• Fire Science and Engineering
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• v.17 no.3
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• pp.26-30
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• 2003

This paper presents a study on the analysis for false alarm of heat detector using HRR(Heat Release Rate). And it is represented to threshold value and domain of false alarm. The HRR threshold value of false alarm is calculated to use parameters obtained by small scale fire-experiment. The experiment is conducted to measure detector activation time and flame spread of wood cribs fire, etc. The results show that HRR threshold value of Fixed type detector is 20.24 kW and rate of rise type detector is 13.59 kW, respectively.

• Journal of the Korean Society of Safety
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• v.37 no.2
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• pp.69-75
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• 2022

Fire-alarm systems are safety equipment that facilitate rapid evacuation and early suppression in case of fire. It is highly desirable that fire-alarm systems have low false-alarm rates and are thus reliable. Until now, researchers have attempted to improve detector performance by applying new technologies such as IoT. To this end, IoT-based fire-detection systems have been developed. However, due to scarcity of large-scale operational data, researchers have barely studied malfunctioning in fire-alarm systems or attempted to reduce false-alarm rates in these systems. In this study, we analyzed false-alarm rates of smoke/temperature detectors and unwanted fire-alarm signal patterns at K institution, where Korea's largest IoT-based fire-detection system operates. After analyzing the fire alarm occurrences at the institution for five years, we inferred that the IoT-based fire-detection system showed lower false-alarm rates compared to the automatic fire-detection equipment. We analyzed the detection pattern by dividing it into two parts: normal operation and unwanted fire alarms. When a specific signal pattern was filtered out, the false-alarm rate was reduced to 66.9% in the smoke detector and to 46.9% in the temperature detector.

• Journal of the Korean Society of Safety
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• v.8 no.1
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• pp.6-12
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• 1993

Now it is reported that there are many companies and factories which confront to the problem of false alarm in Automatic Fire Detecting System (AFDS). Though an AFDS is a high-priced and very important device in detecting a fire. the problem degrades the reliability and utility, eventually faith. of the system. This is a research concerned about the problem of false alarm of fire detectors in an AFDS. Structure of detectors was modeled up as a general K out-of n system. and reliability characteristics including false alarm rate and missed alarm rate, are derived from it. For decision of an optimal structure of detectors. preliminary analysis on various criteria is accomplished. It is concluded that appropriate modification of a detector with plural sensors may increase the system reliability and decrease the false alarm rate.

• Convergence Security Journal
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• v.15 no.1
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• pp.27-36
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• 2015

A discussion on false alarm is a series of problems about a waste of police resources. The the false alarm primarily increase machine the cost of security firm but ultimately increase the costs of national and social management. Verified Report System has been in operation since July 1, 2013, We could analyze the actual operation of 112 report on false alarm rate was 82.4% before this system launched, but after implementation of the Verified Report System, this rate level downs below 69.7% records. So 12.7% is declined at the rate of false alarm. However, the actual alarm rate of Electronic Security itself is just only 0.3 % in the total number of cases responding in contrast to Police is considerable. It is more urgent to evolve the Verified Report System, so penalty system against the false alarm, Police registration system of sensors, and strengthening of the task of the company for installation and management of detection equipment are urgently needed.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.719-729
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• 1992

An improved MXTM (maximum trimmed mean) -CFAR (constant false alarm rate) processor is proposed to reduce false alarm rates In detecting radar targets and Its performance character is ticsare analyzed to be compared with those of other CFAR processors. The proposed MXTM-CFAR processor is obtained by combining the GO (greatest of ) -CFAR processor reducing excessive falsealarm rate at riutter edges with the TM-CFAR processor showing good performances In homo-geneous Jnonhornog eneous background. Performance analyses have been done by computing detection probability, constant false alarm rate and detection thresholds under the homogeneous or multiple target environments and at the clutter edges. Analysis results how that the proposed CFAR processor maintains its performance as good as those of,05(order statistics) and TM-CFAR inhomogeneous and multiple target environments and Can reduce the false alarm rate at clutter edges. Overall computing time hfs been also reduced.

• The Korean Journal of Applied Statistics
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• v.33 no.2
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• pp.161-170
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• 2020

The combined ${\bar{X}}-S^2$ chart is a traditional control chart for simultaneously detecting mean and variance. Control limits for the combined ${\bar{X}}-S^2$ chart are determined so that each chart has the same individual false alarm rate while maintaining the required false alarm rate for the combined chart. In this paper, we provide flexibility to allow the two charts to have different individual false alarm rates as well as evaluate the effect of flexibility. The individual false alarm rate of the ${\bar{X}}$ chart is taken to be γ times the individual false alarm rate of the S² chart. To evaluate the effect of selecting the value of γ, we use the out-of-control average run length and relative mean index as the performance measure for the combined ${\bar{X}}-S^2$ chart.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.473-482
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• 2007

Network-based IDS(Intrusion Detection System) gathers network packet data and analyzes them into attack or normal. They raise alarm when possible intrusion happens. But they often output a large amount of low-level of incomplete alert information. Consequently, a large amount of incomplete alert information that can be unmanageable and also be mixed with false alerts can prevent intrusion response systems and security administrator from adequately understanding and analyzing the state of network security, and initiating appropriate response in a timely fashion. So it is important for the security administrator to reduce the redundancy of alerts, integrate and correlate security alerts, construct attack scenarios and present high-level aggregated information. False alarm rate is the ratio between the number of normal connections that are incorrectly misclassified as attacks and the total number of normal connections. In this paper we propose a false alarm classification model to reduce the false alarm rate using classification analysis of data mining techniques. The proposed model can classify the alarms from the intrusion detection systems into false alert or true attack. Our approach is useful to reduce false alerts and to improve the detection rate of network-based intrusion detection systems.

• Convergence Security Journal
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• v.12 no.2
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• pp.61-70
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• 2012

The objective of this study is to achieve the most optimal data mining for effective analysis of false alarm event on mechanical security system. To perform this, this study searches the cause of false alarm and suggests the data conversion and analysis methods to apply to several algorithm of WEKA, which is a data mining program, based on statistical data for the number of case on movement by false alarm, false alarm rate and cause of false alarm. Analysis methods are used to estimate false alarm and set more effective reaction for false alarm by applying several algorithm. To use the suitable data for effective analysis of false alarm event on mechanical security analysis this study uses Decision Tree, Naive Bayes, BayesNet Apriori and J48Tree algorithm, and applies the algorithm by deducting the highest value.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.4
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• pp.479-485
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• 2011

A video flame detection method analyze the temporal and spatial characteristics of the regions which have the flame-like color and moving objects in the input video. The video flame detector should be able to reduce a false alarm rate without the degradation of flame detection capability. The conventional methods can reject the false alarm caused by the car lights and some electric lights. However they make the false alarm caused by the warning lights, neon sign, and some periodic flickering lights which have the flame-like color and temporal features. This paper propose the video flame detection method with periodicity analysis based false alarm rejection. The proposed method can detect the periodicity of the flickering electric lights and can reject the false alarm caused by the periodic electric lights. The computer simulation showed that the proposed method did not make the false alarm in the test video with the periodic electric lights. But the conventional methods made a false alarm in the same test video.