Detection of mortality clusters associated with highly pathogenic avian influenza in poultry: a theoretical analysis

Rapid detection of infectious disease outbreaks is often crucial for their effective control. One example is highly pathogenic avian influenza (HPAI) such as H5N1 in commercial poultry flocks. There are no quantitative data, however, on how quickly the effects of HPAI infection in poultry flocks can be detected. Here, we study, using an individual-based mathematical model, time to detection in chicken flocks. Detection is triggered when mortality, food or water intake or egg production in layers pass recommended thresholds suggested from the experience of past HPAI outbreaks. We suggest a new threshold for caged flocks—the cage mortality detection threshold—as a more sensitive threshold than current ones. Time to detection is shown to depend nonlinearly on R₀ and is particularly sensitive for R₀<10. It also depends logarithmically on flock size and number of birds per cage. We also examine how many false alarms occur in uninfected flocks when we vary detection thresholds owing to background mortality. The false alarm rate is shown to be sensitive to detection thresholds, dependent on flock size and background mortality and independent of the length of the production cycle. We suggest that current detection thresholds appear sufficient to rapidly detect the effects of a high R₀ HPAI strain such as H7N7 over a wide range of flock sizes. Time to detection of the effects of a low R₀ HPAI strain such as H5N1 can be significantly improved, particularly for large flocks, by lowering detection thresholds, and this can be accomplished without causing excessive false alarms in uninfected flocks. The results are discussed in terms of optimizing the design of disease surveillance programmes in general.     

GPS接收机伪码捕获自适应判决门限估计算法(英文)

GPS接收机输入信号的功率随时间而变化,如果采用固定门限判决是否完成捕获会出现检测概率降低或虚警概率提高的问题.鉴于此,提出了采用自适应捕获门限判决策略完成信号捕获.采用最大似然估计原理,根据判决统计量的样本值得到了噪声方差的最大似然估计,进而求得恒虚警率下的自适应捕获门限.同时,信号的平均捕获时间是GPS接收机性能的重要指标之一,研究了载波频率与伪码相位均为不确定值时的平均捕获时间的计算方法,推导了平均捕获时间与信噪比及门限值之间的约束关系.分析结果表明,自适应门限捕获方法能减小平均捕获时间,实现信号的快速捕获.     

Target detection with randomized thresholds for lidar applications

Light detection and ranging (lidar) systems use binary hypothesis tests to detect the presence of a target in a range interval. For systems that count photon detections, hypothesis test thresholds are normally set so that a target detection is declared if the number of detections exceeds a particular number. When this method is employed, the false alarm probability can not be selected arbitrarily. In this paper, a hypothesis test that uses randomized thresholds is described. This randomized method of thresholding allows lidar operation at any false alarm probability. When there is a maximum allowable false alarm probability, the hypothesis test that uses randomized thresholds generally produces higher target detection probabilities than the conventional (nonrandom) hypothesis test.     

Conditional data processing for single-shot spectral analysis by use of laser-induced breakdown spectroscopy

Schemes of conditional data processing are evaluated based on either the peak-to-base ratio or the signal-to-noise ratio as a metric for analyte detection in single-shot laser-induced breakdown spectra. The analyte signal investigated is the 288.1-nm Si I emission line provided by an aerosol stream of monodisperse 2.5-microm-sized silica microspheres. Both the Si emission line and a spectral region corresponding to continuum emission are used to evaluate the statistical distribution of spectral noise. The probability of false hits is determined by evaluating various conditional processing thresholds. As the detection threshold increases, the rate of detected silica particle hits decreases along with the expected fraction of false-particle hits (i.e., spectral noise). For all threshold values the signal-to-noise ratio is found to provide a more robust metric for single-shot analyte detection compared with the peak-to-base ratio.     

A Bivariate Semiparametric Control Chart Based on Order Statistics

In this article, a new bivariate semiparametric Shewhart‐type control chart is presented. The proposed chart is based on the bivariate statistic (X_(r), Y_(s)), where X_(r) and Y_(s) are the order statistics of the respective X and Y test samples. It is created by considering a straightforward generalization of the well‐known univariate median control chart and can be easily applied because it calls for the computation of two single order statistics. The false alarm rate and the in‐control run length are not affected by the marginal distributions of the monitored characteristics. However, its performance is typically affected by the dependence structure of the bivariate observations under study; therefore, the suggested chart may be characterized as a semiparametric control chart. An explicit expression for the operating characteristic function of the new control chart is obtained. Moreover, exact formulae are provided for the calculation of the alarm rate given that the characteristics under study follow specific bivariate distributions. In addition, tables and graphs are given for the implementation of the chart for some typical average run length values and false alarm rates. The performance of the suggested chart is compared with that of the traditional χ² chart as well as to the nonparametric SN² and SR² charts that are based on the multivariate form of the sign test and the Wilcoxon signed‐rank test, respectively. Finally, in order to demonstrate the applicability of our chart, a case study regarding a real‐world problem related to winery production is presented. Copyright © 2016 John Wiley & Sons, Ltd.     

诊断间歇故障降低BIT虚警

间歇故障的存在及影响是产生BIT(Built-in Test)虚警的一个重要原因,为降低间歇故障引起的虚警,提出一种三状态方法,利用双阈值来区分系统的正常、间歇、永久故障三种状态,并分析得到间歇故障对BIT影响的定量关系,与传统两状态方法相比,基于三状态方法的BIT通过诊断间歇故障,不仅有效地抑制间歇故障引起的虚警,而且还能提高故障检测率。     

Nondestructive Evaluation of Microcracks in Ceramics with Controlled Probability of False Alarm

In this paper we present a complete detection scheme for ultrasonic crack detection in wet ceramics by means of ultrasonic nondestructive techniques. The detection scheme is based on a previous statistical characterization of the ultrasonic grain noise. In these previous works we obtained that the ultrasonic grain noise can be modeled as a K -noise [1, 2]. From this characterization we will prove that the expression of the probability of false alarm (PFA) obtained for a detector of Gaussian envelope pulses over K -noise using the Wigner-Ville Transform (WVT) proposed in reference [3] is valid for the real problem of ultrasonic microcrack detection in wet ceramics.     

Intermittent failure process and false alarm interaction modelling of threshold-based monitoring built-in tests (BITs)

Built-in tests (BITs) are widely used in manufacturing and production systems to find whether system failures occur, whereas the problem of BIT false alarms caused by intermittent failures adds to much trouble for the precise failure detection and diagnosis. Fighting with false alarms caused by intermittent failures is an urgent issue. However, the nature and temporal regularity of intermittent failures are not fully exploited, as well as the relationship between intermittent failure and BIT false alarms. The present paper introduces the method of constructing failure test profile for false alarm assessments. Probabilistic models are proposed of the failure evolution process, as well as the interactions between intermittent failures and false alarms. The false alarm time expectation is derived with the given model, serving as the foundation for the optimisation problem to find the best test threshold to enable the highest BIT capability. A numerical analysis is made to illustrate the proposed model and examine the threshold determination method. An application study is also carried out to show how the model can be applicable in real engineering practices.     

A Distribution-Free Multivariate Control Chart

Monitoring multivariate quality variables or data streams remains an important and challenging problem in statistical process control (SPC). Although the multivariate SPC has been extensively studied in the literature, designing distribution-free control schemes are still challenging and yet to be addressed well. This article develops a new nonparametric methodology for monitoring location parameters when only a small reference dataset is available. The key idea is to construct a series of conditionally distribution-free test statistics in the sense that their distributions are free of the underlying distribution given the empirical distribution functions. The conditional probability that the charting statistic exceeds the control limit at present given that there is no alarm before the current time point can be guaranteed to attain a specified false alarm rate. The success of the proposed method lies in the use of data-dependent control limits, which are determined based on the observations online rather than decided before monitoring. Our theoretical and numerical studies show that the proposed control chart is able to deliver satisfactory in-control run-length performance for any distributions with any dimension. It is also very efficient in detecting multivariate process shifts when the process distribution is heavy-tailed or skewed. Supplementary materials for this article are available online.     

A Cepstral F Statistic for Detecting Delay-Fired Seismic Signals

Time- and frequency-domain approaches to detecting a consistent pattern of reflections on an ensemble of seismic recordings are developed. Such patterns are characteristic of mining bursts and not of nuclear explosions or earthquakes so that detecting a ripple delay structure can serve as one component for discrimination. In the frequency-domain approach, a generalization of cepstral analysis is used to derive an F statistic for detecting delay-fired events observed on an array. Detrended log-spectra are considered as realizations of stationary processes whose periodic components are frequencies, with periods proportional to delay-time differences. An F statistic is derived that is proportional to the stacked or summed cepstra and the spectrum of the stacked log-spectra. Advantages of the cepstral F statistic accrue from better resolution and the fact that statistical significance can be established for delay peaks. It is also easily incorporated into automatic detection systems. The frequency-domain approach is compared to a time-domain approach that involves searching seasonal autoregressive models with a fixed regular delay structure. Simulated array data and data from several mining explosions, measured at an array in northern Norway, are analyzed by both approaches.     

Goodness-of-fit tests in parametric regression based on the estimation of the error distribution

Consider a heteroscedastic regression model Y=m(X)+σ(X)ε, where m(X)=E(Y|X) and σ ²(X)=Var (Y|X) are unknown, and the error ε is independent of the covariate X. We propose a new type of test statistic for testing whether the regression curve m(⋅) belongs to some parametric family of regression functions. The proposed test statistic measures the distance between the empirical distribution function of the parametric and of the nonparametric residuals. The asymptotic theory of the proposed test is developed, and the proposed testing procedure is illustrated by means of a small simulation study and the analysis of a data set.     

Analysis of an automatic sinusoidal detector with FFT and cell‐averaging processor

Abstract

A sinusoidal detector with adaptive thresholding CFAR (constant‐false‐alarm‐rate) processor operating in noise of changing statistics is analyzed for some very general situations. The detector contains FFT and a cell‐averaging processor, which can be easily implemented for real time applications, such as in pulse doppler radar. For signals in stationary noise in particular, the second‐order statistics of the FFT‐based periodogram of the windowed data are derived. In the AWGN case, this detector possesses the desired CFAR characteristics. For Gaussian colored noise, the detector performance is derived analytically with certain reasonable approximations. It is shown from some numerical examples that in this case the variation of the false alarm probability with respect to the unknown sinusoidal frequency and noise spectrum is not significant, which reveals the property of robustness. Moreover, the detection probability is satisfactory for most cases.     

The Effect of Autocorrelation on the Hotelling T2 Control Chart

One of the basic assumptions for traditional univariate and multivariate control charts is that the data are independent in time. For the latter, in many cases, the data are serially dependent (autocorrelated) and cross‐correlated because of, for example, frequent sampling and process dynamics. It is well known that the autocorrelation affects the false alarm rate and the shift‐detection ability of the traditional univariate control charts. However, how the false alarm rate and the shift‐detection ability of the Hotelling T² control chart are affected by various autocorrelation and cross‐correlation structures for different magnitudes of shifts in the process mean is not fully explored in the literature. In this article, the performance of the Hotelling T² control chart for different shift sizes and various autocorrelation and cross‐correlation structures are compared based on the average run length using simulated data. Three different approaches in constructing the Hotelling T² chart are studied for two different estimates of the covariance matrix: (i) ignoring the autocorrelation and using the raw data with theoretical upper control limits; (ii) ignoring the autocorrelation and using the raw data with adjusted control limits calculated through Monte Carlo simulations; and (iii) constructing the control chart for the residuals from a multivariate time series model fitted to the raw data. To limit the complexity, we use a first‐order vector autoregressive process and focus mainly on bivariate data. © 2014 The Authors. Quality and Reliability Engineering International Published by John Wiley & Sons Ltd.     

基于控制图的GPS异常监测数据检验方法研究

为了有效地判别GPS异常监测数据,建立了GPS监测序列异常检验的数学模型,提出利用统计过程控制中的控制图对监测序列进行异常检验和预警的新方法;针对GPS监测数据不服从正态分布的问题,提出利用累积分布函数的核密度估计将其转换为Q统计量,并以此为基础构建基于Q统计量的控制图用于GPS异常波动数据的检验;该文文末利用仿真数据对比分析了休哈特控制图与累积和控制图对不同异常偏移值的检验效果,结果表明两种控制图各有利弊、相互补充,休哈特控制图对于3倍以上标准差的异常偏移能够给出有效的预警,但缺乏小偏移检测的能力,累积和控制图能够精确检测出最小达0.5倍标准差的连续小偏移,但是随着偏移值的增大其误警率会有所增加。     

Networked Sensors for Cargo Screening

Sensors to monitor cargo are currently operated in standalone mode because of cost considerations, operational simplicity, and because potential benefits to having sensors share information do not appear to have been seriously considered in many cases. To identify situations where threat detection benefits offset networking costs (and where they do not), we describe a numerical algorithm for approximating the multidimensional integrals defining the false positive rates and detection probabilities for networked systems. The algorithm is sufficiently fast computationally to embed in optimization routines that search over sensor space (e.g., how to best augment an existing sensor network), as well as over threshold space (i.e., how to best determine alarm thresholds for the sensors) for both standalone and networked sensors. Results are applied to detection of elevated gamma and neutron counts. Circumstances under which networking is useful are characterized.     

Bearing Fault Prediction System Design Based on SPC

According to the requirement of failure prediction and maintenance of high-precision machine tool spindle bearings, this article has designed a bearing fault prediction system using LabVIEW and database connection based on SPC theory. We collect the data of machined workpiece which are analyzed and processed based on SPC theory. And we set up a mathematical model so as to ensure the detection parameters and alarm thresholds. The system predicts the state of bearings through workpiece data. When the detection parameters exceed the threshold, the indicator will be triggered to alarm. The system has achieved the purpose of prediction.     

Evaluation of Satellite Retrieval Algorithm to Ground Rainfall Estimates Over Malaysia

Satellite precipitation data is an indirect measurement for land and water area over the globe. The information from this data is important especially to the area where direct rain gauge measurement is limited. However, the precipitation estimates require calibration and validation to assure the accuracy. Satellite rainfall estimations together with comprehensive ground validation and calibration have been found to offer good prospect for accurate and global rainfall database especially for remote areas and large bodies of water. This work presents performance evaluation of TRMM 3B43 V7 rainfall retrieval algorithms over Malaysia. Inter-comparison and validation of TRMM 3B43 V7 rainfall product with ground measurement is analysed statistically. The result of continuous statistical evaluation shows good agreement, in which, the best correlation for the algorithm 3B43 versus rain gauge is 0.9384. At lower percentage bias threshold, the 2 by 2 categorical statistic of rain or no rain occurrence for annual estimation reveals lower value of probability of detection and higher value of false alarm ratio. However, reverse results are shown at higher bias threshold. The accuracy of the algorithm for a threshold of 1–10 % which falls within International Telecommunication Union—Radio recommendation for radio propagation to discriminate between rain and no rain is 0.53, 0.49 and 0.48 for annual, monthly and wet season, respectively. From analysis, the categorical statistical approach has been able to reveal the level of accuracy of the algorithms as applicable to detection and estimation of rainfall.     

A Detection Theoretic approach to Modeling Aviation Security Problems using the Knapsack Problem

Designers, operators and users of multiple-device, access-control security systems are challenged by the false alarm, false clear tradeoff. Given a particular access control security system, and a prescribed false clear standard, there is an optimal (minimal) false alarm rate that can be achieved. The objective of this research is to develop a new methodology for determining this false alarm rate. A static grid estimation procedure is used to estimate the joint conditional probability density functions for the security device responses. The concept of a system response function is introduced and the problem of determining a system response function that minimizes the false alarm rate, while meeting the false clear standard, is formulated as a decision problem and proven to be NP-complete. A Greedy Algorithm and a Dynamic Programming algorithm are presented to address this problem. Computational results using simulated security data are reported. These results are compared to analytical results obtained for a pre-specified system response function form. Directions for future research are also discussed     

Performance analysis of multi-homed transport protocols with network failure tolerance

The performance of multi-homed transport protocols tolerant of network failure is studied. It evaluates the performance of different retransmission policies combined with path failure detection thresholds, infinite or finite receive buffers for various path bandwidths, delays and loss rate conditions through stream control transmission protocol simulation. The results show that retransmission policies perform differently with different path failure detection threshold configurations. It identifies that retransmission of all data on an alternate path with the path failure detection threshold set to zero performs the best in symmetric path conditions but its performance degrades acutely in asymmetric path conditions even when the alternate path delay is shorter than the primary path delay. It illustrates that retransmission of all data on the same path with the path failure detection threshold set to one or zero gives the most stable performance in all path configurations.     

Using a Bayes classifier to optimize alarm generation to electric power generator stator overheating

This paper shows how a Bayes classifier can be implemented for a failure detection system where statistical failure data is not available for one of the classes. Results of field data obtained from a large electric power generator are shown. The classifier is further improved by the iterative re-evaluation of the prior probabilities, which results in the use of higher alarm threshold values when a good agreement between the monitored quantity and its estimated value is observed, while large disagreement values result in smaller thresholds. As expected, the proposed system is an improvement over a classical Bayesian implementation and a large improvement over a fixed, arbitrary value threshold classifier.