Space Station RT and E Utilization Study

Descriptive information on a set of 241 mission concepts was reviewed to establish preliminary Space Station outfitting needs for technology development missions. The missions studied covered the full range of in-space technology development activities envisioned for early Space Station operations and included both pressurized volume and attached payload requirements. Equipment needs were compared with outfitting plans for the life sciences and microgravity user communities, and a number of potential outfitting additions were identified. Outfitting implementation was addressed by selecting a strawman mission complement for each of seven technical themes, by organizing the missions into flight scenarios, and by assessing the associated outfitting buildup for planning impacts

Investigation of direct solar-to-microwave energy conversion techniques

Identification of alternative methods of producing microwave energy from solar radiation for purposes of directing power to the Earth from space is investigated. Specifically, methods of conversion of optical radiation into microwave radiation by the most direct means are investigated. Approaches based on demonstrated device functioning and basic phenomenologies are developed. There is no system concept developed, that is competitive with current baseline concepts. The most direct methods of conversion appear to require an initial step of production of coherent laser radiation. Other methods generally require production of electron streams for use in solid-state or cavity-oscillator systems. Further development is suggested to be worthwhile for suggested devices and on concepts utilizing a free-electron stream for the intraspace station power transport mechanism

Dielectric function and plasmons in graphene

The electromagnetic response of graphene, expressed by the dielectric function, and the spectrum of collective excitations are studied as a function of wave vector and frequency. Our calculation is based on the full band structure, calculated within the tight-binding approximation. As a result, we find plasmons whose dispersion is similar to that obtained in the single-valley approximation by Dirac fermions. In contrast to the latter, however, we find a stronger damping of the plasmon modes due to inter-band absorption. Our calculation also reveals effects due to deviations from the linear Dirac spectrum as we increase the Fermi energy, indicating an anisotropic behavior with respect to the wave vector of the external electromagnetic field

Dynamical polarization, screening, and plasmons in gapped graphene

The one-loop polarization function of graphene has been calculated at zero temperature for arbitrary wavevector, frequency, chemical potential (doping), and band gap. The result is expressed in terms of elementary functions and is used to find the dispersion of the plasmon mode and the static screening within the random phase approximation. At long wavelengths the usual square root behaviour of plasmon spectra for two-dimensional (2D) systems is obtained. The presence of a small (compared to a chemical potential) gap leads to the appearance of a new undamped plasmon mode. At greater values of the gap this mode merges with the long-wavelength one, and vanishes when the Fermi level enters the gap. The screening of charged impurities at large distances differs from that in gapless graphene by slower decay of Friedel oscillations ($1/r^2$ instead of $1/r^3$), similarly to conventional 2D systems.Comment: 8 pages, 8 figures, v2: to match published versio

A Commodity Trading Model Based on a Neural Network-Expert System Hybrid

Demonstrates a system that combines a neural network approach with an expert system to provide superior performance compared to either approach alone. Learning capability is provided in a software-based approach to commodity trading systems. The authors used the backpropagation network with some parameters selected experimentally. They used a human expert to implicitly define patterns, using hindsight, that an intelligent system might have been able to use for an accurate prediction. Desired outputs were found by a combination of observing the behavior of technical indices that normally precede a certain kind of market behavior, and by observing the actual market behavior in retrospect. Thus, the network learns to give signals based on data that look favorable to a human expert. The authors show the results of a rule-based daily trading system that has been augmented by a neural network market predictor

Coulomb Screening of 2D Massive Dirac Fermions

A model of 2D massive Dirac fermions, interacting with a instantaneous $1/r$ Coulomb interaction, is presented to mimic the physics of gapped graphene. The static polarization function is calculated explicitly to analyze screening effect at the finite temperature and density. Results are compared with the massless case . We also show that various other works can be reproduced within our model in a straightforward and unified manner

Online Identification of Turbogenerator\u27s Dynamics using a Neuro-Identifier

The increasing complexity of modern power systems highlights the need for effective system identification techniques for the successful control of power system. This paper proposes a robust continually online trained neuroidentifier to predict the outputs of turbogenerator - terminal voltage and speed deviation. The inputs to the neuro-identifier are the changes of the plant\u27s outputs and plant\u27s inputs. It overcomes the drawback of calculating deviation signals from reference signals for different operating points in previous work. Simulation results show that the neuro-identifier can provide accurate identification under different operating conditions. Furthermore, the neuro-identifier can learn the dynamics of the system in a short period of time, which makes it suitable for use with an online adaptive controller for the control of turbogenerators

A Heuristic-Dynamic-programming-Based Power System Stabilizer for a Turbogenerator in a Single-Machine Power System

Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared with the CPSS and the indirect-adaptive-neurocontrol-based PSS under small and large disturbances. In addition, the impact of different discount factors in the HDP PSS\u27s performance is presented

A Heuristic Dynamic Programming Based Power System Stabilizer for a Turbogenerator in a Single Machine Power System

Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design of power system stabilizer (PSS) based on heuristic dynamic programming (HDP) is proposed in this paper. HDP combining the concepts of dynamic programming and reinforcement learning is used in the design of a nonlinear optimal power system stabilizer. The proposed HDP based PSS is evaluated against the conventional power system stabilizer and indirect adaptive neurocontrol based PSS under small and large disturbances in a single machine infinite bus power system setup. Results are presented to show the effectiveness of this new technique