In this thesis two collaborative localization techniques are studied: multidimensional scaling (MDS) and maximum likelihood estimator (MLE). A synthesis of a new location estimation method through a serial integration of these two techniques, such that an estimate is first obtained using MDS and then MLE is employed to fine-tune the MDS solution, was the subject of this research using various simulation and experimental studies. In the simulations, important issues including the effects of sensor node density, reference node density and different deployment strategies of reference nodes were addressed. In the experimental study, the path loss model of indoor environments is developed by determining the environment-specific parameters from the experimental measurement data. Then, the empirical path loss model is employed in the analysis and simulation study of the performance of collaborative localization techniques.

abstract: For the last 50 years, oscillator modeling in ranging systems has received considerable attention. Many components in a navigation system, such as the master oscillator driving the receiver system, as well the master oscillator in the transmitting system contribute significantly to timing errors. Algorithms in the navigation processor must be able to predict and compensate such errors to achieve a specified accuracy. While much work has been done on the fundamentals of these problems, the thinking on said problems has not progressed. On the hardware end, the designers of local oscillators focus on synthesized frequency and loop noise bandwidth. This does nothing to mitigate, or reduce frequency stability degradation in band. Similarly, there are not systematic methods to accommodate phase and frequency anomalies such as clock jumps. Phase locked loops are fundamentally control systems, and while control theory has had significant advancement over the last 30 years, the design of timekeeping sources has not advanced beyond classical control. On the software end, single or two state oscillator models are typically embedded in a Kalman Filter to alleviate time errors between the transmitter and receiver clock. Such models are appropriate for short term time accuracy, but insufficient for long term time accuracy. Additionally, flicker frequency noise may be present in oscillators, and it presents mathematical modeling complications. This work proposes novel H∞ control methods to address the shortcomings in the standard design of time-keeping phase locked loops. Such methods allow the designer to address frequency stability degradation as well as high phase/frequency dynamics. Additionally, finite-dimensional approximants of flicker frequency noise that are more representative of the truth system than the tradition Gauss Markov approach are derived. Last, to maintain timing accuracy in a wide variety of operating environments, novel Banks of Adaptive Extended Kalman Filters are used to address both stochastic and dynamic uncertainty. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2020

No description available.

Lam Suet-man. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2001. / Includes bibliographical references (leaves 111-117). / Abstracts in English and Chinese. / ACKNOWOLEDGMENTS --- p.iii / LIST OF TABLES --- p.iv / LIST OF ILLUSTRATIONS --- p.vi / CHAPTER / Chapter ONE --- INTRODUCTION --- p.1 / Chapter TWO --- REVIEW OF THE LITERATURE --- p.6 / Parametric Models / Nonparametric Estimation Techniques / Chapter THREE --- METHODOLOGY --- p.21 / Parametric Models / Nonparametric Models / Chapter FOUR --- DATA DESCRIPTION --- p.33 / Chapter FIVE --- EMPIRICAL FINDINGS --- p.39 / Estimation Results / Evaluation of Model Performance / Out-of-sample Forecast Evaluation / Chapter SIX --- CONCLUSION --- p.58 / TABLES --- p.62 / ILLUSTRATIONS --- p.97 / APPENDIX --- p.107 / BIBOGRAPHY --- p.111

This thesis consists of three parts: a genetic fuzzy trading model for stock trading, incremental intraday information for financial time series forecasting, and intraday effects in conditional variance estimation. Part A investigates a genetic fuzzy trading model for stock trading. This part contributes to use a fuzzy trading model to eliminate undesirable discontinuities, incorporate vague trading rules into the trading model and use genetic algorithm to select an optimal trading ruleset. Technical indicators are used to monitor the stock price movement and assist practitioners to set up trading rules to make buy-sell decision. Although some trading rules have a clear buy-sell signal, the signals are always detected with 'hard' logical. These trigger the undesirable discontinuities due to the jumps of the Boolean variables that may occur for small changes of the technical indicator. Some trading rules are vague and conflicting. They are difficult to incorporate into the trading system while they possess significant market information. Various performance comparisons such as total return, maximum drawdown and profit-loss ratios among different trading strategies were examined. Genetic fuzzy trading model always gave moderate performance. Part B studies and contributes to the literature that focuses on the forecasting of daily financial time series using intraday information. Conventional daily forecast always focuses on the use of lagged daily information up to the last market close while neglecting intraday information from the last market close to current time. Such intraday information are referred to incremental intraday information. They can improve prediction accuracy not only at a particular instant but also with the intraday time when an appropriate predictor is derived from such information. These are demonstrated in two forecasting examples, predictions of daily high and range-based volatility, using linear regression and Neural Network forecasters. Neural Network forecaster possesses a stronger causal effect of incremental intraday information on the predictand. Predictability can be estimated by a correlation without conducting any forecast. Part C explores intraday effects in conditional variance estimation. This contributes to the literature that focuses on conditional variance estimation with the intraday effects. Conventional GARCH volatility is formulated with an additive-error mean equation for daily return and an autoregressive moving-average specification for its conditional variance. However, the intra-daily information doesn't include in the conditional variance while it should has implication on the daily variance. Using Engle's multiplicative-error model formulation, range-based volatility is proposed as an intraday proxy for several GARCH frameworks. The impact of significant changes in intraday data is reflected in the MEM-GARCH variance. For some frameworks, it is possible to use lagged values of range-based volatility to delay the intraday effects in the conditional variance equation. / Ng, Hoi Shing Raymond. / Adviser: Kai-Pui Lam. / Source: Dissertation Abstracts International, Volume: 72-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 107-114). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

This thesis consists of four chapters. Chapter 1 focuses on theoretical results on high-order laplacian-based regularization in function estimation. We studied the iterated laplacian regularization in the context of supervised learning in order to achieve both nice theoretical properties (like thin-plate splines) and good performance over complex region (like soap film smoother). In Chapter 2, we propose an innovative static path-planning algorithm called m-A* within an environment full of obstacles. Theoretically we show that m-A* reduces the number of vertex. In the simulation study, our approach outperforms A* armed with standard L1 heuristic and stronger ones such as True-Distance heuristics (TDH), yielding faster query time, adequate usage of memory and reasonable preprocessing time. Chapter 3 proposes m-LPA* algorithm which extends the m-A* algorithm in the context of dynamic path-planning and achieves better performance compared to the benchmark: lifelong planning A* (LPA*) in terms of robustness and worst-case computational complexity. Employing the same beamlet graphical structure as m-A*, m-LPA* encodes the information of the environment in a hierarchical, multiscale fashion, and therefore it produces a more robust dynamic path-planning algorithm. Chapter 4 focuses on an approach for the prediction of spot electricity spikes via a combination of boosting and wavelet analysis. Extensive numerical experiments show that our approach improved the prediction accuracy compared to those results of support vector machine, thanks to the fact that the gradient boosting trees method inherits the good properties of decision trees such as robustness to the irrelevant covariates, fast computational capability and good interpretation.

The ability to implement Wide Area Monitoring and Control in power systems is developing into a need in order to prevent wide scale cascading outages. Monitoring of events in the power system provides a great deal of insight into the behaviour of the system. The research work presented in this thesis focussed on two tools that aid in monitoring: State Estimation and Synchronised Phasors provided by Phasor Measurement Units (PMU). State Estimation is essentially an on-line data processing scheme used to estimate the best possible state (i.e. voltage phasors) from a monitored set of measurements (active and reactive powers/voltage phasor measurements). The ever growing complexity and developments in the state of art calls for robust state estimators that converge accurately and rapidly. Newton’s method forms the basis for most of the solution approaches. For real-time application in modern power systems, the existing Newton-based state estimation algorithms are too fragile numerically. It is known that Newton’s algorithm may fail to converge if the initial nominal point is far from the optimal point. Sometimes Newton’s algorithm can converge to a local minima. Also Newton’s step can fail to be a descent direction if the gain matrix is nearly singular or ill-conditioned. This thesis proposes a new and more robust method that is based on linear programming and trust region techniques. The proposed formulation is suitable for Upper Bound Linear Programming. The formulation is first introduced and its convergence characteristics with the use of Upper Bound Linear Programming is studied. In the subsequent part, the solution to the same formulation is obtained using trust region algorithms. Proposed algorithms have been tested and compared with well known methods. The trust region method-based state estimator is found to be more reliable. This enhanced reliability justifies the additional time and computational effort required for its execution. One of the key elements in the synchrophasor based wide area monitoring is the Phasor Measurement Unit. Synchronized, real time, voltage phasor angle, phasor measurements over a distributed power network presents an excellent opportunity for major improvements in power system control and protection. Two of the most significant applications include state estimation and instability prediction. In recent years, there has been a significant research activity on the problem of finding the suitable number of PMUs and their optimal locations. For State Estimation, such procedures, which basically ensure observability based on network topology, are sufficient. However for instability prediction, it is very essential that the PMUs are located such that important/vulnerable buses are also directly monitored. In this thesis a method for optimal placement of PMUs, considering the vulnerable buses is developed. This method serves two purposes viz., identifying optimal locations for PMU (planning stage), and identifying the set PMUs to be closely monitored for instability prediction. The major issue is to identify the key buses when the angular and voltage stability prediction is taken into account. Integer Linear Programming technique with equality and inequality constraints is used to find out the optimal placement set. Further, various aspects of including the Phasor Measurements in state estimation algorithms are addressed. Studies are carried out on various sample test systems, an IEEE 30-bus system and real life Indian southern grid equivalents of 24-bus system, 72-bus system and 205-bus system.

To overcome the curse of dimensionality, dimension reduction is important and necessary for understanding the underlying phenomena in a variety of fields. Dimension reduction is the transformation of high-dimensional data into a meaningful representation in the low-dimensional space. It can be further classified into feature selection and feature extraction. In this thesis, which is composed of four projects, the first two focus on feature selection, and the last two concentrate on feature extraction. The content of the thesis is as follows. The first project presents several efficient methods for the sparse representation of a multiple measurement vector (MMV); some theoretical properties of the algorithms are also discussed. The second project introduces the NP-hardness problem for penalized likelihood estimators, including penalized least squares estimators, penalized least absolute deviation regression and penalized support vector machines. The third project focuses on the application of manifold learning in the analysis and prediction of 24-hour electricity price curves. The last project proposes a new hessian regularized nonlinear time-series model for prediction in time series.

Generally in most of the applications of estimation theory using the Method of Maximum Likelihood Estimation (MMLE) to dynamical systems one deals with a situation where only the measurement noise alone is present. However in many present day applications where modeling errors and random state noise input conditions occur it has become necessary for MMLE to handle measurement noise as well as process noise. The numerical algorithms accounting for both measurement and process noise require significantly an order of magnitude higher computer time and memory. Further more, implementation difficulties and convergence problems are often encountered. Here one has to estimate the quantities namely, the initial state error covariance matrix Po, measurement noise covariance matrix R, the process noise covariance matrix Q and the system parameter 0 and the present work deals with the above. Since the above problem is fairly involved we need to have a good reference solution. For this purpose we utilize the approach and results of Gemson who considered the above problem via the extended Kalman filter (EKF) route to compare the present results from the MMLE route. The EKF uses the unknown parameters as additional states unlike in MMLE which uses only the system states. Chapter 1 provides a brief historical perspective followed by parameter identification in the presence of process and measurement noises. The earlier formulations such as natural, innovation, combined, and adaptive approaches are discussed. Chapter 2 deals with the heuristic adaptive tuning of the Kalman filter parameters for the matrices Q and R by Myers and Tapley originally developed for state estimation problems involving satellite orbit estimation. It turns out that for parameter estimation problems apart from the above matrices even the choice of the initial covariance matrix Po is crucial for obtaining proper parameter estimates with a finite amount of data and for this purpose the inverse of the information matrix for Po is used. This is followed by a description of the original Controlled Random Search (CRS) of Price and its variant as implemented and used in the present work to estimate or tune Q, R, and 0 which is the aim of the present work. The above help the reader to appreciate the setting under which the present study has been carried out. Chapter 3 presents the results and the analysis of the estimation procedure adopted with respect to a specific case study of the lateral dynamics of an aircraft involving 15 unknown parameters. The reference results for the present work are the ones based on the approach of Gemson and Ananthasayanam (1998). The present work proceeds in two phases. In the first case (i) the EKF estimates for Po, Q, and R are used to obtain 0 and in the second case (ii) the estimate of Po and Q together with a reasonable choice of R are utilized to obtain 0 from the CRS algorithm. Thus one is able to assess the capability of the CRS to estimate only the unknown parameters. The next Chapter 4 presents the results of utilizing the CRS algorithm with R based on a reasonable choice and for Po from the inverse of the information matrix to estimate both Q and 0. This brings out the efficiency of MMLE with CRS algorithm in the estimation of unknown process noise characteristics and unknown parameters. Thus it demonstratesthofcdifficult Q can be estimated using CRS technique without the attendant difficulties of the earlier MMLE formulations in dealing with process noise. Chapter 5 discusses the - implementation of CRS to estimate the unknown measurement noise covariance matrix R together with the unknown 0 by utilizing the values of Po and Q obtained through EKF route. The effect of variation of R in the parameter estimation procedure is also highlighted in This Chapter. This Chapter explores the importance of Po in the estimation procedure. It establishes the importance of Po though most of the earlier works do not appear to have recognized such a feature. It turned out that the CRS algorithm does not converge when some arbitrary value of Po is chosen. It has to be necessarily obtained from a scouting pass of the EKF. Some sensitivity studies based on variations of Po shows its importance. Further studies shows the sequence of updates, the random nature of process and measurement noise effects, the deterministic nature of the parameter, play a critical role in the convergence of the algorithm. The last Chapter 6 presents the conclusions from the present work and suggestions for further work.

published_or_final_version / Education / Master / Master of Education