Research on Switching Strategy of Mobile Cloud Computing based on Fusion of Threshold Judgment and Network Selection

Jun Wu,Zhijun Chen 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.2

In order to improve the service performance of mobile cloud computing, the paper strategy launched a study about switching strategy of mobile cloud computing, we propose a fused mobile cloud computing switching method based on threshold judgment and network selection .This method divided the switching strategy of mobile cloud computing process into two steps, first by a threshold judgment to determine whether to switch, followed by the use of multi-attribute decision making method to select the best candidate network for the switching .Experimental results show that the proposed switching method has more excellent switching performance than Saw method and TOPSIS method, at the three indicators of switching frequency, completion time, the energy consumption of the mobile terminal have shown outstanding advantages.

Counterweight optimization of an asymmetrical hybrid machine tool based on dynamic isotropy

Jun Wu,Tie-Min Li,Liping Wang 대한기계학회 2013 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.27 No.7

This paper deals with the counterweight optimization of an asymmetrical hybrid machine tool based on dynamic isotropy. The dynamic model is derived and the dynamic manipulability ellipsoid is studied. Based on dynamic manipulability ellipsoid, the dynamic isotropy is defined. Counterweight block and fluid counterweight are presented to balance the weight of moving parts. The mass of the counterweight block is optimized to obtain the maximum dynamic isotropy in the workspace. The dynamic isotropy of the machine tool after counterweight optimization is compared with that before counterweight optimization. Furthermore, the dynamic isotropy of the machine tool with fluid counterweight is investigated and compared with that of the machine tool with counterweight block.

Stochastic Encoding for Enhanced Resistance against Power Analysis Attacks in Crypto-Hardware

Jun Wu,Hengsi Qin,Yiyu Shi,Minsu Choi,Kyung Ki Kim,Ho Joon Lee,Yong-Bin Kim 한국산업정보학회 2014 한국산업정보학회 학술대회논문집 Vol.2014 No.1

A newly recorded genus Tanvia Solovyev & Witt, 2009 from China (Lepidoptera: Limacodidae)

Jun Wu,Ting-Ting Zhao,Hui Lin HAN 국립중앙과학관 2021 Journal of Asia-Pacific Biodiversity Vol.14 No.1

A genus Tanvia Solovyev & Witt, 2009, belonging to family Limacodidae, is reported for the first timefrom Yunnan province, China, with redescription of a newly recorded species: T. zolotuhini Solovyev &Witt, 2009. In this study, illustrations of adults and genitalia of the species and the distribution areprovided.

A Temporal Error Concealment based on Motion Vector Recovery for H.264/AVC

( Jun Wu ),( Xingang Liu ),( Kook-yeol Yoo ) 한국정보처리학회 2007 한국정보처리학회 학술대회논문집 Vol.14 No.1

In this paper, a new temporal error concealment method for the new coding standard H.264/AVC is presented, which uses the high correlation between the motion vectors of neighboring blocks. By using the motion vector of neighboring MB of the lost MB, the MV of the lost MB are recovered. It is shown that under FMO coding method of H.264/AVC, the proposed method increases PSNR gain up to 2.85dB compared to build-in algorithm in the H.264/AVC test model and 2.59dB compared to Lagrange interpolation.

Practical Residual Force Decomposition Method for Damage Identifi cation of Existing Reticulated Shells

Jun Wu,Yongfeng Luo,Lei Wang 한국강구조학회 2019 International Journal of Steel Structures Vol.19 No.6

The non-destructive damage identifi cation approaches for existing structures become more of a concern due to safety requirements. However, the application of damage identifi cation method is still limited for complex spatial structures. In this paper, a practical residual force decomposition (RFD) method is proposed to identify damaged members in existing reticulated shell structures. A parameterized model is built by taking the stiff ness reduction into consideration. In the proposed RFD method, the signifi cant damage can be recognized corresponding to the changes of the observed eigenvector of the reticulated shells, and the eigenvector changes can be easily transferred to a set of multivariate linear equations to locate the members with signifi cant stiff ness reduction. The inputs of the equations are discussed including the additional constraints and the selection of eff ective modes. According to the diff erent proportion of the numbers of members and joints, three situations of the solution are discussed. Especially when the solution is not unique, the importance coeffi cients of structural members are introduced as additional constraints through Ritz vector sensitivity analysis and application of the Modal Assurance Criterion. Two illustrative examples including a single-layer Kiewitt-6 reticulated shell and a structural model of international convention center are adopted to demonstrate the eff ectiveness of the approach. The results show that the proposed method can yield a suitable damage identifi cation.

Secure and Efficient Cooperative Spectrum Sensing Against Byzantine Attack for Interweave Cognitive Radio System

Jun Wu,Ze Chen,Jianrong Bao,Jipeng Gan,Zehao Chen,Jia Zhang 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.11

Due to increasing spectrum demand for new wireless devices applications, cooperative spectrum sensing (CSS) paradigm is the most promising solution to alleviate the spectrum shortage problem. However, in the interweave cognitive radio (CR) system, the inherent nature of CSS opens a hole to Byzantine attack, thereby resulting in a significant drop of the CSS security and efficiency. In view of this, a weighted differential sequential single symbol (WD3S) algorithm based on MATLAB platform is developed to accurately identify malicious users (MUs) and benefit useful sensing information from their malicious reports in this paper. In order to achieve this, a dynamic Byzantine attack model is proposed to describe malicious behaviors for MUs in an interweave CR system. On the basis of this, a method of data transmission consistency verification is formulated to evaluate the global decision’s correctness and update the trust value (TrV) of secondary users (SUs), thereby accurately identifying MUs. Then, we innovatively reuse malicious sensing information from MUs by the weight allocation scheme. In addition, considering a high spectrum usage of primary network, a sequential and differential reporting way based on a single symbol is also proposed in the process of the sensing information submission. Finally, under various Byzantine attack types, we provide in-depth simulations to demonstrate the efficiency and security of the proposed WD3S.

Note for the tripled and quadruple fixed points of the mixed monotone mappings

Jun Wu,Yicheng Liu 대한수학회 2013 대한수학회보 Vol.50 No.3

In this paper, to include more generalized cases, the authors present a modified concept for the tripled and quadruple fixed point of the mixed monotone mappings. Also, they investigate the existence and uniqueness of fixed point of the ordered monotone operator with the Matkowski contractive conditions in the partial ordered metric spaces. As the direct consequences, the existence of coupled fixed point, tripled fixed point and quadruple fixed point are explored at the common framework and some previous results in [T. G. Bhaskar and V. Lakshmikantham, {\it Fixed point theory in partially ordered metric spaces and applications}, Nonlinear Anal. {\bf 65} (2006), 1379--1393; V. Berinde and M. Borcut, {\it Tripled fixed point theorems for contractive type mappings in partially ordered metric spaces}, Nonlinear Anal. {\bf 74} (2011), no. 15, 4889--4897; E. Karapinar and N. V. Luong, {\it Quadruple fixed point theorems for nonlinear contractions}, Computers and Mathematics with Applications (2012), doi:10.1016/j.camwa.2012.02061] are improved. Finally, some fixed point theorems are proved.

Variation of cassiicolin genes among Chinese isolates of Corynespora cassiicola

Jun Wu,Xue-Wen Xie,Yan-Xia Shi,A-Li Chai,Qi Wang,Bao-ju Li 한국미생물학회 2018 The journal of microbiology Vol.56 No.9

Corynespora cassiicola is a species of fungus that is a plant pathogen of many agricultural crop plants, including severe target spot disease on cucumber. Cassiicolin is an important effector of pathogenicity of this fungus. In this study, we collected 141 Corynespora isolates from eighteen hosts, and the casscolin gene was detected in 82 C. cassiicola strains. The deduced protein sequences revealed that 72 isolates contained the Cas2 gene, two strains from Gynura bicolor harboured the Cas2.2 gene, and 59 isolates without a cassiicolin gene were classified as Cas0. Phylogenetic analyses was performed for the 141 isolates using four loci (ITS, ga4, caa5, and act1) and revealed two genetic clusters. Cluster A is composed of four subclades: subcluster A1 includes all Cas2 isolates plus 18 Cas0 strains, subcluster A2 includes the eight Cas5 isolates and one Cas0 isolate, and subclusters A3 and A4 contain Cas0 strains. Cluster B consists of 21 Cas0 isolates. Twenty-two C. cassiicola strains from different toxin classes showed varying degrees of virulence against cucumber. Cas0 or Cas2 strains induced diverse responses on cucumber, from no symptoms to symptoms of moderate or severe infection, but all Cas5 isolates exhibited avirulence on cucumber.

Asymmetric Semi-Supervised Boosting Scheme for Interactive Image Retrieval

Jun Wu,Ming-Yu Lu 한국전자통신연구원 2010 ETRI Journal Vol.32 No.5

Support vector machine (SVM) active learning plays a key role in the interactive content-based image retrieval (CBIR) community. However, the regular SVM active learning is challenged by what we call “the small example problem” and “the asymmetric distribution problem.” This paper attempts to integrate the merits of semi-supervised learning, ensemble learning, and active learning into the interactive CBIR. Concretely, unlabeled images are exploited to facilitate boosting by helping augment the diversity among base SVM classifiers, and then the learned ensemble model is used to identify the most informative images for active learning. In particular, a bias-weighting mechanism is developed to guide the ensemble model to pay more attention on positive images than negative images. Experiments on 5000 Corel images show that the proposed method yields better retrieval performance by an amount of 0.16 in mean average precision compared to regular SVM active learning, which is more effective than some existing improved variants of SVM active learning.