Mitigating tag collisions is paramount for enhancing throughput in Radio Frequency Identification(RFID)systems.However,traditional algorithms encounter challenges like slot wastage and inefficient frame length adjustm...Mitigating tag collisions is paramount for enhancing throughput in Radio Frequency Identification(RFID)systems.However,traditional algorithms encounter challenges like slot wastage and inefficient frame length adjustments.To tackle these challenges,the Slot Prediction Q(SPQ)algorithm was introduced,integrating the VogtII prediction algorithm and slot grouping to improve the initial Q value by predicting the first frame.This method quickly estimates the number of tags based on slot utilization,accelerating Q value adjustments when slot utilization is low.Furthermore,a Markov decision chain is used to optimize the relationship between the number of slot groupings(x)and the Q value.The Whale Optimization Algorithm(WOA)is applied to fine-tune the learning rate(C)and Q value in the traditional Q algorithm.Simulation results demonstrate that SPQ significantly reduces the total slots used during the reading process and improves RFID system throughput compared to traditional Q,FastQ,Subset Enhanced Performance-Q(SUBEP-Q),and Threshold Grouping Dynamic Q(TGDQ)algorithms.Specifically,compared to the traditional Q algorithm,SPQ increases the average Identification Speed by 7.20%,System Efficiency by 11.08%,and Time Efficiency by 5.69%.展开更多
In this work,an optimal Q algorithm based on a collision recovery scheme is presented. Tags use BIBD-( 16,4,1) codes instead of RN16 s. Therefore,readers can make a valid recognition even in collision slots. A way of ...In this work,an optimal Q algorithm based on a collision recovery scheme is presented. Tags use BIBD-( 16,4,1) codes instead of RN16 s. Therefore,readers can make a valid recognition even in collision slots. A way of getting the optimal slot-count parameter is studied and an optimal Q algorithm is proposed. The theoretical and simulation results show that the proposed algorithm can improve reading efficiency by 100% more than the conventional Q algorithm. Moreover,the proposed scheme changes little to the existing standard. Thus,it is easy to implement and compatible with ISO 18000-6C.展开更多
Objective:To explore core acupoints and acupoint selection principles in acupuncture and moxibustion for obesity,from syndrome differentiation prescriptions of the acupuncture-moxibustion therapy in 808 obesity prescr...Objective:To explore core acupoints and acupoint selection principles in acupuncture and moxibustion for obesity,from syndrome differentiation prescriptions of the acupuncture-moxibustion therapy in 808 obesity prescriptions,by using node centrality and cluster analysis methods in complex network.Methods:Firstly,an acupoint network model is established,and acupoint nodes are assessed and calculated in multiple aspects by introducing the node centrality analysis idea of complex network,to excavate core acupoint nodes.Secondly,a cluster analysis is carried out on acupoint network by the cluster algorithm Q-PSO for complex network,to investigate the acupoint combination principles.Results:Zusanli(足三里ST36),Tianshu(天枢ST25),Fenglong(丰隆ST40),Zhongwan(中脘CV12)and Qihai(气海CV6),etc.,were included into the core acupoint Sanyinjiao(三阴交SP6)community.Zhigou(支沟TE6),Neiting(内庭ST44),Shangjuxu(上巨虚ST37),and Pishu(脾俞BL20)etc.,were included into the core acupoint Yinlingquan(阴陵泉SP9)community.Baihuanshu(白环俞BL30)and Zhiyang(至阳GV9)were included into the core acupoint Dachangshu(大肠俞BL25)community.Biguan(髀关ST31)was a single core community.Among all the acupoint nodes,SP6,ST25,SP9,ST36,CV6,Quchi(曲池L111),and Guanyuan(关元CV4)were of high degree centrality and eigenvector centrality,directly reflecting their importance in acupoint selection prescriptions.Conclusion:The Q-PSO algorithm is characterized with high precision and high efficiency,etc.The core acupoints and their combination principles explored by this algorithm are in accordance with clinical experiences.展开更多
基金supported by National Key Research and Development Program of China(2022YFB4703102)National Natural Science Foundation of China(62273105).
文摘Mitigating tag collisions is paramount for enhancing throughput in Radio Frequency Identification(RFID)systems.However,traditional algorithms encounter challenges like slot wastage and inefficient frame length adjustments.To tackle these challenges,the Slot Prediction Q(SPQ)algorithm was introduced,integrating the VogtII prediction algorithm and slot grouping to improve the initial Q value by predicting the first frame.This method quickly estimates the number of tags based on slot utilization,accelerating Q value adjustments when slot utilization is low.Furthermore,a Markov decision chain is used to optimize the relationship between the number of slot groupings(x)and the Q value.The Whale Optimization Algorithm(WOA)is applied to fine-tune the learning rate(C)and Q value in the traditional Q algorithm.Simulation results demonstrate that SPQ significantly reduces the total slots used during the reading process and improves RFID system throughput compared to traditional Q,FastQ,Subset Enhanced Performance-Q(SUBEP-Q),and Threshold Grouping Dynamic Q(TGDQ)algorithms.Specifically,compared to the traditional Q algorithm,SPQ increases the average Identification Speed by 7.20%,System Efficiency by 11.08%,and Time Efficiency by 5.69%.
基金Supported by the National Natural Science Foundation of China(No.61340005)Beijing Natural Science Foundation(No.4132012)+2 种基金Beijing Education Committee Science and Technology Development Plan(No.KM201411232011)Beijing Outstanding Personnel Training Project(No.2013D005007000006)Scientific Research Improving Project-Intelligent Sense and Information Processing(No.5211524100)
文摘In this work,an optimal Q algorithm based on a collision recovery scheme is presented. Tags use BIBD-( 16,4,1) codes instead of RN16 s. Therefore,readers can make a valid recognition even in collision slots. A way of getting the optimal slot-count parameter is studied and an optimal Q algorithm is proposed. The theoretical and simulation results show that the proposed algorithm can improve reading efficiency by 100% more than the conventional Q algorithm. Moreover,the proposed scheme changes little to the existing standard. Thus,it is easy to implement and compatible with ISO 18000-6C.
基金Supported by Hubei Health & Family Planning Commission Notice (No. [2017]20)Wuhan training project of the sixth batch of young and middle-aged medical talents, wuhan health & family planning commission (Wuhan Health & Family Planning Commission Notice No. [2018]116)Training project of the first batch of tanhualin famous doctors and students (Hubei TCM Hospital No. [2018]72)
文摘Objective:To explore core acupoints and acupoint selection principles in acupuncture and moxibustion for obesity,from syndrome differentiation prescriptions of the acupuncture-moxibustion therapy in 808 obesity prescriptions,by using node centrality and cluster analysis methods in complex network.Methods:Firstly,an acupoint network model is established,and acupoint nodes are assessed and calculated in multiple aspects by introducing the node centrality analysis idea of complex network,to excavate core acupoint nodes.Secondly,a cluster analysis is carried out on acupoint network by the cluster algorithm Q-PSO for complex network,to investigate the acupoint combination principles.Results:Zusanli(足三里ST36),Tianshu(天枢ST25),Fenglong(丰隆ST40),Zhongwan(中脘CV12)and Qihai(气海CV6),etc.,were included into the core acupoint Sanyinjiao(三阴交SP6)community.Zhigou(支沟TE6),Neiting(内庭ST44),Shangjuxu(上巨虚ST37),and Pishu(脾俞BL20)etc.,were included into the core acupoint Yinlingquan(阴陵泉SP9)community.Baihuanshu(白环俞BL30)and Zhiyang(至阳GV9)were included into the core acupoint Dachangshu(大肠俞BL25)community.Biguan(髀关ST31)was a single core community.Among all the acupoint nodes,SP6,ST25,SP9,ST36,CV6,Quchi(曲池L111),and Guanyuan(关元CV4)were of high degree centrality and eigenvector centrality,directly reflecting their importance in acupoint selection prescriptions.Conclusion:The Q-PSO algorithm is characterized with high precision and high efficiency,etc.The core acupoints and their combination principles explored by this algorithm are in accordance with clinical experiences.
