The evaluation of a metered mixer for RTV silicone for RSRM nozzle backfill operations

Metered mixing specifically for the RSRM backfill operation was investigated. Projected advantages were the elimination of waste RTV silicone produced in the operation and the elimination of entrapped air during the mix. Although metered mixing proved to be a viable method for mixing the Dow Corning DC 90-0006 rubber with its catalyst, applying the technology to the RSRM backfill operation has several disadvantages that are decisive. Use of a metered mixer would increase the amount of material that was being scraped for each backfill and increase the amount of time required to clean up the equipment after each operation. Therefore, use of metered static mixers is not recommended for use in the RSRM nozzle backfill operations. Because metered mixers proved to have significant disadvantages other methods of mixing and dispensing the RTV during the backfill operation are being investigated, and will be reported in a separate document

Transport Equation Approach to Calculations of Hadamard Green functions and non-coincident DeWitt coefficients

Building on an insight due to Avramidi, we provide a system of transport equations for determining key fundamental bi-tensors, including derivatives of the world-function, \sigma(x,x'), the square root of the Van Vleck determinant, \Delta^{1/2}(x,x'), and the tail-term, V(x,x'), appearing in the Hadamard form of the Green function. These bi-tensors are central to a broad range of problems from radiation reaction to quantum field theory in curved spacetime and quantum gravity. Their transport equations may be used either in a semi-recursive approach to determining their covariant Taylor series expansions, or as the basis of numerical calculations. To illustrate the power of the semi-recursive approach, we present an implementation in \textsl{Mathematica} which computes very high order covariant series expansions of these objects. Using this code, a moderate laptop can, for example, calculate the coincidence limit a_7(x,x) and V(x,x') to order (\sigma^a)^{20} in a matter of minutes. Results may be output in either a compact notation or in xTensor form. In a second application of the approach, we present a scheme for numerically integrating the transport equations as a system of coupled ordinary differential equations. As an example application of the scheme, we integrate along null geodesics to solve for V(x,x') in Nariai and Schwarzschild spacetimes.Comment: 32 pages, 5 figures. Final published version with correction to Eq. (3.24

Application of Fuzzy State Aggregation and Policy Hill Climbing to Multi-Agent Systems in Stochastic Environments

Reinforcement learning is one of the more attractive machine learning technologies, due to its unsupervised learning structure and ability to continually even as the operating environment changes. Applying this learning to multiple cooperative software agents (a multi-agent system) not only allows each individual agent to learn from its own experience, but also opens up the opportunity for the individual agents to learn from the other agents in the system, thus accelerating the rate of learning. This research presents the novel use of fuzzy state aggregation, as the means of function approximation, combined with the policy hill climbing methods of Win or Lose Fast (WoLF) and policy-dynamics based WoLF (PD-WoLF). The combination of fast policy hill climbing (PHC) and fuzzy state aggregation (FSA) function approximation is tested in two stochastic environments; Tileworld and the robot soccer domain, RoboCup. The Tileworld results demonstrate that a single agent using the combination of FSA and PHC learns quicker and performs better than combined fuzzy state aggregation and Q-learning lone. Results from the RoboCup domain again illustrate that the policy hill climbing algorithms perform better than Q-learning alone in a multi-agent environment. The learning is further enhanced by allowing the agents to share their experience through a weighted strategy sharing

Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults

An Examination of the Association Between the Graduation Coach Program and Georgia\u27s Graduation Rate

As result of the dropout problem in the United States and in Georgia, many school systems around the nation have placed much emphasis on reducing the incidents of students dropping out of high school. The purpose of this study was to examine the association between the graduation coach program and Georgia\u27s graduation rate of over a 7 year period of time, 2004-2010. The research sought to determine if differences existed between graduation rates pre the induction of the graduation coach programs and post the induction of the graduation coach program when controlling for variables such as, school locale, free and reduced lunch percentages, science achievement data and race and ethnicity percentages. I used quantitative design to gather descriptive statistics and to test differences in means scores pre and post the induction of the graduation coach program. The participants were 343 public high schools in the state of Georgia with pre coach program graduation rates and post coach program graduation rates. The spreadsheet was developed so that pre graduation coach program data and post graduation coach data was easily distinguishable. The data set contained statistical information on all 343 schools with pre and post graduation rate data. The results of this study indicate that graduation rates were statistically significant higher after the induction of the graduation coach program when compared to the period prior to the induction of the graduation coach program. In fact, this advantage persisted across city high schools, rural high schools, suburban high schools, town high schools, metropolitan Atlanta high schools and high schools outside of metropolitan Atlanta. However, when looking at Atlanta Public Schools, Dekalb County Schools and Clayton County Schools, no significant difference was found for Atlanta Public Schools

Glyoxal 4-nitrophenylhydrazone : triple helices linked into a three-dimensional channel structure

Peer reviewedPublisher PD