Affine Lie Algebras in Massive Field Theory and Form-Factors from Vertex Operators

We present a new application of affine Lie algebras to massive quantum field theory in 2 dimensions, by investigating the $q\to 1$ limit of the q-deformed affine $\hat{sl(2)}$ symmetry of the sine-Gordon theory, this limit occurring at the free fermion point. Working in radial quantization leads to a quasi-chiral factorization of the space of fields. The conserved charges which generate the affine Lie algebra split into two independent affine algebras on this factorized space, each with level 1 in the anti-periodic sector, and level $0$ in the periodic sector. The space of fields in the anti-periodic sector can be organized using level-$1$ highest weight representations, if one supplements the \slh algebra with the usual local integrals of motion. Introducing a particle-field duality leads to a new way of computing form-factors in radial quantization. Using the integrals of motion, a momentum space bosonization involving vertex operators is formulated. Form-factors are computed as vacuum expectation values in momentum space. (Based on talks given at the Berkeley Strings 93 conference, May 1993, and the III International Conference on Mathematical Physics, String Theory, and Quantum Gravity, Alushta, Ukraine, June 1993.)Comment: 13 pages, CLNS 93/125

Spin fluctuations with two-dimensional XY behavior in a frustrated S = 1/2 square-lattice ferromagnet

The spin dynamics of the layered square-lattice vanadate Pb2VO(PO4)2 is investigated by electron spin resonance at various magnetic fields and at temperatures above magnetic ordering. The linewidth divergence towards low temperatures seems to agree with isotropic Heisenberg-type spin exchange suggesting that the spin relaxation in this quasi-two dimensional compound is governed by low-dimensional quantum fluctuations. However, a weak easy- plane anisotropy of the g factor points to the presence of a planar XY type of exchange. Indeed, we found that the linewidth divergence is described best by XY-like spin fluctuations which requires a single parameter only. Therefore, ESR-probed spin dynamics could establish Pb2VO(PO4)2 as the first frustrated square lattice system with XY-inherent spin topological fluctuations.Comment: 5 pages, 3 figure

Fabrication of wide-IF 200–300 GHz superconductor–insulator–superconductor mixers with suspended metal beam leads formed on silicon-on-insulator

We report on a fabrication process that uses SOI substrates and micromachining techniques to form wide-IF SIS mixer devices that have suspended metal beam leads for rf grounding. The mixers are formed on thin 25 µm membranes of Si, and are designed to operate in the 200–300 GHz band. Potential applications are in tropospheric chemistry, where increased sensitivity detectors and wide-IF bandwidth receivers are desired. They will also be useful in astrophysics to monitor absorption lines for CO at 230 GHz to study distant, highly redshifted galaxies by reducing scan times. Aside from a description of the fabrication process, electrical measurements of these Nb/Al–AlNx/Nb trilayer devices will also be presented. Since device quality is sensitive to thermal excursions, the new beam lead process appears to be compatible with conventional SIS device fabrication technology

Spin correlations and exchange in square lattice frustrated ferromagnets

The J1-J2 model on a square lattice exhibits a rich variety of different forms of magnetic order that depend sensitively on the ratio of exchange constants J2/J1. We use bulk magnetometry and polarized neutron scattering to determine J1 and J2 unambiguously for two materials in a new family of vanadium phosphates, Pb2VO(PO4)2 and SrZnVO(PO4)2, and we find that they have ferromagnetic J1. The ordered moment in the collinear antiferromagnetic ground state is reduced, and the diffuse magnetic scattering is enhanced, as the predicted bond-nematic region of the phase diagram is approached.Comment: 4 pages, 4 figure

Chiral Vertex Operators in Off-Conformal Theory: The Sine-Gordon Example

We study chiral vertex operators in the sine-Gordon [SG] theory, viewed as an off-conformal system. We find that these operators, which would have been primary fields in the conformal limit, have interesting and, in some ways, unexpected properties in the SG model. Some of them continue to have scale- invariant dynamics even in the presence of the non-conformal cosine interaction. For instance, it is shown that the Mandelstam operator for the bosonic representation of the Fermi field does {\it not} develop a mass term in the SG theory, contrary to what the real Fermi field in the massive Thirring model is expected to do. It is also shown that in the presence of the non-conformal interactions, some vertex operators have unique Lorentz spins, while others do not.Comment: 32 pages, Univ. of Illinois Preprint # ILL-(TH)-93-1

Finite-size effects in amorphous Fe90Zr10/Al75Zr25 multilayers

The thickness dependence of the magnetic properties of amorphous Fe90Zr10 layers has been explored using Fe90Zr10/Al75Zr25 multilayers. The Al75Zr25 layer thickness is kept at 40 \AA, while the thickness of the Fe90Zr10 layers is varied between 5 and 20 \AA. The thickness of the Al75Zr25 layers is sufficiently large to suppress any significant interlayer coupling. Both the Curie temperature and the spontaneous magnetization decrease non-linearly with decreasing thickness of the Fe90Zr10 layers. No ferromagnetic order is observed in the multilayer with 5 {\AA} Fe90Zr10 layers. The variation of the Curie temperature $T_c$ with the Fe90Zr10 layer thickness $t$ is fitted with a finite-size scaling formula [1-\Tc(t)/\Tc(\infty)]=[(t-t')/t_0]^{-\lambda}, yielding $\lambda=1.2$, and a critical thickness $t'=6.5$ \AA, below which the Curie temperature is zero.Comment: 8 pages, 8 figure

Artificial Neural Network-based error compensation procedure for low-cost encoders

An Artificial Neural Network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behavior. The ANN-based error compensation procedure has been implemented for 4 encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ~6 arc-min to ~0.65 arc-min when their corresponding ANN-generated weight files are used for determining the corrected encoder angle.Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science and Technology (MST

Output Feedback Controller for Operation of Spark Ignition Engines at Lean Conditions Using Neural Networks

Spark ignition (SI) engines operating at very lean conditions demonstrate significant nonlinear behavior by exhibiting cycle-to-cycle bifurcation of heat release. Past literature suggests that operating an engine under such lean conditions can significantly reduce NO emissions by as much as 30% and improve fuel efficiency by as much as 5%-10%. At lean conditions, the heat release per engine cycle is not close to constant, as it is when these engines operate under stoichiometric conditions where the equivalence ratio is 1.0. A neural network controller employing output feedback has shown ability in simulation to reduce the nonlinear cyclic dispersion observed under lean operating conditions. This neural network (NN) output controller consists of three NNs: a) an NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. The uniform ultimate boundedness of all closed-loop signals is demonstrated by using the Lyapunov analysis without using the separation principle. Persistency of the excitation condition, the certainty equivalence principle, and the linearity in the unknown parameter assumptions are also relaxed. The controller is implemented for a research engine as a program running on an embeddable PC that communicates with the engine through a custom hardware interface, and the results are similar to those observed in simulation. Experimental results at an equivalence ratio of 0.77 show a drop in NO emissions by around 98% from stoichiometric levels with an improvement of fuel efficiency by 5%. A 30% drop in unburned hydrocarbons from uncontrolled case is observed at this equivalence ratio of 0.77. Similar performance was observed with the controller on a different engine

Geometrical dependence of low frequency noise in superconducting flux qubits

A general method for directly measuring the low-frequency flux noise (below 10 Hz) in compound Josephson junction superconducting flux qubits has been used to study a series of 85 devices of varying design. The variation in flux noise across sets of qubits with identical designs was observed to be small. However, the levels of flux noise systematically varied between qubit designs with strong dependence upon qubit wiring length and wiring width. Furthermore, qubits fabricated above a superconducting ground plane yielded lower noise than qubits without such a layer. These results support the hypothesis that localized magnetic impurities in the vicinity of the qubit wiring are a key source of low frequency flux noise in superconducting devices.Comment: 5 pages, 5 figure

Neural Network Control of Spark Ignition Engines with High EGR Levels

Research has shown substantial reductions in the oxides of nitrogen (NOx) concentrations by using 10% to 25% exhaust gas recirculation (EGR) in spark ignition (SI) engines [1]. However under high EGR levels the engine exhibits strong cyclic dispersion in heat release which may lead to instability and unsatisfactory performance. A suite of neural network (NN)-based output feedback controllers with and without reinforcement learning is developed to control the SI engine at high levels of EGR even when the engine dynamics are unknown by using fuel as the control input. A separate control loop was designed for controlling EGR levels. The neural network controllers consists of three NN: a) A NN observer to estimate the states of the engine such as total fuel and air; b) a second NN for generating virtual input; and c) a third NN for generating actual control input. For reinforcement learning, an additional NN is used as the critic. The stability analysis of the closed loop system is given and the boundedness of all signals is ensured without separation principle. Online training is used for the adaptive NN and no offline training phase is needed. Experimental results obtained by testing the controller on a research engine indicate an 80% drop of NOx from stoichiometric levels using 10% EGR. Moreover, unburned hydrocarbons drop by 25% due to NN control as compared to the uncontrolled scenario