Characterization of the Non-Uniqueness of Used Nuclear Fuel Burnup Signatures through a Mesh-Adaptive Direct Search

The use of passive gamma and neutron signatures from fission indicators is a common means of estimating used fuel burnup, enrichment, and cooling time. However, while characteristic fission product signatures such as 134Cs, 137Cs and 154Eu, and others are generally reliable estimators for used fuel burnup within the context where the assembly initial enrichment and the discharge time are known, in the absence of initial enrichment and/or cooling time information (such as when applying NDA measurements in a safeguards/verification context), these fission product indicators no longer yield a unique solution for assembly enrichment, burnup, and cooling time after discharge. Through the use of a new mesh-adaptive direct search (MADS) algorithm, it is possible to directly probe the shape of this ``degeneracy space\u27\u27 characteristic of individual nuclides (and combinations thereof), both as a function of constrained parameters (such as the assembly irradiation history) and unconstrained parameters (e.g., the cooling time before measurement and the measurement precision for particular indicator nuclides). In doing so, this affords the identification of potential means of narrowing the uncertainty space of potential assembly enrichment, burnup, and cooling time combinations, thereby bounding estimates of assembly plutonium content. In particular, combinations of gamma-emitting nuclides with distinct half-lives (e.g., 134Cs with 137Cs and 154Eu) in conjunction with gross neutron counting (via 244Cm) are able to reasonably constrain the degeneracy space of possible solutions to a space small enough to perform useful discrimination and verification of fuel assemblies based on their irradiation history

A Framework for the Use of Mobile Sensor Networks in System Identification

System identification (SID, also known as structural identification in this context) is the process of extracting a system’s modal properties from sensor measurements. Typically, a mathematical model is chosen for data fitting and the identification of model parameters yields modal property estimates. Historically, SID has relied on measurements from fixed sensors, which remain at specific locations throughout data collection. The ultimate flaw in fixed sensors is they provide restricted spatial information, which can be addressed by mobile sensors. In this dissertation, a framework is developed for extracting structural modal estimates from data collected by mobile sensors. The current state of mobile sensor networks applications in SHM is developing; research has been diverse, however limited. Reduced setup requirements for mobile sensor networks facilitate data collection, thus enable expedited information updates on a structure’s health and improved emergency response times to natural disasters. This research focuses on using mobile sensor data, i.e., data from sensors simultaneously recording in time, while moving in space, for comprehensive system identification of real structural systems. Mobile sensing data is analyzed from two perspectives, each requires different modeling techniques: an incomplete data perspective and a complete data perspective. In Chapter 2, Structural Identification using Expectation Maximization (STRIDE) is introduced, a novel application of the Expectation Maximization (EM) algorithm and approach for output-only modal identification. Chapter 3 revisits STRIDE for consideration of incomplete datasets, i.e., data matrices containing missing entries. Such instances may occur as a result of failed communications or packet losses in a wireless sensor network or as a result of sensing and sampling methods, e.g., mobile sensing. It is demonstrated that sensor network data containing a significant amount of missing observations can be used to achieve a comprehensive modal identification. Moreover, a successful real-world identification with simulated mobile sensors quantifies the preservation of spatial information, establishing benefits of this type of network, and emphasizing an inquiry for future SHM implementations. In Maximum Likelihood (ML) estimation theory, on which STRIDE is based, the precision of ML point estimates can be measured by the curvature of the likelihood function. Chapter 4 presents closed-form partial derivatives, observed information, and variance expressions for discrete-time stochastic state-space model entities. Confidence intervals are constructed for natural frequencies, damping ratios, and mode shapes using the asymptotic normality property of ML estimators. In anticipation of high-resolution scanning, mobile sensor data is also perceived to belong to a general class of data called dynamic sensor networks (DSNs), which inherently contain spatial discontinuities. Chapter 5 introduces state-space approaches for processing data from sensor networks with time-variant configurations for which a novel truncated physical model (TPM) is proposed. In typical state-space frameworks, a spatially dense observation space on the physical structure dictates a large state variable space, i.e., more total sensing nodes require a more complex dynamic model. The result is an overly complex dynamic model for the structural system. As sensor networks evolve and with increased use of novel sensing techniques in practice, it is desirable to decouple the size of the structural dynamic system from spatial sampling resolution during instrumentation. The TPM is presented as a novel technique to reduce physical state sizes and permit a general class of DSN data, with an emphasis on mobile sensing. Also, the role of basis functions in the approximation of mode shape regression is established. Chapter 6 discusses the identification of the TPM using an adjusted STRIDE methodology

Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks

The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state–space (SS) representation of vector autoregressive (VAR) models. Despite their high computational reliability these tools still suffer from estimation problems which emerge, in the case of low ratio between data points available and the number of time series, when VAR identification is performed via the standard ordinary least squares (OLS). In this work we propose to replace the OLS with penalized regression performed through the Least Absolute Shrinkage and Selection Operator (LASSO), prior to computation of the measures of information transfer and information modification. First, simulating networks of several coupled Gaussian systems with complex interactions, we show that the LASSO regression allows, also in conditions of data paucity, to accurately reconstruct both the underlying network topology and the expected patterns of information transfer. Then we apply the proposed VAR-SS-LASSO approach to a challenging application context, i.e., the study of the physiological network of brain and peripheral interactions probed in humans under different conditions of rest and mental stress. Our results, which document the possibility to extract physiologically plausible patterns of interaction between the cardiovascular, respiratory and brain wave amplitudes, open the way to the use of our new analysis tools to explore the emerging field of Network Physiology in several practical applications

Analysis of human mobility patterns from GPS trajectories and contextual information

This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994).Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. While this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age or gender of the tracked individuals. Further, we explore theorised “third places”, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.PostprintPeer reviewe

Technological Leap, Statutory Gap, and Constitutional Abyss: Remote Biometric Identification Comes of Age

Federal interest in using facial recognition technology (“FRT”) to collect, analyze, and use biometric information is rapidly growing. Despite the swift movement of agencies and contractors into this realm, however, Congress has been virtually silent on the current and potential uses of FRT. No laws directly address facial recognition—much less the pairing of facial recognition with video surveillance—in criminal law. Limits placed on the collection of personally identifiable information, moreover, do not apply. The absence of a statutory framework is a cause for concern. FRT represents the first of a series of next generation biometrics, such as hand geometry, iris, vascular patterns, hormones, and gait, which, when paired with surveillance of public space, give rise to novel questions of law and policy. These technologies constitute what can be termed Remote Biometric Identification (“RBI”). That is, they give the government the ability to ascertain the identity (1) of multiple people, (2) at a distance, (3) in public space, (4) absent notice and consent, and (5) in a continuous and on-going manner. RBI fundamentally differs from what can be understood as Immediate Biometric Identification (“IBI”)--i.e., the use of biometrics to determine identity at the point of arrest, following conviction, or in conjunction with access to secure facilities. IBI, in contrast, tends to be focused (1) on a single individual, (2) close-up, (3) in relation either to custodial detention or in the context of a specific physical area related to government activity, (4) in a manner often involving notice and often consent, and (5) is a one-time or limited occurrence. The types of legal and policy questions raised by RBI significantly differ from those accompanying IBI. In the absence of a statutory framework, we are driven to Constitutional considerations, where the Court’s jurisprudence proves inadequate as a way of addressing the concerns that present in the realm of RBI. The Fourth Amendment’s guarantee to protection against unreasonable search and seizure and the probable cause requirement for the issuance of warrants; the Fifth Amendment’s right against self-incrimination; the First Amendment’s protection of speech and assembly; and the Fifth and Fourteenth Amendments’ due process protections fail to account for the way in which such measures fundamentally challenge the current norms. The article calls for Congressional action and a judicial framing commensurate with the threat posed by these new and emerging technologies

Identifying hidden contexts

In this study we investigate how to identify hidden contexts from the data in classification tasks. Contexts are artifacts in the data, which do not predict the class label directly. For instance, in speech recognition task speakers might have different accents, which do not directly discriminate between the spoken words. Identifying hidden contexts is considered as data preprocessing task, which can help to build more accurate classifiers, tailored for particular contexts and give an insight into the data structure. We present three techniques to identify hidden contexts, which hide class label information from the input data and partition it using clustering techniques. We form a collection of performance measures to ensure that the resulting contexts are valid. We evaluate the performance of the proposed techniques on thirty real datasets. We present a case study illustrating how the identified contexts can be used to build specialized more accurate classifiers

Sequence effects in categorization of simple perceptual stimuli

Categorization research typically assumes that the cognitive system has access to a (more or less noisy) representation of the absolute magnitudes of the properties of stimuli and that this information is used in reaching a categorization decision. However, research on identification of simple perceptual stimuli suggests that people have very poor representations of absolute magnitude information and that judgments about absolute magnitude are strongly influenced by preceding material. The experiments presented here investigate such sequence effects in categorization tasks. Strong sequence effects were found. Classification of a borderline stimulus was more accurate when preceded by a distant member of the opposite category than by a distant member of the same category. It is argued that this category contrast effect cannot be accounted for by extant exemplar or decision-bound models of categorization. The effect suggests the use of relative magnitude information in categorization. A memory and contrast model illustrates how relative magnitude information may be used in categorization

Nonparametric Stochastic Contextual Bandits

We analyze the $K$-armed bandit problem where the reward for each arm is a noisy realization based on an observed context under mild nonparametric assumptions. We attain tight results for top-arm identification and a sublinear regret of $\widetilde{O}\Big(T^{\frac{1+D}{2+D}}\Big)$, where $D$ is the context dimension, for a modified UCB algorithm that is simple to implement ($k$NN-UCB). We then give global intrinsic dimension dependent and ambient dimension independent regret bounds. We also discuss recovering topological structures within the context space based on expected bandit performance and provide an extension to infinite-armed contextual bandits. Finally, we experimentally show the improvement of our algorithm over existing multi-armed bandit approaches for both simulated tasks and MNIST image classification.Comment: AAAI 201