58,154 research outputs found

    Change Acceleration and Detection

    Full text link
    A novel sequential change detection problem is proposed, in which the change should be not only detected but also accelerated. Specifically, it is assumed that the sequentially collected observations are responses to treatments selected in real time. The assigned treatments not only determine the pre-change and post-change distributions of the responses, but also influence when the change happens. The problem is to find a treatment assignment rule and a stopping rule that minimize the expected total number of observations subject to a user-specified bound on the false alarm probability. The optimal solution to this problem is obtained under a general Markovian change-point model. Moreover, an alternative procedure is proposed, whose applicability is not restricted to Markovian change-point models and whose design requires minimal computation. For a large class of change-point models, the proposed procedure is shown to achieve the optimal performance in an asymptotic sense. Finally, its performance is found in two simulation studies to be close to the optimal, uniformly with respect to the error probability

    Human-machine diversity in the use of computerised advisory systems: a case study

    Get PDF
    Computer-based advisory systems form with their users composite, human-machine systems. Redundancy and diversity between the human and the machine are often important for the dependability of such systems. We discuss the modelling approach we applied in a case study. The goal is to assess failure probabilities for the analysis of X-ray films for detecting cancer, performed by a person assisted by a computer-based tool. Differently from most approaches to human reliability assessment, we focus on the effects of failure diversity β€” or correlation β€” between humans and machines. We illustrate some of the modelling and prediction problems, especially those caused by the presence of the human component. We show two alternative models, with their pros and cons, and illustrate, via numerical examples and analytically, some interesting and non-intuitive answers to questions about reliability assessment and design choices for human-computer systems

    Characterization of Model-Based Detectors for CPS Sensor Faults/Attacks

    Full text link
    A vector-valued model-based cumulative sum (CUSUM) procedure is proposed for identifying faulty/falsified sensor measurements. First, given the system dynamics, we derive tools for tuning the CUSUM procedure in the fault/attack free case to fulfill a desired detection performance (in terms of false alarm rate). We use the widely-used chi-squared fault/attack detection procedure as a benchmark to compare the performance of the CUSUM. In particular, we characterize the state degradation that a class of attacks can induce to the system while enforcing that the detectors (CUSUM and chi-squared) do not raise alarms. In doing so, we find the upper bound of state degradation that is possible by an undetected attacker. We quantify the advantage of using a dynamic detector (CUSUM), which leverages the history of the state, over a static detector (chi-squared) which uses a single measurement at a time. Simulations of a chemical reactor with heat exchanger are presented to illustrate the performance of our tools.Comment: Submitted to IEEE Transactions on Control Systems Technolog

    Online Nonparametric Anomaly Detection based on Geometric Entropy Minimization

    Full text link
    We consider the online and nonparametric detection of abrupt and persistent anomalies, such as a change in the regular system dynamics at a time instance due to an anomalous event (e.g., a failure, a malicious activity). Combining the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we propose a computationally efficient online anomaly detection method that is applicable to high-dimensional datasets, and at the same time achieve a near-optimum average detection delay performance for a given false alarm constraint. We provide new insights to both GEM and CUSUM, including new asymptotic analysis for GEM, which enables soft decisions for outlier detection, and a novel interpretation of CUSUM in terms of the discrepancy theory, which helps us generalize it to the nonparametric GEM statistic. We numerically show, using both simulated and real datasets, that the proposed nonparametric algorithm attains a close performance to the clairvoyant parametric CUSUM test.Comment: to appear in IEEE International Symposium on Information Theory (ISIT) 201

    BNP-Seq: Bayesian Nonparametric Differential Expression Analysis of Sequencing Count Data

    Full text link
    We perform differential expression analysis of high-throughput sequencing count data under a Bayesian nonparametric framework, removing sophisticated ad-hoc pre-processing steps commonly required in existing algorithms. We propose to use the gamma (beta) negative binomial process, which takes into account different sequencing depths using sample-specific negative binomial probability (dispersion) parameters, to detect differentially expressed genes by comparing the posterior distributions of gene-specific negative binomial dispersion (probability) parameters. These model parameters are inferred by borrowing statistical strength across both the genes and samples. Extensive experiments on both simulated and real-world RNA sequencing count data show that the proposed differential expression analysis algorithms clearly outperform previously proposed ones in terms of the areas under both the receiver operating characteristic and precision-recall curves.Comment: To appear in Journal of the American Statistical Associatio
    • …
    corecore