Context-aware Dynamic Data-driven Pattern Classification

AbstractThis work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems. We present definitions for both intrinsic context, i.e. factors which directly affect sensor measurements for a given event, as well as extrinsic context, i.e. factors which do not affect the sensor measurements directly, but do affect the interpretation of collected data. Supervised and unsupervised modeling techniques to derive context and context labels from sensor data are formulated. Here, supervised modeling incorporates the a priori known factors affecting the sensing modalities, while unsupervised modeling autonomously discovers the structure of those factors in sensor data. Context-aware event classification algorithms are developed by adapting the classification boundaries, dependent on the current operational context. Improvements in context-aware classification have been quantified and validated in an unattended sensor-fence application for US Border Monitoring. Field data, collected with seismic sensors on different ground types, are analyzed in order to classify two types of walking across the border, namely, normal and stealthy. The classification is shown to be strongly dependent on the context (specifically, soil type: gravel or moist soil)

Symbolic identification for anomaly detection in aircraft gas turbine engines

Abstract—This paper presents a robust and computationally inexpensive technique of fault detection in aircraft gas-turbine engines, based on a recently developed statistical pattern recog-nition tool. The method involves abstraction of a qualitative description from a general dynamical system structure, using state space embedding of the output data-stream and dis-cretization of the resultant pseudo state and input spaces. The system identification is achieved through grammatical inference techniques, and the deviation of the plant output from the nominal estimated language gives a metric for fault detection. The algorithm is validated on a numerical simulation test-bed that is built upon the NASA C-MAPSS model of a generic commercial aircraft engine

Mathematical foundations of sensor network design based on linguistic informatics

Abstract-We propose a design approach for sensor networks based on formal linguistic representations of information. The approach exploits the concepts of space-time neighborhoods for dynamic sensor grid formation in the vicinity of an event, and symbolization and nonlinear filtering to formulate rigorous mathematical methods that capture the causal dynamics of distributed fusion processes. We formulate the Fundamental Equation of Linguistic Sensing relating physical design parameters to those in the Information Space, and lays the framework for design and operation of sensor networks that dynamically cluster sensing, processing and communications resources in space-time neighborhoods of emergent hotspots for efficient event tracking. Index Terms-Sensor Networks; Probabilistic Finite State machines; Dynamic Clustering I. I & M A sensor network operates on an infrastructure of sensing, computation, and communication, through which it perceives the evolution of physical dynamic processes in its environment. Sensors require physical interaction with the sensed phenomena and are subject to a number of noise factors. To get reliable performance from less reliable individual sensors, collaborative intelligent inference in the vicinity of a stimulus is necessary to circumvent limitations of sensor data, communications, power, and equipment faults. Basic tradeoffs exist between energy, information, and their time critical effect on operations. Tradeoffs of architectural design parameters like number of nodes, node placement, routing, clustering density, and resource constraints for in-situ fusion of spatial-temporal information studied in recent years We envision a fundamentally new approach to sensor network operations. Instead of specifying parameters for worst-case design, we postulate designing these systems by dynamically organizing a scalable set of diverse sensing and computational resources-that interact to best support fusion needs in operational environments. The central idea is to affect dynamic space-time sensor clustering in the vicinity of the stimulus, specifically keeping in mind that sensors in the vicinity of a stimulus may need to generate more data than the communication network can effectively handle. Using quantization (referred to as symbolization in the sequel) for low level autonomous aggregation of data, formal linguistic representations of sensed data and patterns are generated, which lead to reliable and efficient handling of information. Thus the proposed approach can effectively relate the overall design problem to pattern complexity, sensor resolution, and other relevant effects. A key contribution of this paper is the formulation of the Fundamental Equation of Linguistic Sensing which relates the parameters of the physical design space to those in the abstract information space, and consequently allows the formulation of a general design methodology based on linguistic sensing. The rest of the paper is organized in five sections. Section II discusses the requisite preliminary notions for the subsequent development. Section III presents the analytical framework for linguistic sensing. Section IV discusses relevant issues concerning decision stability and presents the fundamental equation for linguistic senssing. A general design methodology is proposed that guarantees decision stability while ensuring perfect sensor coverage and the approach is demonstrated under simplifying assumptions. Section ?? presents the experimental validation of the theoretical development in the example of distributed target tracking in the laboratory environment. The paper is summarized and concluded in Section V with recommendations for future work. II. P: L R I This paper proposes an operational architecture for sensor networks that handles information via symbolic representions. Except at the local node level where continuous domain signal conditioning may be employed for denoising, the rest of the architecture replaces classical signal processing and classification with automated recognition of abstract representations of sets of symbolic strings via recognizers of probabilistic formal languages. Signal patterns are encoded as probabilistic generators of symbolic strings over pre-specified abstract alphabets and the linguistic formalism used for representation of such generators is that of Probabilistic Finite State Machine (PFSM). Definition 2.1: A PFSM is defined to be the quintuple G ≔ (Q, Σ, δ, Π, q o ) where Q is the set of states, Σ is the alphabet,δ : Q × Σ ⋆ → Q is the transition map, Π : Q × Σ → [0, 1] is the map specifying the event generation probabilities such that ∀i, j Π ij = 1, and q 0 ∈ Q is the initial state. Note that Σ ⋆ (known as the Kleene closure of Σ) is the set of all finite strings on the alphabet Σ. The key advantage of such a formalism is the availability of simple and fast algorithms that construct or learn PFSMs from scalar and vector time series. Such construction-algorithms have been studied extensively • Devising a partition function that maps continuous values from the time series to discrete symbols. • Compute a PFSM that best captures the statistics and the underlying causal semantics of the symbolic series generated in Step 1. A. PFSM Construction Algorithms The key assumption in constructing PFSM representations of evolving dynamics is that the system admits a suitable time scale separation as follows: Definition 2.2: The fast scale is defined to be the time scale over which the statistical properties of process behavior are assumed to remain invariant, i.e., the process has statistically stationary dynamics. The slow scale is defined to be the time scale over which the processic system may exhibit nonstationary dynamics