Engineering topics

Jarman, Kenneth D

Publications and source records attributed to Jarman, Kenneth D.

8 records

DOE OSTI2013

CDF Solutions of Buckley-Leverett Equation with Uncertain Parameters

The Buckley-Leverett (nonlinear advection) equation is often used to describe two-phase flow in porous media. We develop a new probabilistic method to quantify parametric uncertainty in the Buckley-Leverett model. Our approach is based on the concept of fine-grained cumulative density function (CDF) and provides a full statistical description of the system states. Hence, it enables one to obtain not only average system response but also the probability of rare events, which is critical for risk assessment. We obtain a closed-form, semi-analytical solution and test it against the results from Monte Carlo simulations.

Wang, Peng↗

DOE OSTI2012

Modeling Dynamic Regulatory Processes in Stroke.

The ability to examine in silico the behavior of biological systems can greatly accelerate the pace of discovery in disease pathologies, such as stroke, where in vivo experimentation is lengthy and costly. In this paper we describe an approach to in silico examination of blood genomic responses to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) relating regulators and functional clusters from the data. These ODEs were used to develop dynamic models that simulate the expression of regulated functional clusters using system dynamics as the modeling paradigm. The dynamic model has the considerable advantage of only requiring an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. The manipulation of input model parameters, such as changing the magnitude of gene expression, made it possible to assess the behavior of the networks through time under varying conditions. We report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different preconditioning paradigms.

dynamic modeling↗

DOE OSTI2012

Imaging for dismantlement verification: information management and analysis algorithms

The level of detail discernible in imaging techniques has generally excluded them from consideration as verification tools in inspection regimes. An image will almost certainly contain highly sensitive information, and storing a comparison image will almost certainly violate a cardinal principle of information barriers: that no sensitive information be stored in the system. To overcome this problem, some features of the image might be reduced to a few parameters suitable for definition as an attribute, which must be non-sensitive to be acceptable in an Information Barrier regime. However, this process must be performed with care. Features like the perimeter, area, and intensity of an object, for example, might reveal sensitive information. Any data-reduction technique must provide sufficient information to discriminate a real object from a spoofed or incorrect one, while avoiding disclosure (or storage) of any sensitive object qualities. Ultimately, algorithms are intended to provide only a yes/no response verifying the presence of features in the image. We discuss the utility of imaging for arms control applications and present three image-based verification algorithms in this context. The algorithms reduce full image information to non-sensitive feature information, in a process that is intended to enable verification while eliminating the possibility of image reconstruction. The underlying images can be highly detailed, since they are dynamically generated behind an information barrier. We consider the use of active (conventional) radiography alone and in tandem with passive (auto) radiography. We study these algorithms in terms of technical performance in image analysis and application to an information barrier scheme.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2011

Low-Intrusion Techniques and Sensitive Information Management for Warhead Counting and Verification: FY2011 Annual Report

Future arms control treaties may push nuclear weapons limits to unprecedented low levels and may entail precise counting of warheads as well as distinguishing between strategic and tactical nuclear weapons. Such advances will require assessment of form and function to confidently verify the presence or absence of nuclear warheads and/or their components. Imaging with penetrating radiation can provide such an assessment and could thus play a unique role in inspection scenarios. Yet many imaging capabilities have been viewed as too intrusive from the perspective of revealing weapon design details, and the potential for the release of sensitive information poses challenges in verification settings. A widely held perception is that verification through radiography requires images of sufficient quality that an expert (e.g., a trained inspector or an image-matching algorithm) can verify the presence or absence of components of a device. The concept of information barriers (IBs) has been established to prevent access to relevant weapon-design information by inspectors (or algorithms), and has, to date, limited the usefulness of radiographic inspection. The challenge of this project is to demonstrate that radiographic information can be used behind an IB to improve the capabilities of treaty-verification weapons-inspection systems.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2011

Integrating Correlated Bayesian Networks Using Maximum Entropy

We consider the problem of generating a joint distribution for a pair of Bayesian networks that preserves the multivariate marginal distribution of each network and satisfies prescribed correlation between pairs of nodes taken from both networks. We derive the maximum entropy distribution for any pair of multivariate random vectors and prescribed correlations and demonstrate numerical results for an example integration of Bayesian networks.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2011

Non-Invertible Transforms for Image-Based Verification

Imaging may play a unique role in verifying the presence and distribution of warhead components in warhead counting and dismantlement settings where image information content can distinguish among shapes, forms, and material composition of items. However, a major issue with imaging is the high level of intrusiveness, and in particular, the possible need to store sensitive comparison images in the inspection system that would violate information barrier (IB) principles. Reducing images via transformations or feature extraction can produce image features (e.g. attributes) for verification, but with enough prior information about structure the reduced information itself may be sufficient to deduce sensitive details of the original image. Further reducing resolution of the transformed image information is an option, but too much reduction destroys the quality of the attribute. We study the possibility of a one-way transform that allows storage of non-sensitive reference information and analysis to enable comparison of transformed images within IB constraints. In particular, we consider the degree to which images can be reconstructed from image intensity histograms depending on the number of pixel intensity bins and the degree of frequency data quantization, as well as assumed knowledge of configuration of objects in the images. We also explore the concept of a 'perceptual hash' as a class of transforms that may enable verification with provable non-invertibility, leading to an effective one-way transform that preserves the nature of the image feature data without revealing sufficient information to reconstruct the original image.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2011

Peptide identification

Peptides are identified from a list of candidates using collision-induced dissociation tandem mass spectrometry data. A probabilistic model for the occurrence of spectral peaks corresponding to frequently observed partial peptide fragment ions is applied. As part of the identification procedure, a probability score is produced that indicates the likelihood of any given candidate being the correct match. The statistical significance of the score is known without necessarily having reference to the actual identity of the peptide. In one form of the invention, a genetic algorithm is applied to candidate peptides using an objective function that takes into account the number of shifted peaks appearing in the candidate spectrum relative to the test spectrum.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2011

Bayesian Radiation Source Localization

Locating illicit radiological sources using gamma ray or neutron detection is a key challenge for both homeland security and nuclear nonproliferation. Localization methods using an array of detectors or a sequence of observations in time and space must provide rapid results while accounting for a dynamic attenuating environment. In the presence of significant attenuation and scatter, more extensive numerical transport calculations in place of the standard analytical approximations may be required to achieve accurate results. Numerical adjoints based on deterministic transport codes provide relatively efficient detector response calculations needed to determine the most likely location of a true source. Probabilistic representations account for uncertainty in the source location resulting from uncertainties in detector responses, the approximations that are used, and the potential for nonunique solutions. A Bayesian approach improves on previous likelihood methods for source localization by allowing the incorporation of all available information to help constrain solutions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗