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     

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.     

Flows in clumpy planetary nebulae

The dynamics of clumps observed in planetary nebulae are considered. The possibility that SiO maser spots in evolved stars and the planetary nebula clumps are formed by the Parker instability behind shocks in pulsating stars' atmospheres is raised. Molecular observations of the clumps are suggested. The effects of the ablation of clumps on the global flow structure of a more tenuous plasma in which they are embedded are reviewed.     

Variations in the natural abundance of 15N in estuarine suspended particulate matter: A specific indicator of biological processing

In this study, the ¹⁴N:¹⁵N ratio of suspended particulate material collected from the Tamar river estuary, south-west England, is described. Three populations of particles, distinguishable by their ¹⁵N content, were observed. This investigation has shown that populations of estuarine particles are generated by biological transformations in situ and that the ¹⁵N content of estuarine particles does not merely reflect hydrodynamic mixing of the freshwater and seawater source particulate material.     

Particulate organic matter in the coastal and estuarine waters of Goa and its relationship with phytoplankton production

In the coastal and estuarine waters of Goa, particulate organic carbon (POC) varied from 0.52 to 2.51 mg l^?1 and from 0.28 to 5.24 mg l^?1 and particulate phosphorus (PP) varied from 0.71 to 5.18 μg l^?1 and from 0.78 to 20.34 μg l^?1, respectively. The mean values of chlorophyll and primary productivity were 1.94 mg m^?3 and 938.1 mg C m^?2 day^?1 in the coastal waters and 4.3 mg m^?3 and 636.5 mg C m^?1 day^?1 in the estuarine waters, respectively.$\text{POC}\text{chl}$ ratios were low in June and October even when POC values were quite high. The POC in surface waters was linearly correlated with the chlorophyll content. Also PP increased when chlorophyll and primary productivity remained high. The results suggest that the phytoplankton was sharply increasing and contributed to POC and PP content. The percentage of detritus calculated from the intercept values of chlorophyll on POC varied from 46 to 76% depending on season. Results indicate that the major portion of POC and PP during postmonsoon (October–January) is derived from phytoplankton production while the allochthonous matter predominate during monsoon (June–September).     

A one-dimensional model for the interaction between continental-scale ice sheets and atmospheric stationary waves

 The great continental ice sheets of the Pleistocene represented a significant topographic obstacle to the westerly winds in northern midlatitudes. This work explores how consequent changes in the atmospheric stationary wave pattern might have affected the shape and growth of the ice sheets themselves. A one dimensional (1-D) model is developed which permits an examination of the types and magnitudes of the feedbacks that might be expected. When plausible temperature perturbations are introduced at the ice-sheet margin which are proportional to the stationary wave amplitude, the equilibrium shape of the ice sheet is significantly altered, and depends on the sign of the perturbation. The proposed feedback also affects the response of the ice sheet to time-varying climate forcing. The results suggest that the evolution of a continental-scale ice sheet with a land-based margin may be significantly determined by the changes it induces in the atmospheric circulation. Received: 1 October 1999 / Accepted: 17 July 2000     

New developments in eclipsing binary light curve modeling

The light curve modeling of binary stars has continued to evolve since its founding by Henry Norris Russell (see Russell and Merrill 1952 and citations therein) nearly a century ago, accelerated in the 1950s by Kopal's introduction of Roche geometry into models and by the development of synthetic light curve computer code in the 1970's. Improved physics and the use of more kinds of observational input are providing another round of important advances that promise to enlarge our knowledge of both binary stars and ensembles containing them. Here we discuss the newer horizons of light curve modeling and the steps being taken toward them.