Propositional and Relational Bayesian Networks Associated with Imprecise and Qualitative Probabilistic Assessments

This paper investigates a representation language with flexibility inspired by probabilistic logic and compactness inspired by relational Bayesian networks. The goal is to handle propositional and first-order constructs together with precise, imprecise, indeterminate and qualitative probabilistic assessments. The paper shows how this can be achieved through the theory of credal networks. New exact and approximate inference algorithms based on multilinear programming and iterated/loopy propagation of interval probabilities are presented; their superior performance, compared to existing ones, is shown empirically.

An Architecture of a Multi-Agent System for SCADA - Dealing With Uncertainty, Plans and Actions

This paper presents a multi-agent system approach to address the difficulties encountered in traditional SCADA systems deployed in critical environments such as electrical power generation, transmission and distribution. The approach models uncertainty and combines multiple sources of uncertain information to deliver robust plan selection. We examine the approach in the context of a simplified power supply/demand scenario using a residential grid connected solar system and consider the challenges of modelling and reasoning with
uncertain sensor information in this environment. We discuss examples of plans and actions required for sensing, establish and discuss the effect of uncertainty on such systems and investigate different uncertainty theories and how they can fuse uncertain information from multiple sources for effective decision making in
such a complex system.

Reducing Parametric Uncertainty in Limit Cycle Oscillations

The assimilation of discrete higher fidelity data points with model predictions can be used to achieve a reduction in the uncertainty of the model input parameters which generate accurate predictions. The problem investigated here involves the prediction of limit-cycle oscillations using a High-Dimensional Harmonic Balance method (HDHB). The efficiency of the HDHB method is exploited to enable calibration of structural input parameters using a Bayesian inference technique. Markov-chain Monte Carlo is employed to sample the posterior distributions. Parameter estimation is carried out on both a pitch/plunge aerofoil and Goland wing configuration. In both cases significant refinement was achieved in the distribution of possible structural parameters allowing better predictions of their
true deterministic values.

Propagation of intense laser pulses in strongly magnetized plasmas

Propagation of intense circularly polarized laser pulses in strongly magnetized inhomogeneous plasmas is investigated. It is shown that a left-hand circularly polarized laser pulse propagating up the density gradient of the plasma along the magnetic field is reflected at the left-cutoff density. However, a right-hand circularly polarized laser can penetrate up the density gradient deep into the plasma without cutoff or resonance and turbulently heat the electrons trapped in its wake. Results from particle-in-cell simulations are in good agreement with that from the theory. (c) 2015 AIP Publishing LLC.

Analysing Web-Orchestrations Under Stress Using Uncertainty Profiles

An orchestration is a multi-threaded computation that invokes a number of remote services. In practice, the responsiveness of a web-service fluctuates with demand; during surges in activity service responsiveness may be degraded, perhaps even to the point of failure. An uncertainty profile formalizes a user's perception of the effects of stress on an orchestration of web-services; it describes a strategic situation, modelled by a zero-sum angel–daemon game. Stressed web-service scenarios are analysed, using game theory, in a realistic way, lying between over-optimism (services are entirely reliable) and over-pessimism (all services are broken). The ‘resilience’ of an uncertainty profile can be assessed using the valuation of its associated zero-sum game. In order to demonstrate the validity of the approach, we consider two measures of resilience and a number of different stress models. It is shown how (i) uncertainty profiles can be ordered by risk (as measured by game valuations) and (ii) the structural properties of risk partial orders can be analysed.

Uncertainty in the Cloud: An Angel-Daemon Approach to Modelling Performance

Uncertainty profiles are used to study the effects of contention within cloud and service-based environments. An uncertainty profile provides a qualitative description of an environment whose quality of service (QoS) may fluctuate unpredictably. Uncertain environments are modelled by strategic games with two agents; a daemon is used to represent overload and high resource contention; an angel is used to represent an idealised resource allocation situation with no underlying contention. Assessments of uncertainty profiles are useful in two ways: firstly, they provide a broad understanding of how environmental stress can effect an application’s performance (and reliability); secondly, they allow the effects of introducing redundancy into a computation to be assessed

Deep Salience: Visual Salience Modeling via Deep Belief Propagation

Visual salience is an intriguing phenomenon observed in biological neural systems. Numerous attempts have been made to model visual salience mathematically using various feature contrasts, either locally or globally. However, these algorithmic models tend to ignore the problem’s biological solutions, in which visual salience appears to arise during the propagation of visual stimuli along the visual cortex. In this paper, inspired by the conjecture that salience arises from deep propagation along the visual cortex, we present a Deep Salience model where a multi-layer model based on successive Markov random fields (sMRF) is proposed to analyze the input image successively through its deep belief propagation. As a result, the foreground object can be automatically separated from the background in a fully unsupervised way. Experimental evaluation on the benchmark dataset validated that our Deep Salience model can consistently outperform eleven state-of-the-art salience models, yielding the higher rates in the precision-recall tests and attaining the best F-measure and mean-square error in the experiments.

Propagation Characteristics of Groove Gap Waveguide Below and Above Cutoff

Recently, gap waveguides have been shown as a potential alternative to conventional waveguides in the millimeterwave band. Until now, Groove Gap Waveguide (GGW) has been studied through direct correspondence with rectangular waveguide with the same physical dimensions. However there have been observed differences in the above cutoff propagation characteristics between these two waveguide types. Furthermore, the behavior of GGW below cutoff remains unknown. This work presents a discussion of both below and above cutoff propagation characteristics of GGW, and introduces a simple model that explains the observed GGW behavior and establishes well its propagation characteristics. Two TRL calibration kits have been manufactured, and the measurements have good agreement with the proposed analysis model results.

Signal Reception Characteristics in the Proximity of Alice and Bob for Secure Indoor Peer-to-Peer Communications At 2.45 GHz

Mutual variation of the received signal which occurs as a consequence of the channel reciprocity property has recently been proposed as a viable method for secret key generation. However, this cannot be strictly maintained in practice as the property is applicable only in the absence of interference. To ensure the propagation defined key remains secret, one requirement is that there remain high degrees of uncertainty between the legitimate users channel response and that of any eavesdropper's. In this paper, we investigate whether such de-correlation occurs for an indoor point-to-point link at 2.45 GHz. This is achieved by computing the localized correlation coefficient between the simultaneous channel response measured by the legitimate users and that of multiple distributed eavesdroppers for static and dynamic scenarios.