Frequent False Alarms Research Articles

Online Change Detection for a Poisson Process with a Phase-Type Change-Time Prior Distribution

We consider a change detection problem in which the arrival rate of a Poisson process changes suddenly at some unknown and unobservable disorder time. It is assumed that the prior distribution of the disorder time is known. The objective is to detect the disorder time with an online detection rule (a stopping time) in a way that balances the frequency of false alarms and detection delay. So far in the study of this problem, the prior distribution of the disorder time is taken to be exponential distribution for analytical tractability. Here, we will take the prior distribution to be a phase-type distribution, which is the distribution of the absorption time of a continuous time Markov chain with a finite state space. We find an optimal stopping rule for this general case. We illustrate our findings on two numerical examples.

베이지안 네트워크를 이용한 자동 화재 감지 시스템

본 논문에서는 실시간 화재 감지를 위해 비전 기반의 새로운 화재 감지 기법을 제안한다. 기존의 비전기반 화재감지 기법에서는 컬러정보와 픽셀들의 시간적인 변화량 검출을 위해 다수의 휴리스틱한 특징들을 적용함으로써 실험결과가 환경의 변화에 민감한 문제들이 존재했다. 또한 정확한 화재감지를 위해서 많은 연산을 수행함으로써 감지시간 길어지는 단점이 있었다. 이러한 문제점들을 극복하기 위해서 본 논문에서는 시간축 상에서 불규칙하게 변화하는 화재의 특성을 분석하고 이를 토대로 확률 모델을 구성하여 이를 베이지안 네트워크(Bayesian network)에 적용하는 새로운 방법을 제안한다. 우선, 배경 모델링과 컬러 모델을 적용하여 화재 후보 영역을 검출하고, 이 후보 영역에서 명암도에 평준화된 Red 색상의 왜도(skewness)와 웨이블릿 변환을 통하여 얻어진 3가지 고주파 성분의 왜도를 노드로 갖는 베이지안 네트워크를 구성하여 최종 화재를 감별한다. 실생활 환경에서 촬영된 화재 영상에 대한 실험 결과는 빠른 검출 속도와 우수한 화재 검출 성능을 보여주고 있다. In this paper, we propose a new vision-based fire detection method for a real-life application. Most previous vision-based methods using color information and temporal variation of pixel produce frequent false alarms because they used a lot of heuristic features. Furthermore there is also computation delay for accurate fire detection. To overcome these problems, we first detected candidated fire regions by using background modeling and color model of fire. Then we made probabilistic models of fire by using a fact that fire pixel values of consecutive frames are changed constantly and applied them to a Bayesian Network. In this paper we used two level Bayesian network, which contains the intermediate nodes and uses four skewnesses for evidence at each node. Skewness of R normalized with intensity and skewnesses of three high frequency components obtained through wavelet transform. The proposed system has been successfully applied to many fire detection tasks in real world environment and distinguishes fire from moving objects having fire color.

CUSUM: a tool for ongoing assessment of performance

CUSUM: a tool for ongoing assessment of performance

Detection of Oscillatory Control Loops in Irrigation Channels

In this paper the algorithm for detection of oscillatory control loops developed in Hägglund (1995) is applied to irrigation channels. The water levels in irrigation channels are controlled using mechanical gates. The controller configuration is a decentralized distant downstream scheme where a gate controls the water level upstream of the next downstream gate. The controller is a PI controller augmented with a low pass filter together with a feedforward term from the downstream gate. The algorithm is applied to real data from six consecutive reaches of an irrigation channel, and it detected the control loops that gave oscillatory response. Furthermore, due to the decentralized configuration, one is also able to localize the cause of the oscillation. Given that there can be many control loops in a channel network, the ability to localize the cause of an oscillation speeds up the trouble shooting process. However, care must be taken in choosing the thresholds in the algorithm in order to avoid frequent false alarm.

Contour-integration deficits on the intact side of the visual field in hemianopia patients

Objective Visual impairments in hemianopia are thought to be exclusively caused by the reduced visual field size. However, the primary lesion may affect the contralateral hemisphere through damage of interhemispheric projections. The question therefore arises if the presumed “intact” hemifield is perceptually impaired. Methods Three hemianopia patients and three matched controls carried out a Yes/No figure detection task with their intact side of the visual field. The figure (square) contours were composed of non-contiguous Gabor patches embedded in a random patch array of different background densities (low, Δ = 2; high, Δ = 1). Response accuracy and reaction times were recorded. Results A temporal–parietal patient revealed figure detection impairments, with accuracy rate, 77% ( Δ = 2) and 53% ( Δ = 1), below compared control values. An occipital patient was comparable to his match: 99% ( Δ = 2); 84% ( Δ = 1). Both patients exhibited frequent false alarms to random patterns and required longer presentation times to perform the task. In the third patient, with optic tract lesion, figure detection was nearly normal at low density (92%, Δ = 2) but impaired at noisy background (62%, Δ = 1). Conclusion The “intact” visual field in hemianopes is impaired in detection of incomplete figures embedded in a noisy background. This deficit may be caused by damage to higher visual centers and/or loss of interhemispheric interactions.

DEVELOPMENT OF AN ON-SITE DETECTION METHOD FOR CHEMICAL AND BIOLOGICAL WARFARE AGENTS

We evaluated commercially available, portable, on-site equipment for chemical warfare agent detection (a gas detection tube, ion mobility spectrometer, surface acoustic wavelength detector, flame photometric detector, photoionization detector, Fourier-transformed infrared spectrometer and a gas chromatograph-mass spectrometer) using authentic, vaporized chemical-warfare agents from the standpoint of their qualitative detection characteristics, detection limits, response times, frequency of false alarms and residubility on the devices. False alarms and the strong adsorption of agents by the devices are typical drawbacks of such equipment. As a screening method for biological warfare agents, on-site methods using flow cytometry, bioluminescence assay, and lateral flow immunoassay were developed.

Begging in the absence of parents: a “quick on the trigger” strategy to minimize costly misses

Nestling begging in the absence of parents may reflect ‘‘false alarms’’ due to cognitive constraints or signaling activity toward nest mates (sibling negotiation). According to signal detection theory, cognitive constraints should result in both false alarms (begging in the absence of parents or to inappropriate stimuli) and misses (failure to beg during parental visits). In our study of house sparrows, nestling begging in the absence of parents comprised up to 50% of the begging events at the nest and was more frequent at an early age and among hungrier (lower ranked) nestlings. In contrast, the probability of begging during parental visits was constantly high (80% or more), suggesting that the rate of misses must have been low even at an early age. These results have 2 main implications. First, the observation that begging in the absence of parents decreases with nestling age favors the cognitive constraints hypothesis over functional explanations such as the sibling negotiation hypothesis. Second, the low proportion of ‘‘misses’’ among young nestlings suggests that nestling respond to their cognitive constraints by using low decision criteria (a ‘‘quick on the trigger’’ strategy) that increases the frequency of false alarms but minimizes costly misses. Key words: cognitive constraints, false alarm, nestling begging, Passer domesticus, signal detection theory. [Behav Ecol]

A Dynamic Decision Model Applied to Hurricane Landfall

AbstractThe decision to prepare for an oncoming hurricane is typically framed as a static cost:loss problem, based on a strike-probability forecast. The value of waiting for updated forecasts is therefore neglected. In this paper, the problem is reframed as a sequence of interrelated decisions that more accurately represents the situation faced by a decision maker monitoring an evolving tropical cyclone. A key feature of the decision model is that the decision maker explicitly anticipates and plans for future forecasts whose accuracy improves as lead time declines. A discrete Markov model of hurricane travel is derived from historical tropical cyclone tracks and combined with the dynamic decision model to estimate the additional value that can be extracted from existing forecasts by anticipating updated forecasts, rather than incurring an irreversible preparation cost based on the instantaneous strike probability. The value of anticipating forecasts depends on the specific alternatives and cost profile of each decision maker, but conceptual examples for targets at Norfolk, Virginia, and Galveston, Texas, yield expected savings ranging up to 8% relative to repeated static decisions. In real-time decision making, forecasts of improving information quality could be used in combination with strike-probability forecasts to evaluate the trade-off between lead time and forecast accuracy, estimate the value of waiting for improving forecasts, and thereby reduce the frequency of false alarms.

Statistical analysis and modelling of weather radar beam propagation conditions in the Po Valley (Italy)

Abstract. Ground clutter caused by anomalous propagation (anaprop) can affect seriously radar rain rate estimates, particularly in fully automatic radar processing systems, and, if not filtered, can produce frequent false alarms. A statistical study of anomalous propagation detected from two operational C-band radars in the northern Italian region of Emilia Romagna is discussed, paying particular attention to its diurnal and seasonal variability. The analysis shows a high incidence of anaprop in summer, mainly in the morning and evening, due to the humid and hot summer climate of the Po Valley, particularly in the coastal zone. Thereafter, a comparison between different techniques and datasets to retrieve the vertical profile of the refractive index gradient in the boundary layer is also presented. In particular, their capability to detect anomalous propagation conditions is compared. Furthermore, beam path trajectories are simulated using a multilayer ray-tracing model and the influence of the propagation conditions on the beam trajectory and shape is examined. High resolution radiosounding data are identified as the best available dataset to reproduce accurately the local propagation conditions, while lower resolution standard TEMP data suffers from interpolation degradation and Numerical Weather Prediction model data (Lokal Model) are able to retrieve a tendency to superrefraction but not to detect ducting conditions. Observing the ray tracing of the centre, lower and upper limits of the radar antenna 3-dB half-power main beam lobe it is concluded that ducting layers produce a change in the measured volume and in the power distribution that can lead to an additional error in the reflectivity estimate and, subsequently, in the estimated rainfall rate.

Alarm Algorithms in Critical Care Monitoring

The alarms of medical devices are a matter of concern in critical and perioperative care. The frequent false alarms not only are a nuisance for patients and caregivers but can also compromise patient safety and effectiveness of care. The development of alarm systems has lagged behind the technological advances of medical devices over the last 20 years. From a clinical perspective, major improvements of alarm algorithms are urgently needed. We give an overview of the current clinical situation and the underlying problems and discuss different methods from statistics and computational science and their potential for clinical application.

Poisson Disorder Problem with Exponential Penalty for Delay

We solve the Poisson disorder problem when the delay is penalized exponentially. Our objective is to detect as quickly as possible the unobservable time of the change (or disorder) in the intensity of a Poisson process. The disorder time delimits two different regimes in which one employs distinct strategies (e.g., investment, advertising, manufacturing). We seek a stopping rule that minimizes the frequency of false alarms and an exponential (unlike previous formulations, which use a linear) cost function of the detection delay. In the financial applications, the exponential penalty is a more apt measure for the delay cost because of the compounding of the investment growth. The Poisson disorder problem with a linear delay cost was studied by Peskir and Shiryaev [2002. Solving the Poisson Disorder Problem. Advances in Finance and Stochastics. Springer, Berlin, Germany, 295–312], which is a limiting case of ours.

Sequential change-point detection when unknown parameters are present in the pre-change distribution

In the sequential change-point detection literature, most research specifies a required frequency of false alarms at a given pre-change distribution $f_{\theta}$ and tries to minimize the detection delay for every possible post-change distribution $g_{\lambda}$. In this paper, motivated by a number of practical examples, we first consider the reverse question by specifying a required detection delay at a given post-change distribution and trying to minimize the frequency of false alarms for every possible pre-change distribution $f_{\theta}$. We present asymptotically optimal procedures for one-parameter exponential families. Next, we develop a general theory for change-point problems when both the pre-change distribution $f_{\theta}$ and the post-change distribution $g_{\lambda}$ involve unknown parameters. We also apply our approach to the special case of detecting shifts in the mean of independent normal observations.

Optimal and Asymptotically Optimal CUSUM Rules for Change Point Detection in the Brownian Motion Model with Multiple Alternatives

This work examines the problem of sequential change detection in the constant drift of a Brownian motion in the case of multiple alternatives. As a performance measure an extended Lorden's criterion is proposed. When the possible drifts, assumed after the change, have the same sign, the \hbox{CUSUM} rule, designed to detect the smallest in absolute value drift, is proven to be the optimum. If the drifts have opposite signs, then a specific 2-\hbox{CUSUM} rule is shown to be asymptotically optimal as the frequency of false alarms tends to infinity.

Evaluating Detection of an Inhalational Anthrax Outbreak

Timely detection of an inhalational anthrax outbreak is critical for clinical and public health management. Syndromic surveillance has received considerable investment, but little is known about how it will perform relative to routine clinical case finding for detection of an inhalational anthrax outbreak. We conducted a simulation study to compare clinical case finding with syndromic surveillance for detection of an outbreak of inhalational anthrax. After simulated release of 1 kg of anthrax spores, the proportion of outbreaks detected first by syndromic surveillance was 0.59 at a specificity of 0.9 and 0.28 at a specificity of 0.975. The mean detection benefit of syndromic surveillance was 1.0 day at a specificity of 0.9 and 0.32 days at a specificity of 0.975. When syndromic surveillance was sufficiently sensitive to detect a substantial proportion of outbreaks before clinical case finding, it generated frequent false alarms.

Optimality of the 2-CUSUM drift equalizer rules for detecting two-sided alternatives in the Brownian motion model

This work employs the Brownian motion model in which observations are taken sequentially. The objective is to detect a two-sided change in the constant drift by means of a stopping rule. As a performance measure, an extended Lorden criterion is used. The goal is to minimize the worst-case detection delay subject to a constraint in the frequency of false alarms. In a companion paper, attention is drawn to a first category of 2-CUSUM rules for which the harmonic mean rule holds. It is further seen that a special class of 2-CUSUM stopping rules within this category, called drift equalizer rules, perform strictly better than non-equalizer rules, according to this specific performance measure.

Optimality of the 2-CUSUM drift equalizer rules for detecting two-sided alternatives in the Brownian motion model

This work employs the Brownian motion model in which observations are taken sequentially. The objective is to detect a two-sided change in the constant drift by means of a stopping rule. As a performance measure, an extended Lorden criterion is used. The goal is to minimize the worst-case detection delay subject to a constraint in the frequency of false alarms. In a companion paper, attention is drawn to a first category of 2-CUSUM rules for which the harmonic mean rule holds. It is further seen that a special class of 2-CUSUM stopping rules within this category, called drift equalizer rules, perform strictly better than non-equalizer rules, according to this specific performance measure.

Design and test methods for a video-based cargo fire verification system for commercial aircraft

The Cargo Fire Verification System was developed to address the problem of frequent false smoke alarms that are of particular concern in long range flights of passenger aircraft. The system uses low-cost CCD cameras operating in the near infra red range to directly detect fire and hotspots. In addition, LED illumination units are appropriately switched on and off, and the obtained images are analyzed to detect smoke. Fusion of image processing results with temperature and humidity readings allows reliable detection of true fires and elimination of false alarms due to fog and dust. It was necessary to create a suite of fire sensitivity and false alarm immunity tests applicable to these vision-based fire detection systems. This paper will concentrate on the design of the system, testing aspects, and test cell modifications.

Sensing technology for chemical-warfare agents and its evaluation using authentic agents

Some commercially available portable on-site detection equipment (detection paper, gas detection tube, ion mobility spectrometer, surface acoustic wavelength detector, flame photometric detector, gas chromatograph–mass spectrometer) were evaluated using authentic vaporized chemical-warfare agents, from the standpoint of their qualitative detection characteristics, detection limits, response time, frequency of false alarms and residubility on the devices. False alarms and the strong adsorption of agents by the devices are typical drawbacks of such equipment.

A Video-based Cargo Fire Verification System For Commercial Aircraft: Design And Test Methods

The Cargo Fire Verification System was developed to address the problem of frequent false smoke alarms that are of particular concern in long range flights of passenger aircraft. The system uses low-cost CCD cameras operating in the near infrared range to directly detect fire and hotspots. In addition, LED illumination units are appropriately switched on and off, and the obtained images are analysed to detect smoke. Fusion of image processing results with temperature and humidity readings allows reliable detection of true fires and elimination of false alarms due to condensed moisture (fog) and dust. It was necessary to create a suite of fire sensitivity and false alarm immunity tests applicable to these vision based fire detection systems. This paper will concentrate on the design of the system, testing aspects, and test cell modifications.

Process monitoring for continuous process with periodic characteristics

AbstractApplication of conventional statistical monitoring methods to periodic processes can result in frequent false alarms and/or missed faults due to the non‐stationary behavior seen over a period. To address this problem, we propose to identify and use a stochastic state space model that describes the statistical behavior of changes occurring from period to period. This model, when retooled as a periodically time‐varying model, can be used for on‐line monitoring and estimation with the aid of a Kalman filter. The same model can also be used for inferential estimation of the variables that are difficult or slow to measure on‐line. The proposed approach is applied to a simulation benchmark of a waste water treatment process, which exhibits strong diurnal changes in the feed stream, and is compared against the principal component analysis (PCA) and partial least squares (PLS) methods. Copyright © 2004 John Wiley & Sons, Ltd.