• Journal of the Korea Computer Industry Society
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• v.8 no.2
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• pp.103-106
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• 2007

In order to construct a Video Database, shot change detection should be made first. But, because these processes are not automated perfectly, we need a lot of time and efforts now. And, there are many shot change detection algorithms, which can't always insure the perfect result because of the editing effects such as cut, wipe, and dissolves used in film production. Therefore, in order to receive the exact shot change, It needs the verification and correction by manual processing at any cost. Spatiotemporal slice is a simple image condensing method for the content changes of video. The editing effects are expressed on the Spatiotemporal slice in the visually noticed form of vertical line, diagonal line, curved line and gradual color changes, etc. Accordingly the parts doubted as a shot change can be easily detected by the change of the Spatiotemporal slice without replaying the video. The system proposed in this study makes it possible to delete the false detected key frames, and create the undetected key frames on the Spatiotemporal slice.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.4
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• pp.1307-1323
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• 2014

With the exhaustion of global IPv4 addresses, IPv6 technologies have attracted increasing attentions, and have been deployed widely. Meanwhile, new applications running over IPv6 networks will change the traditional traffic characteristics obtained from IPv4 networks. Traditional models obtained from IPv4 cannot be used for IPv6 network monitoring directly and there is a need to investigate those changes. In this paper, we explore the flow features of IPv6 traffic and compare its difference with that of IPv4 traffic from flow level. Firstly, we analyze the differences of the general flow statistical characteristics and users' behavior between IPv4 and IPv6 networks. We find that there are more elephant flows in IPv6, which is critical for traffic engineering. Secondly, we find that there exist many one-way flows both in the IPv4 and IPv6 traffic, which are important information sources for abnormal behavior detection. Finally, in light of the challenges of analyzing massive data of large-scale network monitoring, we propose a group flow model which can greatly reduce the number of flows while capturing the primary traffic features, and perform a comparative measurement analysis of group users' behavior dynamic characteristics. We find there are less sharp changes caused by abnormity compared with IPv4, which shows there are less large-scale malicious activities in IPv6 currently. All the evaluation experiments are carried out based on the traffic traces collected from the Northwest Regional Center of CERNET (China Education and Research Network), and the results reveal the detailed flow characteristics of IPv6, which are useful for traffic management and anomaly detection in IPv6.

• Smart Structures and Systems
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• v.32 no.5
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• pp.319-338
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• 2023

The study presents a new hybrid data-driven method by combining radial basis functions neural networks (RBF-NN) with the Jaya algorithm (JA) to provide effective structural health monitoring of arch dams. The novelty of this approach lies in that only one user-defined parameter is required and thus can increase its effectiveness and efficiency, as compared to other machine learning techniques that often require processing a large amount of training and testing model parameters and hyper-parameters, with high time-consuming. This approach seeks rapid damage detection in arch dams under dynamic conditions, to prevent potential disasters, by utilizing the RBF-NNN to seamlessly integrate the dynamic elastic modulus (DEM) and modal parameters (such as natural frequency and mode shape) as damage indicators. To determine the dynamic characteristics of the arch dam, the JA sequentially optimizes an objective function rooted in vibration-based data sets. Two case studies of hyperbolic concrete arch dams were carefully designed using finite element simulation to demonstrate the effectiveness of the RBF-NN model, in conjunction with the Jaya algorithm. The testing results demonstrated that the proposed methods could exhibit significant computational time-savings, while effectively detecting damage in arch dam structures with complex nonlinearities. Furthermore, despite training data contaminated with a high level of noise, the RBF-NN and JA fusion remained the robustness, with high accuracy.

• BMB Reports
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• v.40 no.5
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• pp.611-616
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• 2007

$E^{rns}$ is an envelope glycoprotein of classical swine fever virus (CSFV) and has an unusual feature of RNase activity. In the present study, we demonstrate that $E^{rns}$ counteracts Newcastle disease virus (NDV)-mediated induction of IFN-$\beta$. For this purpose, $E^{rns}$ fused to the enhanced green fluorescent protein (EGFP) was transiently expressed in porcine kidney 15 (PK15) cells. In luciferase activity assay, $E^{rns}$-EGFP was found to prevent IFN-$\beta$ promoter-driven luciferase expression and block the induction of IFN-$\beta$ promoter mediated by NDV in a dose-dependent manner. Through IFN-specific semi-quantitative RT-PCR detection, obvious decrease of IFN-$\beta$ mRNA in NDV-infected PK15 cells was observed in the presence of $E^{rns}$-EGFP. In contrast, EGFP alone showed none of this block capacity. In addition, $E^{rns}$-EGFP mutations with RNase inactivation were also found to block NDV-mediated induction of IFN-$\beta$. These evidences establish a novel function for CSFV $E^{rns}$ glycoprotein in counteraction of the IFN-$\beta$ induction pathway.

• Asian-Australasian Journal of Animal Sciences
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• v.21 no.8
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• pp.1080-1087
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• 2008

Lipopolysaccharide binding protein (LBP) and CD14 protein play important roles in the defense against infection of Gram-negative bacteria. In the present study, tissue distribution and polymorphism of porcine LBP and CD14 genes were analyzed. Real-time PCR results showed that the porcine LBP gene was especially highly expressed in liver, while CD14 gene was highly expressed in liver and spleen tissues. A 1,732 bp cDNA fragment of porcine LBP gene and a 1,682 bp genomic DNA fragment of CD14 gene were isolated. Polymorphisms were identified in these two fragments and showed that there were 14 potential SNPs in the porcine LBP gene and 3 potential SNPs in the porcine CD14 gene. Three SNPs, 292G/A (Gly/Ser), 1168G/A (Ala/Thr) of the LBP gene and -61G/A of the CD14 gene, were genotyped using restriction fragment length polymorphism (RFLP) method. Association analyses indicated that polymorphism of the 292G/A locus was significantly associated with porcine immune traits hematocrit (HCT), IgG and delayed-type hypersensitivity (DTH) (p<0.01), and the 1168G/A locus was significantly associated with HCT and mean corpuscular volume (MCV) traits (p<0.05). No significant association was found between the -61G/A locus and immune traits of the pig. Our data indicated that the LBP gene was significantly associated with immune traits of pig. Also, we identified some SNPs which may be useful markers for disease-resistant breeding of pigs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.6
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• pp.3230-3253
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• 2017

Android malware steals users' private information, and embedded unsafe advertisement (ad) libraries, which execute unsafe code causing damage to users. The majority of such traffic is HTTP and is mixed with other normal traffic, which makes the detection of malware and unsafe ad libraries a challenging problem. To address this problem, this work describes a novel HTTP traffic flow mining approach to detect and categorize Android malware and unsafe ad library. This work designed AndroCollector, which can automatically execute the Android application (app) and collect the network traffic traces. From these traces, this work extracts HTTP traffic features along three important dimensions: quantitative, timing, and semantic and use these features for characterizing malware and unsafe ad libraries. Based on these HTTP traffic features, this work describes a supervised classification scheme for detecting malware and unsafe ad libraries. In addition, to help network operators, this work describes a fine-grained categorization method by generating fingerprints from HTTP request methods for each malware family and unsafe ad libraries. This work evaluated the scheme using HTTP traffic traces collected from 10778 Android apps. The experimental results show that the scheme can detect malware with 97% accuracy and unsafe ad libraries with 95% accuracy when tested on the popular third-party Android markets.

• Journal of Electrical Engineering and Technology
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• v.10 no.5
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• pp.2179-2188
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• 2015

Recently, many researches have been done to solve the challenging problem of Blind Source Separation (BSS) problems in the underdetermined cases, and the “Two-step” method is widely used, which estimates the mixing matrix first and then extracts the sources. To estimate the mixing matrix, conventional algorithms such as Single-Source-Points (SSPs) detection only exploits the sparsity of original signals. This paper proposes a new underdetermined mixing matrix estimation method for time-delayed mixtures based on the receiver prior exploitation. The prior information is extracted from the specific structure of the complex-valued mixing matrix, which is used to derive a special criterion to determine the SSPs. Moreover, after selecting the SSPs, Agglomerative Hierarchical Clustering (AHC) is used to automaticly cluster, suppress, and estimate all the elements of mixing matrix. Finally, a convex-model based subspace method is applied for signal separation. Simulation results show that the proposed algorithm can estimate the mixing matrix and extract the original source signals with higher accuracy especially in low SNR environments, and does not need the number of sources before hand, which is more reliable in the real non-cooperative environment.

• Structural Engineering and Mechanics
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• v.75 no.4
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• pp.477-485
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• 2020

Finite element (FE) model based structural damage detection (SDD) methods play vital roles in effectively locating and quantifying structural damages. Among these methods, structural model updating should be conducted before SDD to obtain benchmark models of real structures. However, the characteristics of updating parameters are not reasonably considered in existing studies. Inspired by the l_∞ norm regularization, a novel anti-sparse representation method is proposed for structural model updating in this study. Based on sensitivity analysis, both frequencies and mode shapes are used to define an objective function at first. Then, by adding l_∞ norm penalty, an optimization problem is established for structural model updating. As a result, the optimization problem can be solved by the fast iterative shrinkage thresholding algorithm (FISTA). Moreover, comparative studies with classical regularization strategy, i.e. the l₂ norm regularization method, are conducted as well. To intuitively illustrate the effectiveness of the proposed method, a 2-DOF spring-mass model is taken as an example in numerical simulations. The updating results show that the proposed method has a good robustness to measurement noises. Finally, to further verify the applicability of the proposed method, a six-storey aluminum alloy frame is designed and fabricated in laboratory. The added mass on each storey is taken as updating parameter. The updating results provide a good agreement with the true values, which indicates that the proposed method can effectively update the model parameters with a high accuracy.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1121-1137
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• 2015

This study presents probabilistic-based damage identification technique for highlighting damage in metallic structures. This technique utilizes distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. Diagnostic signals were used to define the scatter signals of different paths. The energy of scatter signals till different times were calculated by taking root mean square of the scatter signals. For each pair of parallel paths an error function based on the energy of scatter signals is introduced. The resultant error function then is used to estimate the probability of the presence of damage in the monitoring area. The presented method with an autofocusing feature is applied to aluminum plates for method verification. The results identified using both simulation and experimental Lamb wave signals at different central frequencies agreed well with the actual situations, demonstrating the potential of the presented algorithm for identification of damage in metallic structures. An obvious merit of the presented technique is that in addition to damages located inside the region between transducers; those who are outside this region can also be monitored without any interpretation of signals. This novelty qualifies this method for online structural health monitoring.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6247-6251
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• 2014

Background: The aim of the present study was to investigate the involvement of emodin on the growth of human breast cancer MCF-7 and MDA-MB-231 cells and the estrogen (E2) signal pathway in vitro. Materials and Methods: MTT assays were used to detect the effects of emodin on E2 induced proliferation of MCF-7 and MDA-MB-231 cells. Flow cytometry (FCM) was applied to determine the effect of emodin on E2-induced apoptosis of MCF-7 cells. Western blotting allowed detection of the effects of emodin on the expression of estrogen receptor ${\alpha}$, cyclin D1 and B-cell lymphoma-2 (Bcl-2), mitogen-activated protein kinases (MAPK) and phosphatidylinostiol 3-kinases (PI3K). Luciferase assays were emplyed to assess transcriptional activity of $ER{\alpha}$. Results: Emodin could inhibit E2-induced MCF-7 cell proliferation and anti-apoptosis effects, and arrest the cell cycle in G0/G1 phase, further blocking the effect of E2 on expression and transcriptional activity of $ER{\alpha}$. Moreover, Emodin influenced the ER ${\alpha}$ genomic pathway via downregulation of cyclin D1 and Bcl-2 protein expression, and influenced the non-genomic pathway via decreased PI3K/Akt protein expression. Conclusions: These findings indicate that emodin exerts inhibitory effects on MCF-7 cell proliferation via inhibiting both non-genomic and genomic pathways.