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A new neural network architecture is proposed based upon effects of non-Lipschitzian dynamics. The network is fully connected, but these connections are active only during vanishingly short time periods. The advantages of this architecture are discussed.
Logic is presented for a time-integrated NDVI that is modified by an AVHRR derived surface evaporation resistance factor sigma, and truncated by temperatures that cause plant dormancy, to improve environmental sensitivity. With this approach, NDVI observed during subfreezing temperatures is not integrated. Water stress-related impairment in plant activity is incorporated by reducing the effective NDVI at each integration with sigma, which is derived from the slope of the surface temperature to NDVI ratio for climatically similar zones of the scene. A comparison of surface resistance before and after an extended drought period for a 1200 sq km region of coniferous forest in Montana is presented.
The numerical vertical diffusivity K(num), embedded in a numerical ocean general circulation model with nonuniform vertical grid, is estimated. It is shown that in a downwelling region, K(num) is negative for a grid with grid size increasing with depth. When the grid size increment, or the downward vertical velocity, is large, K(num) may exceed the vertical diffusivity specified and may result in a negative effective vertical diffusivity. Therefore care needs to be taken to specify the vertical diffusivity in a numerical model with nonuniform grid, and a lower bound is generally imposed in order to avoid an unphysical negative value. Some possible effects of the negative effective diffusivity are discussed.
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Software metrics provide an effective method for characterizing software. Metrics have traditionally been composed through the definition of an equation. This approach is limited by the fact that all the interrelationships among all the parameters be fully understood. This paper explores an alternative, neural network approach to modeling metrics. Experiments performed on two widely accepted metrics, McCabe and Halstead, indicate that the approach is sound, thus serving as the groundwork for further exploration into the analysis and design of software metrics.
The torque and flow ripple effects which result when multiple hydraulic motors are used to drive a single motion of a mechanical device can significantly affect the way in which the device performs. This article presents a mathematical model describing the torque and flow ripple effects of a bent-axis hydraulic piston motor. The model is used to show how the ripple magnitude can be reduced when multiple motors are used to drive a motion. A discussion of the hydraulic servo system of the 70-m antennas located with the Deep Space Network is included to demonstrate the application of the concepts presented.
An expandable stochastic digital architecture for recurrent (Hopfield like) neural networks is proposed. The main features and basic principles of stochastic processing are presented. The stochastic digital architecture is based on a chip with n full interconnected neurons with a pipeline, bit processing structure. For large applications, a flexible way to interconnect many such chips is provided.
The goal is to develop an approach to automating the alignment and adjustment of optical measurement, visualization, inspection, and control systems. Classical controls, expert systems, and neural networks are three approaches to automating the alignment of an optical system. Neural networks were chosen for this project and the judgements that led to this decision are presented. Neural networks were used to automate the alignment of the ubiquitous laser-beam-smoothing spatial filter. The results and future plans of the project are presented.
The hardware and algorithms used to vector quantize (VQ) predicted pixel intensity differences for real-time video compression are described. The hardware is designed for rapid vector quantization performance, which entails the development of application-specific associative memory circuits. A modified DPCM algorithm is originally examined to determine how neural circuitry could enhance its operation. It was determined that quantization and encoding could be improved by consolidating these two functions into one, and by increasing the amount of information (i.e. number of pixels) quantized at a time. The result is a predictive scheme that vector quantizes differential values. Some of the disadvantages of VQ algorithms are solved using associative memories. The video compression algorithm and the associative memory design are described.
Nearest neighbor approaches and a new neural network, the Binary Diamond, are used for the classification of images of ground pixels obtained by LANDSAT satellite. The performances are evaluated by comparing classifications of a scene in the vicinity of Washington DC. The problem of optimal selection of categories is addressed as a step in the classification process.
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This paper discusses the application of neural network pattern analysis algorithms to the IC fault diagnosis problem. A fault diagnostic is a decision rule combining what is known about an ideal circuit test response with information about how it is distorted by fabrication variations and measurement noise. The rule is used to detect fault existence in fabricated circuits using real test equipment. Traditional statistical techniques may be used to achieve this goal, but they can employ unrealistic a priori assumptions about measurement data. Our approach to this problem employs an adaptive pattern analysis technique based on feedforward neural networks. During training, a feedforward network automatically captures unknown sample distributions. This is important because distributions arising from the nonlinear effects of process variation can be more complex than is typically assumed. A feedforward network is also able to extract measurement features which contribute significantly to making a correct decision. Traditional feature extraction techniques employ matrix manipulations which can be particularly costly for large measurement vectors. In this paper we discuss a software system which we are developing that uses this approach. We also provide a simple example illustrating the use of the technique for fault detection in an operational amplifier.
This report describes work supported by a post-launch grant (NCC 2-679) that originally covered the period from 1 January 1990 through 31 December 1992. This was extended an additional nine months at no cost until 30 September 1993, and once more until to 30 November 93. A final report on the effort from 1 January 1990 through 30 November 1993 (anticipated) is provided following the background discussion. Tasks required during the post launch period are briefly as follows: (1) attend PSG (Project Science Group) meetings; (2) support in-flight checkouts; maintain and keep safe the spare instrument and GSE (Ground Support Equipment); (3) organize and maintain documentation; (4) finish calibration measurements, documentation, and analysis; (5) characterize and diagnose instrument anomalies; (6) develop descent data analysis tools; and (7) science data analysis and publication. The following sections provide background information on the NFR instrument followed by the complete progress report.
Models for simulating some aspects of neural intelligence are presented and discussed. Special attention is given to terminal neurodynamics as a particular architecture of terminal dynamics suitable for modeling information flows. Applications of terminal chaos to information fusion as well as to planning and modeling coordination among neurons in biological systems are disussed.
Comparisons of satellite-derived temperatures with correlative temperatures indicate that the LIMS temperatures are accurate and contain more of the needed vertical resolution for calculating a residual mean circulation for transporting tracer-like species. Generally, the LIMS temperatures are accurate to at least 2 K. Other satellite data sets are comprised of temperatures with coarser vertical resolution, leading to biases that occur with an error pattern that is characteristic of their resolution. Their biases exceed 2 K at some altitudes. Retrievals of species using an infrared limb emission technique are sensitive to any temperature bias. Generally, the IMS comparisons with other data sets for ozone and water vapor are good to better than 20 percent; this represents an independent confirmation of the quality of LIMS and temperatures. Zonal mean comparisons between LIMS and SAMS temperatures also indicate agreement to better than 2 K from about 7 to 2hPa. Therefore, we are confident that SAMS N2O and CH4 are relatively free of temperature bias in that region. These factors support the generally good agreement in G90 between model N2O transported using a LIMS-derived RMC and the N2O contours from SAMS.
The invention is an apparatus and method for forming woven fabrics through the use of an adjustable reed. The adjustable reed has multiple groups of reed wires that guide the warp yarns. The groups of reed wires move on reed rails parallel to the warp direction. In addition, rail expanders permit the space between the reed wires to be modified and telescoping rods attached to the rail sliders can be turned to permit the reed wires to be skewed to alter the fill yarn angle. These adjustments to the reed permit simultaneous variation of fill yarn angles and fabric widths and allow these variations to be made during fabrication, without the need to halt production.
An evaluation of a prototyped user interface is best supported by a simulation of the system. A simulation allows for dynamic evaluation of the interface rather than just a static evaluation of the screen's appearance. This allows potential users to evaluate both the look (in terms of the screen layout, color, objects, etc.) and feel (in terms of operations and actions which need to be performed) of a system's interface. Because of the need to provide dynamic evaluation of an interface, there must be support for producing active simulations. The high-fidelity training simulators are normally delivered too late to be effectively used in prototyping the displays. Therefore, it is important to build a low fidelity simulator, so that the iterative cycle of refining the human computer interface based upon a user's interactions can proceed early in software development.
Determinations of the zonally averaged and diabatically derived residual mean circulation (RMC) are particularly sensitive to the assumed zonal mean temperature distribution used as input. Several different middle atmosphere satellite temperature distributions have been employed in models and are compared here: a 4-year (late 1978 to early 1982) National Meteorological Center (NMC) climatology, the Barnett and Corney (or BC) climatology, and the 7 months of Nimbus 7 limb infrared monitor of the stratosphere (LIMS) temperatures. All three climatologies are generally accurate below the 10 hPa level, but there are systematic differences between them of up to +/-5 K in the upper stratosphere and lower mesosphere. The NMC/LIMS differences are evaluated using time series of rocketsonde and reconstructed satellite temperatures at station locations. Much of those biases can be explained by the differing vertical resolutions for the satellite-derived temperatures; the time series of reconstructed LIMS profiles have higher resolution and are more accurate. Because the LIMS temperatures are limited to just two full seasons, one cannot obtain monthly RMCs from them for an annual model calculation. Two alternate monthly climatologies are examined briefly: the 4-year Nimbus 7 stratospheric and mesospheric sounder (SAMS) temperatures and for the mesosphere the distribution from the Solar Mesosphere Explorer (SME), both of which are limb viewers of medium vertical resolution. There are also differences of the order of +/-5 K for those data sets. It is concluded that a major source of error in the determination of diabatic RMCs is a persistent pattern of temperature bias whose characteristics vary according to the vertical resolution of each individual climatology.