Efficient and robust missing key tag identification for large-scale RFID systems

Radio Frequency Identification(RFID)technology has been widely used to identify missing items.In many applications,rapidly pinpointing key tags that are attached to favorable or valuable items is critical.To realize this goal,interference from ordinary tags should be avoided,while key tags should be efficiently verified.Despite many previous studies,how to rapidly and dynamically filter out ordinary tags when the ratio of ordinary tags changes has not been addressed.Moreover,how to efficiently verify missing key tags in groups rather than one by one has not been explored,especially with varying missing rates.In this paper,we propose an Efficient and Robust missing Key tag Identification(ERKI)protocol that consists of a filtering mechanism and a verification mechanism.Specifically,the filtering mechanism adopts the Bloom filter to quickly filter out ordinary tags and uses the labeling vector to optimize the Bloom filter's performance when the key tag ratio is high.Furthermore,the verification mechanism can dynamically verify key tags according to the missing rates,in which an appropriate number of key tags is mapped to a slot and verified at once.Moreover,we theoretically analyze the parameters of the ERKI protocol to minimize its execution time.Extensive numerical results show that ERKI can accelerate the execution time by more than 2.14compared with state-of-the-art solutions.

Identification of Key Links in Electric Power Operation Based-Spatiotemporal Mixing Convolution Neural Network

As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore,more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method,this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks,first,the video stream is framed adaptively according to the pixel changes in the video stream.Then,the optimized MobileNet is used to extract the feature map of the video stream,which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally,training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study,and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation,and has good real-time performance.The average accuracy of the algorithm can reach 87.8%,and the frame rate is 61 frames/s,which is of great significance for improving the reliability and accuracy of security control methods.

Analysis of identification methods of key nodes in transportation network

The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.

The Fuzzy Cluster Analysis in Identification of Key Temperatures in Machine Tool

The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.

Identification of Key Genes in Cotton Fiber

Twenty-eight candidate genes provided by other sub-projects were used to produce transgenic cotton plants.There were over 1000 individuals,and some of them were generation T2 or T3.All

A new species of Phyllochaetopterus Grube,1863(Polychaeta:Chaetopteridae)from Hainan Island,South China Sea

Abstract Phyllochaetopterus species are widely distributed on the coast of China. Here, Phyllochaetopterus hainanensis n. sp., a new species collected from Hainan Island （China）, is reported. It is characterized by having a V-shaped peristomium, two eyespots covered by a pair of large curved peristomial notopodia （cirri located beneath the palps）, 13-14 chaetigers in the anterior body region, with three enlarged modified chaetae on the fourth notopodium, and more than five chaetigers in the middle body region. The modified chaeta has a slightly inflated head with an obliquely truncate end. The new species resembles Phyllochaetopterus socialis Clapar6de, 1869, but differs in the shape of peristomial notopodia and peristomium. Twelve species of Phyllochaetopterus have been described from the Pacific Ocean, including the new species described here. An identification key to the known Pacific species is provided together with a brief discussion of the taxonomic value of the eyespots for the genus.

Re-evaluation of Rostraria bierii Tokioka,1970(Annelida)from Seto,Japan as a magelonid,with a review of the Magelonidae of the western Pacific

The identity of Rostraria bierii,originally described as a larval amphinomid from Cape Setozaki,Pacific coast of Japan,is investigated.Based on the original description and illustrations,reinterpretations conclude the“larva”to represent a partial juvenile or adult magelonid specimen,broken after the first chaetiger.The original figures are compared with several known magelonid species to justify the new placement.The authors suggest the supposed amphinomid larva is a Magelonidae taxon inquirendum.The identity of the species is discussed in line with the current knowledge of the Magelonidae in the western Pacific and a key to all known species within the region is provided to aid identifications.Current gaps in our taxonomic knowledge of the Magelonidae of the western Pacific are highlighted and discussed.

Fault Diagnosis Based on Fuzzy Support Vector Machine with Parameter Tuning and Feature Selection

This study describes a classification methodology based on support vector machines(SVMs),which offer superior classification performance for fault diagnosis in chemical process engineering.The method incorporates an efficient parameter tuning procedure(based on minimization of radius/margin bound for SVM's leave-one-out errors)into a multi-class classification strategy using a fuzzy decision factor,which is named fuzzy support vector machine(FSVM).The datasets generated from the Tennessee Eastman process(TEP)simulator were used to evaluate the clas-sification performance.To decrease the negative influence of the auto-correlated and irrelevant variables,a key vari-able identification procedure using recursive feature elimination,based on the SVM is implemented,with time lags incorporated,before every classifier is trained,and the number of relatively important variables to every classifier is basically determined by 10-fold cross-validation.Performance comparisons are implemented among several kinds of multi-class decision machines,by which the effectiveness of the proposed approach is proved.

Molecular evidence for novel Cantharellus (Cantharellales, Basidiomycota) from tropical African miombo woodland and a key to all tropical African chanterelles

The authors present a combined morphological and molecular approach of the genus Cantharellus in Africa.Morphological descriptions and detailed illustrations are provided for five new species from the Zambezian savannah woodlands in tropical Africa:C.afrocibarius,C.gracilis,C.humidicolus,C.miomboensis and C.tanzanicus.A maximum likelihood analysis of tef-1 sequences obtained for 83 collections of Cantharellus that are representative of all major groups in world wide Cantharellus,places a total of 13 African chanterelles,including the five newly described taxa.The recognition of a separate genus Afrocantharellus is rejected.An identification key based on the re-examination of all existing type material is provided for all presently known African Cantharellus.

Sequence data reveal a high diversity of Cantharellus associated with endemic vegetation in Madagascar

This revision of Cantharellus in Madagascar deals with species that are associated with strictly endemic host trees and shrubs.Based on morphological differences and molecular sequence data of the tef-1 gene,five new species are proposed(C.albidolutescens,C.ambohitantelyensis,C.ibityensis,C.paucifurcatus and C.sebosus),as well as one new subspecies,C.subincarnatus ssp.rubrosalmoneus,whereas C.decolorans and C.platyphyllus ssp.bojeriensis are epitypified.A key is provided to all Cantharellus that grow with native vegetation in Madagascar.