• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• Nuclear Engineering and Technology
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• v.55 no.10
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• pp.3716-3724
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• 2023

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.7
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• pp.719-729
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• 1992

An improved MXTM (maximum trimmed mean) -CFAR (constant false alarm rate) processor is proposed to reduce false alarm rates In detecting radar targets and Its performance character is ticsare analyzed to be compared with those of other CFAR processors. The proposed MXTM-CFAR processor is obtained by combining the GO (greatest of ) -CFAR processor reducing excessive falsealarm rate at riutter edges with the TM-CFAR processor showing good performances In homo-geneous Jnonhornog eneous background. Performance analyses have been done by computing detection probability, constant false alarm rate and detection thresholds under the homogeneous or multiple target environments and at the clutter edges. Analysis results how that the proposed CFAR processor maintains its performance as good as those of,05(order statistics) and TM-CFAR inhomogeneous and multiple target environments and Can reduce the false alarm rate at clutter edges. Overall computing time hfs been also reduced.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.247-249
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• 2004

IDS에서 발생되는 경보의 수는 최근 인터넷 애플리케이션의 발달로 인하여 급격히 증가하고 있으며. 그로 인해 오 경보의 수도 함께 증가하고 있다. 발생된 경보들은 침입탐지 시스템의 성능저하와 alert flooding 의 원인이 된다. 따라서 이 논문에서는 다량의 경보 중에서 오 경보(False Alarm)의 발생을 감소시킬 수 있는 오 경보 분류 모델을 제안한다. 제안된 오 경보 분류 모델은 데이터 마이닝 기법들 중에서 분류 기법을 기반으로 구현되었다. 실험 을 통해서 IDS에서 발생하는 경보 중에서 정상데이터이나 공격으로 잘못 판단하여 발생하는 False Positive의 발생율이 현저히 감소됨을 확인할 수 있었다. 제안된 오 경보 분류 모델은 경보메시지 축약의 효과가 있으며 침입탐지 시스템의 탐지율을 높이는데 활용될 수 있다.

• Proceedings of the KSR Conference
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• 1999.05a
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• pp.307-314
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• 1999

The potential use of ranging sensors for reducing the occurrence of accidents in real environment is explored by many companies and laboratories. Most of the sensors under investigation utilize the FMCW(Frequency Modulated Continuous Wave) waveforms. The automotive environment presents to the FMCW radar sensor a multitude of moving and fixed targets and the sensor must detect and track only the targets which may pose a threat of collision or passengers accident. The sensor must function accurately in the presence of background echoes generated by moving and fixed targets, ground reflections, atmospheric noises, including rains, fog, and, snow and noise generated within the receiver. False detection of the desired target in this environment may issue false alarms. That may be dangerous to the passenger and the vehicle. A high false alarm rate is totally unacceptable. The false alarm mechanism consists of noise peaks, crossing the threshold and the undesired response of the system to off lane targets which are not potentially hazardous to the radar equipped vehicle. This paper presents an improve technique safety performance for driver-less operation using FMCW radar sensors.

• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.2
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• pp.83-92
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• 2018

Using ground moving target indicators equipped with synthetic aperture radars for locating moving targets within a wide background clutter in a short time is an excellent method for monitoring traffic. Although the displaced phase center antenna (DPCA) technique and along track interferometry (ATI) are real time methods with low computational complexity, they are essential for reducing cases of false alarm that can result in poor performance. In this paper, we propose two detection methods using DPCA and ATI-the parallel fusion method and serial fusion method. Simulation results demonstrate that the proposed detection methods are characterized by low probability of false alarm along with good performance. In particular, the serial fusion method possesses high detection probability along with low probability of false alarm (1/5th of the false alarm probability of the DPCA technique).

• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.645-653
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• 2014

A Bridge Navigational Watch Alarm System (hereafter 'BNWAS') is to monitor and detect if an officer of watch(hereafter 'OOW') keeps a sharp lookout on the bridge. The careless lookout of an OOW could lead to marine accidents. For this reason on June 5th, 2009, IMO decided that a ship is equipped with a BNWAS. However, an existing BNWAS gives the OOW a lot of inconvenience and stress in its operation. It requires that the OOW should press reset buttons to confirm their alert watch on the bridge at every three to twelve minute. Many OOWs have complained that at some circumstances they cannot focus on their bridge activities including watch-keeping due to a lots of resetting inputs of BNWAS. Accordingly, IMO has allowed the use of a motion sensor as a resetting device. The motion sensor detects the movements of human body on the bridge and subsequently sends reset signals directly to BNWAS automatically. As a result, OOWs can work uninterrupted. However, some of classification societies and flag authorities have a slightly different stance on the use of motion sensor as a resetting method for BNWAS. The reason is that the motion sensor may trigger false reset signals caused by the motion of objects on the bridge, especially a slight movement such as toss and turn of human body which can extend the period of careless watch. As a basic study to minimize the false reset signals, this paper proposes a simple configuration of BNWAS, which consists of only three motion sensors associated with 'AND' and 'OR' logic gates. Additionally, several considerations are also proposed for the implementation of motion sensors. This study found that the proposed configuration which consists of three motion sensors is better than an existing one by reducing false reset signals caused by a slight movement of human body in one's sleep. The proposed configuration in this paper filters false reset signals and is simple to be implemented on existing vessels. In addition, it can be easily installed just by a basic electrical knowledge.

• Korean Journal of Remote Sensing
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• v.31 no.2
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• pp.111-125
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• 2015

In this study, the target detection using both high-resolution satellite SAR and Elecro-Optical (EO) images such as TerraSAR-X and WorldView-2 is performed, considering the characteristics of targets. The targets of our interest are featured by being stationary and appearing as cluster targets. After the target detection of SAR image by using Constant False Alarm Rate (CFAR) algorithm, a series of processes is performed in order to reduce false alarms, including pixel clustering, network clustering and coherence analysis. We extend further our algorithm by adopting the fast and effective ellipse detection in EO image using randomized hough transform, which is significantly reducing the number of false alarms. The performance of proposed algorithm has been tested and analyzed on TerraSAR-X SAR and WordView-2 EO images. As a result, the average false alarm for group of targets is 1.8 groups/$64km^2$ and the false alarms of single target range from 0.03 to 0.3 targets/$km^2$. The results show that groups of targets are successfully identified with very low false alarms.

• Journal of the Korea Society of Computer and Information
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• v.14 no.5
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• pp.65-75
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• 2009

The development of IT technology, Internet popularity is increasing geometrically. However, as its side effect, the intrusion behaviors such as information leakage for key system and infringement of computation network etc are also increasing fast. The attack traffic detection method which is suggested in this study utilizes the Snort, traditional NIDS, filters the packet with false positive among the detected attack traffics using Nmap information. Then, it performs the secondary filtering using nessus vulnerability information and finally performs correlation analysis considering appropriateness of management system, severity of signature and security hole so that it could reduce false positive alarm message as well as minimize the errors from false positive and as a result, it raised the overall attack detection results.