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Adaptation of a cubic smoothing spline algortihm for multi-channel data stitching at the National Ignition Facility

Description: Some diagnostics at the National Ignition Facility (NIF), including the Gamma Reaction History (GRH) diagnostic, require multiple channels of data to achieve the required dynamic range. These channels need to be stitched together into a single time series, and they may have non-uniform and redundant time samples. We chose to apply the popular cubic smoothing spline technique to our stitching problem because we needed a general non-parametric method. We adapted one of the algorithms in the literature, by Hutchinson and deHoog, to our needs. The modified algorithm and the resulting code perform a cubic smoothing spline fit to multiple data channels with redundant time samples and missing data points. The data channels can have different, time-varying, zero-mean white noise characteristics. The method we employ automatically determines an optimal smoothing level by minimizing the Generalized Cross Validation (GCV) score. In order to automatically validate the smoothing level selection, the Weighted Sum-Squared Residual (WSSR) and zero-mean tests are performed on the residuals. Further, confidence intervals, both analytical and Monte Carlo, are also calculated. In this paper, we describe the derivation of our cubic smoothing spline algorithm. We outline the algorithm and test it with simulated and experimental data.
Date: December 28, 2010
Creator: Brown, C; Adcock, A; Azevedo, S; Liebman, J & Bond, E
Partner: UNT Libraries Government Documents Department

Local Area Signal-to-Noise Ratio (LASNR) algorithm for Image Segmentation

Description: Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object. This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation.
Date: July 3, 2007
Creator: Kegelmeyer, L; Fong, P; Glenn, S & Liebman, J
Partner: UNT Libraries Government Documents Department

Biological conversion of organic refuse to methane. Final report, July 1, 1973--November 30, 1976

Description: In order to predict accurately the cost of producing methane from urban refuse, studies were initiated on the dewatering of the fermentor residue and the disposal of the residue from the system. Results of the research are reported under the following subject headings: fermentation system; rheological properties of reactor slurry; filterability of reactor effluent; residue dewatering--vacuum filtration; settleability of solids in the reactor effluent; settleability of sludges from centrate treatment; centrifugation of reactor effluent; leachate potential of dewatered reactor residue; calorific value of the reactor residue; residue incineration; the production of panelboards; caustic treatment of digester feed; and, treatment of filtrate--centrate. Appendixes A, B, D, and E are included; Appendix C, which includes computer programs and documentation, is bound separately as Volume II of this report. (JGB)
Date: November 1, 1976
Creator: Brown, J W; Pfeffer, J T & Liebman, J C
Partner: UNT Libraries Government Documents Department

Final optics damage inspection (FODI) for the National Ignition Facility

Description: The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory (LLNL) will routinely fire high energy shots (approaching 10 kJ per beamline) through the final optics, located on the target chamber. After a high fluence shot, exceeding 4J/cm2 at 351 nm wavelength, the final optics will be inspected for laser-induced damage. The FODI (Final Optics Damage Inspection) system has been developed for this purpose, with requirements to detect laser-induced damage initiation and to track and size it's the growth to the point at which the optic is removed and the site mitigated. The FODI system is the 'corner stone' of the NIF optic recycle strategy. We will describe the FODI system and discuss the challenges to make optics inspection a routine part of NIF operations.
Date: October 23, 2007
Creator: Conder, A; Alger, T; Azevedo, S; Chang, J; Glenn, S; Kegelmeyer, L et al.
Partner: UNT Libraries Government Documents Department

Detection of Laser Optic Defects Using Gradient Direction Matching

Description: That National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) will be the world's largest and most energetic laser. It has thousands of optics and depends heavily on the quality and performance of these optics. Over the past several years, we have developed the NIF Optics Inspection Analysis System that automatically finds defects in a specific optic by analyzing images taken of that optic. This paper describes a new and complementary approach for the automatic detection of defects based on detecting the diffraction ring patterns in downstream optic images caused by defects in upstream optics. Our approach applies a robust pattern matching algorithm for images called Gradient Direction Matching (GDM). GDM compares the gradient directions (the direction of flow from dark to light) of pixels in a test image to those of a specified model and identifies regions in the test image whose gradient directions are most in line with those of the specified model. For finding rings, we use luminance disk models whose pixels have gradient directions all pointing toward the center of the disk. After GDM identifies potential rings locations, we rank these rings by how well they fit the theoretical diffraction ring pattern equation. We perform false alarm mitigation by throwing out rings of low fit. A byproduct of this fitting procedure is an estimate of the size of the defect and its distance from the image plane. We demonstrate the potential effectiveness of this approach by showing examples of rings detected in real images of NIF optics.
Date: December 14, 2005
Creator: Chen, B Y; Kegelmeyer, L M; Liebman, J A; Salmon, J T; Tzeng, J & Paglieroni, D W
Partner: UNT Libraries Government Documents Department

Management Of Experiments And Data At The National Ignition Facility

Description: Experiments, or 'shots', conducted at the National Ignition Facility (NIF) are discrete events that occur over a very short time frame (tens of nanoseconds) separated by many hours. Each shot is part of a larger campaign of shots to advance scientific understanding in high-energy-density physics. In one campaign, scientists use energy from the 192-beam, 1.8-Megajoule pulsed laser in the NIF system to symmetrically implode a hydrogen-filled target, thereby creating conditions similar to the interior of stars in a demonstration of controlled fusion. Each NIF shot generates gigabytes of data from over 30 diagnostics that measure optical, x-ray, and nuclear phenomena from the imploding target. We have developed systems to manage all aspects of the shot cycle. Other papers will discuss the control of the lasers and targets, while this paper focuses on the setup and management of campaigns and diagnostics. Because of the low duty cycle of shots, and the thousands of adjustments for each shot (target type, composition, shape; laser beams used, their power profiles, pointing; diagnostic systems used, their configuration, calibration, settings) it is imperative that we accurately define all equipment prior to the shot. Following the shot, and capture of the data by the automatic control system, it is equally imperative that we archive, analyze and visualize the results within the required 30 minutes post-shot. Results must be securely archived, approved, web-visible and downloadable in order to facilitate subsequent publication. To-date NIF has successfully fired over 2,500 system shots, as well as thousands of test firings and dry-runs. We will present an overview of the highly-flexible and scalable campaign management systems and tools employed at NIF that control experiment configuration of the facility all the way through presentation of analyzed results.
Date: March 18, 2011
Creator: Azevedo, S; Casey, A; Beeler, R; Bettenhausen, R; Bond, E; Chandrasekaran, H et al.
Partner: UNT Libraries Government Documents Department