• ETRI Journal
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• 제37권3호
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• pp.502-511
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• 2015

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.

• 반도체디스플레이기술학회지
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• 제19권2호
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• pp.31-36
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• 2020

Most of the background subtraction algorithms show good performance in static scenes. In the case of dynamic scenes, they frequently cause false alarm to "temporal clutter", a repetitive motion within a certain area. In this paper, we propose a robust technique for the multiple interval pixel sampling (MIS) algorithm to handle highly dynamic scenes. An adaptive threshold scheme is used to suppress false alarms in low-confidence regions. We also utilize multiple background models in the foreground segmentation process to handle repetitive background movements. Experimental results revealed that our approach works well in handling various temporal clutters.

• ETRI Journal
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• 제43권1호
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• pp.31-39
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• 2021

One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제2권2호
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• pp.95-99
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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 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 clime. Half Period amplitude. The result shored that the neural network would be applied to LPMS. Also, applying the neural network to thin practical false alarm data during startup and impact test signal at nuclear power plant, the false alarms are reduced effectively.

• 한국통신학회:학술대회논문집
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• 한국통신학회 1991년도 추계종합학술발표회논문집
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• pp.92-95
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• 1991

Constant false alarm rate(CFAR) processors are useful for detecting radar targets in background for which all parameters in the statistical distribution are not known and may be nonstationary. The well known "cell averging" (CA) CFAR processor is known to yield best performance in homogeneous case, but exhibits severe performance in the presence of an interfering target in the reference window or/and in the region of clutter edges. The "order statistics"(OS) CFAR processor is known to have a good performance above two nonhomogeneous cases. The modified OS-CFAR processor, known as "trimmed mean"(TM) CFAR processor performs somewhat better than the OS-CFAR processor by judiciously trimming the ordered samples. This paper proposes and analyzes the performance of a new CFAR processor called the "maximum trimmed mean"(MX-TM) CFAR processor combining the "greatest of"(GO) CFAR and TM-CFAR processors. The MAX operation is included to control false alarms at clutter edges. Our analyses show that the proposed CFAR processor has similar performance TM- and OS-CFAR processors in homogeneous case and in the precence of interfering targets, but can control the false rate in clutter edges. Simulation results are presented to demonstrate the qualitative effects of various CFAR processors in nonhomogeneous clutter environments.

• 대한임베디드공학회논문지
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• 제13권6호
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2576-2581
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• 2003

We consider fault detection and isolation (FDI) problem for inertial navigation systems (INS) which use redundant inertial sensors and propose an FDI method using average of multiple parity vectors which reduce false alarm and wrong isolation, and improve correct isolation. We suggest optimal isolation threshold based on navigation performance, and suggest optimal sample number to obtain short detection time and to enhance detectability of faults little larger than threshold.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(1)
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• pp.189-192
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• 2000

In this paper, we consider a code acquisition of W-CDMA signals over multipath Rayleigh fading channel when double-dwell serial search code acquisition is used for initial synchronization. We derive the detection and false alarm probability, and mean acquisition time mathematically by taking into account of multiple H$\_$l/ cells and double-dwell serial search. It is noteworthy that the more the number of the post-detection integration, the shorter the mean acquisition time in low SNR.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2001년도 제14회 신호처리 합동 학술대회 논문집
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• pp.635-638
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• 2001

본 논문에서는 수중 환경에서 분산 소나 시스템의 최적 정보 융합에 관한 알고리즘을 제시하였다. 기존의 방법은 Bayesian 법칙을 이용하여 local 소나와 퓨전 센터의 문턱치를 적절히 조절하여 분산 소나 시스템을 최적화했다. 그러나, 이러한 최적화 과정에서 소나의 개수를 늘려감에 따라 P/sub F/(false alarm probability)가 단조 증가하는 현상이 발생하였고 이러한 단점을 보완하기 위해 P/sub F/를 작은 간에 제한시키고 Bayesian 법칙과 Neyman-Pearson 법칙을 함께 적용하여 분산 소나 시스템을 최적화시킨다. 그러나, 이러한 조건 하에 시스템을 최적화시키는 것은 N-P hard 문제에 의해 계산 부하가 매우 크므로 unate 함수와 SQP(Sequential Quadratic Programming)을 이용하여 계산 부하를 감소시켰다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 제13회 신호처리 합동 학술대회 논문집
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• pp.233-236
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• 2000

In this paper, we consider a code acquisition of W-CDMA signals over multipath Rayleigh fading channel when double-dwell serial search code acquisition is used for initial synchronization. We derive the detection and false alarm probability, and mean acquisition time mathematically by taking into account of multiple H$_1$ cells and double-dwell serial search. It is noteworthy that the more the number of the post-detection integration, the shorter the mean acquisition time in low SNR.