Predictive algorithm for the roll control autopilot of a jet fighter aircraft

Predictive controllers are often only applicable for open-loop stable systems. In this paper two such controllers are designed to operate on open-loop critically stable systems, each of which is used to find the control inputs for the roll control autopilot of a jet fighter aircraft. It is shown how it is quite possible for good predictive control to be achieved on open-loop critically stable systems.

Digital signal processing of optical monitoring by means of a Kalman filter

A Kalman filter algorithm has been applied to interpret the optical reflectance excursions during vacuum deposition of infrared coatings and multilayer thin-film filters. The application has been described in detail elsewhere and this paper now reports on-line experience for estimating deposition rate and thickness. The estimation proved sufficiently reliable to firstly 'navigate' regular manufacture (as controlled by a skilled operator) and to subsequently reproduce the skill without interpretation or intervention whilst maintaining exemplary product quality. Optical control by means of this Kalman filter application is therefore considered suitable as a basis for the automated manufacture of infrared coatings and multilayer thin-film filters.

Variable selection for financial distress classification using a genetic algorithm

This paper is concerned with the use of a genetic algorithm to select financial ratios for corporate distress classification models. For this purpose, the fitness value associated to a set of ratios is made to reflect the requirements of maximizing the amount of information available for the model and minimizing the collinearity between the model inputs. A case study involving 60 failed and continuing British firms in the period 1997-2000 is used for illustration. The classification model based on ratios selected by the genetic algorithm compares favorably with a model employing ratios usually found in the financial distress literature.

Application of a hybrid tracking algorithm to motion analysis

An algorithm for tracking multiple feature positions in a dynamic image sequence is presented. This is achieved using a combination of two trajectory-based methods, with the resulting hybrid algorithm exhibiting the advantages of both. An optimizing exchange algorithm is described which enables short feature paths to be tracked without prior knowledge of the motion being studied. The resulting partial trajectories are then used to initialize a fast predictor algorithm which is capable of rapidly tracking multiple feature paths. As this predictor algorithm becomes tuned to the feature positions being tracked, it is shown how the location of occluded or poorly detected features can be predicted. The results of applying this tracking algorithm to data obtained from real-world scenes are then presented.

A new algorithm for tracking people in a pervasive mining environment

This paper analyze and study a pervasive computing system in a mining environment to track people based on RFID (radio frequency identification) technology. In first instance, we explain the RFID fundamentals and the LANDMARC (location identification based on dynamic active RFID calibration) algorithm, then we present the proposed algorithm combining LANDMARC and trilateration technique to collect the coordinates of the people inside the mine, next we generalize a pervasive computing system that can be implemented in mining, and finally we show the results and conclusions.

Are greenhouse gas signals of Northern Hemisphere winter extra-tropical cyclone activity dependent on the identification and tracking algorithm?

For Northern Hemisphere extra-tropical cyclone activity, the dependency of a potential anthropogenic climate change signal on the identification method applied is analysed. This study investigates the impact of the used algorithm on the changing signal, not the robustness of the climate change signal itself. Using one single transient AOGCM simulation as standard input for eleven state-of-the-art identification methods, the patterns of model simulated present day climatologies are found to be close to those computed from re-analysis, independent of the method applied. Although differences in the total number of cyclones identified exist, the climate change signals (IPCC SRES A1B) in the model run considered are largely similar between methods for all cyclones. Taking into account all tracks, decreasing numbers are found in the Mediterranean, the Arctic in the Barents and Greenland Seas, the mid-latitude Pacific and North America. Changing patterns are even more similar, if only the most severe systems are considered: the methods reveal a coherent statistically significant increase in frequency over the eastern North Atlantic and North Pacific. We found that the differences between the methods considered are largely due to the different role of weaker systems in the specific methods.

Comprehensive ground-based and in situ observations of substorm expansion phase onset

In this paper, we present comprehensive ground-based and space-based in situ geosynchronous observations of a substorm expansion phase onset on 1 October 2005. The Double Star TC-2 and GOES-12 spacecraft were both located within the substorm current wedge during the substorm expansion phase onset, which occurred over the Canadian sector. We find that an onset of ULF waves in space was observed after onset on the ground by extending the AWESOME timing algorithm into space. Furthermore, a population of low-energy field-aligned electrons was detected by the TC-2 PEACE instrument contemporaneous with the ULF waves in space. These electrons appear to be associated with an enhancement of field-aligned Poynting flux into the ionosphere which is large enough to power visible auroral displays. The observations are most consistent with a near-Earth initiation of substorm expansion phase onset, such as the Near-Geosynchronous Onset (NGO) substorm scenario. A lack of data from further downtail, however, means other mechanisms cannot be ruled out.

Trade-offs and responsiveness of the single-layer urban canopy parametrization in WRF: an offline evaluation using the MOSCEM optimization algorithm and field observations

For an increasing number of applications, mesoscale modelling systems now aim to better represent urban areas. The complexity of processes resolved by urban parametrization schemes varies with the application. The concept of fitness-for-purpose is therefore critical for both the choice of parametrizations and the way in which the scheme should be evaluated. A systematic and objective model response analysis procedure (Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm) is used to assess the fitness of the single-layer urban canopy parametrization implemented in the Weather Research and Forecasting (WRF) model. The scheme is evaluated regarding its ability to simulate observed surface energy fluxes and the sensitivity to input parameters. Recent amendments are described, focussing on features which improve its applicability to numerical weather prediction, such as a reduced and physically more meaningful list of input parameters. The study shows a high sensitivity of the scheme to parameters characterizing roof properties in contrast to a low response to road-related ones. Problems in partitioning of energy between turbulent sensible and latent heat fluxes are also emphasized. Some initial guidelines to prioritize efforts to obtain urban land-cover class characteristics in WRF are provided. Copyright © 2010 Royal Meteorological Society and Crown Copyright.

A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method

In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach.

Motherhood, evolutionary psychology and mirror neurons or: ‘Grammar is politics by other means’

Through a close analysis of socio-biologist Sarah Blaffer Hrdy’s work on motherhood and ‘mirror neurons’ it is argued that Hrdy’s claims exemplify how research that ostensibly bases itself on neuroscience, including in literary studies ‘literary Darwinism’, relies after all not on scientific, but on political assumptions, namely on underlying, unquestioned claims about the autonomous, transparent, liberal agent of consumer capitalism. These underpinning assumptions, it is further argued, involve the suppression or overlooking of an alternative, prior tradition of feminist theory, including feminist science criticism.

Photometric redshifts and k-corrections for the Sloan Digital Sky Survey Data Release 7

We present a catalogue of galaxy photometric redshifts and k-corrections for the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7), available on the World Wide Web. The photometric redshifts were estimated with an artificial neural network using five ugriz bands, concentration indices and Petrosian radii in the g and r bands. We have explored our redshift estimates with different training sets, thus concluding that the best choice for improving redshift accuracy comprises the main galaxy sample (MGS), the luminous red galaxies and the galaxies of active galactic nuclei covering the redshift range 0 < z < 0.3. For the MGS, the photometric redshift estimates agree with the spectroscopic values within rms = 0.0227. The distribution of photometric redshifts derived in the range 0 < z(phot) < 0.6 agrees well with the model predictions. k-corrections were derived by calibration of the k-correct_v4.2 code results for the MGS with the reference-frame (z = 0.1) (g - r) colours. We adopt a linear dependence of k-corrections on redshift and (g - r) colours that provide suitable distributions of luminosity and colours for galaxies up to redshift z(phot) = 0.6 comparable to the results in the literature. Thus, our k-correction estimate procedure is a powerful, low computational time algorithm capable of reproducing suitable results that can be used for testing galaxy properties at intermediate redshifts using the large SDSS data base.

Deletion of Tumor Necrosis Factor-alpha Receptor 1 (TNFR1) Protects against Diet-induced Obesity by Means of Increased Thermogenesis

In diet-induced obesity, hypothalamic and systemic inflammatory factors trigger intracellular mechanisms that lead to resistance to the main adipostatic hormones, leptin and insulin. Tumor necrosis factor-alpha (TNF-alpha) is one of the main inflammatory factors produced during this process and its mechanistic role as an inducer of leptin and insulin resistance has been widely investigated. Most of TNF-alpha inflammatory signals are delivered by TNF receptor 1 (R1); however, the role played by this receptor in the context of obesity-associated inflammation is not completely known. Here, we show that TNFR1 knock-out (TNFR1 KO) mice are protected from diet-induced obesity due to increased thermogenesis. Under standard rodent chow or a high-fat diet, TNFR1 KO gain significantly less body mass despite increased caloric intake. Visceral adiposity and mean adipocyte diameter are reduced and blood concentrations of insulin and leptin are lower. Protection from hypothalamic leptin resistance is evidenced by increased leptin-induced suppression of food intake and preserved activation of leptin signal transduction through JAK2, STAT3, and FOXO1. Under the high-fat diet, TNFR1 KO mice present a significantly increased expression of the thermogenesis-related neurotransmitter, TRH. Further evidence of increased thermogenesis includes increased O(2) consumption in respirometry measurements, increased expressions of UCP1 and UCP3 in brown adipose tissue and skeletal muscle, respectively, and increased O(2) consumption by isolated skeletal muscle fiber mitochondria. This demonstrates that TNF-alpha signaling through TNFR1 is an important mechanism involved in obesity-associated defective thermogenesis.

Indirect study of (11)B(p,alpha(0))(8)Be and (10)B(p,alpha)(7)Be reactions at astrophysical energies by means of the Trojan Horse Method: recent results

Nuclear (p,alpha) reactions destroying the so-called ""light-elements"" lithium, beryllium and boron have been largely studied in the past mainly because their role in understanding some astrophysical phenomena, i.e. mixing-phenomena occurring in young F-G stars [1]. Such mechanisms transport the surface material down to the region close to the nuclear destruction zone, where typical temperatures of the order of similar to 10(6) K are reached. The corresponding Gamow energy E(0)=1.22 (Z(x)(2)Z(X)(2)T(6)(2))(1/3) [2] is about similar to 10 keV if one considers the ""boron-case"" and replaces in the previous formula Z(x) = 1, Z(X) = 5 and T(6) = 5. Direct measurements of the two (11)B(p,alpha(0))(8)Be and (10)B(p,alpha)(7)Be reactions in correspondence of this energy region are difficult to perform mainly because the combined effects of Coulomb barrier penetrability and electron screening [3]. The indirect method of the Trojan Horse (THM) [4-6] allows one to extract the two-body reaction cross section of interest for astrophysics without the extrapolation-procedures. Due to the THM formalism, the extracted indirect data have to be normalized to the available direct ones at higher energies thus implying that the method is a complementary tool in solving some still open questions for both nuclear and astrophysical issues [7-12].