No description available.

In Statistical Process Control, it is usually assumed that observations taken from the process at different times are independent with a constant mean and with variation due only to measurement error. In many processes this assumption of independence is not satisfied. The lack of independence of observations taken at different times may have a significant effect on the properties of a process monitoring technique. A first order autoregressive process which is observed subject to measurement error is considered. Integral equation, Markov chain and simulation approaches are used to evaluate the average run length (ARL) of exponentially weighted moving average (EWMA) and one-sided cumulative sum (CUSUM) control charts used to monitor the process. The effects of correlation and measurement error on the ARL's of the control charts are studied for a process which is in control and for a process which has undergone a shift in mean level away from the target value. Methods of estimation of the parameters of the process are examined, and tables are given to assist in the design of EWMA and CUSUM control charts for AR(1) processes. Examples of designing an EWMA and a CUSUM chart for an AR(1) process are presented. / Ph. D.

published_or_final_version / Computer Science and Information Systems / Doctoral / Doctor of Philosophy

published_or_final_version / abstract / Statistics and Actuarial Science / Master / Master of Philosophy

This thesis is part of an ongoing larger scale research study started in 2004 at the Naval Postgraduate School (NPS) which aims to develop a speech-driven human-machine interface for the operation of semi-autonomous military robots in noisy operational environments. Earlier work included collecting a small database of isolated word utterances of seven words from 20 adult subjects using an in-ear microphone. The research conducted here develops a speaker-independent isolated word recognizer from these acoustic signals based on a discrete-observation Hidden Markov Model (HMM). The study implements the HMM-based isolated word recognizer in three steps. The first step performs the endpoint detection and speech segmentation by using short-term temporal analysis. The second step includes speech feature extraction using static and dynamic MFCC parameters and vector quantization of continuous-valued speech features. Finally, the last step involves the discrete-observation HMM-based classifier for isolated word recognition. Experimental results show the average classification performance around 92.77%. The most significant result of this study is that the acoustic signals originating from speech organs and collected within the external ear canal via the in-ear microphone can be used for isolated word recognition. The second dataset collected under low signal-to-noise ratio conditions with additive noise results in 79% recognition accuracy in the HMM-based classifier. We also compared the classification results of the data collected within the ear canal and outside the mouth via the same microphone. The second dataset collected under low signal-to-noise ratio conditions with additive noise results in 79% recognition accuracy in the HMM-based classifier. We also compared the classification results of the data collected within the ear canal and outside the mouth via the same microphone. Average classification rates obtained for the data collected outside the mouth shows significant performance degradation (down to 63%), over that observed with the data collected from within the ear canal (down to 86%). The ear canal dampens high frequencies. As a result, the HMM model derived for the data with dampened higher frequencies does not accurately fit the data collected outside the mouth, resulting in degraded recognition performances.

The mean-field game theory is the study of strategic decision making in very large populations of weakly interacting individuals. Mean-field games have been an active area of research in the last decade due to its increased significance in many scientific fields. The foundations of mean-field theory go back to the theory of statistical and quantum physics. One may describe mean-field games as a type of stochastic differential game for which the interaction between the players is of mean-field type, i.e the players are coupled via their empirical measure. It was proposed by Larsy and Lions and independently by Huang, Malhame, and Caines. Since then, the mean-field games have become a rapidly growing area of research and has been studied by many researchers. However, most of these studies were dedicated to diffusion-type games. The main purpose of this thesis is to extend the theory of mean-field games to jump case in both discrete and continuous state space. Jump processes are a very important tool in many areas of applications. Specifically, when modeling abrupt events appearing in real life. For instance, financial modeling (option pricing and risk management), networks (electricity and Banks) and statistics (for modeling and analyzing spatial data). The thesis consists of two papers and one technical report which will be submitted soon: In the first publication, we study the mean-field game in a finite state space where the dynamics of the indistinguishable agents is governed by a controlled continuous time Markov chain. We have studied the control problem for a representative agent in the linear quadratic setting. A dynamic programming approach has been used to drive the Hamilton Jacobi Bellman equation, consequently, the optimal strategy has been achieved. The main result is to show that the individual optimal strategies for the mean-field game system represent 1/N-Nash equilibrium for the approximating system of N agents. As a second article, we generalize the previous results to agents driven by a non-linear pure jump Markov processes in Euclidean space. Mathematically, this means working with linear operators in Banach spaces adapted to the integro-differential operators of jump type and with non-linear partial differential equations instead of working with linear transformations in Euclidean spaces as in the first work. As a by-product, a generalization for the Koopman operator has been presented. In this setting, we studied the control problem in a more general sense, i.e. the cost function is not necessarily of linear quadratic form. We showed that the resulting unique optimal control is of Lipschitz type. Furthermore, a fixed point argument is presented in order to construct the approximate Nash Equilibrium. In addition, we show that the rate of convergence will be of special order as a result of utilizing a non-linear pure jump Markov process. In a third paper, we develop our approach to treat a more realistic case from a modelling perspective. In this step, we assume that all players are subject to an additional common noise of Brownian type. We especially study the well-posedness and the regularity for a jump version of the stochastic kinetic equation. Finally, we show that the solution of the master equation, which is a type of second order partial differential equation in the space of probability measures, provides an approximate Nash Equilibrium. This paper, unfortunately, has not been completely finished and it is still in preprint form. Hence, we have decided not to enclose it in the thesis. However, an outlook about the paper will be included.

In this dissertation the results of Jerrum and Sinclair on the conductance of Markov chains are used to prove that almost all generalized Steinhaus graphs are rapidly mixing and an algorithm for the uniform generation of 2 - (4k + 1,4,1) cyclic Mendelsohn designs is developed.

Natural gas markets are incomplete due to physical limitations and low liquidity, but most valuation approaches for natural gas storage contracts assume a complete market. We propose an alternative approach based on indifference pricing which does not require this assumption but entails the solution of a high- dimensional stochastic-dynamic optimization problem under a risk measure. To solve this problem, we develop a method combining stochastic dual dynamic programming with a novel quantization method that approximates the continuous process of natural gas prices by a discrete scenario lattice. In a computational experiment, we demonstrate that our solution method can handle the high dimensionality of the optimization problem and that solutions are near-optimal. We then compare our approach with rolling intrinsic valuation, which is widely used in the industry, and show that the rolling intrinsic value is sub-optimal under market incompleteness, unless the decision-maker is perfectly risk-averse. We strengthen this result by conducting a backtest using historical data that compares both trading strategies. The results show that up to 40% more profit can be made by using our indifference pricing approach.

by Patrick Shu Pui Ko. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1993. / Includes bibliographical references (leaves 77-78). / Chapter 1. --- Introduction --- p.1 / Chapter 1.1 --- Introduction to Speech Recognition --- p.1 / Chapter 1.2 --- Classifications and Constraints of Speech Recognition Systems --- p.1 / Chapter 1.2.1 --- Isolated Subword Unit Recognition --- p.1 / Chapter 1.2.2 --- Isolated Word Recognition --- p.2 / Chapter 1.2.3 --- Continuous Speech Recognition --- p.2 / Chapter 1.3 --- Objective of the Thesis --- p.3 / Chapter 1.3.1 --- What is the Problem --- p.3 / Chapter 1.3.2 --- How the Problem is Approached --- p.3 / Chapter 1.3.3 --- The Organization of this Thesis --- p.3 / Chapter 2. --- Literature Review --- p.5 / Chapter 2.1 --- Approaches to the Problem of Speech Recognition --- p.5 / Chapter 2.1.1 --- Template-Based Approaches --- p.6 / Chapter 2.1.2 --- Knowledge-Based Approaches --- p.9 / Chapter 2.1.3 --- Stochastic Approaches --- p.10 / Chapter 2.1.4 --- Connectionist Approaches --- p.14 / Chapter 3. --- Discrimination Issues of HMM --- p.16 / Chapter 3.1 --- Maximum Likelihood Estimation (MLE) --- p.16 / Chapter 3.2 --- Maximum Mutual Information (MMI) --- p.17 / Chapter 4. --- Neural Networks --- p.19 / Chapter 4.1 --- History --- p.19 / Chapter 4.2 --- Basic Concepts --- p.20 / Chapter 4.3 --- Learning --- p.21 / Chapter 4.3.1 --- Supervised Training --- p.21 / Chapter 4.3.2 --- Reinforcement Training --- p.22 / Chapter 4.3.3 --- Self-Organization --- p.22 / Chapter 4.4 --- Error Back-propagation --- p.22 / Chapter 5. --- Proposal of a Discriminative Neural Network Layer --- p.25 / Chapter 5.1 --- Rationale --- p.25 / Chapter 5.2 --- HMM Parameters --- p.27 / Chapter 5.3 --- Neural Network Layer --- p.28 / Chapter 5.4 --- Decision Rules --- p.29 / Chapter 6. --- Data Preparation --- p.31 / Chapter 6.1 --- TIMIT --- p.31 / Chapter 6.2 --- Feature Extraction --- p.34 / Chapter 6.3 --- Training --- p.43 / Chapter 7. --- Experiments and Results --- p.52 / Chapter 7.1 --- Experiments --- p.52 / Chapter 7.2 --- Experiment I --- p.52 / Chapter 7.3 --- Experiment II --- p.55 / Chapter 7.4 --- Experiment III --- p.57 / Chapter 7.5 --- Experiment IV --- p.58 / Chapter 7.6 --- Experiment V --- p.60 / Chapter 7.7 --- Computational Issues --- p.62 / Chapter 7.8 --- Limitations --- p.63 / Chapter 8. --- Conclusion --- p.64 / Chapter 9. --- Future Directions --- p.67 / Appendix / Chapter A. --- Linear Predictive Coding --- p.69 / Chapter B. --- Implementation of a Vector Quantizer --- p.70 / Chapter C. --- Implementation of HMM --- p.73 / Chapter C.1 --- Calculations Underflow --- p.73 / Chapter C.2 --- Zero-lising Effect --- p.75 / Chapter C.3 --- Training With Multiple Observation Sequences --- p.76 / References --- p.77

by Chow Ka Fai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves [124-132]). / Text in English; abstract also in Chinese. / by Chow Ka Fai. / Chapter 1 --- INTRODUCTION --- p.1 / Chapter 1.1 --- Speech Recognition Technology --- p.4 / Chapter 1.2 --- Automatic Recognition of Cantonese Speech --- p.6 / Chapter 1.3 --- Objectives of the thesis --- p.8 / Chapter 1.4 --- Thesis Outline --- p.11 / Chapter 2 --- FUNDAMENTALS OF HMM BASED RECOGNITION SYSTEM --- p.13 / Chapter 2.1 --- Introduction --- p.13 / Chapter 2.2 --- HMM Fundamentals --- p.13 / Chapter 2.2.1 --- HMM Structure and Behavior --- p.13 / Chapter 2.2.2 --- HMM-based Speech Modeling --- p.15 / Chapter 2.2.3 --- Mathematics --- p.18 / Chapter 2.3 --- hmm Based Speech Recognition System --- p.22 / Chapter 2.3.1 --- Isolated Speech Recognition --- p.23 / Chapter 2.3.2 --- Connected Speech Recognition --- p.25 / Chapter 2.4 --- Algorithms for Finding Hidden State Sequence --- p.28 / Chapter 2.4.1 --- Forward-backward algorithm --- p.29 / Chapter 2.4.2 --- Viterbi Decoder Algorithm --- p.31 / Chapter 2.5 --- Parameter Estimation --- p.32 / Chapter 2.5.1 --- Basic Ideas for Estimation --- p.32 / Chapter 2.5.2 --- Single Model Re-estimation Using Best State-Time Alignment (HINIT) --- p.36 / Chapter 2.5.3 --- Single Model Re-estimation Using Baum- Welch Method (HREST) --- p.39 / Chapter 2.5.4 --- HMM Embedded Re-estimation (HEREST) --- p.41 / Chapter 2.6 --- Feature Extraction --- p.42 / Chapter 2.7 --- Summary --- p.47 / Chapter 3 --- CANTONESE PHONOLOGY AND LANGUAGE PROPERTIES --- p.48 / Chapter 3.1 --- Introduction --- p.48 / Chapter 3.2 --- Cantonese and Chinese Language --- p.48 / Chapter 3.2.1 --- Chinese Words and Characters --- p.48 / Chapter 3.2.2 --- The Relationship between Cantonese and Chinese Characters --- p.50 / Chapter 3.3 --- Basic Syllable structure --- p.51 / Chapter 3.3.1 --- CVC structure --- p.51 / Chapter 3.3.2 --- Cantonese Phonemes --- p.52 / Chapter 3.3.3 --- The Initial-Final structure --- p.55 / Chapter 3.3.4 --- Cantonese Nine Tone System --- p.57 / Chapter 3.4 --- Acoustic Properties of Cantonese --- p.58 / Chapter 3.5 --- Cantonese Phonology for Speech Recognition --- p.60 / Chapter 3.6 --- Summary --- p.62 / Chapter 4 --- CANTONESE SPEECH DATABASES --- p.64 / Chapter 4.1 --- Introduction --- p.64 / Chapter 4.2 --- The Importance of Speech Data --- p.64 / Chapter 4.3 --- The Demands of Cantonese Speech Databases --- p.67 / Chapter 4.4 --- Principles in Cantonese Database Development --- p.67 / Chapter 4.5 --- Resources and Limitations for Database Designs --- p.69 / Chapter 4.6 --- Details of Speech Databases --- p.69 / Chapter 4.6.1 --- Multiple speakers' Speech Database (CUWORD) --- p.70 / Chapter 4.6.2 --- Single Speaker's Speech Database (MYVOICE) --- p.72 / Chapter 4.7 --- Difficulties and Solutions in Recording Process --- p.76 / Chapter 4.8 --- Verification of Phonetic Transcription --- p.78 / Chapter 4.9 --- Summary --- p.79 / Chapter 5 --- TRAINING OF AN HMM BASED CANTONESE SPEECH RECOGNITION SYSTEM --- p.80 / Chapter 5.1 --- Introduction --- p.80 / Chapter 5.2 --- Objectives of HMM Development --- p.81 / Chapter 5.3 --- The Design of Initial-Final Models --- p.83 / Chapter 5.4 --- Initialization of Basic Initial-Final Models --- p.84 / Chapter 5.4.1 --- The Initialization Training with HEREST --- p.85 / Chapter 5.4.2 --- Refinement of Initialized Models --- p.88 / Chapter 5.4.3 --- Evaluation of the Models --- p.90 / Chapter 5.5 --- Training of Connected Speech Speaker Dependent Models --- p.93 / Chapter 5.5.1 --- Training Strategy --- p.93 / Chapter 5.5.2 --- Preliminary Result --- p.94 / Chapter 5.6 --- Design and Training of Context Dependent Initial Final Models --- p.95 / Chapter 5.6.1 --- Intra-syllable Context Dependent Units --- p.96 / Chapter 5.6.2 --- The Inter-syllable Context Dependent Units --- p.97 / Chapter 5.6.3 --- Model Refinement by Using Mixture Incrementing --- p.98 / Chapter 5.7 --- Training of Speaker Independent Models --- p.99 / Chapter 5.8 --- Discussions --- p.100 / Chapter 5.9 --- Summary --- p.101 / Chapter 6 --- PERFORMANCE ANALYSIS --- p.102 / Chapter 6.1 --- Substitution Errors --- p.102 / Chapter 6.1.1 --- Confusion of Long Vowels and Short Vowels for Initial Stop Consonants --- p.102 / Chapter 6.1.2 --- Confusion of Nasal Endings --- p.103 / Chapter 6.1.3 --- Confusion of Final Stop Consonants --- p.104 / Chapter 6.2 --- Insertion Errors and Deletion Errors --- p.105 / Chapter 6.3 --- Accuracy of Individual Models --- p.106 / Chapter 6.4 --- The Impact of Individual Models --- p.107 / Chapter 6.4.1 --- The Expected Error Rate of Initial Models --- p.110 / Chapter 6.4.2 --- The Expected Error Rate of Final Models --- p.111 / Chapter 6.5 --- Suggested Solutions for Error Reduction --- p.113 / Chapter 6.5.1 --- Duration Constraints --- p.113 / Chapter 6.5.2 --- The Use of Language Model --- p.113 / Chapter 6.6 --- Summary --- p.114 / Chapter 7 --- APPLICATIONS EXAMPLES OF THE HMM RECOGNITION SYSTEM --- p.115 / Chapter 7.1 --- Introduction --- p.115 / Chapter 7.2 --- Application 1: A Hong Kong Stock Market Inquiry System --- p.116 / Chapter 7.3 --- Application 2： A Navigating System for Hong Kong Street Map --- p.117 / Chapter 7.4 --- Automatic Character-to-Phonetic Conversion --- p.118 / Chapter 7.5 --- Summary --- p.119 / Chapter 8 --- CONCLUSIONS AND SUGGESTIONS FOR FURTHER WORK --- p.120 / Chapter 8.1 --- Conclusions --- p.120 / Chapter 8.2 --- Suggestions for Future Work --- p.122 / Chapter 8.2.1 --- Development of Continuous Speech Recognition System --- p.122 / Chapter 8.2.2 --- Implementation of Statistical Language Models --- p.122 / Chapter 8.2.3 --- Tones for Continuous Speech --- p.123 / BIBILOGRAPHY / APPENDIX