scholarly journals Determination of level and time thresholds for detectors with normalized indicator process

2020 ◽  
Author(s):  
С.Б. Егоров ◽  
Р.И. Горбачев
Keyword(s):  
False Alarm ◽  
Average Rate ◽  
Probability Model ◽  
False Alarm Probability ◽  
Statistical Characteristics ◽  
False Alarms ◽  
Time Interval ◽  
Number Of False Alarms ◽  
Time Threshold ◽  
Selection Threshold

«Выбросовая» вероятностная модель работы обнаружителя в режиме ожидания сигнала, предложенная авторами в [1], использована для оценки влияния селекции выбросов по длительности на вероятность ложной тревоги. Флюктуационные выбросы помехового индикаторного процесса, превысившие пороги селекции по уровню и длительности, трактуются как редкие события на интервале ожидания сигнала, подчиняющиеся вероятностному закону Пуассона. При условии, что средний период следования ложных выбросов превышает интервал корреляции индикаторного процесса, получено соотношение между средним числом выбросов любой длительности и средним числом выбросов, превысивших пороговую длительность. На основании известных числовых и вероятностных характеристик выбросов нормального стационарного случайного процесса получен уравнения, связывающие относительные пороги селекции по уровню и длительности с вероятностью ложной тревоги на интервале ожидания сигнала. Предложена методика определения порога селекции по длительности для снижения порога селекции по уровню до заданной величины. «Emissional» probability model of the detector in stand-by mode proposed by the authors in [1], is intended for estimation of false alarm rate dependence from the value of time-selection threshold. Fluctuation emissions of the noise indicator process are interpreted as rare events correspond to Poisson distribution. Assuming that average rate of false alarms exceeds the correlation interval of indicator process, obtained equation between average number of false alarms of any duration and average number of false alarms exceed the time threshold. Based on known numerical and statistical characteristics of emissions of normal stationary random process obtained equations, relating time and level thresholds with false alarm probability on stand-by mode time interval. Also suggested a method of determining time threshold intended to reduce level threshold.

scholarly journals Determination of time-varying periodicities in unequally spaced time series of OH* temperatures using a moving Lomb–Scargle periodogram and a fast calculation of the false alarm probabilities

2020 ◽  
Vol 13 (2) ◽  
pp. 467-477 ◽  
Author(s):  
Christoph Kalicinsky ◽  
Robert Reisch ◽  
Peter Knieling ◽  
Ralf Koppmann
Keyword(s):  
Time Series ◽  
False Alarm ◽  
False Alarm Probability ◽  
Time Interval ◽  
Time Resolved ◽  
Fast Calculation ◽  
Unequally Spaced ◽  
Data Gaps ◽  
Analyse Time

Abstract. We present an approach to analyse time series with unequal spacing. The approach enables the identification of significant periodic fluctuations and the derivation of time-resolved periods and amplitudes of these fluctuations. It is based on the classical Lomb–Scargle periodogram (LSP), a method that can handle unequally spaced time series. Here, we additionally use the idea of a moving window. The significance of the results is analysed with the typically used false alarm probability (FAP). We derived the dependencies of the FAP levels on different parameters that either can be changed manually (length of the analysed time interval, frequency range) or that change naturally (number of data gaps). By means of these dependencies, we found a fast and easy way to calculate FAP levels for different configurations of these parameters without the need for a large number of simulations. The general performance of the approach is tested with different artificially generated time series and the results are very promising. Finally, we present results for nightly mean OH* temperatures that have been observed from Wuppertal (51∘ N, 7∘ E; Germany).

Effects of Stimulus Presentation and Instructions on Pure-Tone Thresholds and False-Alarm Responses

1976 ◽  
Vol 41 (3) ◽  
pp. 315-324 ◽  
Author(s):  
Jess Dancer ◽  
Ira M. Ventry ◽  
Wathina Hill
Keyword(s):  
False Alarm ◽  
Stimulus Presentation ◽  
False Alarms ◽  
Clinical Consideration ◽  
Experimental Conditions ◽  
Number Of False Alarms ◽  
Time Out ◽  
Stimulus Mode ◽  
Presentation Methods ◽  
Male Subjects

The effects of three instructional sets (conventional Carhart-Jerger, strict, and lax) and of two stimulus presentation methods (continuous tones, pulsed tones) on puretone thresholds and false-alarm responses were determined for 20 male subjects. False alarms were tallied during hearing measurement periods and during 30-second time-out periods totaling nine minutes of time-out per subject. Results showed that 50% of the subjects made false-alarm responses to some extent at 250, 1000, and 4000 Hz. Instructions and stimulus mode, along with frequency, affected the number of false alarms, but thresholds under the experimental conditions were unchanged. It is suggested that a method for assessing and controlling false alarms is an important clinical consideration.

scholarly journals Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Chrystinne Fernandes ◽  
Simon Miles ◽  
Carlos José Pereira Lucena
Keyword(s):  
Statistical Analysis ◽  
False Alarm ◽  
Improve Patient Care ◽  
False Alarm Probability ◽  
False Alarms ◽  
Context Information ◽  
Health Providers ◽  
Notification System ◽  
Alarm Fatigue ◽  
Reasoning Algorithm

BACKGROUND Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as <i>alarm fatigue</i>. OBJECTIVE This paper’s main goal is to propose a solution to mitigate <i>alarm fatigue</i> by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. METHODS We present a new approach to cope with the <i>alarm fatigue</i> problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to <i>alarm fatigue</i>. RESULTS The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid <i>alarm fatigue</i>. CONCLUSIONS Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.

Deterrence and false alarms in seismic discrimination

1973 ◽  
Vol 63 (3) ◽  
pp. 1119-1132
Author(s):  
D. H. Weichert ◽  
P. W. Basham
Keyword(s):  
False Alarm ◽  
False Alarm Probability ◽  
False Alarms ◽  
Correct Identification ◽  
Operating Characteristics ◽  
Linear Discrimination ◽  
Seismic Discrimination ◽  
Discrimination Parameter ◽  
Test Ban ◽  
Global Data

abstract The statistical capabilities of Ms:mb earthquake-explosion discrimination are derived from cumulative distributions of a linear discrimination parameter with confidence limits estimated by a distribution-free method. Cumulative discriminant distributions are shown to be preferable to previously employed “operating characteristics” because of difficulties of construction and interpretation of the latter. Three sets of Ms:mb discrimination data, two regional (North American and Eurasian) and one global, are employed to estimate false alarm probabilities at given “deterrence” (probability of correct identification of an explosion) for situations of both a ban and no ban on underground testing. In the hypothetical situation of monitoring a test ban using the Ms:mb criterion, reasonable deterrence (nominally 30 per cent, with a 95 per cent confidence that it is greater than 10 per cent) will be accompanied by a false alarm probability estimated from global data of about 0.1 per cent.

scholarly journals Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Glen Debard ◽  
Marc Mertens ◽  
Toon Goedemé ◽  
Tinne Tuytelaars ◽  
Bart Vanrumste
Keyword(s):  
False Alarm ◽  
Detection System ◽  
Real Life ◽  
Fall Detection ◽  
Training Data ◽  
False Alarms ◽  
Detection Systems ◽  
Number Of False Alarms ◽  
Real Life Data ◽  
Precision Recall Curve

More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.

scholarly journals Adaptive Early Warning Method Based on Similar Proportion and Probability Model

2020 ◽  
Vol 10 (12) ◽  
pp. 4278
Author(s):  
Yazhou Li ◽  
Wei Dai ◽  
Tingting Huang ◽  
Meihua Shi ◽  
Weifang Zhang
Keyword(s):  
Early Warning ◽  
Probability Model ◽  
Similar Proportion ◽  
False Alarms ◽  
Mechanical Equipment ◽  
Threshold Method ◽  
Health States ◽  
Number Of False Alarms ◽  
Fixed Threshold ◽  
Equipment State

This paper presents a multi-state adaptive early warning method for mechanical equipment and proposes an adaptive dynamic update model of the equipment alarm threshold based on a similar proportion and state probability model. Based on the similarity of historical equipment, the initial thresholds of different health states of equipment can be determined. The equipment status is divided into four categories and analyzed, which can better represent its status and provide more detailed and reasonable guidance. The obtained dynamic alarm lines at all levels can regulate the operation range of equipment in the different health states. Compared to the traditional method of a fixed threshold, this method can effectively reduce the number of false alarms and attains a higher prediction accuracy, which demonstrates its effectiveness and superiority. Finally, the method was verified by means of lifetime data of a rolling bearings. The results show that the model improves the timely detection of the abnormal state of the equipment, greatly reduces the false alarm rate, and even overcomes the limitation of independence between the fixed threshold method and equipment state. Moreover, multi-state division can accurately diagnose the current equipment state, which should be considered in maintenance decision-making.

scholarly journals Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation

10.2196/15407 ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. e15407 ◽  
Author(s):  
Chrystinne Fernandes ◽  
Simon Miles ◽  
Carlos José Pereira Lucena
Keyword(s):  
Statistical Analysis ◽  
False Alarm ◽  
Improve Patient Care ◽  
False Alarm Probability ◽  
False Alarms ◽  
Context Information ◽  
Health Providers ◽  
Notification System ◽  
Alarm Fatigue ◽  
Reasoning Algorithm

Background Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. Objective This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. Methods We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. Results The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. Conclusions Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety.

scholarly journals Multi-Dimensional Automatic Detection of Scanning Radar Images of Marine Targets Based on Radar PPInet

2021 ◽  
Vol 13 (19) ◽  
pp. 3856
Author(s):  
Xiaolong Chen ◽  
Jian Guan ◽  
Xiaoqian Mu ◽  
Zhigao Wang ◽  
Ningbo Liu ◽  
...  
Keyword(s):  
False Alarm ◽  
Target Detection ◽  
False Alarm Probability ◽  
False Alarms ◽  
Constant False Alarm Rate ◽  
Sea Clutter ◽  
Detection Model ◽  
Radar Images ◽  
Frame Fusion ◽  
Target Characteristics

Traditional radar target detection algorithms are mostly based on statistical theory. They have weak generalization capabilities for complex sea clutter environments and diverse target characteristics, and their detection performance would be significantly reduced. In this paper, the range-azimuth-frame information obtained by scanning radar is converted into plain position indicator (PPI) images, and a novel Radar-PPInet is proposed and used for marine target detection. The model includes CSPDarknet53, SPP, PANet, power non-maximum suppression (P-NMS), and multi-frame fusion section. The prediction frame coordinates, target category, and corresponding confidence are directly given through the feature extraction network. The network structure strengthens the receptive field and attention distribution structure, and further improves the efficiency of network training. P-NMS can effectively improve the problem of missed detection of multi-targets. Moreover, the false alarms caused by strong sea clutter are reduced by the multi-frame fusion, which is also a benefit for weak target detection. The verification using the X-band navigation radar PPI image dataset shows that compared with the traditional cell-average constant false alarm rate detector (CA-CFAR) and the two-stage Faster R-CNN algorithm, the proposed method significantly improved the detection probability by 15% and 10% under certain false alarm probability conditions, which is more suitable for various environment and target characteristics. Moreover, the computational burden is discussed showing that the Radar-PPInet detection model is significantly lower than the Faster R-CNN in terms of parameters and calculations.

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

scholarly journals Fast radio burst detection in the presence of coloured noise

2021 ◽  
Vol 503 (4) ◽  
pp. 5223-5231
Author(s):  
C F Zhang ◽  
J W Xu ◽  
Y P Men ◽  
X H Deng ◽  
Heng Xu ◽  
...  
Keyword(s):  
False Alarm ◽  
Radio Burst ◽  
Pulse Width ◽  
False Alarm Probability ◽  
False Positives ◽  
Parameter Distribution ◽  
Correlated Noise ◽  
Width Distribution ◽  
Fast Radio Burst ◽  
The Impact

ABSTRACT In this paper, we investigate the impact of correlated noise on fast radio burst (FRB) searching. We found that (1) the correlated noise significantly increases the false alarm probability; (2) the signal-to-noise ratios (S/N) of the false positives become higher; (3) the correlated noise also affects the pulse width distribution of false positives, and there will be more false positives with wider pulse width. We use 55-h observation for M82 galaxy carried out at Nanshan 26m radio telescope to demonstrate the application of the correlated noise modelling. The number of candidates and parameter distribution of the false positives can be reproduced with the modelling of correlated noise. We will also discuss a low S/N candidate detected in the observation, for which we demonstrate the method to evaluate the false alarm probability in the presence of correlated noise. Possible origins of the candidate are discussed, where two possible pictures, an M82-harboured giant pulse and a cosmological FRB, are both compatible with the observation.

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