• 한국안전학회지
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• 제37권3호
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• pp.16-23
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• 2022

This study aims to provide a fundamental material for identifying fire and no-fire signals using the detection signal characteristics of IoT-based fire-detection systems. Unlike analog automatic fire-detection equipment, IoT-based fire-detection systems employ wireless digital communication and are connected to a server. If a detection signal exceeds a threshold value, the measured values are saved to a server within seconds. This study was conducted with the detection data saved from seven fire accidents that took place in traditional markets from 2020 to 2021, in addition to 233 fire alarm data that have been saved in the K institute from 2016 to 2020. The saved values demonstrated variable and continuous VC-Signals. Additionally, we discovered that the detection signals of two fire accidents in the K institution had a VC-Signal. In the 233 fire alarms that took place over the span of 5 years, 31% of smoke alarms and 30% of temperature alarms demonstrated a VC-Signal. Therefore, if we selectively recognize VC-Signals as fire signals, we can reduce about 70% of false alarms.

• 한국안전학회지
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• 제37권5호
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• pp.14-21
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• 2022

The purpose of this study was to apply the signal pattern recognition technique to the digital wireless fire-alarm system and to reduce unwanted fire alarms. In this study, the fire alarms of the K Institute, which operates the largest digital wireless fire-alarm system in Korea, were classified into normal operations and unwanted fire alarms, and these were analyzed and compared with actual fire signals. In addition, by designing a non-fire signal filter and applying it to the K Institute, we confirmed that the monthly unwanted fire alarm rate of all 5,713 detectors decreased sharply. In particular, the unwanted fire alarm rate for flame decreased from 1.09% to 0.11% and the unwanted fire alarm rate for smoke decreased from 0.65% to 0.035%.

• 한국화재소방학회논문지
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• 제30권5호
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• pp.108-115
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• 2016

화재감지기는 화재를 조기에 감지하여 관계인에 통보함으로써 자산보호 및 인명안전에 중요한 역할을 한다. 따라서 화재를 조기에 탐지할 수 있는 성능과 비화재보를 발생하지 않는 신뢰성이 중요하다. 최근 공동주택 오피스텔 숙박시설 노유자시설 의료시설 등과 같이 취침, 숙박, 입원 등 이와 유사한 용도로 사용되는 거실에는 조기화재 감지를 위한 연기감지기를 설치하도록 국가 화재안전기준이 개정되어 연기감지기의 적용이 증가되고 있다. 반면 현재 비화재보를 예방하기 위한 연구가 부족하다. 본 연구는 아날로그 광전식 연기감지기의 순간적으로 발생되는 먼지 등으로 인해 발생되는 비화재보를 예방하기 위한 알고리즘 개선과 감지기의 자기보상기능을 통해 감지기의 오염과 화재신호를 구분할 수 있도록 개선하였다. 본 연구를 통해 최근 수요가 증가하는 아날로그 광전식 연기감지기의 비화재보 개선으로 제품의 신뢰성을 확보하여 비화재보로 인한 경제적 손실을 예방할 것으로 기대된다.

• 대한원격탐사학회지
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• 제36권4호
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• pp.535-544
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• 2020

In synthetic aperture radar (SAR) based ship detection application, false alarms frequently occur due to various noises caused by the radar imaging process. Among them, radio frequency interference (RFI) and azimuth smearing produce substantial false alarms; the latter also yields longer length estimation of ships than the true length. These two noises are prominent at cross-polarization and relatively weak at co-polarization. However, in general, the cross-polarization data are suitable for ship detection, because the radar backscatter from background sea surface is much less in comparison with the co-polarization backscatter, i.e., higher ship-sea image contrast. In order to improve the ship detection accuracy further, the RFI and azimuth smearing need to be mitigated. In the present letter, Sentinel-1 VV- and VH-polarization intensity data are used to show a novel technique of removing these noises. In this method, median image intensities of noises and background sea surface are calculated to yield arithmetic tendency. A band-math formula is then designed to replace the intensities of noise pixels in VH-polarization with adjusted VV-polarization intensity pixels that are less affected by the noises. To verify the proposed method, the adaptive threshold method (ATM) with a sliding window was used for ship detection, and the results showed that the 74.39% of RFI false alarms are removed and 92.27% false alarms of azimuth smearing are removed.

• Clinics in Shoulder and Elbow
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• 제26권4호
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• pp.416-422
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• 2023

Background: Advancements in airport screening measures in response to 9/11 have resulted in increased false alarm rates for patients with orthopedic and metal implants. With the implementation of millimeter-wave scanning technology, it is important to assess the changes in airport screening experiences of patients who underwent total shoulder arthroplasty (TSA). Methods: Here, 197 patients with prior anatomic and reverse TSA completed between 2013 and 2020 responded to a questionnaire regarding their experiences with airport travel screening after their operation. Of these patients, 86 (44%) stated that they had traveled by plane, while 111 (56%) had not. The questionnaire addressed several measures including the number of domestic and international flights following the operation, number of false alarm screenings by the millimeter-wave scanner, patient body habitus, and presence of additional metal implants. Results: A total of 53 patients (62%) responded "yes" to false screening alarms due to shoulder arthroplasty. The odds of a false screening alarm for patients with other metal implants was 5.87 times that of a false screening alarm for patients with no other metal implants (P<0.1). Of a reported 662 flights, 303 (45.8%) resulted in false screening alarms. Greater body mass index was not significantly lower in patients who experienced false screening alarms (P=0.30). Conclusions: Patients with anatomic and reverse TSA trigger false alarms with millimeter-wave scanners during airport screening at rates consistent with prior reports following 9/11. Patient education on the possibility of false alarms during airport screening is important until improvements in implant identification are made. Level of evidence: IV.

• ETRI Journal
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• 제35권1호
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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.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1995년도 추계학술발표회논문집(1)
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• pp.147-152
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• 1995

During transients or major upsets, operators of a nuclear power plant are faced with a significant amount of information which oftentimes exceeds their capability of processing information in such a time-critical situation. To help resolve this problem of information overload, considerable work is underway worldwide to improve its man-machine interface systems (MMISs). The I&C research team of KAERI is developing a DYNamic Alarm processing System, called DYNAS, to suppress unnecessary or nuisance alarms, and at the same time, emphasize vital information. This paper describes our basic strategy to process alarms in DYNAS.

• 한국지진공학회논문집
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• 제25권2호
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• pp.71-81
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• 2021

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

• ETRI Journal
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• 제32권6호
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• pp.881-890
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• 2010

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE $L^*a^*b^*$ color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state-of-the-art fire detection method.

• 한국군사과학기술학회지
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• 제14권1호
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• pp.100-105
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• 2011

Generally, because received signals from radar are very bulky, the data are divided into manageable size called section, and sections are distributed into several digital signal processors. And then, target detection algorithms are applied simultaneously in each processor. CFAR(Constant False Alarm Rate) algorithm, which is the most popular target detection algorithm, can estimate accurate threshold values to determine which signals are targets or noises within center-cut of section allocated to each processor. However, its estimation precision is diminished in section edge data because of insufficient surrounding data to be referred. Especially this edge problem of CFAR is too serious if we have many sections to be processed, because it causes many false alarms in most every section edges. This paper describes false alarm issues on MCA(Minimum Cell Average)-CFAR, and proposes a false alarm elimination method by changing section size alternatively. Real received data from multi-function radar were used to evaluate a proposed method, and we show that our method drastically decreases false alarms without missing real targets, and improves detection performance.