The objective of this thesis is to design and implement a speech inputting system for addresses in Taiwan,Mainland china and HongKong,The completed system has the capability to identify full census and posting addresses in Taiwan and full posting addresses in Peking¡BShanghai¡BTien-Jin and Chungchin of China¡CFor HongKong,a partial address system,including region/street name or school,hotal and other public location names,is implemented¡C In this thesis,Mel frequency cepstrum coefficient,Hidden Mavkov model and lexicon search strategy are applied to choose the initial address candidates¡FMandarin intonation classification technique is then used to increase the final correct rate,under speaker dependent case,a 90%correct rate can be reached by using a Intel Celeron 2.4GHz CPU and RedHat Linux 9.0 operating system¡CThe total address-inputting task can be completed within 3 seconds¡C

The customers perceived all 5 SERVQUAL dimensions; tangibles, reliability, assurance, responstiveness, and empathy, pertaining service quality performance at low level when compared with their expectation. Thailand Resor AB should fulfill these 5 gaps and pay attention at assurance dimension concerning conveying trust and confidence because of the highest gap. The dimension of tangibles (equipment, physical facilities, etc.), empathy (ability to see through the customer’s eyes), responstiveness (willingness to help and provide prompt service), and reliability (doing what company have promised) were adjusted consequencely. The respondents highlighted the following key quality factors of their expectations; be treated with respect, be listened to and be dedicated attention, more friendly staffs, get the accoracy information. Some respondent have fuzzy expectations that he company have to further discover the real expectations by providing gauidance to their staffs to encourage them to ask questions.

No description available.

This thesis develops new hidden Markov models and applies them to financial market and macroeconomic time series. Chapter 1 proposes a probabilistic model of the return distribution with rich and heterogeneous intra-regime dynamics. It focuses on the characteristics and dynamics of bear market rallies and bull market corrections, including, for example, the probability of transition from a bear market rally into a bull market versus back to the primary bear state. A Bayesian estimation approach accounts for parameter and regime uncertainty and provides probability statements regarding future regimes and returns. A Value-at-Risk example illustrates the economic value of our approach. Chapter 2 develops a new efficient approach to model and forecast time series data with an unknown number of change-points. The key is assuming a conjugate prior for the time-varying parameters which characterize each regime and treating the regime duration as a state variable. Conditional on this prior and the time-invariant parameters, the predictive density and the posterior of the change-points have closed forms. The conjugate prior is further modeled as hierarchical to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients or both. In addition to the time-invariant structural change probability, one extension assumes the regime duration has a Poisson distribution. A new Markov Chain Monte Carlo sampler draws the parameters from the posterior distribution efficiently. The model is applied to Canadian inflation time series. Chapter 3 proposes an infinite dimension Markov switching model to accommodate regime switching and structural break dynamics or a combination of both in a Bayesian framework. Two parallel hierarchical structures, one governing the transition probabilities and another governing the parameters of the conditional data density, keep the model parsimonious and improve forecasts. This nonparametric approach allows for regime persistence and estimates the number of states automatically. A global identification algorithm for structural changes versus regime switching is presented. Applications to U.S. real interest rates and inflation compare the new model to existing parametric alternatives. Besides identifying episodes of regime switching and structural breaks, the hierarchical distribution governing the parameters of the conditional data density provides significant gains to forecasting precision.

This thesis develops new hidden Markov models and applies them to financial market and macroeconomic time series. Chapter 1 proposes a probabilistic model of the return distribution with rich and heterogeneous intra-regime dynamics. It focuses on the characteristics and dynamics of bear market rallies and bull market corrections, including, for example, the probability of transition from a bear market rally into a bull market versus back to the primary bear state. A Bayesian estimation approach accounts for parameter and regime uncertainty and provides probability statements regarding future regimes and returns. A Value-at-Risk example illustrates the economic value of our approach. Chapter 2 develops a new efficient approach to model and forecast time series data with an unknown number of change-points. The key is assuming a conjugate prior for the time-varying parameters which characterize each regime and treating the regime duration as a state variable. Conditional on this prior and the time-invariant parameters, the predictive density and the posterior of the change-points have closed forms. The conjugate prior is further modeled as hierarchical to exploit the information across regimes. This framework allows breaks in the variance, the regression coefficients or both. In addition to the time-invariant structural change probability, one extension assumes the regime duration has a Poisson distribution. A new Markov Chain Monte Carlo sampler draws the parameters from the posterior distribution efficiently. The model is applied to Canadian inflation time series. Chapter 3 proposes an infinite dimension Markov switching model to accommodate regime switching and structural break dynamics or a combination of both in a Bayesian framework. Two parallel hierarchical structures, one governing the transition probabilities and another governing the parameters of the conditional data density, keep the model parsimonious and improve forecasts. This nonparametric approach allows for regime persistence and estimates the number of states automatically. A global identification algorithm for structural changes versus regime switching is presented. Applications to U.S. real interest rates and inflation compare the new model to existing parametric alternatives. Besides identifying episodes of regime switching and structural breaks, the hierarchical distribution governing the parameters of the conditional data density provides significant gains to forecasting precision.

The goal of the Master's Thesis was to extend an existing cardiovascular model to include the mechanics of the lung, thus allowing to simulate breathing maneuvers such as the Valsalva maneuver and the Forced Vital Capacity maneuver. This included a remodeling of the pulmonary capillaries and of the existing interactions of the model with the intrathoracic pressure. The existing description of the vascular compartments was found to be insufficient to describe the hemodynamic response to orthostatic changes and was extended to include a compartment representing the upper body. Stress relaxation was included into all the larger vascular compartments. The results showed an improved accuracy of the extended model when subjected to large intrathoracic pressure changes and during orthostatic stress. The internal responses of the newly modeled pulmonary capillaries were studied and verified against literature with satisfying results.

No description available.

A business activity generates both negative and positive externality simultaneously for both the environment and the society. The basic idea is to minimize the negative externality and generate more positive externality.Expectations of the society towards businesses have grown tremendously during the past few decades, the stress is upon a socially, environmentally and legally responsible businesses. Organizations have responded to this expectation by indulging in a set of activities which are termed as Corporate Social Responsibility, but that has not been enough, businesses cannot resolve social and environmental problems of the society unless they know where to focus and what is the strategy to become better businesses and yet not be criticized. The need is to utilize a firm‘s resources in the most effective manner. Thus, strategic CSR comes in as a solution for better performance.Strategic CSR began the evolution of more responsible and favorable businesses in terms of societal pressures and expectations. In our research, we have tried to develop a framework which when incorporated with the CSR concept helps make more strategic sense to the firm, so that it benefits not only the society but also the firm itself. The research builds a strategic framework and analyses in light of it the CSR activities of three major retail companies namely, IKEA, Starbucks and H&M.The EFQM model which is a self assessment tool for organizations has been incorporated with CSR to make it a strategic assessment framework. This has been a new concept, and has proven quite effective in assessing the strategies and outcomes regarding CSR of the three companies being used as examples.

A survey performed over existing two pilot-scale and two full-scale RO desalination facilities to study the current status of boron rejection showed a highest rejection 85% leading to permeate boron concentration of 0.52 mg/L, and recent studies predicted a cost increase due to incorporation of boron reduction systems. Mathematical models were developed to study the process performance and related cost implications. The deterministic process model was verified with pilot-scale experiment performed using a single spiral wound module and was later modified to represent the full-scale design options available to meet the required water quality criteria. Then the selected full-scale design options were simulated to predict their performance in terms of recovery and boron rejection. For cost analysis, to account for uncertainty probability models were developed for stochastic inputs to the cost estimation model and were used with operating parameters from the full-scale simulations to determine the expected total cost of water produced. Later, a sensitivity analysis was performed to observe the effect of change in uncertainty of inputs. Further, the applications of the deterministic process model are suggested.

A Multifactor model is used to explain asset return and risk and its explanatory power depends on common factors that the model uses. Researchers strive to find reasonable factors to enhance multifactor model¡¦s efficiency. However, there are still some unknown factors to be discovered. Miller (2006) presents a general concept and structure of hybrid factor model. The study follows the idea of Miller (2006) and aims to build a complete flow of constructing hybrid factor model that is based on fundamental factor model and statistical factor models. We also apply the hybrid factor model to the Taiwan stock market. We assume that a fundamental factor model is already developed and therefore this study focuses on building the second stage, statistical factor model. Principal Component Analysis is used to form statistical factor and spectral decomposition is used to prepare data for principal component analysis. Those methods are applied to stocks on the Taiwan Stock Exchange in the period of January 1, 2000 to December 31, 2009. This study presents a complete construction flow of hybrid factor models and further confirms that a hybrid factor model is able to find missing factors in a developing market such as Taiwan¡¦s stock market. The study also discovers that the missing factors might be market factor and extensive electronic industry factor.