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This paper discusses an approach to representing and reasoning about constraints over strings. We discuss how many string domains can often be concisely represented using regular languages, and how constraints over strings, and domain operations on sets of strings, can be carried out using this representation.
All of the International Space Station (ISS) systems which require computer control depend upon the hardware and software of the Command and Data Handling System (C&DH) system, currently a network of over 30 386-class computers called Multiplexor/Dimultiplexors (MDMs)[18]. The Caution and Warning System (C&W)[7], a set of software tasks that runs on the MDMs, is responsible for detecting, classifying, and reporting errors in all ISS subsystems including the C&DH. Fault Detection, Isolation and Recovery (FDIR) of these errors is typically handled with a combination of automatic and human effort. We are developing an Advanced Diagnostic System (ADS) to augment the C&W system with decision support tools to aid in root cause analysis as well as resolve differing human and machine C&DH state estimates. These tools which draw from sources in model-based reasoning[ 16,291, will improve the speed and accuracy of flight controllers by reducing the uncertainty in C&DH state estimation, allowing for a more complete assessment of risk. We have run tests with ISS telemetry and focus on those C&W events which relate to the C&DH system itself. This paper describes our initial results and subsequent plans.
In this paper, we introduce JNET, a novel constraint representation and reasoning framework that supports procedural constraints and constraint attachments, providing a flexible way of integrating the constraint system with a runtime software environment and improving its applicability. We describe how JNET is applied to a real-world problem - NASA's Earth-science data processing domain, and demonstrate how JNET can be extended, without any knowledge of how it is implemented, to meet the growing demands of real-world applications.
This viewgraph slide tutorial reviews methods for planning and scheduling events. The presentation reviews several methods and uses several examples of scheduling events for the successful and timely completion of the overall plan. Using constraint based models the presentation reviews planning with time, time representations in problem solving and resource reasoning.
In this paper, we show that force-based reasoning, for identifying a cluster of parts that can be decomposed naturally by the applied force, plays an important role in selecting feasible subassemblies and analyzing subassembly stability in assembly planning.
A technique for finding an optimal threshold for binarization of a gray scale image employs fuzzy reasoning. A triangular membership function is employed which is dependent on the degree to which the pixels in the image belong to either the foreground class or the background class. Use of a simplified linear fuzzy entropy factor function facilitates short execution times and use of membership values between 0.0 and 1.0 for improved accuracy. To improve accuracy further, the membership function employs lower and upper bound gray level limits that can vary from image to image and are selected to be equal to the minimum and the maximum gray levels, respectively, that are present in the image to be converted. To identify the optimal binarization threshold, an iterative process is employed in which different possible thresholds are tested and the one providing the minimum fuzzy entropy measure is selected.
A computer-based technique for detecting edges in gray level digital images employs fuzzy reasoning to analyze whether each pixel in an image is likely on an edge. The image is analyzed on a pixel-by-pixel basis by analyzing gradient levels of pixels in a square window surrounding the pixel being analyzed. An edge path passing through the pixel having the greatest intensity gradient is used as input to a fuzzy membership function, which employs fuzzy singletons and inference rules to assigns a new gray level value to the pixel that is related to the pixel's edginess degree.
A computer-based technique for detecting edges in gray level digital images employs fuzzy reasoning to analyze whether each pixel in an image is likely on an edge. The image is analyzed on a pixel-by-pixel basis by analyzing gradient levels of pixels in a square window surrounding the pixel being analyzed. An edge path passing through the pixel having the greatest intensity gradient is used as input to a fuzzy membership function, which employs fuzzy singletons and inference rules to assigns a new gray level value to the pixel that is related to the pixel's edginess degree.
A computer program for the detection of present and prediction of future discrete states of a complex, real-time engineering system utilizes a combination of symbolic processing and numerical model-based reasoning. One of the biggest weaknesses of a purely symbolic approach is that it enables prediction of only future discrete states while missing all unmodeled states or leading to incorrect identification of an unmodeled state as a modeled one. A purely numerical approach is based on a combination of statistical methods and mathematical models of the applicable physics and necessitates development of a complete model to the level of fidelity required for prediction. In addition, a purely numerical approach does not afford the ability to qualify its results without some form of symbolic processing. The present software implements numerical algorithms to detect unmodeled events and symbolic algorithms to predict expected behavior, correlate the expected behavior with the unmodeled events, and interpret the results in order to predict future discrete states. The approach embodied in this software differs from that of the BEAM methodology (aspects of which have been discussed in several prior NASA Tech Briefs articles), which provides for prediction of future measurements in the continuous-data domain.
A general-purpose computer program for planning the actions of a spacecraft or other complex system has been augmented by incorporating a subprogram that reasons about uncertainties in such continuous variables as times taken to perform tasks and amounts of resources to be consumed. This subprogram computes parametric probability distributions for time and resource variables on the basis of user-supplied models of actions and resources that they consume. The current system accepts bounded Gaussian distributions over action duration and resource use. The distributions are then combined during planning to determine the net probability distribution of each resource at any time point. In addition to a full combinatoric approach, several approximations for arriving at these combined distributions are available, including maximum-likelihood and pessimistic algorithms. Each such probability distribution can then be integrated to obtain a probability that execution of the plan under consideration would violate any constraints on the resource. The key idea is to use these probabilities of conflict to score potential plans and drive a search toward planning low-risk actions. An output plan provides a balance between the user s specified averseness to risk and other measures of optimality.
A set of imaging techniques based on Fuzzy Reasoning (FR) approach was built for NASA at Kennedy Space Center (KSC) to perform complex real-time visual-related safety prototype tasks, such as detection and tracking of moving Foreign Objects Debris (FOD) during the NASA Space Shuttle liftoff and visual anomaly detection on slidewires used in the emergency egress system for Space Shuttle at the launch pad. The system has also proved its prospective in enhancing X-ray images used to screen hard-covered items leading to a better visualization. The system capability was used as well during the imaging analysis of the Space Shuttle Columbia accident. These FR-based imaging techniques include novel proprietary adaptive image segmentation, image edge extraction, and image enhancement. Probabilistic Neural Network (PNN) scheme available from NeuroShell(TM) Classifier and optimized via Genetic Algorithm (GA) was also used along with this set of novel imaging techniques to add powerful learning and image classification capabilities. Prototype applications built using these techniques have received NASA Space Awards, including a Board Action Award, and are currently being filed for patents by NASA; they are being offered for commercialization through the Research Triangle Institute (RTI), an internationally recognized corporation in scientific research and technology development. Companies from different fields, including security, medical, text digitalization, and aerospace, are currently in the process of licensing these technologies from NASA.
Accomplishing the best-performing composite matrix (resin) requires that not only the processing method but also the cure cycle generate low-void-content structures. If voids are present, the performance of the composite matrix will be significantly reduced. This is usually noticed by significant reductions in matrix-dominated properties, such as compression and shear strength. Voids in composite materials are areas that are absent of the composite components: matrix and fibers. The characteristics of the voids and their accurate estimation are critical to determine for high performance composite structures. One widely used method of performing void analysis on a composite structure sample is acquiring optical micrographs or Scanning Electron Microscope (SEM) images of lateral sides of the sample and retrieving the void areas within the micrographs/images using an image analysis technique. Segmentation for the retrieval and subsequent computation of void areas within the micrographs/images is challenging as the gray-scaled values of the void areas are close to the gray-scaled values of the matrix leading to the need of manually performing the segmentation based on the histogram of the micrographs/images to retrieve the void areas. The use of an algorithm developed by NASA and based on Fuzzy Reasoning (FR) proved to overcome the difficulty of suitably differentiate void and matrix image areas with similar gray-scaled values leading not only to a more accurate estimation of void areas on composite matrix micrographs but also to a faster void analysis process as the algorithm is fully autonomous.
In-space assembly operations require accurate reasoning over the pose, location, and structural organization of both the autonomous agents and assembly materials. In a full six-degree-of-freedom space, an accurate understanding of the full three-dimensional structure of the object of interest greatly enriches information for pose estimation and collision planning. Current methods of predicting pose estimation require a priori understanding of the shape of the object. Additionally, visual information in the space environment is impacted by variations in contrast and illumination. Using synthetic data allows us to rapidly generate large datasets with in varying environments and lighting conditions.This work details the generation of synthetic data used to explore the use of a region-based convolutional neural networks to detect objects of interest and predict a voxel-based three-dimensional mesh in order to understand their full three-dimensional shape. This mesh provides useful spatial information during in-space assembly operations without requiring either the complexity of maintaining models over the progress of building an object or observations from multiple angles. The generated meshes are then compared to that of ground truth in order to measure its performance.
In-space assembly operations require accurate reasoning over the pose, location, and structural organization of both the autonomous agents and assembly materials. In a full six-degree-of-freedom space, an accurate understanding of the full three-dimensional structure of the object of interest greatly enriches information for pose estimation and collision planning. Current methods of predicting pose estimation require a priori understanding of the shape of the object. Additionally, visual information in the space environment is impacted by variations in contrast and illumination. Using synthetic data allows us to rapidly generate large datasets with in varying environments and lighting conditions. This work details the generation of synthetic data used to explore the use of a region-based convolutional neural networks to detect objects of interest and predict a voxel-based three-dimensional mesh in order to understand their full three-dimensional shape. This mesh provides useful spatial information during in-space assembly operations without requiring either the complexity of maintaining models over the progress of building an object or observations from multiple angles. The generated meshes are then compared to that of ground truth in order to measure its performance.
Many of the approaches proposed to date for assembly planning have focused on the geometric reasoning of path interferences among parts to generate assembly sequences efficiently.
This work is focused on the formalized reasoning of operational volumes as it relates to the current and future technologies developed by NASA to aid in wildfire fighting operations. One such technology is the unmanned aircraft system pilot kit (UASP-kit) developed by the Scalable Traffic Management for Emergency Response Operations (STEReO) project at NASA, which is used to increase situation awareness for a ground operator in the field. The UASP-kit utilizes operational volumes which represent mission areas and alerting volumes, to alert when another aircraft is within one of these volumes from received ADS-B data. This work is focused on developing a rigorous foundation for the concept of operational volumes for modeling and prototyping operations in such a tool as the UASP-kit. This includes establishing a class of algorithms to detect when an object is in an operational volume, and when an operational volume is intersecting or contained within another. Additionally, this work is focused on providing rigorous proof in an interactive theorem prover that the algorithms work as intended. Scenarios are presented that model current UASP-kit operations and extend past the current capabilities of the technology to modeling more complex scenarios such as mission planning.
This work is focused on the formalized reasoning of operational volumes as it relates to the current and future technologies developed by NASA to aid in wildland firefighting operations. One such technology is the Unmanned Aircraft System Pilot Kit (UASP-kit) developed by the Scalable Traffic Management for Emergency Response Operations (STEReO) project at NASA, which is used to increase situational awareness for a ground operator in the field. The UASP-kit utilizes operational volumes to represent mission areas and alerting volumes; these volumes, in combinations with ADS-B data, can then be used to alert the ground operator when another aircraft has entered one of these areas. This work presents a rigorous foundation for the concept of operational volumes for modeling and prototyping operations in such a tool as the UASP-kit. This includes establishing a class of algorithms to detect when an object is in an operational volume, and when one operational volume intersects or is contained in another. Additionally, this work provides rigorous proof that the algorithms work as intended. Scenarios are presented that model current UASP-kit operations and extend past the current capabilities of the technology to modeling more complex scenarios such as mission planning.
The visual analytic system enables information retrieval within large text collections. Typically, users have to directly and explicitly query information to retrieve it. With this system and process, the reasoning of the user is inferred from the user interaction they perform in a visual analytic tool, and the appropriate information to query, process, and visualize is systematically determined.