Test Case Generation as an AI Planning Problem

While Artificial Intelligence techniques have been applied to a variety of software engineering applications, the area of automated software testing remains largely unexplored. Yet, test cases for certain types of systems (e.g., those with command language interfaces and transaction based systems) are similar to plans. We have exploited this similarity by constructing an automated test case generator with an AI planning system at its core. We compared the functionality and output of two systems, one based on Software Engineering techniques and the other on planning, for a real application: the StorageTek robot tape library command language. From this, we showed that AI planning is a viable technique for test case generation and that the two approaches are complementary in their capabilities.     

The road to maturity: navigating between craft and science

C. Ebert presents his views on the state of software engineering as a field, its roots and inherent conflicts, its relationship to other engineering disciplines, where it is headed, and what we can do to influence that direction. T. Matsubara, T. Webb, M. Pezze, and O.W. Bertelsen offer a spectrum of further insights     

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.     

Spontaneous disappearance of traumatic macular holes in young patients

PURPOSE: To report the disappearance of traumatic macular hole in three eyes of three patients. METHODS: Clinical data of the patients were reviewed. RESULTS: The three patients were relatively young, ranging in age from 12 to 18 years old. In one eye of each patient, a small traumatic macular hole was observed at the first visit. Visual acuities ranged from 20/100 to 20/40. The macular holes resolved spontaneously 3 to 4 months after the trauma, and final visual acuity improved to 20/20 in all patients. CONCLUSION: Small traumatic macular holes in young patients can resolve spontaneously, and this can be associated with good visual recovery.     

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.     

Estimation of deformation gradient and strain from cine-PC velocity data

Phase contrast magnetic resonance imaging (MRI) can provide in vivo myocardial velocity field measurements. These data allow densely spaced material points to be tracked throughout the whole heart cycle using, for example, the Fourier tracking algorithm. To process the tracking results for myocardial deformation and strain quantification, we developed a method that is based on fitting the tracking results to an appropriate local deformation model. We further analyzed the accuracy and precision of the method and provided performance predictions for several local models. In order to validate the method and the theoretical performance analysis, we conducted controlled computer simulations and a phantom study. The results agreed well with expectations. Human heart data were also acquired and analyzed, and provided encouraging results. At the signal-to-noise ratio (SNR) level and spatial resolution expected in clinical settings, the study predicts strain quantification accuracy and precision that may allow the technique to become a practical and powerful noninvasive approach for the study of cardiac function, although clinically acceptable data acquisition strategies for three-dimensional (3-D) data are still a challenge.