NonLinear Principal Component Analysis (NLPCA) using three-hidden-layer feed-forward neural networks can produce solutions that over-fit the data and are non-unique. These problems have been dealt with by subjective methods during the network training. This study shows that these problems are intrinsic due to the three-hidden-layer architecture. A simplified two-hidden-layer feed-forward neural network that has no encoding layer and no bottleneck and output biases is proposed. This new, compact NLPCA model alleviates these problems without employing the subjective methods and is called quasi-objective. The compact NLPCA is applied to the zonal winds observed at seven pressure levels between 10 and 70 hPa in the equatorial stratosphere to represent the Quasi-Biennial Oscillation (QBO) and investigate its variability and structure. The two nonlinear principal components of the dataset offer a clear picture of the QBO. In particular, their structure shows that the QBO phase consists of a predominant 28.4-month cycle that is modulated by an 11-year cycle and a longer-period cycle. The significant difference in variability of the winds between cold and warm seasons and the tendency for a seasonal synchronization of the QBO phases are well captured. The one-dimensional NLPCA approximation of the dataset provides a better representation of the QBO than the classical principal component analysis and a better description of the asymmetry of the QBO between westerly and easterly shear zones and between their transitions. The compact NLPCA is then applied to the Arctic Oscillation (AO) index and aforementioned zonal winds to investigate the relationship of the AO with the QBO. The NLPCA of the AO index and zonal-winds dataset shows clearly that, of covariation of the two oscillations, the phase defined by the two nonlinear principal components progresses with a predominant 28.4-month periodicity, plus the 11-year and longer-period modulations. Large positive values of the AO index occur when westerlies prevail near the middle and upper levels of the equatorial stratosphere. Large negative values of the AO index arise when easterlies occupy over half the layer of the equatorial stratosphere.

Electrical machines are subject to different types of failures. Early detection of the incipient faults and fast maintenance may prevent costly consequences. Fault diagnosis of wind turbine is especially important because they are situated at extremely high towers and therefore inaccessible. For offshore plants, bad weather can prevent any repair actions for several weeks. In some of the new wind turbines synchronous generators are used and directly connected to the grid without the need of power converters. Despite intensive research efforts directed at rotor fault diagnosis in induction machines, the research work pertinent to damper winding failure of synchronous machines is very limited. This dissertation is concerned with the in-depth study of damper winding failure and its traceable symptoms in different machine signals and parameters. First, a model of a synchronous machine with damper winding based on the winding function approach is presented. Next, simulation and experimental results are presented and discussed. A specially designed inside-out synchronous machine with a damper winding is employed for the experimental setup. Finally, a novel analytical method is developed to predict the behavior of the left sideband amplitude for different numbers and locations of the broken bars. This analysis is based on the magnetic field theory and the unbalanced multiphase circuits. It is found that due to the asymmetrical structure of damper winding, the left sideband component in the stator current spectrum of the synchronous machine during steady state asynchronous operation is not similar to that of the induction machine with broken bars. As a result, the motor current signature analysis (MCSA) for detection rotor failures in the induction machine is usable to detect broken damper bars in synchronous machines. However, a novel intelligent-systems based approach is developed that can identify the severity of the damper winding failure. This approach potentially can be used in a non-invasive condition monitoring system to monitor the deterioration of a synchronous motor damper winding as the number of broken bars increase over time. Some other informative features such as speed spectrum, transient time, torque-speed curve and rotor slip are also found for damper winding diagnosis.

The Singular Value Decomposition is one of the most useful matrix factorizations in applied linear algebra, the Principal Component Analysis has been called one of the most valuable results of applied linear algebra. How and why principal component analysis is intimately related to the technique of singular value decomposition is shown. Their properties and applications are described. Assumptions behind this techniques as well as possible extensions to overcome these limitations are considered. This understanding leads to the real world applications, in particular, image processing of neurons. Noise reduction, and edge detection of neuron images are investigated.

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A component is built to be reused and reusability has significant impact on component generality and flexibility requirement. A component model plays a critical role in reusability of software component and defines a set of standards for component implementation, evolution, composition, deployment and standardization of the run-time environment for execution of component. In component based development (CBD), standardization of the runtime environment includes specification of component’s interfaces – general and domain specific run-time services. For rapid development of component based system for distributed information systems, COM+ and .NET technologies introduced the idea of Software Component Services that are achieved by proxy objects which intercept message calls between components to provide service like transaction handling, security management, etc. An embedded real-time system (ERTS) designed for a specific purpose in which software tasks have hard real-time constraints to execute and it interact directly with its environment via buses, sensors and other devices. In an embedded real time system, it is great importance to ensure that all tasks of the software system should respond within the time limits. Component based development process have vital role in development of embedded real time system and recently software component models applied in the embedded real-time systems (ERTS) and introduced software component services (SCS) for ERTS. The purpose of this Master thesis is to investigate how to make tool that targets smart device projects in Microsoft Visual Studio and generates proxy objects that can be compiled with Microsoft Visual Studio.NET. The tool generates a complete set of files for a Microsoft Visual Studio smart device project within solution, including both source code and configuration files according to a specific version of Visual Studio by invoking Visual Studio’s automation object model. The tool also generates code for simple logging services for the .NET component running on top of the .NET Compact framework.

Currently, the development of high-integrity embedded component-based software systems is not supported by well-integrated means allowing for quality evaluation and design support within a development process. Quality, especially dependability, is very important for such systems. The CHESS (Composition with Guarantees for High-integrity Embedded Software Components Assembly) project aims at providing a new systems development methodology to capture extra-functional concerns and extend Model Driven Engineering industrial practices and technology approaches to specifically address the architectural structure, the interactions and the behavior of system components while guaranteeing their correctness and the level of service at run time. The CHESS methodology is expected to be supported by a tool-set which consists of a set of plug-ins integrated within the Eclipse IDE. In the framework of the CHESS project, this thesis addresses the lack of well integrated means concerning quality evaluation and proposes an integrated framework to evaluate the dependability of high-integrity embedded systems. After a survey of various failure behavior analysis techniques, a specific technique, called Failure Propagation and Transformation Calculus (FPTC), is selected and a plug-in, called CHESS-FPTC, is developed within the CHESS tool-set. FPTC technique allows users to calculate the failure behavior of the system from the failure behavior of its building components. Therefore, to fully support FPTC, CHESS-FPTC plug-in allows users to model the failure behavior of the building components, perform the analysis automatically and get the analysis results back into their initial models. A case study about AAL2 Signaling Protocol is presented to illustrate and evaluate the CHESS-FPTC framework. / CHESS Project - http://chess-project.ning.com/

The heat transfer performance of the Helium-cooled Multi-jet (HEMJ) divertor was investigated. The HEMJ design uses impinging jets to significantly enhance its heat transfer capability. The convective heat transfer coefficient predicted by computational fluid dynamics software packages is on the order of 50,000 W/(m2-K). The high predicted values of the convective heat transfer coefficient necessitated experimental validation, which was the focus of this investigation. A test section which simulates the thermal performance of the HEMJ divertor was designed, constructed, and instrumented for testing an in air flow loop. The operating conditions of the air flow loop were chosen to match the non-dimensional operating conditions expected for the HEMJ divertor in a post-ITER fusion power plant. The air flow loop experiments were performed for mass flow rates of 2.0 g/s to 8.0 g/s and with incident nominal heat fluxes of 0.8 MW/m2 and 1.0 MW/m2. The angular variation of the heat transfer coefficient was also investigated. Numerical simulations which matched the experimental operating conditions were performed using the computational fluid dynamics software package, FLUENT® 6.2. Comparisons of the experimental and numerical pressure drop, temperature, and heat transfer coefficient were made. The experimental results agreed with the numerical predictions for all operating conditions in this investigation. This provided a strong degree of confidence in using the FLUENT® software package to analyze the HEMJ divertor design.

Purpose of this research is that capillary optic fiber transmission and energy distribution of internal energy. Contains only single from the initial silica capillary fiber energy transfer within the simulation, and then changing the different input conditions (different incident angles, multi- mode and single mode) optical observation of the capillary wall modes and modal distribution between hole mode. Mode simulation, containing only the capillary in the simple addition of a layer of high-dielectric fiber optic ring to change its transmission mechanism, by analog control of its high dielectric ring geometry (thickness) of air holes the size of its hole mode can simulate the type of transmission mode number. By single-and double- layer energy transfer capillary mode simulation and modal changes in different input conditions, and further understanding of the capillary mode fiber transmission characteristics. Combining the concept of adding fiber coupler and single-mode optical fiber capillary to produce a new original, and then by capillary tube method to make thick plugs can be more convenient and rapid production of a new type of coupler components.

The purpose of this research is to address the problem of fault diagnosis of sensors which measure a set of direct redundant variables. This study proposes: 1. A method for linear senor fault diagnosis 2. An analysis of isolability and detectability of sensor faults 3. A stochastic method for the decision process 4. A nonlinear approach to sensor fault diagnosis. In this study, first a geometrical approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output model in model based methods or from a Principal Component Analysis (PCA) based model in data driven methods. Using this residual generator and the assumption of white Gaussian noise, the effect of noise on the isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Next, for the decision process a probabilistic approach to sensor fault diagnosis is presented. Unlike most existing probabilistic approaches to fault diagnosis, which are based on Bayesian Belief Networks, in this approach the probabilistic model is directly extracted from a parity equation. The relevant parity equation can be found using a model of the system or through PCA analysis of data measured from the system. In addition, a sensor detectability index is introduced that specifies the level of detectability of sensor faults in a set of redundant sensors. This index depends only on the internal relationships of the variables of the system and noise level. Finally, the proposed linear sensor fault diagnosis approach has been extended to nonlinear method by separating the space of measurements into several local linear regions. This classification has been performed by application of Mixture of Probabilistic PCA (MPPCA). The proposed linear and nonlinear methods are tested on three different systems. The linear method is applied to sensor fault diagnosis in a smart structure and to the Tennessee Eastman process model, and the nonlinear method is applied to a data set collected from a fully instrumented HVAC system.

Prior to 1960, most countries over the world used traditional economic indicators to represent their social status. Economists mostly used Gross National Product, GNP, as a measurement of the social welfare standard of a country or a society. However, with the progress of the development, the traditional economic indicators were unable to follow the progress of social welfares. Therefore, it made economists hard to measure the status of social welfares. Sen (1977) thought that the development of human beings is not restricted to the increase of average disposable incomes only. He thought that people should use the indicators with more information to measure the distinctive diversity of welfares. Bauer (1966) first stated the social indicators as a measurement of social status and trends. Then, the so-called ¡§Social Indicators Movement¡¨ was aroused by Bauer¡¦s theory. As a result, to measure the development of a society entirely, people could determine the development from the medicine, health, economy, environment, and welfare aspects. In the researches about social developments, there were many discussions in building indicators of social welfare, quality of living, basic fulfillment, and development of welfare. The research is trying to establish a system of social indicators to measure the development from every aspect and selecting the social indicator index with representative indicators as a measurement of society development. Moreover, this research would analyze the systems of social indicators in Taiwan from 1982 to 2002 to see if the government made an appropriate allocation of resources in the executions of related policies. The research refers to 20 related indicator systems in Taiwan and overseas as their times of quotes and principles of selecting indicators to sum up 9 probable indicators. Then, the Principal Component Analysis method and the Varian method are adopted as research methods to abstract factors. Moreover, there are two abstracted factors. One is economic and environment factor and another one is medical welfare and unemployment factor. The research uses weighted method to find out the synthetic indicators in Taiwan from 1982 to 2002. The weighted multiple gained from factor analysis for the two factors is 0.8353 and 0.1647. Based on the data mining and analysis from second information, three scores were acquired, economic and environment factor, medical welfare and unemployment factor, and entirely performance. Each of these three scores shows the trend and the change year by year. The last, according the result from this analysis, the policy and recommendation was brought up.