Physical layer Authentication of Wired Networks (PAWN) Final Technical Report
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97 MATHEMATICS AND COMPUTING↗
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Results for “pattern recognition”
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Data analysis for valve leak detection of nuclear power plant safety critical components - 336
A nuclear plant consists of several safety-related components. These safety-related components are tested periodically in order to verify their functionality when they are called upon to perform. The data acquired during periodic testing provides information about possible degradation of the equipment or device under test. This research is focused on leak monitoring techniques to provide diagnostic information on the determination of valve aging and degradation. Several commercially available valve diagnostic-monitoring methods are evaluated, especially for the techniques based on measurements of acoustic emission, and accelerometer. The check valve is one of typical components being extensively used in safety systems of nuclear power plants. The failures of check valves have resulted in significant maintenance efforts, on occasion, have resulted in water hammer, over-pressurization of low-pressure systems, and damage to flow system components. In this paper, acoustic emission signals due to leak from check valves with artificial leakage and worn parts are investigated both analytically and experimentally. The results of advanced signal processing and de-noising techniques also have been identified. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Identification of the threshold security applied for fuel under reactivity-initiated conditions by self-organizing maps and neural networks - 14386
In recent times, we have 439 reactors distributed in 31 countries that give about 377 GW, covering around 12% of world capacity of energy. Currently, it accounts for an estimated 40 serious accidents reported in nuclear power plants. In this article, turns out the behavior of fuel rod in light-water reactors under accident conditions. Besides, we investigate deeply about reactivity-initiated accidents, by introducing Nordheim-Fuchs model as state equations. The early research showed that pre-irradiated fuels and afterward subjected to a rapid pulse of energy. It adopts the key items necessary to check the threshold of failure. These items are few limits as burning the fuel; the value applied to the peak of energy, pulse width, oxidation layer, and cladding. The kinetic equations of nuclear reactors modeled as a non-linear system. In this way, it can apply the paradigm of artificial intelligence using the self-organized maps. Where the goal is to find the cluster of failure for irradiated fuels until 75 GWd/MTU. The second model based on statistical is the logistic regression theory. Hence, the tests carried out and compared against the results found by neural networks for classification of clusters. It proofs that both approaches present capacity to find the flaws accurately of about 80% of cases. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
A note on the automatic generation and recognition of textures
Discriminator algorithm for computer pattern generation and texture recognition
Flower, D. W.↗
Improved Accuracy for Automated Communication Pattern Characterization Using Communication Graphs and Aggressive Search Space Pruning
An understanding of a parallel application’s communication behavior is useful for a range of activities including debugging and optimization, job scheduling, target system selection, and system design. Because it can be challenging to understand communication behavior, especially for those who lack expertise or who are not familiar with the application, I and two colleagues recently developed an automated, search-based approach for recognizing and parameterizing application communication behavior using a library of common communication patterns. This initial approach was effective for characterizing the behavior of many workloads, but I identified some combinations of communication patterns for which the method was inefficient or would fail. In this paper, I discuss one such troublesome pattern combination and propose modifications to the recognition method to handle it. Specifically, I propose an alternative approach that uses communication graphs instead of traditional communication matrices to improve recognition accuracy for collective communication operations, and that uses a non-greedy recognition technique to avoid search space dead-ends that trap the original greedy recognition approach. My modified approach uses aggressive search space pruning and heuristics to control the potential for state explosion caused by its non-greedy pattern recognition method. I demonstrate the improved recognition accuracy and pruning efficacy of the modified approach using several synthetic and real-world communication pattern combinations.
Roth, Philip↗
Artificial Immune System for Recognizing Patterns
A method of recognizing or classifying patterns is based on an artificial immune system (AIS), which includes an algorithm and a computational model of nonlinear dynamics inspired by the behavior of a biological immune system. The method has been proposed as the theoretical basis of the computational portion of a star-tracking system aboard a spacecraft. In that system, a newly acquired star image would be treated as an antigen that would be matched by an appropriate antibody (an entry in a star catalog). The method would enable rapid convergence, would afford robustness in the face of noise in the star sensors, would enable recognition of star images acquired in any sensor or spacecraft orientation, and would not make an excessive demand on the computational resources of a typical spacecraft. Going beyond the star-tracking application, the AIS-based pattern-recognition method is potentially applicable to pattern- recognition and -classification processes for diverse purposes -- for example, reconnaissance, detecting intruders, and mining data.
Huntsberger, Terrance↗
Learning algorithms for multi-class pattern classification and problems associated with on-line handwritten character recognition
On-line handwritten alphanumeric character recognition system and learning algorithm for multiclass pattern classification
Li, C. C.↗
Automated reduction of instantaneous flow field images
An automated data reduction system for the analysis of interference fringe patterns obtained using the particle image velocimetry technique is described. This system is based on digital image processing techniques that have provided the flexibility and speed needed to obtain more complete automation of the data reduction process. As approached here, this process includes scanning/searching for data on the photographic record, recognition of fringe patterns of sufficient quality, and, finally, analysis of these fringes to determine a local measure of the velocity magnitude and direction. The fringe analysis as well as the fringe image recognition are based on full frame autocorrelation techniques using parallel processing capabilities.
Reynolds, G. A.↗
Optical-Correlator Neural Network Based On Neocognitron
Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.
Chao, Tien-Hsin↗
Deep learning Hamiltonians from disordered image data in quantum materials
The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length- and timescales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimensionality, and disorder—a spatial encoding of the Hamiltonian driving the pattern formation. Image recognition techniques from machine learning are an excellent tool for interpreting information encoded in the spatial relationships in such images. Here, we develop a deep learning framework for using the rich information available in these spatial correlations in order to discover the underlying Hamiltonian driving the patterns. We first vet the method on a known case, scanning near-field optical microscopy on a thin film of VO 2 . We then apply our trained convolutional neural network architecture to new optical microscope images of a different VO 2 film as it goes through the metal-insulator transition. We find that a two-dimensional Hamiltonian with both interactions and random field disorder is required to explain the intricate, fractal intertwining of metal and insulator domains during the transition. This detailed knowledge about the underlying Hamiltonian paves the way for using the model to control the pattern formation via, e.g., tailored hysteresis protocols. Finally, we also introduce a distribution-based confidence measure on the results of a multilabel classifier, which does not rely on adversarial training. In addition, we propose a machine-learning-based criterion for diagnosing a physical system's proximity to criticality.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Comparison of measurement and simulation of ATLAS cavern radiation background
Sixteen Medipix2 pixel detector based (MPX) devices were operated at various positions within the ATLAS detector and cavern continuously from early 2008 up to 2013. In addition to photons, each MPX detector is capable to detect charged particles, and neutrons as it is covered with a mask of converter materials dividing its area into regions sensitive to thermal or fast neutrons. The MPX detector network was effectively used for real-time measurements of the spectral characteristics and composition of complex radiation fields in ATLAS. This article reports comparison of the results of measurements performed with MPX detectors during the LHC operation period in 2010 and 2011 with Monte Carlo simulations results from the FLUGG and GCALOR codes. For the purpose of this comparison, the MPX detectors were operated in tracking mode with low threshold (8–10 keV) allowing one to distinguish among particle categories based on the recognition of track patterns left by the particles in the MPX sensitive layer. The comparison of measurements with simulations shows that the agreement between measured and simulated data is satisfactory in most cases within a factor of two.
47 OTHER INSTRUMENTATION↗
Mesoscale wake clouds in Skylab pictures.
The recognition of cloud patterns formed in the wake of orographic obstacles was investigated using pictures from Skylab, for the purpose of estimating atmospheric motions. The existence of ship-wake-type wave clouds in contrast to vortex sheets were revealed during examination of the pictures, and an attempt was made to characterize the pattern of waves as well as the transition between waves and vortices. Examples of mesoscale cloud patterns which were analyzed photogrammetrically and meteorologically are presented.
Fujita, T. T.↗
The real-time display of interferometry data for Goldstone radar astronomy data acquisition
A method for visualizing radar interferometry data in real time has been developed for the Goldstone radar astronomy ranging data-acquisition system. The presentation is similar in appearance to a vector field display or data-based grid. This form was selected to facilitate the recognition of characteristic patterns of local variation in the phase and magnitude of complex elements in a two-dimensional data array. The design emphasized efficiency under the demands of real-time processing and remote monitoring. The interferometry 'phase-magnitude' presentation, as it has come to be called, has been used to monitor radar interferometry experiments on three targets, beginning with the asteroid 4179 Toutatis, and continuing with Mars and Mercury.
Howard, S. D.↗
Optimal Correlation Filters for Images with Signal-Dependent Noise
We address the design of optimal correlation filters for pattern detection and recognition in the presence of signal-dependent image noise sources. The particular examples considered are film-grain noise and speckle. Two basic approaches are investigated: (1) deriving the optimal matched filters for the signal-dependent noise models and comparing their performances with those derived for traditional signal-independent noise models and (2) first nonlinearly transforming the signal-dependent noise to signal-independent noise followed by the use of a classical filter matched to the transformed signal. We present both theoretical and computer simulation results that demonstrate the generally superior performance of the second approach in terms of the correlation peak signal-to-noise ratio.
Downie, John D.↗
Complex Event Recognition Architecture
Complex Event Recognition Architecture (CERA) is the name of a computational architecture, and software that implements the architecture, for recognizing complex event patterns that may be spread across multiple streams of input data. One of the main components of CERA is an intuitive event pattern language that simplifies what would otherwise be the complex, difficult tasks of creating logical descriptions of combinations of temporal events and defining rules for combining information from different sources over time. In this language, recognition patterns are defined in simple, declarative statements that combine point events from given input streams with those from other streams, using conjunction, disjunction, and negation. Patterns can be built on one another recursively to describe very rich, temporally extended combinations of events. Thereafter, a run-time matching algorithm in CERA efficiently matches these patterns against input data and signals when patterns are recognized. CERA can be used to monitor complex systems and to signal operators or initiate corrective actions when anomalous conditions are recognized. CERA can be run as a stand-alone monitoring system, or it can be integrated into a larger system to automatically trigger responses to changing environments or problematic situations.
Fitzgerald, William A.↗
Protocadherin cis -dimer architecture and recognition unit diversity
Clustered protocadherins (Pcdhs) mediate numerous neural patterning functions, including neuronal self-recognition and non–self-discrimination to direct self-avoidance among vertebrate neurons. Individual neurons stochastically express a subset of Pcdh isoforms, which assemble to form a stochastic repertoire of cis -dimers. We describe herein the structure of a PcdhγB7 cis -homodimer, which includes the membrane-proximal extracellular cadherin domains EC5 and EC6. The structure is asymmetric with one molecule contributing interface surface from both EC5 and EC6, and the other only from EC6. Structural and sequence analyses suggest that all Pcdh isoforms will dimerize through this interface. Site-directed mutants at this interface interfere with both Pcdh cis -dimerization and cell surface transport. The structure explains the known restrictions of cis -interactions of some Pcdh isoforms, including α-Pcdhs, which cannot form homodimers. The asymmetry of the interface approximately doubles the size of the recognition repertoire, and restrictions on cis -interactions among Pcdh isoforms define the limits of the Pcdh recognition unit repertoire.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Time series analysis of sferics rate data associated with severe weather patterns
Data obtained by an electronic transducer measuring the rate of occurrence of electrical disturbances in the atmosphere (the sferic rate in the form of a time series) over the life of electrical storms are analyzed. It is found that the sferic rate time series are not stationary. The sferics rate time series has a complete life cycle associated with a particular storm. The approach to recognition of a spectral pattern is somewhat similar to real-time recognition of the spoken word.
Wang, P. P.↗
cGAS-like receptors sense RNA and control 3'2'-cGAMP signalling in Drosophila
Cyclic GMP–AMP synthase (cGAS) is a cytosolic DNA sensor that produces the second messenger cG[2'–5']pA[3'–5']p (2'3'-cGAMP) and controls activation of innate immunity in mammalian cells. Animal genomes typically encode multiple proteins with predicted homology to cGAS, but the function of these uncharacterized enzymes is unknown. Here we show that cGAS-like receptors (cGLRs) are innate immune sensors that are capable of recognizing divergent molecular patterns and catalysing synthesis of distinct nucleotide second messenger signals. Crystal structures of human and insect cGLRs reveal a nucleotidyltransferase signalling core shared with cGAS and a diversified primary ligand-binding surface modified with notable insertions and deletions. We demonstrate that surface remodelling of cGLRs enables altered ligand specificity and used a forward biochemical screen to identify cGLR1 as a double-stranded RNA sensor in the model organism Drosophila melanogaster. We show that RNA recognition activates Drosophila cGLR1 to synthesize the novel product cG[3'–5']pA[2'–5']p (3'2'-cGAMP). A crystal structure of Drosophila stimulator of interferon genes (dSTING) in complex with 3'2'-cGAMP explains selective isomer recognition, and 3'2'-cGAMP induces an enhanced antiviral state in vivo that protects from viral infection. Similar to radiation of Toll-like receptors in pathogen immunity, our results establish cGLRs as a diverse family of metazoan pattern recognition receptors.
59 BASIC BIOLOGICAL SCIENCES↗