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.     

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.     

What Drives Star Formation?

Current theoretical models for what drives star formation (especially low-mass star formation) are: (1) magnetic support of self-gravitating clouds with ambipolar diffusion removing support in cores and triggering collapse and (2) compressible turbulence forming self-gravitating clumps that collapse as soon as the turbulent cascade produces insufficient turbulent support. Observations of magnetic fields can distinguish between these two models because of different predictions in three areas: (1) magnetic field morphology, (2) the scaling of field strength with density and non-thermal velocities, and (3) the mass to magnetic flux ratio, M/Φ. We first discuss the techniques and limitations of methods for observing magnetic fields in star formation regions, then describe results for the L1544 prestellar core as an exemplar of the observational results. Application of the three tests leads to the following conclusions. The observational data show that both magnetic fields and turbulence are important in molecular cloud physics. Field lines are generally regular rather than chaotic, implying strong field strengths. But fields are not aligned with the minor axes of oblate spheroidal clouds, suggesting that turbulence is important. Field strengths appear to scale with non-thermal velocity widths, suggesting a significant turbulent support of clouds. Giant Molecular Clouds (GMCs) require mass accumulation over sufficiently large volumes that they would likely have an approximately critical M/Φ. Yet H I clouds are observed to be highly subcritical. If self-gravitating (molecular) clouds form with the subcritical M/Φ of H I clouds, the molecular clouds will be subcritical. However, the observations of molecular cloud cores suggest that they are approximately critical, with no direct evidence for subcritical molecular clouds or cloud envelopes. Hence, the observations remain inconclusive in deciding between the two extreme-case models of what drives star formation. What is needed to further advance our understanding of the role of magnetic fields in the star formation process are additional high sensitivity surveys of magnetic field strengths and other cloud properties in order to further refine the assessment of the importance of magnetic fields in molecular cores and envelopes.     

The Dergaon (H5) chondrite: Fall,classification, petrological and chemical characteristics,cosmogenic effects,and noble gas records

Abstract— A multiple fall of a stony meteorite occurred near the town of Dergaon in Assam, India, on March 2, 2001. Several fragments weighing <2 kg and a single large fragment weighing ～10 kg were recovered from the strewn field, which extended over several tens of square kilometers. Chemical, petrographic, and oxygen isotopic studies indicate it to be, in most aspects, a typical H5 chondrite, except the unusually low K content of ～340 ppm. A cosmic ray exposure of 9.7 Ma is inferred from the cosmogenic noble gas records. Activities of eleven cosmogenic radionuclides were measured. ²⁶Al and ²²Na activities as well as the ²²Na/²⁶Al activity ratio are close to the values expected on the basis of solar modulation of galactic cosmic rays. The low ⁶⁰Co activity (<1 dpm/kg) is indicative of a small preatmospheric size of the meteorite. Cosmic ray heavy nuclei track densities in olivine grains range from ～106 cm^?2 in samples from the largest fragment to approximately (4–9) × 105 cm^?2 in one of the smaller fragments. The combined track, radionuclide, and noble gas data suggest a preatmospheric radius of ～20 cm for the Dergaon meteorite.     

Tidal effects on temperature front in the Yellow Sea

Temperature front (TF) is one of the important features in the Yellow Sea, which forms in spring, thrives in summer, and fades in autumn as thermocline declines. TF intensity ⋎S _T ⋎ is defined to describe the distribution of TF. Based on the MASNUM wave-tide-circulation coupled model, temperature distribution in the Yellow Sea was simulated with and without tidal effects. Along 36°N, distribution of TF from the simulated results are compared with the observations, and a quantitative analysis is introduced to evaluate the tidal effects on the forming and maintaining processes of the TF. Tidal mixing and the circulation structure adapting to it are the main causes of the TF. Supported by the National Basic Research Program of China (No. G1999043809) and the National Science Foundation of China (No. 49736190).     

The energy-dependent radiation transfer in a homogeneous finite cylinder

Radiation transfer problem in the slowing-down region for cylindrical geometry with diffuse reflectivity and internal source is connected with source-free problem with isotropic boundary condition. Modelled kernels are used to represent the slowing-down kernel. Calculations are performed for the radiation flux at the boundary of the cylinder. Numerical results are obtained using the bi-variation technique.