• Journal of the Korea Institute of Construction Safety
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• v.2 no.1
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• pp.36-44
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• 2019

$21^{st}$ century is digitally civilized era. Technologies such as AI, Iot, Big Data, Mobile and etc makes this era digitally advanced. These advancement of the technology greatly impacted detection range of the radar. Human's eye sight can see about 20Km and hear 20 ~ 20000 Hz. These limitations can be overcome using radar. This radar technology is used in military, aircraft, ship, vehicle and etc. to replace human eye. However, radar technology is capable of making False Alarm Rate. This document will propose the fix of these problems. Radar's distortion includes beam refraction, diffraction and reflection. These inaccurate data result in deterioration of human judgements and my cause various casualties and damages. Radar goes through annual testing to test how many false alarm is being produced. Normal radar usually makes 10 to 20 False alarms. In emergency situation, if operator were to follow this false alarm, this might result in following false object or take 12 more seconds to follow the right object. This problem can be overcome by using different radar data from different places and angles. This helps reduces False Alarm rate and track the object twice as fast.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.227-230
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• 2002

The Loose Part Monitoring System(LPMS) has been designed to detect, locate and evaluate detached or loosened parts and foreign objects in the reactor coolant system. In this paper, at first, we presents an application of the back propagation neural network. At the preprocessing step, the moving window average filter is adopted to reject the low frequency background noise components. And then, extracting the acoustic signature such as Starting point of impact signal, Rising time, Half period, and Global time, they are used as the inputs to neural network. Secondly, we applied the neural network algorithm to LPMS in order to estimate the mass of loose parts. We trained the impact test data of YGN3 using the backpropagation method. The input parameter for training is Rising Time, Half Period, Maximum amplitude. The result showed that the neural network would be applied to LPMS. Also, applying the neural network to the Practical false alarm data during startup and impact test signal at nuclear power Plant, the false alarms are reduced effectively. 1.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.3
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• pp.135-144
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• 2003

An intrusion detection system shows different result over overall detection area according to its detection characteristics of inner detection algorithms or techniques. To expand detection areas, we requires an integrated detection which can be archived both by deploying a few detection systems which detect different detection areas and by combining their results. In addition to expand detection areas, we need to decrease the workload of security managers by false alarms and improve the correctness by minimizing false alerts which happen during the process of integration. In this paper, a method for aggregation detection use fuzzy inference to integrate a vague detection results which imply the characteristics of detection systems. Their analyzed detection characteristics are expressed as fuzzy membership functions and fuzzy rule bases which are applied through the process of fuzzy control. And, it integrate a vague decision results and minimize the number of false alerts by reflecting the characteristics of detection systems. Also it does minimize inference objects by applying thresholds decided through several experiments.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.4
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• pp.41-49
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• 2009

In ubiquitous sensor networks, sensor nodes can be compromised by an adversary since they are deployed in hostile environments. False sensing reports can be injected into the network through these compromised nodes, which may cause not only false alarms but also the depletion of limited energy resource in the network. In the security solutions for the filtering of false reports, the choice of a security threshold value which determines the security level is important. In the existing adaptive solutions, a newly determined threshold value is broadcasted to the whole nodes, so that extra energy resource may be consumed unnecessarily. In this paper, we propose an application of the recursive contract net protocol to determine the threshold value which can provide both energy efficiency and sufficient security level. To manage the network more efficiently, the network is hierarchically grouped, and the contract net protocol is applied to each group. Through the protocol, the threshold value determined by the base station using a fuzzy logic is applied only where the security attack occurs on.

• Journal of the Korea Society for Simulation
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• v.19 no.1
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• pp.161-171
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• 2010

Sensor Networks are often deployed in unattended environments, thus leaving these networks vulnerable to false data injection attacks in which an adversary injects forged reports into the network through compromised nodes. Such attacks by compromised sensors can cause not only false alarms but also the depletion of the finite amount of energy in a battery powered network. Ye et al. proposed the Statistical En-route Filtering scheme to overcome this threat. In statistical en-route filtering scheme, all the intermediate nodes perform verification as event reports created by center of stimulus node are forwarded to the base station. This paper applies a probabilistic verification method to the Static Statistical En-route Filtering for energy efficiency. It is expected that the farther from the base station an event source is, the higher energy efficiency is achieved.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.101-111
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• 2023

Many spoofing detection studies have been conducted to cope with the most difficult types of deception among various disturbances of GPS, such as jamming, spoofing, and meaconing. In this paper, we propose a spoofing detection scheme based on elevation masked-relative received power between GPS L1 and L2 signals in a system using a multi-band array antenna. The proposed scheme focuses on enabling spoofing to be normally detected and minimizes the possibility of false detection in an environment where false alarms may occur due to pattern distortion among elements of an array antenna. The pattern distortion weakens the GPS signal strength at low elevation. It becomes confusing to detect a spoofing signal based on the relative power difference between GPS L1 and L2, especially when GPS L2 has weak signal strength. We propose design parameters for the relative power threshold including beamforming gain, the minimum received power difference between L1 and L2, and the patch antenna gain difference between L1 and L2. In addition, in order to eliminate the weak signal strength of GPS L2 in the spoofing detection process, we propose a rotation matrix that sets the elevation mask based on platform coordinates. Array antennas generally do not have high usefulness in commercial areas where receivers are operated alone, but are considered essential in military areas where GPS receivers are used together with signal processing for beamforming in the direction of GPS satellites. Through laboratory and live sky tests using the device under test, the proposed scheme with an elevation mask detects spoofing signals well and reduces the probability of false detection relative to that without the elevation mask.

• Journal of the Korea Society for Simulation
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• v.16 no.2
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• pp.27-35
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• 2007

When sensor networks are deployed in open environments, all the sensor nodes are vulnerable to physical threat. An attacker can physically capture a sensor node and obtain the security information including the keys used for data authentication. An attacker can easily inject false reports into the sensor network through the compromised node. False report can lead to not only false alarms but also the depletion of limited energy resource in battery powered sensor networks. To overcome this threat, Fan Ye et al. proposed that statistical on-route filtering scheme(SEF) can do verify the false report during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power and energy, where security threshold value is the number of message authentication code for verification of false report. In this paper, we propose a fuzzy rule-based system for security threshold determination that can conserve energy, while it provides sufficient detection power in the SEF based sensor networks. The fuzzy logic determines a security threshold by considering the probability of a node having non-compromised keys, the number of compromised partitions, and the remaining energy of nodes. The fuzzy based threshold value can conserve energy, while it provides sufficient detection power.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1A
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• pp.41-48
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• 2011

This paper proposes an efficient code acquisition scheme for direct-sequence spread spectrum (DSSS) based real-time locating systems (RTLS). The proposed scheme employs multiple bit integration to significantly decrease false alarms when no packet signal is transmitted and increase the detection probability when the RTLS tags are excited. The performance of the proposed code acquisition scheme is analyzed and numerically evaluated, and compared with those of single-dwell and traditional double-dwell code acquisition schemes. The simulation results show that the proposed multiple-bit integration scheme outperforms single-dwell and double-dwell schemes.

• Journal of information and communication convergence engineering
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• v.14 no.1
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• pp.1-8
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• 2016

Spectrum sensing in cognitive radio networks allows secondary users to sense the unused spectrum without causing interference to primary users. Cognitive radio requires more accurate sensing results from unused portions of the spectrum. Accurate spectrum sensing techniques can reduce the probability of false alarms and misdetection. In this paper, a two-stage spectrum sensing scheme is proposed for cooperative spectrum sensing in cognitive radio networks. In the first stage, spectrum sensing is executed for each secondary user using energy detection based on double adaptive thresholds to determine the spectrum condition. If the energy value lies between two thresholds, a fuzzy logic scheme is applied to determine the channel conditions more accurately. In the second stage, a fusion center combines the results of each secondary user and uses a fuzzy logic scheme for combining all decisions. The simulation results show that the proposed scheme provides increased sensing accuracy by about 20% in some cases.

• Journal of the Korean Data and Information Science Society
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• v.14 no.2
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• pp.289-301
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• 2003

In this paper, we propose Shewhart control chart and EWMA control chart using the autocorrelated data which are common in chemical and process industries and lead to increase the number of false alarms when conventional control charts are applied. The effect of autocorrelated data is modeled as a autoregressive process, and canonical analysis is used to reduce the dimensionality of the data set and find the canonical variables that explain as much of the data variation as possible. Charting statistics are constructed based on the residual vectors from the canonical variables which are uncorrelated over time, and the control charts for these statistics can attenuate the autocorrelation in the process data. The charting procedures are illustrated with a numerical example and simulation is conducted to investigate the performances of the proposed control charts.