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RISS 인기검색어
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- 저자 : Yurong Wei (검색결과 5 건)
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A Partial Discharge Fault Identification Algorithm based on SGWT Neural Network
Wei Zhang,Qiuli Wu,Yurong Deng 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.5
Based on the second generation wavelet and information entropy, in this paper, we recognize the partial discharge pattern using the second generation wavelet (SGWT) and adaptive BP. Firstly, feature extraction of discharge signals are obtained using the SGWT and information entropy. Then, the extracted features are feed into the training BP network. The learning algorithm employed the conjugate gradient methods and the adaptive adjustment to train the error for BP network. Finally, we get the optimum training network, and the simulation results verified the feasibility of the algorithm.
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Fuzzy reliability analysis of laminated composites
Chen, Jianqiao,Wei, Junhong,Xu, Yurong Techno-Press 2006 Structural Engineering and Mechanics, An Int'l Jou Vol.22 No.6
The strength behaviors of Fiber Reinforced Plastics (FRP) Composites can be greatly influenced by the properties of constitutive materials, the laminate structures, and load conditions etc, accompanied by many uncertainty factors. So the reliability study on FRP is an important subject of research. Many achievements have been made in reliability studies based on the probability theory, but little has been done on the roles played by fuzzy variables. In this paper, a fuzzy reliability model for FRP laminates is established first, in which the loads are considered as random variables and the strengths as fuzzy variables. Then a numerical model is developed to assess the fuzzy reliability. The Monte Carlo simulation method is utilized to compute the reliability of laminas under the maximum stress criterion. In the second part of this paper, a generalized fuzzy reliability model (GFRM) is proposed. By virtue of the fact that there may exist a series of states between the failure state and the function state, a fuzzy assumption for the structure state together with the probabilistic assumption for strength parameters is adopted to construct the GFRM of composite materials. By defining a generalized limit state function, the problem is converted to the conventional reliability formula that enables the first-order reliability method (FORM) applicable in calculating the reliability index. Several examples are worked out to show the validity of the models and the efficiency of the methods proposed in this paper. The parameter sensitivity analysis shows that some of the mean values of the strength parameters have great influence on the laminated composites' reliability. The differences resulting from the application of different failure criteria and different fuzzy assumptions are also discussed. It is concluded that the GFRM is feasible to use, and can provide an effective and synthetic method to evaluate the reliability of a system with different types of uncertainty factors.
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Chen Lu,Cao Kai,Gu Yurong,Luo Chao,Mao Wei,Zhou Weijun,Zhu Jinwei,Zhang Huying 대한독성 유전단백체 학회 2021 Molecular & cellular toxicology Vol.17 No.3
Background Kaempferol (KMF) is a fl avone reported to have anti-oxidant and anti-infl ammatory activity. Objective The present study screened the eff ect of KMF in the animal model of spinal cord injury (SCI). Results KMF caused a signifi cant inhibition of spinal cord injury mediated oxidative stress and also suppressed the infl ammatory reactions. The treatment of KMF also inhibited the levels of p53, TGF-β1 and COX-2 whereas a signifi cant elevation in Bcl-2/Bax ratio was observed after the rats were treated with KMF. The in silico docking analysis suggested potential binding of KMF having lower energy with p53 confi rming the potential target of KMF. Conclusion The treatment of KMF exerted neuroprotective eff ect by improving the anti-oxidant status and infl ammatory response. The signifi cant spinal cord injury protective eff ect of KMF in rats was attributed by targeting p53 and Bcl-2/Bax ratio.
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Wanhe Luo,Yongtao Jiang,Jinhuan Liu,Beibei Sun,Xiuge Gao,Samah Attia Algharib,Dawei Guo,Jie Wei,Yurong Wei The Korean Society of Veterinary Science 2024 Journal of Veterinary Science Vol.25 No.2
Background: Biofilms, such as those from Staphylococcus epidermidis, are generally insensitive to traditional antimicrobial agents, making it difficult to inhibit their formation. Although quercetin has excellent antibiofilm effects, its clinical applications are limited by the lack of sustained and targeted release at the site of S. epidermidis infection. Objectives: Polyethylene glycol-quercetin nanoparticles (PQ-NPs)-loaded gelatin-N,O-carboxymethyl chitosan (N,O-CMCS) composite nanogels were prepared and assessed for the on-demand release potential for reducing S. epidermidis biofilm formation. Methods: The formation mechanism, physicochemical characterization, and antibiofilm activity of PQ-nanogels against S. epidermidis were studied. Results: Physicochemical characterization confirmed that PQ-nanogels had been prepared by the electrostatic interactions between gelatin and N,O-CMCS with sodium tripolyphosphate. The PQ-nanogels exhibited obvious pH and gelatinase-responsive to achieve on-demand release in the micro-environment (pH 5.5 and gelatinase) of S. epidermidis. In addition, PQ-nanogels had excellent antibiofilm activity, and the potential antibiofilm mechanism may enhance its antibiofilm activity by reducing its relative biofilm formation, surface hydrophobicity, exopolysaccharides production, and eDNA production. Conclusions: This study will guide the development of the dual responsiveness (pH and gelatinase) of nanogels to achieve on-demand release for reducing S. epidermidis biofilm formation.
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