Multiband Evanescent Waveguide Antenna

A dual/triband evanescent waveguide antenna elementis presented. The antenna operates in the 740–790, 1.9–2.2, and2.5–2.7 GHz frequency bands. It measures 55 3 27.5 3 53 mm3andoccupies a small volume making it attractive for miniaturized applica-tions

Optical Spectroscopy of Unbound Asteroid Pairs

The recently discovered unbound asteroid pairs have been suggested to be the result of the decoupling of binary asteroids formed either through collision processes or, more likely, rotational fission of a rubble-pile asteroid after spin-up (Vokrouhlicky et al. 2008, AJ 136, 280; Pravec et al., 2010, Nature, 466, 1085). Much of the evidence for linkage of the asteroids in each pair relies solely on the backwards integrations of their orbits. We report new results from our continuing spectroscopic survey of the unbound asteroid pairs, including the youngest known pair, (6070) Rhineland - (54827) 2001 NQ8. The survey goal is to determine whether the asteroids in each unbound pair have similar spectra and therefore composition, expected if they have formed from a common parent body. Low-resolution spectroscopy covering the range 0.4-0.95 microns was conducted using the 3.6m ESO NTT+EFOSC2 during 2011-2012 and the 4.2m WHT+ACAM. We have attempted to maintain a high level of consistency between the observations of the components in each pair to ensure that differences in the asteroid spectra are not the result of the observing method or data reduction, but purely caused by compositional differences. Our WHT data indicates that the asteroids of unbound pair 17198 - 229056 exhibit different spectra and have been assigned different taxonomies, A and R respectively. Initial analysis of our data from the NTT suggests that the asteroids in unbound pairs 6070 - 54827 and 38707 - 32957 are likely silicate-dominated asteroids. The components of pair 23998 - 205383 are potentially X-type asteroids. We present final taxonomic classifications and the likelihood of spectral similarity in each pair.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.