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Tool for Merging Proposals Into DSN Schedules

A Practical Extraction and Reporting Language (Perl) script called “merge7da” has been developed to facilitate determination, by a project scheduler in NASA’s Deep Space Network, of whether a proposal for use of the DSN could create a conflict with the current DSN schedule. Prior to the development of merge7da, there was no way to quickly identify potential schedule conflicts: it was necessary to submit a proposal and wait a day or two for a response from a DSN scheduling facility. By using merge7da to detect and eliminate potential schedule conflicts before submitting a proposal, a project scheduler saves time and gains assurance that the proposal will probably be accepted. merge7da accepts two input files, one of which contains the current DSN schedule and is in a DSN-standard format called “7da.”

Posted in: Briefs, Information Sciences

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Simulation of Stochastic Processes by Coupled ODE-PDE

A document discusses the emergence of randomness in solutions of coupled, fully deterministic ODE-PDE (ordinary differential equations-partial differential equations) due to failure of the Lipschitz condition as a new phenomenon. It is possible to exploit the special properties of ordinary differential equations (represented by an arbitrarily chosen, dynamical system) coupled with the corresponding Liouville equations (used to describe the evolution of initial uncertainties in terms of joint probability distribution) in order to simulate stochastic processes with the proscribed probability distributions. The important advantage of the proposed approach is that the simulation does not require a random-number generator.

Posted in: Briefs, TSP, Information Sciences

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Genetic Algorithm Optimizes Q-LAW Control Parameters

A document discusses a multi-objective, genetic algorithm designed to optimize Lyapunov feedback control law (Q-law) parameters in order to efficiently find Pareto- optimal solutions for low-thrust trajectories for electronic propulsion systems. These would be propellant-optimal solutions for a given flight time, or flight time optimal solutions for a given propellant requirement. The approximate solutions are used as good initial solutions for high-fidelity optimization tools. When the good initial solutions are used, the high-fidelity optimization tools quickly converge to a locally optimal solution near the initial solution.

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Quantum-Inspired Maximizer

A report discusses an algorithm for a new kind of dynamics based on a quantum-classical hybrid-quantum-inspired maximizer. The model is represented by a modified Madelung equation in which the quantum potential is replaced by different, specially chosen “computational” potential. As a result, the dynamics attains both quantum and classical properties: it preserves superposition and entanglement of random solutions, while allowing one to measure its state variables, using classical methods. Such optimal combination of characteristics is a perfect match for quantum-inspired computing. As an application, an algorithm for global maximum of an arbitrary integrable function is proposed. The idea of the proposed algorithm is very simple: based upon the Quantum-inspired Maximizer (QIM), introduce a positive function to be maximized as the probability density to which the solution is attracted. Then the larger value of this function will have the higher probability to appear.

Posted in: Briefs, TSP, Information Sciences

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Spiking Neurons for Analysis of Patterns

High-performance pattern-analysis systems could be implemented as analog VLSI circuits. Artificial neural networks comprising spiking neurons of a novel type have been conceived as improved pattern- analysis and pattern- recognition computational systems. These neurons are represented by a mathematical model denoted the state- variable model (SVM), which among other things, exploits a computational parallelism inherent in spiking-neuron geometry. Networks of SVM neurons offer advantages of speed and computational efficiency, relative to traditional artificial neural networks. The SVM also overcomes some of the limitations of prior spiking-neuron models. There are numerous potential pattern-recognition, tracking, and data-reduction (data preprocessing) applications for these SVM neural networks on Earth and in exploration of remote planets.

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Symmetric Phase-Only Filtering in Particle-Image Velocimetry

Performance is enhanced significantly with little increase in computation time. Symmetrical phase-only filtering (SPOF) can be exploited to obtain substantial improvements in the results of data processing in particle- image velocimetry (PIV). In comparison with traditional PIV data processing, SPOF PIV data processing yields narrower and larger amplitude correlation peaks, thereby providing more-accurate velocity estimates. The higher signal-to-noise ratios associated with the higher amplitude correlation peaks afford greater robustness and reliability of processing. SPOF also affords superior performance in the presence of surface flare light and/or background light. SPOF algorithms can readily be incorporated into pre-existing algorithms used to process digitized image data in PIV, without significantly increasing processing times.

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Supplier Management System

Supplier Management System (SMS) allows for a consistent, agency-wide performance rating system for suppliers used by NASA. This version (2.0) combines separate databases into one central database that allows for the sharing of supplier data. Information extracted from the NBS/Oracle database can be used to generate ratings. Also, supplier ratings can now be generated in the areas of cost, product quality, delivery, and audit data. Supplier data can be charted based on real-time user input. Based on these individual ratings, an overall rating can be generated.

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