Application of a sensitivity analysis technique to high-order digital flight control systems

A sensitivity analysis technique for multiloop flight control systems is studied. This technique uses the scaled singular values of the return difference matrix as a measure of the relative stability of a control system. It then uses the gradients of these singular values with respect to system and controller parameters to judge sensitivity. The sensitivity analysis technique is first reviewed; then it is extended to include digital systems, through the derivation of singular-value gradient equations. Gradients with respect to parameters which do not appear explicitly as control-system matrix elements are also derived, so that high-order systems can be studied. A complete review of the integrated technique is given by way of a simple example: the inverted pendulum problem. The technique is then demonstrated on the X-29 control laws. Results show linear models of real systems can be analyzed by this sensitivity technique, if it is applied with care. A computer program called SVA was written to accomplish the singular-value sensitivity analysis techniques. Thus computational methods and considerations form an integral part of many of the discussions. A user's guide to the program is included. The SVA is a fully public domain program, running on the NASA/Dryden Elxsi computer

Super-resolution imaging using a camera array

The angular resolution of many commercial imaging systems is limited, not by diffraction or optical aberrations, but by pixilation effects. Multiaperture imaging has previously demonstrated the potential for super-resolution (SR) imaging using a lenslet array and single detector array. We describe the practical demonstration of SR imaging using an array of 25 independent commercial-off-the-shelf cameras. This technique demonstrates the potential for increasing the angular resolution toward the diffraction limit, but without the limit on angular resolution imposed by the use of a single detector array

An Investigation of High Order and Low Order Dynamic Modeling of a Complete Pressurized Water Reactor Nuclear Power Plant

A reference high order PWR system model was developed resulting in a 57th order, lumped parameter, state variable dynamic model. Included in the model are representations of the reactor core, pressurizer, U-tube recirculation type steam generator, connecting piping, and turbine-feedwater heaters. Also included are the models of three-element feedwater flow control, nonlinear reactor control, pressurizer pressure control, and megawatt-frequency turbine control systems. A low order PWR system model was developed by reducing the 57th order model to a 25th order model by physical methods. A further reduction on the low order model was demonstrated by a numerical method called the pole-zero deletion method. The results of the physically reduced low order model were compared to the results of the reference high order model. This comparison showed that the low order model could simulate the desired output of turbine shaft power equally as well as the reference high order model. Other intermediate system outputs were also shown to give good results for the low order model as compared to the reference high order model

Factors Influencing The Variability In Social Capital

This research provides insights into three aspects of social capital: the factors that influence its variability; its two-dimensional nature; and the relationship between social capital and membership in a YMCA. These insights have implications for social capital theory, for public policy, for organizational management and for individual well-being. Most social capital research treats the construct as a causal variable and analyzes the implications of different levels of social capital for certain aspects of individual and community well-being. This treatment implies that levels of social capital vary. Little research has been done to analyze the factors that cause social capital variability and therefore the understanding of social capital variability lacks insight. Before social capital variability can be explored, an intermediate issue must be addressed. Social capital is usually conceived of as a single-dimension construct. In fact social capital has two dimensions: the attitudes of social capital and the behaviors of social capital. Unidimensionality is sufficient when social capital is used exogenously but it is insufficiently nuanced when used for the purpose of recommending policies to increase it. This research analyzes the two-dimensional nature of social capital. Finally, a number of social capital behaviors have been studied but membership in the YMCA is not one of them. This research examines the relationship, ceteris paribus, between membership in the Central Florida YMCA and individual social capital. A survey questionnaire was mailed to 10,000 YMCA members in Central Florida and 21,000 residents who were demographically similar. There were 1,881 completed responses. The results were analyzed using structural equation modeling and were guided by social capital theory and the theory of reasoned action. iv The results of the study indicate that the two most influential factors of social capital variability are personal educational attainment and the average educational attainment of the community. The study also confirms that social capital is a two-dimensional construct and the two dimensions are iterative. The study results also revealed that members of the Central Florida YMCA had higher levels of social capital ceteris paribus. This study is significant in four areas: social capital theory, public policy, management of social capital-generating organizations and for individuals. At the theoretical level, insight has been gained into both the causes of social capital variability and the two-dimensional nature of social capital. Regarding public policy, this research provides clear evidence that education provides a greater role in building a community than simply creating human capital; it also creates social capital. Both educational institutions and those organizations that create social capital should be supported. Furthermore, social capital promulgation through public policy should target both dimensions of social capital to be most effective. For managers of social capital-generating organizations social capital can be used as a metric for measuring organizational effectiveness and community impact. For individuals, there is now an evidencebased approach for developing a life plan for creating personal social capital. This research is unique because it simultaneously brings insights into four distinct spheres of social capital