• Journal of Sensor Science and Technology
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• v.33 no.5
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• pp.298-304
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• 2024

In Korea, fires are the second most common type of disaster, causing large-scale damages. The installation of fire detectors is legislated to prevent fires and minimize damage. Conventional fire detectors have limitations in initial suppression of failures because they detect fires when large amounts of smoke and heat are generated. Additionally, frequent malfunctions in fire detectors may cause users to turn them off. To address these issues, recent studies focus on accurately detecting even small-scale fires using multi-sensor and deep-learning technologies. They also aim at quick fire detection and thermal decomposition using gas. However, these studies are not practical because they overlook the heavy computations involved. Therefore, we propose a fast and accurate fire detection system based on multi-sensor and deep-learning technologies. In addition, we propose a computation-reduction method for selecting sensors suitable for detection using the Pearson correlation coefficient. Specifically, we use a moving average to handle outliers and two-stage labeling to reduce false detections during preprocessing. Subsequently, a deep-learning model is selected as LSTM for analyzing the temporal sequence. Then, we analyze the data using a correlation analysis. Consequently, the model using a small data group with low correlation achieves an accuracy of 99.88% and a false detection rate of 0.12%.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.874-882
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• 2003

The condition assessment of engineering systems has increased in importance because the manpower needed to operate and supervise various plants has been reduced. Especially, induction motors are at the core of most engineering processes, and there is an indispensable need to monitor their health and performance. So detection and diagnosis of motor faults is a base to improve efficiency of the industrial plant. In this paper, a model-based fault detection system is developed for induction motors, using steady state vibration signals. Early various fault detection systems using vibration signals are a trivial method and those methods are prone to have missed fault or false alarms. The suggested motor fault detection system was developed using a model-based reference value. The stationary signal had been extracted from the non-stationary signal using a data segmentation method. The signal processing method applied in this research is FFT. A reference model with spectra signal is developed and then the residuals of the vibration signal are generated. The ratio of RMS values of vibration residuals is proposed as a fault indicator for detecting faults. The developed fault detection system is tested on 800 hp motor and it is shown to be effective for detecting faults in the air-gap eccentricities and broken rotor bars. The suggested system is shown to be effective for reducing missed faults and false alarms. Moreover, the suggested system has advantages in the automation of fault detection algorithms in a random signal system, and the reference model is not complicated.

• Korean Journal of Remote Sensing
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• v.35 no.4
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• pp.545-560
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• 2019

Unlike other critical forest diseases, pine pitch canker in Korea has shown rather mild symptoms of partial loss of crown foliage and leaf discoloration. This study used high-resolution satellite images to detect and monitor canopy decline by pine pitch canker. To enhance the subtle change of canopy reflectance in pitch canker damaged tree crowns, multi-temporal analysis was applied to two KOMPSAT multispectral images obtained in 2011 and 2015. To assure the spectral consistency between the two images, radiometric corrections of atmospheric and shadow effects were applied prior to multi-temporal analysis. The normalized difference vegetation index (NDVI) of each image and the NDVI difference (${\Delta}NDVI=NDVI_{2015}-NDVI_{2011}$) between two images were derived. All negative ΔNDVI values were initially considered any pine stands, including both pitch canker damaged trees and other trees, that showed the decrease of crown foliage from 2011 to 2015. Next, $NDVI_{2015}$ was used to exclude the canopy decline unrelated to the pitch canker damage. Field survey data were used to find the spectral characteristics of the damaged canopy and to evaluate the detection accuracy from further analysis.Although the detection accuracy as assessed by limited number of field survey on 21 sites was 71%, there were also many false alarms that were spectrally very similar to the damaged canopy. The false alarms were mostly found at the mixed stands of pine and young deciduous trees, which might invade these sites after the pine canopy had already opened by any crown damages. Using both ${\Delta}NDVI$ and $NDVI_{2015}$ could be an effective way to narrow down the potential area of the pitch canker damage in Korea.

• Journal of the Korean Society of Safety
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• v.34 no.1
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• pp.82-89
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• 2019

The importance of aviation safety has been emphasized recently due to the development of aviation industry. Despite the efforts of each country and the improvement of screening equipment, screening tasks are still difficult and detection failures are frequent. The purpose of this study was to examine the effect of feedback on improving signal detection performance applying a Simulated Baggage Screening Program(SBSP) for improving aviation safety. SBSP consists of three parts: image combination, option setting and experiment. The experimental images were color-coded to reflect the items' transmittance of the x-rays and could be combined as researchers' need. In the option, the researcher could set up the information, incentive, and comments needed for training to be delivered on a number of tasks and times. Experiment was conducted using SBSP and participant's performance information (hit, missed, false alarms, correct rejection, reaction time, etc.) was automatically calculated and stored. A total of 50 participants participated and each participant was randomly assigned to feedback and non-feedback group. Participants performed a total of 200 tasks and 20(10%) contained target object(gun and knife). The results showed that when the feedback was provided, the hit, correct rejection ratio and d′ were increased, however, the false alarms and miss decreased. However, there was no significant difference in response criteria(${\beta}$). In addition, implications, limitations of this study and future research were discussed.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.2
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• pp.385-395
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• 2018

With the development of Internet, cyber attack has become a major threat. To detect cyber attacks, intrusion detection system(IDS) has been widely deployed. But IDS has a critical weakness which is that it generates a large number of false alarms. One of the promising techniques that reduce the false alarms in real time is machine learning. However, there are problems that must be solved to use machine learning. So, many machine learning approaches have been applied to this field. But so far, researchers have not focused on features. Despite the features of IDS alerts are important for performance of model, the approach to feature is ignored. In this paper, we propose new feature set which can improve the performance of model and can be extracted from a single alarm. New features are motivated from security analyst's know-how. We trained and tested the proposed model applied new feature set with real IDS alerts. Experimental results indicate the proposed model can achieve better accuracy and false positive rate than SVM model with ordinary features.

• Korean Journal of Remote Sensing
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• v.36 no.2_2
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• pp.365-377
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• 2020

Continuous monitoring and immediate response is essential to protect the national maritime territory and maritime resources from the activities of illegal ships. Synthetic Aperture Radar (SAR) images with a wide range of images are effective for maritime surveillance asthe weather and day-night conditions rarely affect to image acquisition. However, an effective ship detection is not easy due to the huge data size of SAR images and various characteristics such as the speckle noise. In this study, the Human Visual Attention System (HVAS) algorithm was applied to KOMPSAT-5 to extract the initial targets, and the SAR-Split algorithm depending on the imaging modes was used to remove false alarms. The detected targets were finally selected by the Constant False Alarm Rate (CFAR) algorithm and matched with the ship's Automatic Identification System (AIS) information. Overall, the detected targets were well matched with AIS data, but some false alarms by ship wakes were observed. The detection rate was about 80% in ES mode and about 64% in ST mode. It is expected that the developed ship detection algorithm will contribute to the construction of a wide area maritime surveillance network.

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.133-138
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• 2009

When sensor networks are deployed in open environments, an adversary may compromise some sensor nodes and use them to inject false sensing reports. False report attack can lead to not only false alarms but also the depletion of limited energy resources in battery powered networks. The Interleaved hop-by-hop authentication (IHA) scheme detects such false reports through interleaved authentication. In IHA, when a report is forwarded to the base station, all nodes on the path must spend energies on receiving, authenticating, and transmitting it. An dversary can spend energies in nodes by using the methods as a relaying attack which uses macro. The Adversary aim to drain the finite amount of energies in sensor nodes without sending false reports to BS, the result paralyzing sensor network. In this paper, we propose a countermeasure using fuzzy logic from the Denial of Service(DoS) attack and show an efficiency of energy through the simulataion result.

• Journal of Korean Biological Nursing Science
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• v.20 no.4
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• pp.228-235
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• 2018

Purpose: This study aimed to investigate the clinical alarm occurrence and management of nurses toward clinical alarms in the intensive care unit (ICU). Methods: This observational study was conducted with 40 patients and nurses cases in two ICUs of a university hospital. This study divided 24 hours into the unit of an hour and conducted two times of direct observation per unit hour for 48 hours targeting the medical devices applied to 40 patients. Data were analyzed using IBM SPSS Statistics 23. Results: On average, 3.8 units of medical devices were applied for each patient and the ranges of alarm settings were wide. During 48 hours, 184 cases of clinical alarm were occurred by four types of medical devices including physiological monitors, mechanical ventilators, infusion pumps, and continuous renal replacement therapy. Among them, false alarm was 110 cases (59.8%). As for the alarm management by ICU nurses, two-minute alarm mute took up most at 38.0% (70 cases), and no response was second most at 32.6% (60 cases). When valid alarm sounded, nurses showed no response at 43.2%. Conclusion: The findings suggest that a standard protocol for alarm management should be developed for Korean ICU settings. Based on the protocol, continuous training and education should be provided to nurses for appropriate alarm management.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.53-61
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• 2010

In wireless sensor networks(WSNs) individual sensor nodes are subject to security compromises. An adversary can physically capture sensor nodes and obtain the security information. And the adversary injects false reports into the network using compromised nodes. If undetected, these false reports are forwarded to the base station. False reports injection attacks can not only result in false alarms but also depletion of the limited amount of energy in battery powered sensor nodes. To combat these false reports injection attacks, several filtering schemes have been proposed. The statistical en-routing filtering(SEF) scheme can detect and drop false reports during the forwarding process. In SEF, The number of the message authentication codes(threshold) is important for detecting false reports and saving energy. In this paper, we propose a dynamic threshold determination method for energy efficient SEF using fuzzy-logic in wireless sensor networks. The proposed method consider false reports rate and the number of compromised partitions. If low rate of false reports in the networks, the threshold should low. If high rate of false reports in networks, the threshold should high. We evaluated the proposed method’s performance via simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.8C
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• pp.529-535
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• 2011

After permission for utilization of TV white space by FCC, a lot of attentions are focused on spectrum sensing, and various spectrum sensing methods have been proposed. However, they do not consider real environment, thus they are hard to achieve the required performance. In this paper, we propose resolutions for the problem which could be occurred in implementation of spectrum sensing module and verify performance of the proposed methods with computer simulation. The first proposed method utilizes channel status information to separate received signal and spurious for reducing false alarm probability caused by system internal spurious. The another proposed scheme is subband normalization method to prevent miss detection caused by multiple narrow band signals with different received signal strength. The simulation results verify that we can prevent false alarm cause by spurious components with the proposed system internal spurious cognition. Moreover, the proposed subband normalization method shows that it could overcome performance degradation caused by received signal strength difference.