/10044/1/46807.
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Caldwell, Timothy M. "Second-order optimal estimation of switching times for switched autonomous systems." Thesis, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1464487.
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Kim, Jae-Hak. "Camera Motion Estimation for Multi-Camera Systems." Phd thesis, 2008. http://hdl.handle.net/1885/49364.
Full textAbstract:
The estimation of motion of multi-camera systems is one of the most important tasks in computer vision research. Recently, some issues have been raised about general camera models and multi-camera systems. Using many cameras as a single camera is studied [60], and the epipolar geometry constraints of general camera models is theoretically derived. Methods for calibration, including a self-calibration method for general camera models, are studied [78, 62]. Multi-camera systems are an example of practically implementable general camera models and they are widely used in many applications nowadays because of both the low cost of digital charge-coupled device (CCD) cameras and the high resolution of multiple images from the wide field of views. To our knowledge, no research has been conducted on the relative motion of multi-camera systems with non-overlapping views to obtain a geometrically optimal solution. ¶ In this thesis, we solve the camera motion problem for multi-camera systems by using linear methods and convex optimization techniques, and we make five substantial and original contributions to the field of computer vision. ...
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Haque, Md Zahidul. "A sequential approach of solving second order AC-DC load flow and state estimation problem." Thesis, 1996. https://vuir.vu.edu.au/15410/.
Full textAbstract:
Load flow and state estimation problems are solved for an economic and efficient operation, and an effective and stable control of the power system. Several methods are available to solve these problems, yet new ones are
developed with a view to enhance the computational efficiency and convergence stability. Three new methods each for the ac-dc load flow and the ac-dc state estimation are developed and experimented in this thesis.
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Huang, Chaojun. "Structural Health Monitoring System for Deepwater Risers with Vortex-Induced Vibration: Nonlinear Modeling, Blind Identification, Fatigue/Damage Estimation and Vibration Control." Thesis, 2013. http://hdl.handle.net/1911/71968.
Full textAbstract:
This study focuses on developing structural health monitoring techniques to detect damage in deepwater risers subjected to vortex-induced vibration (VIV), and studying vibration control strategies to extend the service life of offshore structures.
Vibration-based damage detection needs both responses from the undamaged and damaged deepwater risers. Because no experimental data for damaged deepwater risers is available, a model to predict the VIV responses of deepwater risers with given conditions is needed, which is the forward problem. In this study, a new three dimensional (3D) analytical model is proposed considering coupled VIV (in-line and cross-flow) for top-tensioned riser (TTR) with wake oscillators. The model is verified by direct numerical simulations and experimental data.
The inverse problem is to detect damage using VIV responses from the analytical models with/without damage, where the change between dynamic properties obtained from riser responses represents damage. The inverse problem is performed in two steps: blind identification and damage detection. For blind identification, a wavelet modified second order blind identification (WMSOBI) method and a complex WMSOBI (CWMSOBI) method are proposed to extract modal properties from output only responses for standing and traveling wave vibration, respectively. Numerical simulations and experiments validate the effectiveness of proposed methods. For damage detection, a novel weighted distribution force change (WDFC) index (for standing wave) and a phase angle change (PAC) index (for traveling wave) are proposed and proven numerically. Experiments confirm that WDFC can accurately locate damage and estimate damage severity. Furthermore, a new fatigue damage estimation method involving WMSOBI, S-N curve and Miner's rule is proposed and proven to be effective using field test data.
Vibration control is essential to extend the service life and enhance the safety of offshore structures. Literature review shows that semi-active control devices are potentially a good solution. A novel semi-active control strategy is proposed to tune the damper properties to match the dominant frequency of the structural response in real-time. The effectiveness of proposed strategy in vibration reduction for deepwater risers and offshore floating wind turbines is also validated through numerical studies.
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Pal, Madhumita. "Accurate and Efficient Algorithms for Star Sensor Based Micro-Satellite Attitude and Attitude Rate Estimation." Thesis, 2013. http://etd.iisc.ac.in/handle/2005/3428.
Full textAbstract:
This dissertation addresses novel techniques in determining gyroless micro-satellite attitude and attitude rate. The main objective of this thesis is to explore the possibility of using commercially available low cost micro-light star sensor as a stand-alone sensor for micro-satellite attitude as well as attitude rate determination. The objective is achieved by developing accurate and computationally efficient algorithms for the realization of onboard operation of a low fidelity star sensor. All the algorithms developed here are tested with the measurement noise presented in the catalog of the sensor array STAR-1000.
A novel accurate second order sliding mode observer (SOSMO) is designed for discrete time uncertain linear multi-output system. Our design procedure is effective for both matched and unmatched bounded uncertain ties and/or disturbances. The bound on uncertainties and/or disturbances is assumed to be unknown. This problem is addressed in this work using the second order multiple sliding modes approach. Second order sliding manifold and corresponding sliding condition for discrete time system is defined similar on the lines of continuous counterpart. Our design is not restricted to a particular class of uncertain (matched) discrete time system. Moreover, it can handle multiple outputs unlike single out-put systems. The observer design is achieved by driving the state observation error and its first order finite difference to the vicinity of the equilibrium point (0,0) in a finite steps and maintaining them in the neighborhood thereafter. The estimation synthesis is based on Quasi Sliding Mode (QSM) design.
The problem of designing sliding mode observer for a linear system subjected to unknown inputs requires observer matching condition. This condition is needed to ensure that the state estimation error is a asymptotically stable and is independent of the unknown input during the sliding motion. In the absence of a matching condition, asymptotic stability of the reduced order error dynamics on the sliding surface is not guaranteed. However, unknown bounded inputs guarantee bounded error on state estimation. The QSM design guarantees an ultimate error bound by incorporating Boundary Layer (BL) in its design procedure.
The observer achieves one order of magnitude improvement in estimation accuracy than the conventional sliding mode observer (SMO) design for an unknown input. The observer estimation errors, satisfying the given stability conditions, converge to an ultimate finite bound (with in the specified BL) of O(T2), where T Is the sampling period. A relation between sliding mode gain and boundary layer is established for the existence of second order discrete sliding motion. The robustness of the proposed observer with respect to measurement noise is also analyzed. The design algorithm is very simple to apply and is implemented for two examples with different classes of disturbances (matched and unmatched) to show the effectiveness of the design. Simulation results show the robustness with respect to the measurement noise for SOSMO.
Second order sliding mode observer gain can be calculated off-line and the same gain can work for large band of disturbance as long as the disturbance acting on the continuous time system is bounded and smooth. The SOSMO is simpler to implement on board compared to the other traditional nonlinear filters like Pseudo-Linear-Kalman-filter(PLKF); Extended Kalman Filter(EKF). Moreover, SMO possesses an automatic adaptation property same as optimal state estimator(like Kalman filter) with respect to the intensity of the measurement noise. The SMO rejects the noisy measurements automatically, in response to the increased noise intensity. The dynamic performance of the observer on the sliding surface can be altered and no knowledge of noise statistics is required. It is shown that the SOSMO performs more accurately than the PLKF in application to micro-satellite angular rate estimation since PLKF is not an optimal filter.
A new method for estimation of satellite angular rates through derivative approach is proposed. The method is based on optic flow of star image patterns formed on a star sensor. The satellite angular rates are derived directly from the 2D-coordinates of star images. Our algorithm is computationally efficient and requires less memory allocation compared to the existing vector derivative approaches, where there is also no need for star identification. The angular rates are computed using least square solution method, based on the measurement equation obtained by optic flow of star images. These estimates are then fed into discrete time second order sliding mode observer (SOSMO). The performance of angular rate estimation by SOSMO is compared with the discrete time First order SMO and PLKF. The SOSMO gives the best estimates as compared to the other two schemes in estimating micro-satellite angular rates in all three axes. The improvement in accuracy is one order of magnitude (around1.7984 x 10−5 rad/ sec,8.9987 x 10−6 rad/ sec and1.4222 x 10−5 rad/ sec in three body axes respectively) in terms of standard deviation in steady state estimation error.
A new method and algorithm is presented to determine star camera parameters along with satellite attitude with high precision even if these parameters change during long on-orbit operation. Star camera parameters and attitude need to be determined independent of each other as they both can change. An efficient, closed form solution method is developed to estimate star camera parameters (like focal length, principal point offset), lens distortions (like radial distortion) and attitude. The method is based on a two step procedure. In the first step, all parameters (except lens distortion) are estimated using a distortion free camera model. In the second step, lens distortion coefficient is estimated by linear least squares (LS) method. Here the derived camera parameters in first step are used in the camera model that incorporates distortion. However, this method requires identification of observed stars with the catalogue stars. But, on-orbit star identification is difficult as it utilizes the values of camera calibrating parameters that can change in orbit(detector and optical element alignment get change in orbit due to solar pressure or sudden temperature change) from the ground calibrated value. This difficulty is overcome by employing a camera self-calibration technique which only requires four observed stars in three consecutive image frames. Star camera parameters along with lens (radial and decentering) distortion coefficients are determined by camera self calibration technique. Finally Kalman filter is used to refine the estimated data obtained from the LS based method to improve the level of accuracy.
We consider the true values of camera parameters as (u0,v0) = (512.75,511.25) pixel, f = 50.5mm; The ground calibrated values of those parameters are (u0,v0) =( 512,512) pixel, f = 50mm; Worst case radial distortion coefficient affecting the star camera lens is considered to be k1 =5 x 10−3 .Our proposed method of attitude determination achieves accuracy of the order of magnitude around 6.2288 x 10−5 rad,3.3712 x 10−5 radand5.8205 x
10−5 rad in attitude angles φ,θ and ψ. Attitude estimation by existing methods in the literature diverges from the true value since they utilize the ground calibrated values of camera parameters instead of true values.
To summarize, we developed a formal theory of discrete time Second Order Sliding Mode Observer for uncertain multi-output system. Our methods achieve the desired accuracy while estimating satellite attitude and attitude rate using low fidelity star sensor data. Our methods require lower on-board processing requirement and less memory allocation; thus are suitable for micro-satellite applications. Thus, the objective of using low fidelity star sensor as stand-alone sensor in micro-satellite application is achieved.
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Pal, Madhumita. "Accurate and Efficient Algorithms for Star Sensor Based Micro-Satellite Attitude and Attitude Rate Estimation." Thesis, 2013. http://etd.iisc.ernet.in/2005/3428.
Full textAbstract:
This dissertation addresses novel techniques in determining gyroless micro-satellite attitude and attitude rate. The main objective of this thesis is to explore the possibility of using commercially available low cost micro-light star sensor as a stand-alone sensor for micro-satellite attitude as well as attitude rate determination. The objective is achieved by developing accurate and computationally efficient algorithms for the realization of onboard operation of a low fidelity star sensor. All the algorithms developed here are tested with the measurement noise presented in the catalog of the sensor array STAR-1000.
A novel accurate second order sliding mode observer (SOSMO) is designed for discrete time uncertain linear multi-output system. Our design procedure is effective for both matched and unmatched bounded uncertain ties and/or disturbances. The bound on uncertainties and/or disturbances is assumed to be unknown. This problem is addressed in this work using the second order multiple sliding modes approach. Second order sliding manifold and corresponding sliding condition for discrete time system is defined similar on the lines of continuous counterpart. Our design is not restricted to a particular class of uncertain (matched) discrete time system. Moreover, it can handle multiple outputs unlike single out-put systems. The observer design is achieved by driving the state observation error and its first order finite difference to the vicinity of the equilibrium point (0,0) in a finite steps and maintaining them in the neighborhood thereafter. The estimation synthesis is based on Quasi Sliding Mode (QSM) design.
The problem of designing sliding mode observer for a linear system subjected to unknown inputs requires observer matching condition. This condition is needed to ensure that the state estimation error is a asymptotically stable and is independent of the unknown input during the sliding motion. In the absence of a matching condition, asymptotic stability of the reduced order error dynamics on the sliding surface is not guaranteed. However, unknown bounded inputs guarantee bounded error on state estimation. The QSM design guarantees an ultimate error bound by incorporating Boundary Layer (BL) in its design procedure.
The observer achieves one order of magnitude improvement in estimation accuracy than the conventional sliding mode observer (SMO) design for an unknown input. The observer estimation errors, satisfying the given stability conditions, converge to an ultimate finite bound (with in the specified BL) of O(T2), where T Is the sampling period. A relation between sliding mode gain and boundary layer is established for the existence of second order discrete sliding motion. The robustness of the proposed observer with respect to measurement noise is also analyzed. The design algorithm is very simple to apply and is implemented for two examples with different classes of disturbances (matched and unmatched) to show the effectiveness of the design. Simulation results show the robustness with respect to the measurement noise for SOSMO.
Second order sliding mode observer gain can be calculated off-line and the same gain can work for large band of disturbance as long as the disturbance acting on the continuous time system is bounded and smooth. The SOSMO is simpler to implement on board compared to the other traditional nonlinear filters like Pseudo-Linear-Kalman-filter(PLKF); Extended Kalman Filter(EKF). Moreover, SMO possesses an automatic adaptation property same as optimal state estimator(like Kalman filter) with respect to the intensity of the measurement noise. The SMO rejects the noisy measurements automatically, in response to the increased noise intensity. The dynamic performance of the observer on the sliding surface can be altered and no knowledge of noise statistics is required. It is shown that the SOSMO performs more accurately than the PLKF in application to micro-satellite angular rate estimation since PLKF is not an optimal filter.
A new method for estimation of satellite angular rates through derivative approach is proposed. The method is based on optic flow of star image patterns formed on a star sensor. The satellite angular rates are derived directly from the 2D-coordinates of star images. Our algorithm is computationally efficient and requires less memory allocation compared to the existing vector derivative approaches, where there is also no need for star identification. The angular rates are computed using least square solution method, based on the measurement equation obtained by optic flow of star images. These estimates are then fed into discrete time second order sliding mode observer (SOSMO). The performance of angular rate estimation by SOSMO is compared with the discrete time First order SMO and PLKF. The SOSMO gives the best estimates as compared to the other two schemes in estimating micro-satellite angular rates in all three axes. The improvement in accuracy is one order of magnitude (around1.7984 x 10−5 rad/ sec,8.9987 x 10−6 rad/ sec and1.4222 x 10−5 rad/ sec in three body axes respectively) in terms of standard deviation in steady state estimation error.
A new method and algorithm is presented to determine star camera parameters along with satellite attitude with high precision even if these parameters change during long on-orbit operation. Star camera parameters and attitude need to be determined independent of each other as they both can change. An efficient, closed form solution method is developed to estimate star camera parameters (like focal length, principal point offset), lens distortions (like radial distortion) and attitude. The method is based on a two step procedure. In the first step, all parameters (except lens distortion) are estimated using a distortion free camera model. In the second step, lens distortion coefficient is estimated by linear least squares (LS) method. Here the derived camera parameters in first step are used in the camera model that incorporates distortion. However, this method requires identification of observed stars with the catalogue stars. But, on-orbit star identification is difficult as it utilizes the values of camera calibrating parameters that can change in orbit(detector and optical element alignment get change in orbit due to solar pressure or sudden temperature change) from the ground calibrated value. This difficulty is overcome by employing a camera self-calibration technique which only requires four observed stars in three consecutive image frames. Star camera parameters along with lens (radial and decentering) distortion coefficients are determined by camera self calibration technique. Finally Kalman filter is used to refine the estimated data obtained from the LS based method to improve the level of accuracy.
We consider the true values of camera parameters as (u0,v0) = (512.75,511.25) pixel, f = 50.5mm; The ground calibrated values of those parameters are (u0,v0) =( 512,512) pixel, f = 50mm; Worst case radial distortion coefficient affecting the star camera lens is considered to be k1 =5 x 10−3 .Our proposed method of attitude determination achieves accuracy of the order of magnitude around 6.2288 x 10−5 rad,3.3712 x 10−5 radand5.8205 x
10−5 rad in attitude angles φ,θ and ψ. Attitude estimation by existing methods in the literature diverges from the true value since they utilize the ground calibrated values of camera parameters instead of true values.
To summarize, we developed a formal theory of discrete time Second Order Sliding Mode Observer for uncertain multi-output system. Our methods achieve the desired accuracy while estimating satellite attitude and attitude rate using low fidelity star sensor data. Our methods require lower on-board processing requirement and less memory allocation; thus are suitable for micro-satellite applications. Thus, the objective of using low fidelity star sensor as stand-alone sensor in micro-satellite application is achieved.
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AbdelAziz, Salamh Mustafa. "Second-order least squares estimation in dynamic regression models." 2014. http://hdl.handle.net/1993/23512.
Full textAbstract:
In this dissertation we proposed two generalizations of the Second-Order Least Squares (SLS) approach in two popular dynamic econometrics models. The first one is the regression model with time varying nonlinear mean function and autoregressive conditionally heteroskedastic (ARCH) disturbances. The second one is a linear dynamic panel data model.
We used a semiparametric framework in both models where the SLS approach is based only on the first two conditional moments of response variable given the explanatory variables. There is no need to specify the distribution of the error components in both models. For the ARCH model under the assumption of strong-mixing process with finite moments of some order, we established the strong consistency and asymptotic normality of the SLS estimator.
It is shown that the optimal SLS estimator, which makes use of the additional information inherent in the conditional skewness and kurtosis of the process, is superior to the commonly used quasi-MLE, and the efficiency gain is significant when the underlying distribution is asymmetric. Moreover, our large scale simulation studies showed that the optimal SLSE behaves better than the corresponding estimating function estimator in finite sample situation. The practical usefulness of the optimal SLSE was tested by an empirical example on the U.K. Inflation. For the linear dynamic panel data model, we showed that the SLS estimator is consistent and asymptotically normal for large N and finite T under fairly general regularity conditions. Moreover, we showed that the optimal SLS estimator reaches a semiparametric efficiency bound. A specification test was developed for the first time to be used whenever the SLS is applied to real data. Our Monte Carlo simulations showed that the optimal SLS estimator performs satisfactorily in finite sample situations compared to the first-differenced GMM and the random effects pseudo ML estimators. The results apply under stationary/nonstationary process and wih/out exogenous regressors. The performance of the optimal SLS is robust under near-unit root case. Finally, the practical usefulness of the optimal SLSE was examined by an empirical study on the U.S. airfares.
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GAO, WEN-ZHONG, and 郭文中. "An adaptive second-order statistics method for bearing estimation." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/11251266346985281677.
Full text
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Li, He. "Second-order Least Squares Estimation in Generalized Linear Mixed Models." 2011. http://hdl.handle.net/1993/4446.
Full textAbstract:
Maximum likelihood is an ubiquitous method used in the estimation of generalized linear mixed model (GLMM). However, the method entails computational difficulties and relies on the normality assumption for random effects. We propose a second-order least squares (SLS) estimator based on the first two marginal moments of the response variables. The proposed estimator is computationally feasible and requires less distributional assumptions than the maximum likelihood estimator. To overcome the numerical difficulties of minimizing an objective function that involves multiple integrals, a simulation-based SLS estimator is proposed. We show that the SLS estimators are consistent and asymptotically normally distributed under fairly general conditions in the framework of GLMM.
Missing data is almost inevitable in longitudinal studies. Problems arise if the missing data mechanism is related to the response process. This thesis develops the proposed estimators to deal with response data missing at random by either adapting the inverse probability weight method or applying the multiple imputation approach.
In practice, some of the covariates are not directly observed but are measured with error. It is well-known that simply substituting a proxy variable for the unobserved covariate in the model will generally lead to biased and inconsistent estimates. We propose the instrumental variable method for the consistent estimation of GLMM with covariate measurement error. The proposed approach does not need any parametric assumption on the distribution of the unknown covariates. This makes the method less restrictive than other methods that rely on either a parametric distribution of the covariates, or to estimate the distribution using some extra information.
In the presence of data outliers, it is a concern that the SLS estimators may be vulnerable due to the second-order moments. We investigated the robustness property of the SLS estimators using their influence functions. We showed that the proposed estimators have a bounded influence function and a redescending property so they are robust to outliers.
The finite sample performance and property of the SLS estimators are studied and compared with other popular estimators in the literature through simulation studies and real world data examples.
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Chen, Xin. "Robust second-order least squares estimation for linear regression models." Thesis, 2009. http://hdl.handle.net/1828/3087.
Full textAbstract:
The second-order least-squares estimator (SLSE), which was proposed by Wang (2003), is asymptotically more efficient than the least-squares estimator (LSE) if the third moment of the error distribution is nonzero. However, it is not robust against outliers. In this paper. we propose two robust second-order least-squares estimators (RSLSE) for linear regression models. RSLSE-I and RSLSE-II, where RSLSE-I is robust against X-outliers and RSLSE-II is robust. against X-outliers and Y-outliers. The basic idea is to choose proper weight matrices, which give a zero weight to an outlier. The RSLSEs are asymptotically normally distributed and are highly efficient with high breakdown point.. Moreover, we compare the RSLSEs with the LSE, the SLSE and the robust MM-estimator through simulation studies and real data examples. The results show that they perform very well and are competitive to other robust regression estimators.
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Chuang, Chih-Yang, and 莊智仰. "The estimation of the second harmonic with differential concentration microbubbles." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/27042820996795884713.
Full textAbstract:
碩士國立臺灣大學
電機工程學研究所
95
Although the behavior of a bubble in an acoustic field has been studied for a long time, few theoretical describe the simulation of a real population of variably sized micro bubbles in a finite beam. In this thesis, we use the modified Rayleigh-Plesset equation to simulation each single bubble’s oscillator under the different acoustic pressure, and summation all the radiation signals from large number of such bubbles. Consider the effect from the waveform beam. Finally we will compare with the experience results. The nonlinear response (second harmonic , sub harmonic) come from the micro bubbles provide an easy way to separate from bubbles and tissue. We would like to use this identity to build up an ultrasound drug deliver system. We use the liposome as the ultrasound contrast agents for the drug carrier, furthermore trying to find the best region to manipulate the variations of liposome concentration and the amplitude of received spectrum. In conclusion, we wish that we could infer the amount of liposome by the amplitude of harmonic in this area, then we could not only to monitor, but also to forecast the amount of the drug carries. We set each parameter of the contrast agents, and choose the best model、input pulse waveform for the experience environment. We measure the micro bubble’s size distribution by N4-plus coulter and consider the probe effect made different acoustic pressure. We discuss the monitor range when input cycle number 15、20、25. Through experimentation result, we know that all the feasible manipulate areas occur in lower concentrations. We glad to see it, because all the clinical application are under the lower concentration.
49
盧契佑. "The Use of Second-Order Statistics for Instantaneous Frequency Estimation." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/27785113481827785193.
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碩士國立中山大學
電機工程研究所
81
A new adaptive scheme based on the second-order statistics of the received signal is devised, in this thesis, for estimating the digital instantaneous frequency of the narrowband input buried in additive white noise. Since the desired signal (the sinusoidal signal) and the additive noise are independent, the correlation between both will be null, ideally. Due to the reason described above, the adaptive second-order statistics method for estimating the parameters of the autoregressive (AR) model can be derived. That is, the second-order statistics of the received signal samples is used instead of the received signal samples of the input sequence in the conventional method. To estimate the AR parameters, a gradient-type algorithm is employed to approximate the optimum solution. The estimated AR parameters can be then applied to obtain the spectrum via the modified maximum entropy method. In consequence, the instantaneous frequency can be identified by searching the peak of the spectrum. To reduce the computation effort and make the hardware implementation more easier, the sign algorithm is adopted instead of the gradient-type algorithm in the second-order moment method. From the simulation result, we found that the performance of the presented methods, viz., the gradient-type algorithm and the sign algorithm, are performed very similar very similar but superior to the conventional first-order moment method.
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JIAN, JIN-WAN, and 簡進萬. "On the regression function estimation by second kind Tchebichef polynomial." Thesis, 1989. http://ndltd.ncl.edu.tw/handle/01455561865457199198.
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