False Alarm Conditions Research Articles

An AN ANALYSIS OF EMERGENCY RESPONSE COSTS DUE TO FALSE ALARM SYSTEM

Losses from fire events can be minimized if the fire detection and alarm system installed in an area function properly. The conditions will be different if an alarm that sounds is not the result of real conditions that can trigger a fire - also known as a false alarm. The false alarm condition gives a loss to the company. In this research, an analysis and comparison of costs in the detection and alarm systems conducted on the existing company fires with the same risk of false alarms, repairs fire detection and alarm system, and investment costs. The probability of a false alarm calculated in the existing condition, to know the potential losses charged to the company due to unnecessary emergency response activities. As well, investment costs were analysed to improve the performance of the system. Two alternative conditions were found to improve company’s performance.

Linear Frequency Modulated Reverberation Suppression Using Non-Negative Matrix Factorization Methods, Dechirping Transformation and Modulo Operation

In this study, we propose a reverberation suppression algorithm for linear frequency-modulated (LFM) pulse sonar systems using a non-negative matrix factorization (NMF) method. Because conventional NMF-based reverberation suppression algorithms are only applicable to continuous wave reverberation, we propose two pre-processing methods, namely dechirping transformation and modulo operation, to facilitate application of the NMF method to LFM reverberation. Moreover, we impose additional sparse constraints on the NMF method to improve its performance. To evaluate the proposed algorithm, an experiment involving simulated LFM reverberation is performed. The results thereof show improved detection performance at several signal-to-reverberation ratios and false alarm conditions. Moreover, the proposed algorithm is also applied to sea experiment data. According to the sea experiment analysis, the algorithm is able to suppress the LFM reverberation effectively and improve detection performance in practical LFM pulse sonar systems.

Monitoring of severe weather events using RGB scheme of INSAT-3D satellite

In this study, real-time analysis of products and information dissemination (RAPID), a web-based quick visualisation and analysis tool for INSAT satellite data on a real-time basis has been introduced for identification of pre-monsoon severe weather events. The tool introduces the next generation weather data access and advanced visualisation. The combination of channels using red–green–blue (RGB) composites of INSAT-3D satellite and its physical significant value contents are presented. The solar reflectance and brightness temperatures (BTs) are the major components of the RGB composite. The solar reflectance component of the shortwave thermal infrared (IR) (\(1.6~\upmu \hbox {m}\)), visible (\(0.5~\upmu \hbox {m}\)) and thermal IR channels (\(10.8~\upmu \hbox {m}\)) representing the cloud microstructure is known as Day Microphysics (DMP) RGB and the BT differences between 10.8, 12.0 and \(3.9~\upmu \hbox {m}\) is known as Night Microphysics (NMP) RGB. The threshold technique has been developed separately for both the RGB products of the year 2015–2016 and 2016–2017 of March–June, prior to the event (1–3 hr) for the detection of the thunderstorms. A validation analysis was conducted using the Forecast Demonstration Project of Storm Bulletins for pre-monsoon weather systems prepared by the India Meteorological Department and RADAR observations, demonstrating that this approach is extremely useful in recognising the area of convection prior to the occurrence of the events by the RGB thresholds. The validation of these thresholds has been carried out for March–June 2017. Both the RGBs i.e., DMP and NMP have a reasonable agreement with the ground-based observations and RADAR data. This threshold technique yields a very good probability of thunderstorm detection more than 94% and 93% with acceptable false alarm conditions, less than 3% and 5% for DMP and NMP, respectively. Furthermore, the limitations of these RGB products are additionally highlighted, and the future extent of refinement of these products in perspective of a rapid scan strategy is proposed. The threshold techniques are found to be useful for nowcasting application and are being used operationally using the RAPID tool.

Identification of Weather Events from INSAT-3D RGB Scheme using RAPID Tool

Real-time analysis of products and information dissemination (RAPID), a web-based quick visualization and analysis tool for INSAT satellite data has been presented for identification of weather events. The combination of channels using red-green-blue (RGB) composites of INSAT-3D satellite and its physical significant value content is presented. The solar reflectance and brightness temperatures are the major components of this scheme. The shortwave thermal infrared (1.6 μm), visible (0.5 μm) and thermal IR channels (10.8 μm) representing cloud microstructure is known as Day Microphysics (DMP) and the brightness temperature (BT) differences between 10.8, 12.0 and 3.9 μm is referred to as Night Microphysics (NMP). The thresholds technique have been developed separately for both the RGB products of two years (2015-17 of December to February) of data for the identification of fog, snow and low clouds. The validation of these thresholds has been carried out against in situ visibility data from IMD observatories. The RGBs, i.e. DMP and NMP have a reasonable good agreement with ground-based observations and Moderate Resolution Imaging Spectroradiometer (MODIS) data. This threshold technique yields a very good probability of fog detection more than 94% and 85% with acceptable false alarm conditions less than 8% and 10% for DMP and NMP respectively. The technique has significantly minimized the misclassification between low clouds, snow, and fog and found useful for day-to-day weather forecast.

Conveying Automation Reliability and Automation Error Type An Empirical Study in the Cyber Domain

Background Emails have become an integral part of our daily life and work. Phishing emails are often disguised as trustworthy ones and attempt to obtain sensitive information for malicious reasons (Egelman, Cranor, Hong, 2008;). Anti-phishing tools have been designed to help users detect phishing emails or websites (Egelman, et al., 2008; Yang, Xiong, Chen, Proctor, & Li, 2017). However, like any other types of automation aids, these tools are not perfect. An anti-phishing system can make errors, such as labeling a legitimate email as phishing (i.e., a false alarm) or assuming a phishing email as legitimate (i.e., a miss). Human trust in automation has been widely studied as it affects how the human operator interacts with the automation system, which consequently influences the overall system performance (Dzindolet, Peterson, Pomranky, Pierce, & Beck, 2003; Lee & Moray, 1992; Muir, 1994; Sheridan & Parasuraman, 2006). With interacting with an automation system, the human operator should calibrate his or her trust level to trust a system that is capable but distrust a system that is incapable (i.e., trust calibration; Lee & Moray, 1994; Lee & See, 2004; McGuirl & Sarter, 2006). Among the various system capabilities, automation reliability is one of the most important factors that affect trust, and it is widely accepted that higher reliability levels lead to higher trust levels (Desai et al., 2013; Hoff & Bashir, 2015). How well these capabilities are conveyed to the operator is essential (Lee & See, 2004). There are two general ways of conveying the system capabilities: through an explicit description of the capabilities (i.e., description), or through experiencing the system (i.e., experience). These two ways of conveying information have been studied widely in human decision-making literature (Wulff, Mergenthaler-Canseco, & Hertwig, 2018). Yet, there has not been systematic investigation on these different methods of conveying information in the applied area of human-automation interaction (but see Chen, Mishler, Hu, Li, & Proctor, in press; Mishler et al., 2017). Furthermore, trust and reliance on automation is not only affected by the reliability of the automation, but also by the error types, false alarms and misses (Chancey, Bliss, Yamani, & Handley, 2017; Dixon & Wickens, 2006). False alarms and misses affect human performance in qualitatively different ways, with more serious damage being caused by false-alarmprone automation than by miss-prone automation (Dixon, Wickens, & Chang, 2004). In addition, false-alarm-prone automation reduces compliance (i.e., the operator’s reaction when the automation presents a warning); and miss-prone automation reduces reliance (i.e., the operator’s inaction when the automation remains silent; Chancey et al., 2017). Current Study The goal of the current study was to examine how the methods of conveying system reliability and automation error type affect human decision making and trust in automation. The automation system was a phishing-detection system, which provided recommendations to users as to whether an email was legitimate or phishing. The automation reliability was defined as the percentage of correct recommendations (60% vs. 90%). For each reliability level, there were a false-alarm condition, with all the automation errors being false alarms, and a miss condition, with all the errors being misses. The system reliability was conveyed through description (with an exact percentage described to the user) or experience (with immediate feedback to help the user learn; Barron, & Erev, 2003). A total of 510 participants were recruited and completed the experiment online through Amazon Mechanical Turk. The experimental task consisted of classifying 20 emails as phishing and legitimate, with a phishing-detection system providing recommendations. At the end of the experiment, participants rated their trust in this automated aid system. The measures included a performance measure (the decision accuracy made by the participants), as well as two trust measures (participants’ agreement rate with the phishing-detection system, and their self-reported trust in the system). Our results showed that higher system reliability and feedback increased accuracy significantly, but description or error type alone did not affect accuracy. In terms of the trust measures, false alarms led to lower agreement rates than did misses. With a less reliable system, though, the misses caused a problem of inappropriately higher agreement rates; this problem was reduced when feedback was provided for the unreliable system, indicating a trust-calibration role of feedback. Self-reported trust showed similar result patterns to agreement rates. Performance was improved with higher system reliability, feedback, and explicit description. Design implications of the results included that (1) both feedback and description of the system reliability should be presented in the interface of an automation aid whenever possible, provided that the aid is reliable, and (2) for systems that are unreliable, false alarms are more desirable than misses, if one has to choose between the two.

Looming Auditory Collision Warnings for Driving

A driving simulator was used to compare the effectiveness of increasing intensity (looming) auditory warning signals with other types of auditory warnings. Auditory warnings have been shown to speed driver reaction time in rear-end collision situations; however, it is not clear which type of signal is the most effective. Although verbal and symbolic (e.g., a car horn) warnings have faster response times than abstract warnings, they often lead to more response errors. Participants (N=20) experienced four nonlooming auditory warnings (constant intensity, pulsed, ramped, and car horn), three looming auditory warnings ("veridical," "early," and "late"), and a no-warning condition. In 80% of the trials, warnings were activated when a critical response was required, and in 20% of the trials, the warnings were false alarms. For the early (late) looming warnings, the rate of change of intensity signaled a time to collision (TTC) that was shorter (longer) than the actual TTC. Veridical looming and car horn warnings had significantly faster brake reaction times (BRT) compared with the other nonlooming warnings (by 80 to 160 ms). However, the number of braking responses in false alarm conditions was significantly greater for the car horn. BRT increased significantly and systematically as the TTC signaled by the looming warning was changed from early to veridical to late. Looming auditory warnings produce the best combination of response speed and accuracy. The results indicate that looming auditory warnings can be used to effectively warn a driver about an impending collision.

Infrared target detection with probability density functions of wavelet transform subbands.

We report the development of a wavelet multiresolution texture-based algorithm that uses the probability density functions (PDFs) of the subband of the wavelet decomposition of an image. The moments of these pdfs are used in a clustering algorithm to segment the targets from their background clutter. Using the tools of experimental methodology, we evaluate the performance of this algorithm on real infrared imagery under varying algorithm parameter sets as well as scene, image, and false-alarm conditions. We estimate a set of multidimensional predictive analytic performance models that relate the detection probabilities as functions of false alarm, algorithm internal parameter, target pixel number, target-to-background interference ratio, target-interference ratio, and Fechner-Weber and local entropy metrics in the scene. These models can be used to predict performance in regions were no data are available and to optimize performance by selection of the optimum parameter and constant false-alarm values in regions with known scene and metric conditions.