13,823 research outputs found

    Bayesian learning of models for estimating uncertainty in alert systems: application to air traffic conflict avoidance

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    Alert systems detect critical events which can happen in the short term. Uncertainties in data and in the models used for detection cause alert errors. In the case of air traffic control systems such as Short-Term Conflict Alert (STCA), uncertainty increases errors in alerts of separation loss. Statistical methods that are based on analytical assumptions can provide biased estimates of uncertainties. More accurate analysis can be achieved by using Bayesian Model Averaging, which provides estimates of the posterior probability distribution of a prediction. We propose a new approach to estimate the prediction uncertainty, which is based on observations that the uncertainty can be quantified by variance of predicted outcomes. In our approach, predictions for which variances of posterior probabilities are above a given threshold are assigned to be uncertain. To verify our approach we calculate a probability of alert based on the extrapolation of closest point of approach. Using Heathrow airport flight data we found that alerts are often generated under different conditions, variations in which lead to alert detection errors. Achieving 82.1% accuracy of modelling the STCA system, which is a necessary condition for evaluating the uncertainty in prediction, we found that the proposed method is capable of reducing the uncertain component. Comparison with a bootstrap aggregation method has demonstrated a significant reduction of uncertainty in predictions. Realistic estimates of uncertainties will open up new approaches to improving the performance of alert systems

    Evaluator services for optimised service placement in distributed heterogeneous cloud infrastructures

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    Optimal placement of demanding real-time interactive applications in a distributed heterogeneous cloud very quickly results in a complex tradeoff between the application constraints and resource capabilities. This requires very detailed information of the various requirements and capabilities of the applications and available resources. In this paper, we present a mathematical model for the service optimization problem and study the concept of evaluator services as a flexible and efficient solution for this complex problem. An evaluator service is a service probe that is deployed in particular runtime environments to assess the feasibility and cost-effectiveness of deploying a specific application in such environment. We discuss how this concept can be incorporated in a general framework such as the FUSION architecture and discuss the key benefits and tradeoffs for doing evaluator-based optimal service placement in widely distributed heterogeneous cloud environments

    RF Localization in Indoor Environment

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    In this paper indoor localization system based on the RF power measurements of the Received Signal Strength (RSS) in WLAN environment is presented. Today, the most viable solution for localization is the RSS fingerprinting based approach, where in order to establish a relationship between RSS values and location, different machine learning approaches are used. The advantage of this approach based on WLAN technology is that it does not need new infrastructure (it reuses already and widely deployed equipment), and the RSS measurement is part of the normal operating mode of wireless equipment. We derive the Cramer-Rao Lower Bound (CRLB) of localization accuracy for RSS measurements. In analysis of the bound we give insight in localization performance and deployment issues of a localization system, which could help designing an efficient localization system. To compare different machine learning approaches we developed a localization system based on an artificial neural network, k-nearest neighbors, probabilistic method based on the Gaussian kernel and the histogram method. We tested the developed system in real world WLAN indoor environment, where realistic RSS measurements were collected. Experimental comparison of the results has been investigated and average location estimation error of around 2 meters was obtained

    Railway track condition assessment at network level by frequency domain analysis of GPR data

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    The railway track system is a crucial infrastructure for the transportation of people and goods in modern societies. With the increase in railway traffic, the availability of the track for monitoring and maintenance purposes is becoming significantly reduced. Therefore, continuous non-destructive monitoring tools for track diagnoses take on even greater importance. In this context, Ground Penetrating Radar (GPR) technique results yield valuable information on track condition, mainly in the identification of the degradation of its physical and mechanical characteristics caused by subsurface malfunctions. Nevertheless, the application of GPR to assess the ballast condition is a challenging task because the material electromagnetic properties are sensitive to both the ballast grading and water content. This work presents a novel approach, fast and practical for surveying and analysing long sections of transport infrastructure, based mainly on expedite frequency domain analysis of the GPR signal. Examples are presented with the identification of track events, ballast interventions and potential locations of malfunctions. The approach, developed to identify changes in the track infrastructure, allows for a user-friendly visualisation of the track condition, even for GPR non-professionals such as railways engineers, and may further be used to correlate with track geometric parameters. It aims to automatically detect sudden variations in the GPR signals, obtained with successive surveys over long stretches of railway lines, thus providing valuable information in asset management activities of infrastructure managers
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