2,827 research outputs found

    A Neural Network Approach for Non-contact Defect Inspection of Flat Panel Displays

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    AbstractThis paper proposes a neural network-based approach for the inspection of electrical defects on thin film transistor lines of flat panel displays. The inspection is performed on digitized waveform data of voltage signals that are captured by a capacitor-based non-contact sensor by scanning over thin film transistor lines on the surface of the mother glass of flat panels. The sudden deep falls (open circuits) or sharp rises (short circuits) on the captured noisy waveform are classified and detected by employing a four-layer feed-forward neural network with two hidden layers. The topology of the network comprises an input layer with two units, two hidden layers with two and three units, and an output layer with one unit; a standard sigmoid function as the activation function for each unit. The network is trained with a fast adaptive back-propagation algorithm to find an optimal set of associated weights of neurons by feeding a known set of input data. The ambiguity of the threshold definition does not arise in this method because it uses only local features of waveform data at and around selected candidate points as inputs to the network, unlike the existing thresholding-based method, which is inherently prone to missed detections and false detections

    Building and Infrastructure Defect Detection and Visualization Using Drone and Deep Learning Technologies

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    This paper presents an accurate and stable method for object and defect detection and visualization on building and infrastructural facilities. This method uses drones and cameras to collect three- dimensional (3D) point clouds via photogrammetry, and uses orthographic or arbitrary views of the target objects to generate the feature images of points’ spectral, elevation, and normal features. U-Net is implemented in the pixelwise segmentation for object and defect detection using multiple feature images. This method was validated on four applications, including on-site path detection, pavement cracking detection, highway slope detection, and building facade window detection. The comparative experimental results confirmed that U-Net with multiple features has a better pixelwise segmentation performance than separately using each single feature. The developed method can implement object and defect detection with different shapes, including striped objects, thin objects, recurring and regularly shaped objects, and bulky objects, which will improve the accuracy and efficiency of inspection, assessment, and management of buildings and infrastructural facilities

    NASA SBIR abstracts of 1991 phase 1 projects

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    The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

    Proceedings of the FAA-NASA Symposium on the Continued Airworthiness of Aircraft Structures

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    This publication contains the fifty-two technical papers presented at the FAA-NASA Symposium on the Continued Airworthiness of Aircraft Structures. The symposium, hosted by the FAA Center of Excellence for Computational Modeling of Aircraft Structures at Georgia Institute of Technology, was held to disseminate information on recent developments in advanced technologies to extend the life of high-time aircraft and design longer-life aircraft. Affiliations of the participants included 33% from government agencies and laboratories, 19% from academia, and 48% from industry; in all 240 people were in attendance. Technical papers were selected for presentation at the symposium, after a review of extended abstracts received by the Organizing Committee from a general call for papers

    Development of quality assurance procedures and methods for the CBM Silicon Tracking System

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    The Compressed Baryonic Matter (CBM) experiment at the future Facility for Antiproton and Ion Research (FAIR) aims to study the properties of nuclear matter at high net-baryon densities and moderate temperatures. It is expected that, utilizing ultra-relativistic heavy-ion collisions, a phase transition from hadronic matter to QCD matter will be probed. Among the key objectives are the determination of the nature and order of the transition (deconfinement and/or chiral) and the observation of a critical end-point. To measure and determine the physics phenomena occurring in these collisions, appropriate detectors are required. The Silicon Tracking System (STS) is the key detector to reconstruct charged particle tracks created in heavy-ion collisions. In order to assure the necessary detector performance, about 900 silicon microstrip sensors must be checked and tested for their quality. For these tasks highly efficient and highly automated procedures and methods have to be developed. The first part of this dissertation reports on a novel automated inspection system developed for the optical quality control of silicon microstrip sensors. Proposed methods and procedures allow to scan along the individual sensors to recognize and classify sensor defects. Examples of these defects are: surface scratches, implant defects, metalization layer lithography defects and others. In order to separate and classify these defects various image-processing algorithms based on machine vision are used. The silicon sensors are also characterized geometrically to ensure the mechanical precision targeted for the detector assembly procedures. Since the STS detector will be operated in a high radiation environment with a total non-ionizing radiation dose up to 1x10^14 n_eq/cm^2 over 6 years of operation, the silicon sensors need to be kept in the temperature range of -5 to -10 °C at all times to minimize reverse annealing effects and to avoid thermal runaway. The second part of this work is devoted to the development and optimization of the design of cooling bodies, which remove the thermal energy of overall more than 40 kW produced by the front-end readout electronics. In particular, thermodynamical models were developed to estimate the cooling regimes and thermal simulations of the cooling bodies were carried out. Based on the performed calculations an innovative bi-phase CO2 cooling system of up to 200 W cooling power was built and allowed to verify the simulated cooling body designs experimentally.In der geplanten Experimentieranlage für Antiprotonen- und Ionenforschung (Facility for Antiproton and Ion Research, FAIR) wird das Compressed Baryonic Matter Experiment (CBM) nukleare Materie bei hoher Baryonendichte und moderaten Temperaturen untersuchen. Der Phasenübergang zwischen hadronischer und QCD-Materie kann mithilfe von ultrarelativistischen Schwerionenkollisionen untersucht werden. Die wichtigsten Ziele sind die Bestimmung der Art des Übergangs (Deconfinement- und/oder chiraler Phasenübergang) und die Untersuchung des kritischen Endpunktes im Phasendiagramm. Um diese Phänomene zu untersuchen, sind geeignete Detektorsysteme notwendig. Das Silicon Tracking System (STS) ist der zentrale Detektor, mit Hilfe dessen die Spuren der in den Schwerionenkollisionen erzeugten geladenen Teilchen rekonstruiert werden. Um die volle Funktionsfähigkeit des STS sicherzustellen, müssen die mehr als 900 Siliziumstreifensensoren vor dem Zusammenbau überprüft und getestet werden. Hierfür müssen die hocheffiziente und automatisierte Prozeduren und Methoden entwickelt werden. In erstem Teil dieser Dissertation wird über ein automatisiertes optisches Inspektionssystem berichtet. Das System erlaubt es, die einzelnen Siliziumsensoren auf potentielle vorhandene Oberflächendefekte zu untersuchen und sie zu klassifizieren. Beispiele hierfür sind: Kratzer auf der Oberfläche, Implantierungsdefekte oder Lithographiedefekte der Metallisierungsschicht. Für das Erkennen dieser Defekte werden mehrere “Machine Vision” Bildbearbeitungsalgorithmen benutzt. Außerdem werden die geometrischen Parameter der Sensoren, die für den Zusammenbau des STS wichtig sind, optisch kontrolliert. Der STS Detektor wird bei extrem hohen Kollisionsraten betrieben. Innerhalb einer Betriebsbszeit von 6 Jahren wird eine Strahlungsdosis von bis zu 1x10^14 n_eq/cm^2 akkumuliert, was zu einer deutlichen Erhöhung des Dunkelstrom führt und letztlich des “end-of-life” Kriterium darstellt. Die Siliziumsensoren müssen deswegen auf -5 bis -10 °C gekühlt werden, um “reverse Annealing” Effekte zu minimieren und das “Thermal Runaway” Phänomen zu verzögern. Durch die Ausleselektronik werden andererseits mehr als 40 kW an thermischer Energie nahe der Sensoren produziert, die deshalb mit Kühlkörpern komplett abgeleitet werden muß. Das zweite Teil dieser Dissertation wurde der Optimierung von Kühlkörpern gewidmet. Dafür wurden thermodynamische Modelle implementiert und entsprechende thermische Simulationen durchgeführt. Im Rahmen der Arbeit wurde ein 200 W CO2 Kühlungssystem gebaut, das es erlaubt, die Modellberechnungen und Simulationen einer Kühlung mit 2-phasigem CO2 zu überprüfen
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