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Research on Hybrid Distributed Computing System Based on Embedded System
Li Zhe,Mu Dejun,Zhang Tianfan,Guo Lantian 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.11
With the rapid development of embedded system and Internet of Things technology, embedded system and smart device based on embedded system is collecting huge amounts of data, and corresponding data processing and application method have been greatly changed, different from the traditional big data and cloud computing focus , local processing and application become important trend. This paper tries to take Cortex-A7 as the main system with the stronger ability , Hadoop distributed computing system is deployed in embedded system and could meet the demand of the future data process with directly managing the resource-constrained sensors, embedded systems and smart devices . Successfully deploying over 20GB data in the test, the system is verified that it can complete most of the functions of data processing cluster , and can also manage the collected sensors and embedded system terminal ,with better research and market promotional value.
Feisheng Yang,Dejun Mu,Jing He,Dongxy Huang 제어로봇시스템학회 2015 제어로봇시스템학회 국제학술대회 논문집 Vol.2015 No.10
A new methodology is presented for the stability problem of genetic networks with time-varying delay in the current article. Novel stability criteria with delay dependency in terms of linear matrix inequalities for gene regulatory nets are derived by the proposed augmented Lyapunov-Krasovski (L-K) functional. Our proof deployment applies the technique of second order convex combination, and the property of quadratic convex function without resort to Jensen`s inequality.
Stochastic Stability of the Improved Maximum Correntropy Kalman Filter Against Non-Gaussian Noises
Xuehua Zhao,Dejun Mu,Zhaohui Gao,Jiahao Zhang,Guo Li 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.3
In this paper, an improved maximum correntropy Kalman filter (IMCKF) algorithm is proposed to enhance the estimation accuracy of conventional correntropy based Kalman filter against the non-Gaussian noise. Toincrease the proposed algorithm estimation precision, a novel cost function is introduced based on weighted factors. Then the IMCKF algorithm is put forward and derived in detail. Furthermore, the stochastic boundness of theestimation error is discussed to illustrate the IMCKF algorithm’s stability. Finally, simulation results demonstratethat the proposed IMCKF algorithm increases the estimation precision and robustness performance in contrast tothe conventional Gaussian Sum Kalman filter and maximum correntropy Kalman filter.
Service Clustering by Leveraging a Context-Sensitive Approach
Lantian Guo,Tao Yang,Huixiang Zhang,Dejun Mu,Zhe Li,Yang Li 보안공학연구지원센터 2016 International Journal of Multimedia and Ubiquitous Vol.11 No.12
Service technology has gained increasing popularity in recent communication software applied in many domains. With a growing number of services that share same or similar functionalities, clustering services help improve both service composition and mashup creation. To achieve service clustering, utilizing probabilistic topic model to extract and characterize the service description documents as corresponding topics is an available scheme. However, unlike short text in social networks, the descriptions of published services possess higher dimensionality and sparse functional information. With traditional LDA (Latent Dirichlet Allocation) model to implement topic extraction makes topics unclear. To address that challenge, we conduct a context sensitive approach to generate context sensitive vector for merging the words with similar context before loading to LDA model, referred to as CV-LDA (Context Vector LDA). Through F1-Measure of clustering and topic perplexity analysis in the real-world dataset, it is shown that the proposed approach outperforms traditional LDA model in service clustering.