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An Improved Nonlinear Multi-Objective Optimization Problem Based on Genetic Algorithm
Yali Yun,Yaping Li 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.7
Genetic algorithms for multi-objective optimization problem to be solved were studied. Through the elitist strategy analysis, it is an improved multi-objective optimization algorithm. The algorithm uses a data warehouse to store the optimal solution produced by individuals in each generation, from the way individuals adopt measures to phase out the individual data warehouse identical or similar, the algorithm also improved selection operator, so that the algorithm adaptive capacity enhancement, the new algorithm improves the algorithm performance, improves the quality of understanding between sets, can get a lot of optimal and balanced.
Yun Deng,Yali Luo,Bingjun Qian,Zhenmin Liu,Yuanrong Zheng,Xiaoyong Song,Shaojuan Lai,Yanyun Zhao 한국식품과학회 2014 Food Science and Biotechnology Vol.23 No.2
The antihypertensive activity of few-flower wildrice was studied in spontaneously hypertensive rats (SHRs)with evaluation of blood pressure lowering effects andtranscriptional levels of the sarcoplasmic reticulum Ca2+-ATPase (SERCA2a) gene that is regulated by AngiotensinII (Ang II). SHRs were randomly divided into 5 groupswith 6 rats each. The systolic blood pressure (SBP) reachedthe lowest point 3 h after administration of a single dose ofpaste made from few-flower wild rice stem powder. TheSBP of SHR in the relatively high amount of RSP (HRSP)administrated group was reduced by approximately 30mmHg, compared to the negative control group, and wasnot significantly different from the positive control IPPcontrol group at a dose of 1.5 mg/kg body weight (p>0.05). RSP administrated SHRs showed a significantly higherSERCA2a transcription level than negative control SHRs(p<0.05). RSP administration had no negative effects onglycometabolism of SHR.
Coverage Holes Compensation Algorithms Based on Event-Driven Strategy in Wireless Sensor Networks
Zeyu Sun,Yali Yun,Yalin Nie,Yuanbo Li 보안공학연구지원센터 2016 International Journal of Future Generation Communi Vol.9 No.9
The process of random deployments, Coverage holes’ phenomena were appeared in wireless sensor network system. This paper presents a probabilistic model by means of event-driven policy coverage holes’ compensation method (Coverage Holes Compensation Algorithms Based on Event-Driven Strategy, CHCAEDS). Firstly, the characteristics of random deployment verified, given the random deployment of representation, followed by the use of probabilistic knowledge within the surveillance area coverage desired and the number of nodes is solved using the minimum number of nodes in order to achieve maximum coverage area; and finally, simulation experiment show, CHCAEDS algorithm with other algorithms in the network life cycle and the algorithm running time increased by 12.59% and10.82%.
CCAJS: A Novel Connect Coverage Algorithm Based on Joint Sensing Model for Wireless Sensor Networks
( Zeyu Sun ),( Yali Yun ),( Houbing Song ),( Huihui Wang ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.10
This paper discusses how to effectively guarantee the coverage and connectivity quality of wireless sensor networks when joint perception model is used for the nodes whose communication ranges are multi-level adjustable in the absence of position information. A Connect Coverage Algorithm Based on Joint Sensing model (CCAJS) is proposed, with which least working nodes are chosen based on probability model ensuring the coverage quality of the network. The algorithm can balance the position distribution of selected working nodes as far as possible, as well as reduce the overall energy consumption of the whole network. The simulation results show that, less working nodes are needed to ensure the coverage quality of networks using joint perception model than using the binary perception model. CCAJS can not only satisfy expected coverage quality and connectivity, but also decrease the energy consumption, thereby prolonging the network lifetime.