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- 저자 : Wenhua Zeng (검색결과 4 건)
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Risk Identification Method for Cloud Computing Safety based on LSA-GCC and LSA-SAM
Fan Lin,Wenhua Zeng,Yue Wang 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.2
This paper proposes a generalized cluster risk evaluation model by applying a data mining method to the cloud computing risk evaluation. The model maps data sets into a semantic space via singular value decomposition (SVD), uses a clustering algorithm to classify them and to extract the prototype vector of a particular category from clustering results, and assigns a definite weight to each category so as to set up an initial prototype vector model. The model is taken as the basis for risk evaluation of information system. After the data to be evaluated were mapped to the same semantic space, they are calculated with the prototype vector of each category, so as to obtain the similarity of the category, and the cumulative sum of the similarity with the weight of the corresponding category comes out. Finally, a mean value is calculated to obtain the risk value of the data to be evaluated, namely, the risk value of the occasion when the data is obtained. In this paper, the safety risk information is obtained from the operating system log and Web application server log of a virtual host; the Latent Semantic Analysis-based Generalized Cluster Classifier (LSA-GCC) is adopted and the MapReduce-based LSA-GCC and LSA-SAM parallel acceleration experiment is conducted. The experimental results show that in a cloud computing environment of large-scale parallel processing, the method used in this paper can identify the log events of a cloud computing system and conduct risk prompt rapidly.
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Shanjie Wang,Kegong Chen,Ye Wang,Zeng Wang,Zhaoying Li,JunChen Guo,Jianfeng Chen,Wenhua Liu,Xiaohui Guo,Guangcan Yan,Chenchen Liang,Huai Yu,Shaohong Fang,Bo Yu 한국생체재료학회 2023 생체재료학회지 Vol.27 No.00
Background Large-dose melatonin treatment in animal experiments was hardly translated into humans, which may explain the dilemma that the protective effects against myocardial injury in animal have been challenged by clinical trials. Ultrasound-targeted microbubble destruction (UTMD) has been considered a promising drug and gene delivery system to the target tissue. We aim to investigate whether cardiac gene delivery of melatonin receptor mediated by UTMD technology optimizes the efficacy of clinically equivalent dose of melatonin in sepsis-induced cardiomyopathy. Methods Melatonin and cardiac melatonin receptors in patients and rat models with lipopolysaccharide (LPS)- or cecal ligation and puncture (CLP)-induced sepsis were assessed. Rats received UTMD-mediated cardiac delivery of RORα/cationic microbubbles (CMBs) at 1, 3 and 5 days before CLP surgery. Echocardiography, histopathology and oxylipin metabolomics were assessed at 16–20 h after inducing fatal sepsis. Results We observed that patients with sepsis have lower serum melatonin than healthy controls, which was observed in the blood and hearts of Sprague–Dawley rat models with LPS- or CLP-induced sepsis. Notably, a mild dose (2.5 mg/kg) of intravenous melatonin did not substantially improve septic cardiomyopathy. We found decreased nuclear receptors RORα, not melatonin receptors MT1/2, under lethal sepsis that may weaken the potential benefits of a mild dose of melatonin treatment. In vivo, repeated UTMD-mediated cardiac delivery of RORα/CMBs exhibited favorable biosafety, efficiency and specificity, significantly strengthening the effects of a safe dose of melatonin on heart dysfunction and myocardial injury in septic rats. The cardiac delivery of RORα by UTMD technology and melatonin treatment improved mitochondrial dysfunction and oxylipin profiles, although there was no significant influence on systemic inflammation. Conclusions These findings provide new insights to explain the suboptimal effect of melatonin use in clinic and potential solutions to overcome the challenges. UTMD technology may be a promisingly interdisciplinary pattern against sepsis-induced cardiomyopathy.
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Sun, Zhe,Lee, Sangsu,Park, Kyu Hyung,Zhu, Xiaojian,Zhang, Wenhua,Zheng, Bin,Hu, Pan,Zeng, Zebing,Das, Soumyajit,Li, Yuan,Chi, Chunyan,Li, Run-Wei,Huang, Kuo-Wei,Ding, Jun,Kim, Dongho,Wu, Jishan American Chemical Society 2013 JOURNAL OF THE AMERICAN CHEMICAL SOCIETY - Vol.135 No.48
<P><I>Clar’s aromatic sextet rule</I> has been widely used for the prediction of the reactivity and stability of polycyclic aromatic hydrocarbons with a <I>closed-shell</I> electronic configuration. Recent advances in <I>open-shell</I> biradicaloids have shown that the number of aromatic sextet rings plays an important role in determination of their ground states. In order to test the validity of this rule in singlet biradicaloids, the two soluble and stable dibenzoheptazethrene isomers <B>DBHZ1</B> and <B>DBHZ2</B> were prepared by different synthetic approaches and isolated in crystalline form. These two molecules have different numbers of aromatic sextet rings in their respective biradical resonance forms and thus are expected to exhibit varied singlet biradical character. This assumption was verified by different experimental methods, including nuclear magnetic resonance (NMR), electron spin resonance (ESR), superconducting quantum interference device (SQUID), steady-state and transient absorption spectroscopy (TA), and X-ray crystallographic analysis, assisted by unrestricted symmetry-broken density functional theory (DFT) calculations. <B>DBHZ2</B>, with more aromatic sextet rings in the biradical form, was demonstrated to possess greater biradical character than <B>DBHZ1</B>; as a result, <B>DBHZ2</B> exhibited an intense one-photon absorption (OPA) in the near-infrared region (λ<SUB>abs</SUB><SUP>max</SUP> <I>=</I> 804 nm) and a large two-photon absorption (TPA) cross-section (σ<SUP>(2)</SUP><SUB>max</SUB> = 2800 GM at 1600 nm). This investigation together with previous studies indicates that Clar’s aromatic sextet rule can be further extended to the singlet biradicaloids to predict their ground states and singlet biradical characters.</P><P><B>Graphic Abstract</B> <IMG SRC='http://pubs.acs.org/appl/literatum/publisher/achs/journals/content/jacsat/2013/jacsat.2013.135.issue-48/ja410279j/production/images/medium/ja-2013-10279j_0012.gif'></P><P><A href='http://pubs.acs.org/doi/suppl/10.1021/ja410279j'>ACS Electronic Supporting Info</A></P>
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A Novel Dataset Generating Method for Fine-Grained Vehicle Classification with CNN
Shaoyong Yu,Zhijun Song,Songzhi Su,Wei Li,Yun Wu,Wenhua Zeng 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.6
We focus on the issue of dataset generation for fine-grained vehicle classification with CNN. Traditionally, to build a large dataset, images must be first collected manually, and then be annotated with a lot of effort. All these work are time-consuming and cost-prohibitive. In this work we propose a novel method that can generate massive images automatically, and these generated images need no annotation. An AutoCAD 3D model of a car of specified make and model is imported into our system, and then images of different views of the car are generated, these images can describe all the details of a car. By taking these images as training dataset, we use a Convolutional Neural Network to train a model for fine-grained vehicle classification. Experimental results show that these images generated virtually by 3D model indeed work as effective as real images.
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