• Title/Summary/Keyword: false alarm

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Automatic False-Alarm Labeling for Sensor Data

  • Adi, Taufik Nur;Bae, Hyerim;Wahid, Nur Ahmad
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.2
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    • pp.139-147
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    • 2019
  • A false alarm, which is an incorrect report of an emergency, could trigger an unnecessary action. The predictive maintenance framework developed in our previous work has a feature whereby a machine alarm is triggered based on sensor data evaluation. The sensor data evaluator performs three essential evaluation steps. First, it evaluates each sensor data value based on its threshold (lower and upper bound) and labels the data value as "alarm" when the threshold is exceeded. Second, it calculates the duration of the occurrence of the alarm. Finally, in the third step, a domain expert is required to assess the results from the previous two steps and to determine, thereby, whether the alarm is true or false. There are drawbacks of the current evaluation method. It suffers from a high false-alarm ratio, and moreover, given the vast amount of sensor data to be assessed by the domain expert, the process of evaluation is prolonged and inefficient. In this paper, we propose a method for automatic false-alarm labeling that mimics how the domain expert determines false alarms. The domain expert determines false alarms by evaluating two critical factors, specifically the duration of alarm occurrence and identification of anomalies before or while the alarm occurs. In our proposed method, Hierarchical Temporal Memory (HTM) is utilized to detect anomalies. It is an unsupervised approach that is suitable to our main data characteristic, which is the lack of an example of the normal form of sensor data. The result shows that the technique is effective for automatic labeling of false alarms in sensor data.

Analysis of Unwanted Fire Alarm Signal Pattern of Smoke / Temperature Detector in the IoT-Based Fire Detection System (IoT 기반 화재탐지시스템의 연기 및 온도감지기 비화재보 신호 패턴 분석)

  • Park, Seunghwan;Kim, Doo-Hyun;Kim, Sung-Chul
    • 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.

A Study of Data Mining Methodology for Effective Analysis of False Alarm Event on Mechanical Security System (기계경비시스템 오경보 이벤트 분석을 위한 데이터마이닝 기법 연구)

  • Kim, Jong-Min;Choi, Kyong-Ho;Lee, Dong-Hwi
    • 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.

Video Flame Detection with Periodicity Analysis Based False Alarm Rejection (주기 신호 검출을 통한 거짓 경보 제거 기능을 갖춘 비디오 화염 감지 기법)

  • Lee, Sang-Hak
    • 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.

A Design of false alarm analysis framework of intrusion detection system by using incremental mining method (점진적 마이닝 기법을 적용한 침입탐지 시스템의 오 경보 분석 프레임워크 설계)

  • Kim Eun-Hee;Ryu Keun-Ho
    • The KIPS Transactions:PartC
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    • v.13C no.3 s.106
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    • pp.295-302
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    • 2006
  • An intrusion detection system writes a lot of alarms against attack behaviors in real time. These alarms contain not only actual attack alarms, but also false alarms that are mistakes made by the intrusion detection system. False alarms are the main reason that reduces the efficiency of the intrusion detection system, and we propose framework for false alarms analysis in the paper. Also, we apply an incremental data mining method for pattern analysis of false alarms increasing continuously. The framework consists of GUI, DB Manager, Alert Preprocessor, and False Alarm Analyzer. We analyze the false alarms increasingly through the experiment of the proposed framework and show that false alarms are reduced by applying the analyzed false alarm rules in the intrusion detection system.

A Study on the False Alarm Management of Alarm Monitoring Service (기계경비의 오경보 관리방안)

  • Chung, Tae-Hwang
    • Journal of the Society of Disaster Information
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    • v.8 no.1
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    • pp.93-99
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    • 2012
  • This study is to present false alarm management of alarm monitoring service, by literature analysis and interview with the persons in charge of service of alarm monitoring service companies. To reduce false alarm by customer's mistake, development of educational tool that could provide practical effect is needed. Also institutional consent to charge a specific sum of money could be considered for unnecessary dispatch that occurred by customer's mistake. To improve the false alarm by defect of equipments and installation of electronic security system, standardization of technical regulation and system installation is necessary. And to improve the performance of passive infrared sensor that cause most false alarm, the development of new type of sensor is required. It could be considered that guideline on educational contents and term definition about false alarm could be come under the security regulation.

Discrimination model using denoising autoencoder-based majority vote classification for reducing false alarm rate

  • Heonyong Lee;Kyungtak Yu;Shiu Kim
    • 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.

Reduction of False Alarm Signals for PIR Sensor in Realistic Outdoor Surveillance

  • Hong, Sang Gi;Kim, Nae Soo;Kim, Whan Woo
    • ETRI Journal
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    • v.35 no.1
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    • pp.80-88
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    • 2013
  • A passive infrared or pyroelectric infrared (PIR) sensor is mainly used to sense the existence of moving objects in an indoor environment. However, in an outdoor environment, there are often outbreaks of false alarms from environmental changes and other sources. Therefore, it is difficult to provide reliable detection outdoors. In this paper, two algorithms are proposed to reduce false alarms and provide trustworthy quality to surveillance systems. We gather PIR signals outdoors, analyze the collected data, and extract the target features defined as window energy and alarm duration. Using these features, we model target and false alarms, from which we propose two target decision algorithms: window energy detection and alarm duration detection. Simulation results using real PIR signals show the performance of the proposed algorithms.

An Analysis of False Alarm Threshold Value by Heat Detector Using Heat Release Rate (열방출률을 이용한 열감지기의 오동작 경계값 분석)

  • 홍성호;김두현
    • 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.

Structural Reliability Analysis of Automatic Fire Detectors for Reduction of False Alarms (화재감지기의 오보 감소를 위한 구조 신뢰도 분석)

  • 김주홍;임현교
    • 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.

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