Speckle interferometry observations of asteroids at tng

The Speckle camera of the 3.5 m Telescopio Nazionale Galileo (TNG) has been used to measure apparent sizes and shapes of a number of main belt asteroids. The average size measurements are in a generally good agreement with the results of indirect IRAS-based radiometric techniques. The measured shapes are compared with predictions based on previously derived spin axis directions and lightcurve photometry of some of the observed objects. Also in this case the agreement is reasonable and the speckle observations allow us to discriminate in some cases between the two pole solutions usually found for each object. No clear evidence of binaries was found. The results show that the TNG speckle camera can be a powerful tool to resolve relatively large main belt asteroids and to calibrate the results of the IRAS survey.     

Observation and preliminary analysis of a recent Jovian satellite phenomenon

The occultation of Io by Ganymede as observed on 10 June 1985 is reported. The middle of the occultation minimum was found to occur at 14h 14m 5.7s UT. In the plane perpendicular to the line of sight the centres of the two satellite disks passed to within ~ 2530 km of each other, at a relative velocity, in this plane, of about 11 kms^–1. The values of these last two quantities, however, depend on what assumptions are made about the light distribution over io's disk.     

A neural network approach to ship track-keeping control

This paper presents an on-line trained neural net work controller for ship track-keeping problems. Following a brief review of the ship track-keeping control development since the 1980's, an analysis of various existing backpropagation-based neural controllers is provided. We then propose a single-input multioutput (SIMO) neural control strategy for situations where the exact mathematical dynamics of the ship are not available. The aim of this study is to build an autonomous neural controller which uses rudder to regulate both the tracking error and heading error. During the whole control process, the proposed SIMO neural controller adapts itself on-line from a direct evaluation of the control accuracy, and hence the need for a “teacher” or an off-line training process can be removed. With a relatively modest amount of quantitative knowledge of the ship behavior, the design philosophy enables real time control of a nonlinear ship model under random wind disturbances and measurement noise. Three different track-keeping tasks have been simulated to demonstrate the effectiveness of the training method and the robust performance of the proposed neural control strategy     

First results from the Gauribidanur Radio heliograph

Observations of the Sun at two frequencies (51 and 77 MHz) using the East-West arm of the Gauribidanur Radio heliograph are presented.     

Solar-planetary cycles in Jupiter's great red spot darkness

The change in the darkness of the Great Red Spot (GRS) of Jupiter (1894–1974) has been analysed with Fourier (FFT), Maximum Entropy and Power spectrum (Blackman-Tukey window) (PSA) methods of spectrum analysis. Significance, non-randomness and stationarity tests assigned high variance to periodicities of 33 ± 4, 13–15, about 11, 9 and 3 yrs. The highest correlation between solar activity and GRS darkness was found for the 14th and 16th solar cycle. The periodicities obtained are interpreted as the combined eftects of solar activity, planetary resonances and internal jovian mechanisms.     

Identification of physical processes inherent in artificial neural network rainfall runoff models

The emergence of artificial neural network (ANN) technology has provided many promising results in the field of hydrology and water resources simulation. However, one of the major criticisms of ANN hydrologic models is that they do not consider/explain the underlying physical processes in a watershed, resulting in them being labelled as black‐box models. This paper discusses a research study conducted in order to examine whether or not the physical processes in a watershed are inherent in a trained ANN rainfall‐runoff model. The investigation is based on analysing definite statistical measures of strength of relationship between the disintegrated hidden neuron responses of an ANN model and its input variables, as well as various deterministic components of a conceptual rainfall‐runoff model. The approach is illustrated by presenting a case study for the Kentucky River watershed. The results suggest that the distributed structure of the ANN is able to capture certain physical behaviour of the rainfall‐runoff process. The results demonstrate that the hidden neurons in the ANN rainfall‐runoff model approximate various components of the hydrologic system, such as infiltration, base flow, and delayed and quick surface flow, etc., and represent the rising limb and different portions of the falling limb of a flow hydrograph. Copyright © 2004 John Wiley & Sons, Ltd.