Search

The Arbitrary Lagrangian-Eulerian (ALE) method is used in a variety of engineering and scientific applications for enabling multi-physics simulations. Unfortunately, the ALE method can suffer from failures that require users to adjust a set of parameters to control mesh relaxation. In this paper, we present a deep learning framework for predicting mesh relaxation in ALE simulations. Our framework is designed to train a neural network using data generated from existing ALE simulations developed by expert users. In order to capture the spatial coherence inherent in simulations, we apply convolutional-deconvolutional neural networks to achieve up to 0.99 F1 score in predicting mesh relaxation.

GeoVisipedia is a new and novel approach to annotating satellite imagery. It uses wiki pages to annotate objects rather than simple labels. The use of wiki pages to contain annotations is particularly useful for annotating objects in imagery of complex geospatial configurations such as industrial facilities. GeoVisipedia uses the PRISM algorithm to project annotations applied to one image to other imagery, hence enabling ubiquitous annotation. This paper derives the PRISM algorithm, which uses image metadata and a 3D facility model to create a view matrix unique to each image. The view matrix is used to project model components onto a mask which aligns the components with the objects in the scene that they represent. Wiki pages are linked to model components, which are in turn linked to the image via the component mask. An illustration of the efficacy of the PRISM algorithm is provided, demonstrating the projection of model components onto an effluent stack. We conclude with a discussion of the efficiencies of GeoVisipedia over manual annotation, and the use of PRISM for creating training sets for machine learning algorithms.

GeoVisipedia (Geospatial Visual Wikipedia) is a new and novel approach to sharing knowledge about complex geospatial entities such as facilities. Facilities are composed of interconnected objects such as buildings, chemical processing units, electrical generation equipment and similar structures. Satellite imagery of a facility reveals a great deal about the organization and visual appearance of objects in a facility, but very little about the identity or function of the object. For example, given a satellite imagery of an oil refinery, an expert in refining readily identifies distillation units and can explain how they work. A non-expert would have a very difficult time identifying these objects let alone explaining how they function. To make highly complex information accessible to non-experts, GeoVisipedia associates a wiki page with objects in satellite imagery. A user selects an object in the image and a wiki page appears that provides the user with detailed information about the object. Experts can author information into the wiki and this information is shared with other users. Additionally, GeoVisipedia automatically transfers all wiki pages from one image of a facility to other imagery of the facility. Consequentially, knowledge about objects in the facility integrates over time as new imagery becomes available and as new wiki pages are created and additional information is added to existing wiki pages. In this respect, satellite imagery becomes a portal to expert knowledge and insight about objects in a facility.

The Image Content Engine (ICE) is a framework of software and underlying mathematical and physical models that enable scientists and analysts to extract features from Terabytes of imagery and search the extracted features for content relevant to their problem domain. The ICE team has developed a set of tools for feature extraction and analysis of image data, primarily based on the image content. The scale and volume of imagery that must be searched presents a formidable computation and data bandwidth challenge, and a search of moderate to large scale imagery quickly becomes intractable without exploiting high degrees of data parallelism in the feature extraction engine. In this paper we describe the software and hardware architecture developed to build a data parallel processing engine for ICE. We discuss our highly tunable parallel process and job scheduling subsystem, remote procedure invocation, parallel I/O strategy, and our experience in running ICE on a 16 node, 32 processing element (CPU) Linux Cluster. We present performance and benchmark results, and describe how we obtain excellent speedup for the imagery searches in our test-bed prototype.

The Image Content Engine (ICE) is being developed to provide cueing assistance to human image analysts faced with increasingly large and intractable amounts of image data. The ICE architecture includes user configurable feature extraction pipelines which produce intermediate feature vector and match surface files which can then be accessed by interactive relational queries. Application of the feature extraction algorithms to large collections of images may be extremely time consuming and is launched as a batch job on a Linux cluster. The query interface accesses only the intermediate files and returns candidate hits nearly instantaneously. Queries may be posed for individual objects or collections. The query interface prompts the user for feedback, and applies relevance feedback algorithms to revise the feature vector weighting and focus on relevant search results. Examples of feature extraction and both model-based and search-by-example queries are presented.

Human analysts are often unable to meet time constraints on analysis and interpretation of large volumes of remotely sensed imagery. To address this problem, the Image Content Engine (ICE) system currently under development is organized into an off-line component for automated extraction of image features followed by user-interactive components for content detection and content-based query processing. The extracted features are vectors that represent attributes of three entities, namely image tiles, image regions and shapes, or suspected matches to models of objects. ICE allows users to interactively specify decision thresholds so that the content (consisting of entities whose features satisfy decision criteria) can be detected. ICE presents detected content to users as a prioritized series of thumbnail images. Users can either accept the detection results or specify a new set of decision thresholds. Once accepted, ICE stores the detected content in database tables and semantic graphs. Users can interactively query the tables and graphs for locations at which prescribed relationships between detected content exist. New queries can be submitted repeatedly until a satisfactory series of prioritized thumbnail image cues is produced. Examples are provided to demonstrate how ICE can be used to assist users in quickly finding prescribed collections of entities (both natural and man-made) in a set of large USGS aerial photos retrieved from TerraserverUSA

Machine controllers built from standardized software components have the greatest potential to reap open architecture benefits--including plug-and-play, reusability and extensibility. A challenge to component-based controllers relates to standardizing behavior in a non- restrictive manner to accommodate component packaging and component integration. Control component packaging requires behavior to be dependable, well-defined, and well-understood among a variety of users to help ensure the reusability of the component, the reliability of the component, and the correctness of the system built using the component. Integration of control components requires that the behavior model is consistent not just within a single component, but across all components in a system so that the components interoperate correctly. At the same time, the component behavioral model must be reasonably flexible to accommodate all behavioral situations and not be restrictive to a single programming methodology. Further, not all the behavior in the system may be pre-packaged as part of a component. Thus, another issue is the suitability of the standard behavior model for programming and integration of new control logic. Ideally, we need a vendor-neutral, tool-neutral, controller- neural behavior model to allow the export/import of any and all types of control logic programs. This paper will analyze the requirements of component-based, machine controller behavior, then offer a refinement of a Finite State Machine as the basis of a behavior model to satisfy these requirements. Examples will be presented based on the behavioral model the efforts of the Open, Modular, Architecture Controller User's Group Application Programming Interface for standardized, interchangeable machine controller components.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Open architecture technology is ushering in new advances in the world of computer numerically controlled machines. Yet, some major benefits of open architecture technology have failed to materialized due to the lack of a standard open architecture specification. We propose an open architecture framework to fill the specification void. The proposed framework supports component-based technology by specifying a control class hierarchy, plug-and-play components and a design framework. This framework can be used to build applications ranging from a single-axis device to a multi- arm robot. An example application applying the framework is documented.

The purpose of this project is to produce a generic high precision computer numerical controller (CNC) for use on microinch- and sub-microinch-resolution machine tools at the Lawrence Livermore National Laboratory. In order to fully utilize the potential of these machine tools, the CNC must include the ability to use multiple feedback sensors on each machine axis, incorporate corrections for quasistatic geometric errors (such as straightness, and squareness), be able to function over a relatively large range of motion (in excess of 60 inches per axis), and be able to produce motion updates at a rate sufficient to take advantage of the high bandwidth of the servo systems. At present, no commercially available CNC can presently meet all of the resolution, feed rate, and length of travel requirements of these machines. In order to minimize the complexity of the system, and thereby increase its reliability and maintainability, the programming was done in a high level language. The number of processors was kept as small as possible while still maintaining the performance requirements. We also used commercially available hardware in preference to building, in order to increase both reliability and maintainability. Special emphasis was given to making the CNC's operator interface asmore » friendly as possible. We have completed a prototype control. We plan to install and test it in 1988. 4 figs.« less