System analysis approach to deriving design criteria (loads) for Space Shuttle and its payloads. Volume 1: General statement of approach

Space shuttle, the most complex transportation system designed to date, illustrates the requirement for an analysis approach that considers all major disciplines simultaneously. Its unique cross coupling and high sensitivity to aerodynamic uncertainties and high performance requirements dictated a less conservative approach than those taken in programs. Analyses performed for the space shuttle and certain payloads, Space Telescope and Spacelab, are used a examples. These illustrate the requirements for system analysis approaches and criteria, including dynamic modeling requirements, test requirements control requirements and the resulting design verification approaches. A survey of the problem, potential approaches available as solutions, implications for future systems, and projected technology development areas are addressed

Learning and Co-operation in Mobile Multi-Robot Systems

Merged with duplicate record 10026.1/1984 on 27.02.2017 by CS (TIS)This thesis addresses the problem of setting the balance between exploration and exploitation in teams of learning robots who exchange information. Specifically it looks at groups of robots whose tasks include moving between salient points in the environment. To deal with unknown and dynamic environments,such robots need to be able to discover and learn the routes between these points themselves. A natural extension of this scenario is to allow the robots to exchange learned routes so that only one robot needs to learn a route for the whole team to use that route. One contribution of this thesis is to identify a dilemma created by this extension: that once one robot has learned a route between two points, all other robots will follow that route without looking for shorter versions. This trade-off will be labeled the Distributed Exploration vs. Exploitation Dilemma, since increasing distributed exploitation (allowing robots to exchange more routes) means decreasing distributed exploration (reducing robots ability to learn new versions of routes), and vice-versa. At different times, teams may be required with different balances of exploitation and exploration. The main contribution of this thesis is to present a system for setting the balance between exploration and exploitation in a group of robots. This system is demonstrated through experiments involving simulated robot teams. The experiments show that increasing and decreasing the value of a parameter of the novel system will lead to a significant increase and decrease respectively in average exploitation (and an equivalent decrease and increase in average exploration) over a series of team missions. A further set of experiments show that this holds true for a range of team sizes and numbers of goals

Design and analysis of numerical algorithms for the solution of linear systems on parallel and distributed architectures

The increasing availability of parallel computers is having a very significant impact on all aspects of scientific computation, including algorithm research and software development in numerical linear algebra. In particular, the solution of linear systems, which lies at the heart of most calculations in scientific computing is an important computation found in many engineering and scientific applications. In this thesis, well-known parallel algorithms for the solution of linear systems are compared with implicit parallel algorithms or the Quadrant Interlocking (QI) class of algorithms to solve linear systems. These implicit algorithms are (2x2) block algorithms expressed in explicit point form notation. [Continues.

Hybrid approaches for mobile robot navigation

The work described in this thesis contributes to the efficient solution of mobile robot navigation problems. A series of new evolutionary approaches is presented. Two novel evolutionary planners have been developed that reduce the computational overhead in generating plans of mobile robot movements. In comparison with the best-performing evolutionary scheme reported in the literature, the first of the planners significantly reduces the plan calculation time in static environments. The second planner was able to generate avoidance strategies in response to unexpected events arising from the presence of moving obstacles. To overcome limitations in responsiveness and the unrealistic assumptions regarding a priori knowledge that are inherent in planner-based and a vigation systems, subsequent work concentrated on hybrid approaches. These included a reactive component to identify rapidly and autonomously environmental features that were represented by a small number of critical waypoints. Not only is memory usage dramatically reduced by such a simplified representation, but also the calculation time to determine new plans is significantly reduced. Further significant enhancements of this work were firstly, dynamic avoidance to limit the likelihood of potential collisions with moving obstacles and secondly, exploration to identify statistically the dynamic characteristics of the environment. Finally, by retaining more extensive environmental knowledge gained during previous navigation activities, the capability of the hybrid navigation system was enhanced to allow planning to be performed for any start point and goal point

SAGA: A project to automate the management of software production systems

The SAGA system is a software environment that is designed to support most of the software development activities that occur in a software lifecycle. The system can be configured to support specific software development applications using given programming languages, tools, and methodologies. Meta-tools are provided to ease configuration. The SAGA system consists of a small number of software components that are adapted by the meta-tools into specific tools for use in the software development application. The modules are design so that the meta-tools can construct an environment which is both integrated and flexible. The SAGA project is documented in several papers which are presented

An empirical investigation of the nature of management development with particular emphasis on the influence of learning styles on the levels of accumulated managerial tacit knowledge in the Malaysian Public Service

This study explored the broad learning patterns associated with the acquisition of managerial tacit knowledge. The study then proceeded to examine whether levels of accumulated managerial tacit knowledge (LAMTK) may be associated with managers' learning styles and/or the extent to which a person's style is consonant with the context of their work environment. The possibility that deliberate learning strategies normally associated with formal rather than informal learning would be unrelated to LAMTK was also examined.The research employed a cross-sectional, mixed-method approach incorporating both qualitative interview and survey data collection. For the qualitative element, interviews were conducted with 14 public sector managers based on a method developed by Nestor-Baker (1999). For the quantitative element, survey data were collected from 356 public sector managers attending management development training courses at the Malaysian National Institute of Public Administration. Respondents completed a questionnaire designed to measure tacit knowledge based on Stemberg et al's (2000) Tacit Knowledge Inventory for Managers, learning styles based on Geiger et al's (1993) normative version of Kolb's Learning Style Inventory, learning strategy based on Warr & Downing's (2000) Learning Strategies Questionnaire, and a range of other selfdeveloped items.The interviews revealed that most managers were unaware of the learning associated with the acquisition of tacit knowledge, as it occurs in an unplanned and unintentional manner. Several adult learning principles such as reflection and learning from experience emerged from the analyses. While learning styles were found to be significant in predicting LAMTK, a rather surprising finding was that learning strategies, believed to be associated with declarative as opposed to tacit knowledge, were also related to LAMTK. Based on these findings it was concluded that the process of tacit knowledge acquisition involves the interaction of learning that takes place in both formal and informal settings. Outcomes of the research suggest that in management development initiatives, formal approaches should be blended with informal approaches in order to achieve effective learning

Learning Gaussisan noise models from high-dimensional sensor data with deep neural networks

Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 87-92).While measurement covariances are often taken to be constant in many robotic state estimation systems, many sensors exhibit different interactions with their environment. Accurate covariance estimation allows graph-based estimation techniques to better optimize state estimates by reasoning about the utility of different methods relative to each other. This thesis describes a method of learning compact feature representations for real-time covariance estimation. A direct log-likelihood optimization technique is used to train a deep convolutional neural network to predict the covariance matrix of a Gaussian measurement model, given representative data. This method is algorithm-agnostic, and therefore does not require the handcoding of representative features. Quantative results are presented, showing that improved measurement covariances on a frame-to-frame visual odometry system reduce trajectory errors after a loop closure is applied.by Katherine Y. Liu.S.M