• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1463-1478
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• 2012

Based on the theory of local-world network, the composition self-similarity (CSS) of network traffic is presented for the first time in this paper for the study of DoS detection. We propose the concept of composition distribution graph and design the relative operations. The $(R/S)^d$ algorithm is designed for calculating the Hurst parameter. Based on composition distribution graph and Kullback Leibler (KL) divergence, we propose the composition self-similarity anomaly detection (CSSD) method for the detection of DoS attacks. We evaluate the effectiveness of the proposed method. Compared to other entropy based anomaly detection methods, our method is more accurate and with higher sensitivity in the detection of DoS attacks.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.1-11
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• 2023

Deep-learning-based image segmentation is one of the most widely employed lane detection approaches, and it requires a post-process for extracting the key points on the lanes. A general approach for key-point extraction is using a fixed threshold defined by a user. However, finding the best threshold is a manual process requiring much effort, and the best one can differ depending on the target data set (or an image). We propose a novel key-point extraction algorithm that automatically adapts to the target image without any manual threshold setting. In our adaptive key-point extraction algorithm, we propose a line-level normalization method to distinguish the lane region from the background clearly. Then, we extract a representative key point for each lane at a line (row of an image) using a kernel density estimation. To check the benefits of our approach, we applied our method to two lane-detection data sets, including TuSimple and CULane. As a result, our method achieved up to 1.80%p and 17.27% better results than using a fixed threshold in the perspectives of accuracy and distance error between the ground truth key-point and the predicted point.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.1
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• pp.133-138
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• 2008

In this paper, we propose a novel anchor shot detection system, named to MASD (Multi-phase Anchor Shot Detection), which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) one class SVM module for determining the anchor shots using a support vector data description. Besides the qualitative analysis, our experiments validate that the proposed system shows not only the comparable accuracy to the recently developed methods, but also more faster detection rate than those of others.

• Journal of Korea Society of Digital Industry and Information Management
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• v.10 no.4
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• pp.89-96
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• 2014

MANET has been applied to a wide variety of areas because it has advantages which can build a network quickly in a difficult situation to build a network. However, it is become a victim of malicious nodes because of characteristics such as mobility of nodes consisting MANET, limited resources, and the wireless network. Therefore, it is required to lightweight attack detection technique which can accurately detect attack without causing a large burden to the mobile node. In this paper, we propose a multistage attack detection techniques that attack detection takes place in routing phase and data transfer phase in order to increase the accuracy of attack detection. The proposed attack detection technique is composed of four modules at each stage in order to perform accurate attack detection. Flooding attack and packet discard or modify attacks is detected in the routing phase, and whether the attack by modification of data is detected in the data transfer phase. We assume that nodes have a public key and a private key in pairs in this paper.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Asian-Australasian Journal of Animal Sciences
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• v.20 no.8
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• pp.1169-1173
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• 2007

The protein encoded by SLC27A2 gene is an isozyme of long-chain fatty-acid-coenzyme A ligase family, and it converts free long-chain fatty acids into fatty acyl-CoA esters, and thereby plays a key role in lipid biosynthesis and fatty acid degradation. In the present study, SLC27A2 located on human chromosome 15 was selected as candidate gene and we isolated and cloned partial fragments of mRNA sequence and genomic fragments of porcine SLC27A2 gene. The coding region of the gene as determined by alignments shared 90% and 82% identity with human and mouse cDNAs, respectively. Detection in LargeWhite and Meishan breeds showed that a single nucleotide polymorphism (SNP) ($A{\rightarrow}G$) existed in exon 7, which caused corresponding amino acid changed for encoding. In LargeWhite pigs it encoded for Val while in Meishan pigs it encoded for Ile, so we developed the PCR-RFLP genotype method for detection of this polymorphism. Association study in 135 $F_2$ reference family indicated that significant correlation existed between the polymorphism and growth and carcass traits.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.9
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• pp.3907-3923
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• 2020

An enhanced irregular repetition slotted ALOHA (IRSA) protocol is proposed by using polarization characteristic of satellite link and MIMO detection in low earth orbit (LEO) satellite networks, which is dubbed polarized MIMO IRSA (PM-IRSA). In the proposed scheme, one or two packets in one slot can be decoded by employing polarized MIMO detection, and more than two collided packets in multiple slots which can construct the virtual MIMO model can be decoded by the MIMO detection algorithm. The performance of the proposed scheme is analyzed with the density evolution (DE) approach and the degree distribution is optimized to maximize the system throughput by using a differential evolution. Numerical results certify our analysis and show that the normalized throughput of the proposed PM-IRSA can achieve 1.89 bits/symbol.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.91-96
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• 2006

Presently, cycle-slip detection is done between adjacent two points in many cycle-slip methods. Inherently, it is simple wavelet analysis. A new idea is put forward that the number of difference point can adjust by a parameter factor; we study this method to smooth raw data and detect cycle-slip with wavelet analysis. Taking CHAMP satellite data for example, we get some significant conclusions. It is showed that it is valid to detect cycle-slip in GPS phase measurement based on this wavelet function, and it is helpful to improve the precision of GPS data pre-processing and positioning.

• ETRI Journal
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• v.35 no.3
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• pp.371-377
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• 2013

An effective signal detection algorithm with low complexity is presented for multiple-input multiple-output orthogonal frequency division multiplexing systems. The proposed technique, QR-MLD, combines the conventional maximum likelihood detection (MLD) algorithm and the QR algorithm, resulting in much lower complexity compared to MLD. The proposed technique is compared with a similar algorithm, showing that the complexity of the proposed technique with T=1 is a 95% improvement over that of MLD, at the expense of about a 2-dB signal-to-noise-ratio (SNR) degradation for a bit error rate (BER) of $10^{-3}$. Additionally, with T=2, the proposed technique reduces the complexity by 73% for multiplications and 80% for additions and enhances the SNR performance about 1 dB for a BER of $10^{-3}$.

• Journal of the Optical Society of Korea
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• v.14 no.3
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• pp.199-203
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• 2010

A mobile direct detection Doppler LIDAR based on molecular backscattering for measurement of wind in the stratosphere has been developed in Hefei, China. First, the principle of wind measurement with direct detection Doppler LIDAR is presented. Then the configuration of the LIDAR system is described. Finally, the primary experimental results are provided and analyzed. The results indicate that the detection range of the designed Doppler LIDAR reached 50 km altitude, and there is good consistency between the molecular Doppler wind LIDAR(DWL) and the wind profile radar(WPR) in the low troposphere.