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RISS 인기검색어
- 검색키워드
- 저자 : ( Weipeng Guo ) (검색결과 5 건)
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DIntrusion Detection in WSN with an Improved NSA Based on the DE-CMOP
( Weipeng Guo ),( Yonghong Chen ),( Yiqiao Cai ),( Tian Wang ),( Hui Tian ) 한국인터넷정보학회 2017 KSII Transactions on Internet and Information Syst Vol.11 No.11
Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective Optimization Problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimumblack hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.
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Oil Pipeline Weld Defect Identification System Based on Convolutional Neural Network
( Jiaze Shang ),( Weipeng An ),( Yu Liu ),( Bang Han ),( Yaodan Guo ) 한국인터넷정보학회 2020 KSII Transactions on Internet and Information Syst Vol.14 No.3
The automatic identification and classification of image-based weld defects is a difficult task due to the complex texture of the X-ray images of the weld defect. Several depth learning methods for automatically identifying welds were proposed and tested. In this work, four different depth convolutional neural networks were evaluated and compared on the 1631 image set. The concavity, undercut, bar defects, circular defects, unfused defects and incomplete penetration in the weld image 6 different types of defects are classified. Another contribution of this paper is to train a CNN model "RayNet" for the dataset from scratch. In the experiment part, the parameters of convolution operation are compared and analyzed, in which the experimental part performs a comparative analysis of various parameters in the convolution operation, compares the size of the input image, gives the classification results for each defect, and finally shows the partial feature map during feature extraction with the classification accuracy reaching 96.5%, which is 6.6% higher than the classification accuracy of other existing fine-tuned models, and even improves the classification accuracy compared with the traditional image processing methods, and also proves that the model trained from scratch also has a good performance on small-scale data sets. Our proposed method can assist the evaluators in classifying pipeline welding defects.
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Gao Ran,Guo Wenjun,Fan Tianfei,Pang Junling,Hou Yangfeng,Feng Xiaohang,Li Bolun,Ge Weipeng,Fan Tianhui,Zhang Tiantian,Lu Jiakai,Jing He,Jin Mu,Yan Chen,Wang Jing 생화학분자생물학회 2022 Experimental and molecular medicine Vol.54 No.-
Abdominal aortic aneurysm (AAA) is a permanent expansion of the abdominal aorta that has a high mortality but limited treatment options. Phosphodiesterase (PDE) 4 family members are cAMP-specific hydrolyzing enzymes and have four isoforms (PDE4A-PDE4D). Several pan-PDE4 inhibitors are used clinically. However, the regulation and function of PDE4 in AAA remain largely unknown. Herein, we showed that PDE4D expression is upregulated in human and angiotensin II-induced mouse AAA tissues using RT-PCR, western blotting, and immunohistochemical staining. Furthermore, smooth muscle cell (SMC)-specific Pde4d knockout mice showed significantly reduced vascular destabilization and AAA development in an experimental AAA model. The PDE4 inhibitor rolipram also suppressed vascular pathogenesis and AAA formation in mice. In addition, PDE4D deficiency inhibited caspase 3 cleavage and SMC apoptosis in vivo and in vitro, as shown by bulk RNA-seq, western blotting, flow cytometry and TUNEL staining. Mechanistic studies revealed that PDE4D promotes apoptosis by suppressing the activation of cAMP-activated protein kinase A (PKA) instead of the exchange protein directly activated by cAMP (Epac). Additionally, the phosphorylation of BCL2-antagonist of cell death (Bad) was reversed by PDE4D siRNA in vitro, which indicates that PDE4D regulates SMC apoptosis via the cAMP-PKA-pBad axis. Overall, these findings indicate that PDE4D upregulation in SMCs plays a causative role in AAA development and suggest that pharmacological inhibition of PDE4 may represent a potential therapeutic strategy.
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Yang, Xiaojuan,Wu, Qingping,Zhang, Jumei,Guo, Weipeng,Mo, Shuping,Liu, Shengrong 한국식품과학회 2016 Food Science and Biotechnology Vol.25 No.4
This study aimed to classify a collection of Enterobacter sakazakii (E. sakazakii) strains previously identified from powdered infant formula (PIF) to species level by recN gene sequencing and biochemical testing to determine the distribution of Cronobacter species in China and investigate the strain diversity by cellular fatty acid (CFA) analysis. Of 24 E. sakazakii isolates, 23 were identified as C. sakazakii and one was C. malonaticus. The 23 C. sakazakii isolates showed the same CFA profiles. The C. malonaticus isolate was discriminated from the C. sakazakii isolates by the significant difference in the amounts of $C_{12:0}$, $C_{14:0}$, and $C_{17:0\;cyclo}$ acids. These results showed that C. sakazakii and C. malonaticus were the common Cronobacter species distributed in PIF in China and that the isolates of the two species exhibited different CFA profiles. These findings are of value for epidemiological investigations and provide an alternative method for confirming various Cronobacter spp.
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Xiaojuan Yang,Qing-Ping Wu,Ju-Mei Zhang,Weipeng Guo,Shuping Mo,Sheng-Rong Liu 한국식품과학회 2016 Food Science and Biotechnology Vol.25 No.4
This study aimed to classify a collection of Enterobacter sakazakii (E. sakazakii) strainspreviously identified from powdered infant formula (PIF) to species level by recN gene sequencing andbiochemical testing to determine the distribution of Cronobacter species in China and investigate thestrain diversity by cellular fatty acid (CFA) analysis. Of 24 E. sakazakii isolates, 23 were identified as C. sakazakii and one was C. malonaticus. The 23 C. sakazakii isolates showed the same CFA profiles. TheC. malonaticus isolate was discriminated from the C. sakazakii isolates by the significant difference inthe amounts of C12:0, C14:0, and C17:0 cyclo acids. These results showed that C. sakazakii and C. malonaticuswere the common Cronobacter species distributed in PIF in China and that the isolates of the twospecies exhibited different CFA profiles. These findings are of value for epidemiological investigationsand provide an alternative method for confirming various Cronobacter spp.
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