• International Journal of Aeronautical and Space Sciences
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• v.7 no.1
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• pp.73-83
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• 2006

In this paper, a redundancy management system for aircraft is studied, and fault detection and isolation algorithms of inertial sensor system are proposed. Contrary to the conventional aircraft systems, UAV system cannot allow triple or quadruple hardware redundancy due to the limitations on space and weight. In the UAV system with dual sensors, it is very difficult to identify the faulty sensor. Also, conventional fault detection and isolation (FDI) method cannot isolate multiple faults in a triple redundancy system. In this paper, two FDI techniques are proposed. First, hardware based FDI technique is proposed, which combines a parity equation approach with a wavelet based technique. Second, analytic FDI technique based on the Kalman filter is proposed, which is a model-based FDI method utilizing the threshold value and the confirmation time. To provide the reference value for detecting the fault, residuals are calculated using the extended Kalman filter. To verify the effectiveness of the proposed FDI methods, numerical simulations are performed.

• Proceedings of the IEEK Conference
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• 2002.06d
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• pp.215-218
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• 2002

The edges of an image hold much of the information in that image. The edges tell where objects are, their shape and size, and something about their texture. An edge is where the intensity of an image moves from a low value to a high value. We introduce the edge detection using the differential operator with Sobel operator and describe a nonparametric Wilcoxon test based on statistical hypothesis testing for the detection of edges. This paper proposes an efficient edge detection using Van der Waerden's statistic in original and noisy images. We use the threshold determined by specifying significance level a and an edge-height parameter. Comparison with our statistical test and Sobel operator shows that Van der Waerden method perform more effectively in both noisy and noise-free images.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.250-251
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• 2013

In this paper, we would like to extract criminals doing this criminal offense to smoke in elevators. Extraction method detect difference value using modified color-$X^2$-test and it was normalized. Next, we find frames that has occurred scene change in successive frames using the four-step algorithm of scene change detection. Finally, we present the method of smoking image retrieval and extraction in stored large amount of video.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.7C
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• pp.642-646
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• 2005

We propose an improved partial response maximum likelihood (PRML) detector with the branch value compensation of Viterbi decoder for asymmetric high-density optical channel. Since the compensation value calculated by a survival path is applied to each branch metric, it reduces the detection errors by the asymmetric channel. The proposed PRML detection scheme improves the detection performance on the $2^{nd},\;3^{rd}\;and\;4^{th}$ order PR targets for asymmetric optical recording channel.

• Proceedings of the KIEE Conference
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• 2004.05a
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• pp.105-107
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• 2004

In this work, we suggested the method which improves the efficiency of the face detection algorithm using Pulse-Coupled Neural Network. Face detection algorithm which uses the color information is independent on size, angle, and obstruction of a face. But the use of color information encounters some problems arising from skin-tone color in the background, intensity variation within faces, and presence of random noise, and so on. Depending on these conditions, we obtained the mean and variance of skin-tone colors by experiments. Then we introduce a preprocess that the pixel with a mean value of skin-tone colors has highest level value(255) and the other pixels in the skin-tone region have values between 0 and 255 according to a normal distribution with a variance. This preprocess leads to an easy decision of the linking parameters.

• ETRI Journal
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• v.43 no.3
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• pp.511-523
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• 2021

The World Health Organization provides guidelines for managing the particulate matter (PM) level because a higher PM level represents a threat to human health. To manage the PM level, a procedure for measuring the PM value is first needed. We use a PM sensor that collects the PM level by laser-based light scattering (LLS) method because it is more cost effective than a beta attenuation monitor-based sensor or tapered element oscillating microbalance-based sensor. However, an LLS-based sensor has a higher probability of malfunctioning than the higher cost sensors. In this paper, we regard the overall malfunctioning, including strange value collection or missing collection data as anomalies, and we aim to detect anomalies for the maintenance of PM measuring sensors. We propose a novel architecture for solving the above aim that we call the hypothesis pruning generative adversarial network (HP-GAN). Through comparative experiments, we achieve AUROC and AUPRC values of 0.948 and 0.967, respectively, in the detection of anomalies in LLS-based PM measuring sensors. We conclude that our HP-GAN is a cutting-edge model for anomaly detection.

• Journal of Software Engineering Society
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• v.25 no.4
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• pp.69-74
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• 2012

This article suggests applicability to community detection algorithm from module recovering process of software architecture through compare to software clustering metric and community dectection metric. in addition to, analyze mutual relation and difference between separated module and measurement value of typical clustering algorithms and community detection algorithms. and then only sugeested several kinds basis that community detection algorithm can use to recovering module view of software architecture and, by so comparing measurement value of existing clustering metric and community algorithms, this article suggested correlation of two result data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.802-804
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• 2014

Edge detection of image for performing the road lane recognition is an essential preprocessing. Various studies are being performed in order to detect such edge and there are conventional edge detection methods such as Sobel, Prewitt and Roberts. Such methods regardless of pixel distribution are processed by applying the same weighted value to the entire pixels and show a somewhat insufficient edge detection results. Therefore, this paper has proposed an algorithm that detects the edge using the suitable weighted value for the road lane recognition considering the pixel distribution shape of the image.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.792-794
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• 2014

Character recognition is an image processing technique for obtaining the character information such as documents and automobile license plate and for this edge detection methods are commonly used. The previous edge detection methods are mostly applying the weighted value mask on the image and because it applies the same mask to the entire areas of the image, the processing results are somewhat insufficient. Therefore, this paper has proposed an edge detection algorithm by applying the weighted value mask considering the distribution and location of pixels to be suitable for the character recognition.

• ETRI Journal
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• v.43 no.1
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• pp.152-162
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• 2021

In the last few years, detection has become a powerful methodology for network protection and security. This paper presents a new detection scheme for data recorded over a computer network. This approach is applicable to the broad scientific field of information security, including intrusion detection and prevention. The proposed method employs bidimensional (time-frequency) data representations of the forms of the short-time Fourier transform, as well as the Wigner distribution. Moreover, the method applies matrix factorization using singular value decomposition and principal component analysis of the two-dimensional data representation matrices to detect intrusions. The current scheme was evaluated using numerous tests on network activities, which were recorded and presented in the KDD-NSL and UNSW-NB15 datasets. The efficiency and robustness of the technique have been experimentally proved.