• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.248-254
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• 2024

Artificial intelligence techniques have improved fire-detection methods; however, false alarms still occur. Conventional methods detect fires using current sensors, which can lead to detection errors due to temporary environmental changes or noise. Thus, fire-detection methods must include a trend analysis of past information. We propose a deep-learning-based fire detection method using multi-sensor data and Kendall's tau. The proposed system used a BiLSTM model to predict fires using pre-processed multi-sensor data and extracted trend information. Kendall's tau indicates the trend of a time-series data as a score; therefore, it is easy to obtain a target pattern. The experimental results showed that the proposed system with trend values recorded an accuracy of 99.93% for BiLSTM and GRU models in a 20-tap moving average filter and 40% fire threshold. Thus, the proposed trend approach is more accurate than that of conventional approaches.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.125-137
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• 2009

Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems used in service. For reliable fault monitoring related to the crack growth, it is important to identify the dynamical characteristics as well as transient crack-related signals. Widely used methods which are based on physical phenomena of the three damage stages for detecting the crack growth have a problem that crack-related acoustic emission activities overlap in time, therefore it is insufficient to estimate the exact crack growth time. The proposed pattern recognition method uses the dynamical characteristics of acoustic emission as inputs for minimizing false alarms and miss alarms and performs the temporal clustering to estimate the crack growth time accurately. Experimental results show that the proposed method is effective for practical use because of its robustness to changes of acoustic emission caused by changes of pressure levels.

• Journal of Korean Society of Transportation
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• v.22 no.6
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• pp.77-88
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• 2004

This study aims at developing the model that is able to detect the compression wave, which is included as a similar situation in incidents, that causes false applicable to the similar character such as incidents in the incident detection model for expressways. In this study, it has been checked whether the number of false alarms is decreased or not by modularizing this model for being able to applicable to other models such as DES and DELOS, etc. which do not perform the compression wave test based on the compression wave test process of APID model which has been being used in the expressway traffic management system currently. The evaluation in this study focuses on the sensitivity of the model and the results analysis is performed classified by each polling cycle. And how well these models are working is evaluated by each polling cycle. In addition to this, the detection rate, the false alarm rate and the average detection time in both the existing models and the model in this study are calcuated. As a result of appling the model in this study, it is found that the false alarm rate is improved through the reasonable decrease in the number of false alarm frequencies and there are not remarkable changes concerning the detection rate and the average detection time. To sum up, it is expected that a good number of improvement effects will be occurred when this model is applied to the actual expressway traffic management system.

• Journal of Internet Computing and Services
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• v.22 no.4
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• pp.83-98
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• 2021

The purpose of this study to find the factors influencing the fire alarms using IoT firefighting facility management system data of Seoul Fire & Disaster Headquarters, and to present academic implications for establishing an effective prevention system of fire situation. As the number of high and complex buildings increases and former bulidings are advanced, the fire detection facilities that can quickly respond to emergency situations are also increasing. However, if the accuracy of the fire situation is incorrectly detected and the accuracy is lowered, the inconvenience of the residents increases and the reliability decreases. Therefore, it is necessary to improve accuracy of the system through efficient inspection and the internal environment investigation of buildings. The purpose of this study is to find out that false detection may occur due to building characteristics such as usage or time, and to aim of emphasizing the need for efficient system inspection and controlling the internal environment. As a result, it is found that the size(total area) of the building had the greatest effect on the fire alarms, and the fire alarms increased as private buildings, R-type receivers, and a large number of failure or shutoff days. In addition, factors that influencing fire alarms were different depending on the main usage of the building. In terms of time, it was found to follow people's daily patterns during weekdays(9 am to 6 pm), and each peaked around 10 am and 2 pm. This study was claimed that it is necessary to investigate the building environment that caused the fire alarms, along with the system internal inspection. Also, it propose additional recording of building environment data in real-time for follow-up research and system enhancement.

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.57-65
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• 2007

In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Fan Ye et al. proposed that statistical en-route filtering scheme(SEF) can do verify the false report during the forwarding process. In this scheme, the choice of a partition value represents a trade off between resilience and energy where the partition value is the total number of partitions which global key pool is divided. If every partition are compromised by an adversary, SEF disables the filtering capability. Also, when an adversary has compromised a very small portion of keys in every partition, the remaining uncompromised keys which take a large portion of the total cannot be used to filter false reports. We propose a fuzzy-based adaptive partitioning method in which a global key pool is adaptively divided into multiple partitions by a fuzzy rule-based system. The fuzzy logic determines a partition value by considering the number of compromised partitions, the energy and density of all nodes. The fuzzy based partition value can conserve energy, while it provides sufficient resilience.

• Atmosphere
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• v.29 no.4
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• pp.501-509
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• 2019

Asian Dust is a meteorological phenomenon that sand particles are raised from the arid and semi-arid regions-Taklamakan Desert, Gobi Desert and Inner Mongolia in China-and transported by westerlies and deposited on the surface. Asian dust results in a negative effect on human health as well as environmental, social and economic aspects. For monitoring of Asian Dust, Korea Meteorological Administration operates 29 stations using a continuous ambient particulate monitor. Kim et al. (2016) developed an automatic quality check (AQC) algorithm for objective and systematic quality check of observed PM₁₀ concentration and evaluated AQC with results of a manual quality check (MQC). The results showed the AQC algorithm could detect abnormal observations efficiently but it also presented a large number of false alarms which result from valid error check. To complement the deficiency of AQC and to develop an AQC system which can be applied in real-time, AQC has been modulated. Based on the analysis of instrument status codes, valid error check process was revised and 6 status codes were further considered as normal. Also, time continuity check and spike check were modified so that posterior data was not referred at inspection time. Two-year observed PM₁₀ concentration data and corresponding MQC results were used to evaluate the modulated AQC compared to the original AQC algorithm. The results showed a false alarm ratio decreased from 0.44 to 0.09 and the accuracy and the probability of detection were conserved well in spite of the exclusion of posterior data at inspection time.

• Journal of the Korea Society of Computer and Information
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• v.25 no.7
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• pp.9-16
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• 2020

In this paper, we propose a prototype design and implementation of a fire detection algorithm using multiple sensors. The proposed topic detection system determines fire by applying rules based on data from multiple sensors. The fire takes about 3 to 5 minutes, which is the optimal time for fire detection. This means that timely identification of potential fires is important for fire management. However, current fire detection devices are very vulnerable to false alarms because they rely on a single sensor to detect smoke or heat. Recently, with the development of IoT technology, it is possible to integrate multiple sensors into a fire detector. In addition, the fire detector has been developed with a smart technology that can communicate with other objects and perform programmed tasks. The prototype was produced with a success rate of 90% and a false alarm rate of 10% based on 10 actual experiments.

• Journal of Korean Tunnelling and Underground Space Association
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• v.17 no.4
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• pp.441-455
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• 2015

In the present study, a face safety monitoring system was developed that will enable judging collapse risks on faces during tunnel construction to secure workers' safety. This system enables detecting abnormal behaviors of faces by analyzing the displacement of faces measured in real time using the x-MR control chart technique. In addition, an algorithm to judge false alarms was developed so that abnormal behaviors of faces and errors occurring in the process of work can be distinguished from each other by comparing the number of measured values exceeding the management criteria and moving range k. The results of the present study are applicable to real-time monitoring of behavior on the face in dangerous ground sections to minimize damage to workers.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.7
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• pp.50-61
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• 1999

\In this paper, we consider the optimal scheduling of detection and tracking parameters in phased array radars to minimize the radar energy required for track maintenance in a cluttered environment. We develop a mathematical model of target detection induced by a search process in phased array radars. In the mathematical development, we take into account the effect of unwanted measurements that may have originated from clutter or false alarms in the detection process. We use and analytic approximation of the modified Riccati equation of the probabilistic data association (PDA) filter to take into account the effect of clutter interference in tracking. Based on the search process and the tracking models, we formulate the optimal scheduling problem into a nonlinear optimal control problem. We solve a constrained nonlinear optimization problem to obtain the solution of the optimal control problem.

• The KIPS Transactions:PartC
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• v.8C no.6
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• pp.741-756
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• 2001

With the development of computer systems, there has been a sharp increase in the threats on these systems including attacks by malicious programs such as virus, vandal, etc. Currently virus vaccines are widely used to thwart these threats, but they have many weaknesses. They cannot guard against unknown threats and sometimes, they also cannot detect the existence of malicious programs before these malicious programs make any destructive results. For lack of an efficient security model, the existing security programs have the problem that they raise many false-positive alarms in spite of normal action. So it becomes very important to develop the improved security program that can make up for the weakness of the existing computer security program and can detect many threats of malicious programs as early as possible. In this paper we describe the design of an improved security model and the implementation of a security program that can filter and handle the threats on computer systems at the kernel level in real time.