Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.     

A Metallographic Study of Abnormal Grain Growth During Aging of a β-Quenched Zr-1.14 Wt% Cr-0.08 Wt% Fe Alloy

Results are reported of a detailed optical and electron metallographic study of the effects of variations in quenching rate from the β-phase and aging temperature in the + ZrCr₂ phase region, on the recrystallization and grain growth characteristics of Zr-1.14 wt% Cr-0.08 wt% Fe alloy used for nuclear fuel sheathing. It is shown that certain quenching rates, which produce a mixed martensitic-Widmanstätten structure, should be avoided during the fabrication of fuel sheathing, since recrystallization and grain growth occurs at a much lower temperature than a material quenched at such a rate that the structure is wholly Widmanstätten or wholly martensitic. Preaging at 350 or 450°C prior to the high-temperature (700–750°C) aging reduces the incidence of recrystallization and grain growth.     

Artificial neural networks for coordination and control: The portability of experiential representations

It is time to locate connectionist representation theory in the new wave of robotics research. The utility of representations developed in artificial neural networks (ANNs) during learning has been demonstrated in cognitive science research since the 1980s. The research reported here puts learned representations to work in a decentered control task, the disembodied arm problem, in which a mobile robot operates an arm fixed to a table to pick up objects. There is no physical linkage between the arm and the robot and so the robot's point of view must be decentered. This is done by developing a modular Artificial Neural Net system in three stages: (i) a classifier net is trained with laser scan data to output transformationally invariant position classes; (ii) an arm net is trained for picking up objects; (iii) an inter net is trained to communicate and coordinate the sensing and acting. The completed system is shown to create new nonsymbolic transformationally invariant representations in order to perform the effective generalization of decentered viewpoints.     

Certain nonlinear partially observable stochastic optimal controlproblems with explicit control laws equivalent to LEQG/LQG problems

This paper is concerned with partially observed stochastic optimal control problems when nonlinearities enter the dynamics of the unobservable state and the observations as gradients of potential functions. Explicit representations for the information state are derived in terms of a finite number of sufficient statistics. Consequently, the partially observed problem is recast as one of complete information with a new state generated by a modified version of the Kalman filter. When the terminal cost is quadratic in the unobservable state and includes the integral of the nonlinearities, the optimal control laws are explicitly computed, similar to linear-exponential-quadratic-Gaussian (LEQG) and linear-quadratic-Gaussian (LQG) tracking problems. The results are applicable to filtering and control of Hamiltonian systems     

Estimating the viewing parameters of random, noisy projections ofasymmetric objects for tomographic reconstruction

The ability to determine the viewing parameters of objects from their projections has enabled well established tomographic techniques to be employed in the 3D reconstruction of objects from images obtained via modalities where the orientation of the objects cannot be controlled. A method is described for the determination of the viewing parameters of randomly acquired projections of asymmetric objects. It extends upon the common lines algorithm by determining the relative orientation of projections from the location of lines of intersection among the Fourier transforms of the projections in 3D Fourier space. A new technique for finding the lines of intersection in the presence of translational displacement, and for subsequently finding the translational displacement, is presented. The complete algorithm is described and its efficacy is demonstrated using real data. A new technique for dealing with noise is also discussed     

On a large-gain theorem

The aim of this paper is to give a general quantitative requirement which the loop gain must satisfy in order to stabilize a given unstable (possibly nonlinear and time-varying) plant, namely that the gain must exceed one.     

Imprudent linking weaves a tangled Web

Hypertext linking is often embraced uncritically by Web authors eager to explore the power of hypertext without first considering its effects on their readers' comprehension. Hypertext linking is not a substitute for thought-we think with ideas, not with dissociated snippets of raw information. Even the most germane bits of information cannot become ideas, regardless of how cleverly they are stacked, listed or linked. Ideas define relevance, provide context and establish patterns. With patterns, most concepts become intelligible, and we need continuity and stability of theme and context to recognize patterns. So, like most powerful technologies, hypertext links are a mixed blessing. Used improperly, they can be detrimental to Web sites. “Loose links” can drive away an audience, dilute the site's message, confuse the reader with irrelevant digressions and become a continuing maintenance headache for site authors and Webmasters     

Large-scale simulation studies in image pattern recognition

Many obstacles to progress in image pattern recognition result from the fact that per-class distributions are often too irregular to be well-approximated by simple analytical functions. Simulation studies offer one way to circumvent these obstacles. We present three closely related studies of machine-printed character recognition that rely on synthetic data generated pseudo-randomly in accordance with an explicit stochastic model of document image degradations. The unusually large scale of experiments - involving several million samples that makes this methodology possible have allowed us to compute sharp estimates of the intrinsic difficulty (Bayes risk) of concrete image recognition problems, as well as the asymptotic accuracy and domain of competency of classifiers