The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms...The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.展开更多
This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to...This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).展开更多
Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.Th...Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.展开更多
Artificial gynogenesis is of great research value in fish genetics and breeding technology. However, existing studies did not explain the mechanism of some interesting phenomena. Severe developmental defects in gynoge...Artificial gynogenesis is of great research value in fish genetics and breeding technology. However, existing studies did not explain the mechanism of some interesting phenomena. Severe developmental defects in gynogenetic haploids can lead to death during hatching. After diploidization of chromosomes, gynogenetic diploids may dispense from the remarkable malformation and restore the viability, although the development time is longer and the survival rate is lower compared with normal diploids. The aim of this study was to reveal key mechanism in haploid syndrome of Japanese flounder, a commercially important marine teleost in East Asia. We measured genome-scale gene expression of flounder haploid, gynogenetic diploid and normal diploid embryos using RNA-Seq, constructed a module-centric co-expression network based on weighted correlation network analysis(WGCNA) and analyzed the biological functions of correlated modules. Module gene content analysis revealed that the formation of gynogenetic haploids was closely related to the abnormality of plasma proteins, and the up-regulation of p53 signaling pathway might rescue gynogenetic embryos from haploid syndrome via regulating cell cycle arrest, apoptosis and DNA repair. Moreover, normal diploid has more robust nervous system. This work provides novel insights into molecular mechanisms in haploid syndrome and the rescue process by gynogenetic diploidization.展开更多
In this paper, an empirical investigation is presented, which focuses on unveiling the universality of connectivity correlations in three spaces (the route space, the stop geographical space and bus-transferring spac...In this paper, an empirical investigation is presented, which focuses on unveiling the universality of connectivity correlations in three spaces (the route space, the stop geographical space and bus-transferring space) of urban bustransport networks (BTNs) in four major cities of China. The underlying features of the connectivity correlations are shown in two statistical ways. One is the correlation between the (weighted) average degree of all the nearest neighbouring vertices with degree k, (Knn^w,(k)) Knn(k), and k, and the other is the correlations between the assortativity coefficient r and, respectively, the network size N, the network diameter D, the averaged clustering coefficient C, and the averaged distance (l). The obtained results show qualitatively the same connectivity correlations of all the considered cities under all the three spaces.展开更多
We analyze the correlation properties of the Erd6s-Rdnyi random graph (RG) and the Barabdsi-Albert scale-free network (SF) under the attack and repair strategy with detrended fluctuation analysis (DFA). The maxi...We analyze the correlation properties of the Erd6s-Rdnyi random graph (RG) and the Barabdsi-Albert scale-free network (SF) under the attack and repair strategy with detrended fluctuation analysis (DFA). The maximum degree kmax, representing the local property of the system, shows similar scaling behaviors for random graphs and scale-free networks. The fluctuations are quite random at short time scales but display strong anticorrelation at longer time scales under the same system size N and different repair probability pre. The average degree 〈k〉, revealing the statistical property of the system, exhibits completely different scaling behaviors for random graphs and scale-free networks. Random graphs display long-range power-law correlations. Scale-free networks are uncorrelated at short time scales; while anticorrelated at longer time scales and the anticorrelation becoming stronger with the increase of pre.展开更多
Complex networks have been applied to model numerous interactive nonlinear systems in the real world. Knowledge about network topology is crucial to an understanding of the function, performance and evolution of compl...Complex networks have been applied to model numerous interactive nonlinear systems in the real world. Knowledge about network topology is crucial to an understanding of the function, performance and evolution of complex systems. In the last few years, many network metrics and models have been proposed to investigate the network topology, dynamics and evolution. Since these network metrics and models are derived from a wide range of studies, a systematic study is required to investigate the correlations among them. The present paper explores the effect of degree correlation on the other network metrics through studying an ensemble of graphs where the degree sequence (set of degrees) is fixed. We show that to some extent, the characteristic path length, clustering coefficient, modular extent and robustness of networks are directly influenced by the degree correlation.展开更多
This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP, FTP and SMTP. First, the phase space is reconstruc...This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP, FTP and SMTP. First, the phase space is reconstructed and the embedding parameters are obtained by the mutual information method. Secondly, the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature, i.e. HTTP and FTP traffics are chaotic, furthermore, the former is more complex than the later; on the other hand, SMTP traffic is stochastic. It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP, FTP and SMTP protocol network traffics. This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components.展开更多
Due to the openness of wireless multiuser networks,the private information transmitted in uplink or downlink is vulnerable to eavesdropping.Especially,when the downlink transmissions use nonorthogonal multiple access(...Due to the openness of wireless multiuser networks,the private information transmitted in uplink or downlink is vulnerable to eavesdropping.Especially,when the downlink transmissions use nonorthogonal multiple access(NOMA)techniques,the system further encounters interior eavesdropping.In order to address these security problems,we study the secret communication in multiuser networks with both uplink and downlink transmissions.Specifically,in uplink transmissions,the private messages transmitted in each slot are correlated,so any loss of the private information at the eavesdropper will prevent the eavesdropper from decoding the private information in later time slots.In downlink transmissions,the messages are correlated to the uplink information.In this way,any unexpected users who lose the expected user’s uplink information cannot decode its downlink information.The intercept probability is used to measure security performance and we analyze it in theory.Finally,simulation results are provided to corroborate our theoretical analysis.展开更多
In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a ...In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys.展开更多
Distributed denial of service(DDoS)attacks launch more and more frequently and are more destructive.Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense.Mos...Distributed denial of service(DDoS)attacks launch more and more frequently and are more destructive.Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense.Most DDoS feature extraction methods cannot fully utilize the information of the original data,resulting in the extracted features losing useful features.In this paper,a DDoS feature representation method based on deep belief network(DBN)is proposed.We quantify the original data by the size of the network flows,the distribution of IP addresses and ports,and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified values.Two feedforward neural networks(FFNN)are initialized by the trained deep belief network,and one of the feedforward neural networks continues to be trained in a supervised manner.The canonical correlation analysis(CCA)method is used to fuse the features extracted by two feedforward neural networks per layer.Experiments show that compared with other methods,the proposed method can extract better features.展开更多
Studied forecasting and controlling the blasting fragmentation by using artifi- cial neural network for multi-ingredients. At the same time, according to the characteris- tic of multi-parameters input to network model...Studied forecasting and controlling the blasting fragmentation by using artifi- cial neural network for multi-ingredients. At the same time, according to the characteris- tic of multi-parameters input to network model, the gray correlation theory was employed to find out key factors, which can not only save time of computation and parameters in- put, but improve the stability of the model.展开更多
Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced...Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.展开更多
Fluidization of non-spherical particles is very common in petroleum engineering.Understanding the complex phenomenon of non-spherical particle flow is of great significance.In this paper,coupled with two-fluid model,t...Fluidization of non-spherical particles is very common in petroleum engineering.Understanding the complex phenomenon of non-spherical particle flow is of great significance.In this paper,coupled with two-fluid model,the drag coefficient correlation based on artificial neural network was applied in the simulations of a bubbling fluidized bed filled with non-spherical particles.The simulation results were compared with the experimental data from the literature.Good agreement between the experimental data and the simulation results reveals that the modified drag model can accurately capture the interaction between the gas phase and solid phase.Then,several cases of different particles,including tetrahedron,cube,and sphere,together with the nylon beads used in the model validation,were employed in the simulations to study the effect of particle shape on the flow behaviors in the bubbling fluidized bed.Particle shape affects the hydrodynamics of non-spherical particles mainly on microscale.This work can be a basis and reference for the utilization of artificial neural network in the investigation of drag coefficient correlation in the dense gas-solid two-phase flow.Moreover,the proposed drag coefficient correlation provides one more option when investigating the hydrodynamics of non-spherical particles in the gas-solid fluidized bed.展开更多
Objective To establish correlation models between various physical examination indexes and traditional Chinese medicine(TCM)constitutions,and explore their relationships based on the radial basis function(RBF)neural n...Objective To establish correlation models between various physical examination indexes and traditional Chinese medicine(TCM)constitutions,and explore their relationships based on the radial basis function(RBF)neural network.Methods The raw data of physical examination indexes and TMC constitutions of 650 subjects who underwent a physical examination were cleaned,classified and sorted,on the basis of which valid data were retrieved and categorized into a training dataset and a test dataset.Subsequently,the RBF neural network was applied to the valid samples in the training set to establish correlation models between various physical examination indexes and TCM constitutions.The accuracy and the error margin of the correlation model were then verified using the valid samples in the test set.Results Of all selected samples,the highest accuracy rates were 80% for the blood lipid index-TCM constitution model;100% for the renal function index-TCM constitution model;100% for the blood routine(male)index-TCM constitution model;88.8% for the blood routine(female)index-TCM constitution model;84.1%for the urine routine index-TCM constitution model;and 100% for the blood transfusion index-TCM constitution model.Conclusions The samples selected in this study suggested that there is a strong correlation between physical examination indexes and TCM constitutions,making it feasible to apply the established correlation models to TCM constitution identification.展开更多
The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental...The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental data.However,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage.Therefore,the collected data may become inaccurate or incomplete.Focusing on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes.The Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more reliable.It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.展开更多
In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and tra...In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.展开更多
In the field of statistical mechanics and system science, it is acknowledged that the financial crisis has a profound influence on stock market. However, the influence of total asset of enterprise on stock quote was n...In the field of statistical mechanics and system science, it is acknowledged that the financial crisis has a profound influence on stock market. However, the influence of total asset of enterprise on stock quote was not considered in the previous studies. In this work, a modified cross-correlation matrix that focuses on the influence of total asset on stock quote is introduced into the analysis of the stocks collected from Asian and American stock markets, which is different from the previous studies. The key results are obtained as follows. Firstly, stock is more greatly correlated with big asset than with small asset. Secondly, the higher the correlation coefficient among stocks, the larger the eigenvector is. Thirdly, in different periods, like the pre-subprime crisis period and the peak of subprime crisis period, Asian stock quotes show that the component of the third eigenvector of the cross-correlation matrix decreases with the asset of the enterprise decreasing.Fourthly, by simulating the threshold network, the small network constructed by 10 stocks with large assets can show the large network state constructed by 30 stocks. In this research we intend to fully explain the physical mechanism for understanding the historical correlation between stocks and provide risk control strategies in the future.展开更多
This paper analyzes the relationship between capacity and dynamics in recurrent correlation neural network, and points out that in some conditions the recurrent correlation neural network has high memory capacity. The...This paper analyzes the relationship between capacity and dynamics in recurrent correlation neural network, and points out that in some conditions the recurrent correlation neural network has high memory capacity. Then this paper presents several methods for improving the performance.展开更多
We investigate the performances of the pairwise correlations(PCs) in different quantum networks consisting of fourwave mixers(FWMs) and beamsplitters(BSs). PCs with quantum correlation in different quantum netwo...We investigate the performances of the pairwise correlations(PCs) in different quantum networks consisting of fourwave mixers(FWMs) and beamsplitters(BSs). PCs with quantum correlation in different quantum networks can be verified by calculating the degree of relative intensity squeezing for any pair of all the output fields. More interestingly, the quantum correlation recovery and enhancement are present in the FWM+BS network and the repulsion effect phenomena(signal(idler)-frequency mode cannot be quantum correlated with the other two idler(signal)-frequency modes simultaneously)between the PCs with quantum correlation are predicted in the FWM + FWM and FWM + FWM + FWM networks. Our results presented here pave the way for the manipulation of the quantum correlation in quantum networks.展开更多
文摘The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.
基金supported by Beijing Natural Science Foundation (L202003)。
文摘This letter proposes a sliced-gated-convolutional neural network with belief propagation(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of SGCNNBP is using Neural Networks(NN) to transform the correlated noise into white noise, setting up the optimal condition for a standard BP decoder that takes the output from the NN. A gate-controlled neuron is used to regulate information flow and an optional operation—slicing is adopted to reduce parameters and lower training complexity. Simulation results show that SGCNN-BP has much better performance(with the largest gap being 5dB improvement) than a single BP decoder and achieves a nearly 1dB improvement compared to Fully Convolutional Networks(FCN).
基金supported by the Science and Technology Project of China Southern Power Grid(GZHKJXM20210043-080041KK52210002).
文摘Traditional distribution network planning relies on the professional knowledge of planners,especially when analyzing the correlations between the problems existing in the network and the crucial influencing factors.The inherent laws reflected by the historical data of the distribution network are ignored,which affects the objectivity of the planning scheme.In this study,to improve the efficiency and accuracy of distribution network planning,the characteristics of distribution network data were extracted using a data-mining technique,and correlation knowledge of existing problems in the network was obtained.A data-mining model based on correlation rules was established.The inputs of the model were the electrical characteristic indices screened using the gray correlation method.The Apriori algorithm was used to extract correlation knowledge from the operational data of the distribution network and obtain strong correlation rules.Degree of promotion and chi-square tests were used to verify the rationality of the strong correlation rules of the model output.In this study,the correlation relationship between heavy load or overload problems of distribution network feeders in different regions and related characteristic indices was determined,and the confidence of the correlation rules was obtained.These results can provide an effective basis for the formulation of a distribution network planning scheme.
基金supported by the Scientific and Technological Innovation Project of Qingdao National Laboratory for Marine Science and Technology (No. 2015A SKJ02)the National Natural Science Foundation of China (No. 31540063)
文摘Artificial gynogenesis is of great research value in fish genetics and breeding technology. However, existing studies did not explain the mechanism of some interesting phenomena. Severe developmental defects in gynogenetic haploids can lead to death during hatching. After diploidization of chromosomes, gynogenetic diploids may dispense from the remarkable malformation and restore the viability, although the development time is longer and the survival rate is lower compared with normal diploids. The aim of this study was to reveal key mechanism in haploid syndrome of Japanese flounder, a commercially important marine teleost in East Asia. We measured genome-scale gene expression of flounder haploid, gynogenetic diploid and normal diploid embryos using RNA-Seq, constructed a module-centric co-expression network based on weighted correlation network analysis(WGCNA) and analyzed the biological functions of correlated modules. Module gene content analysis revealed that the formation of gynogenetic haploids was closely related to the abnormality of plasma proteins, and the up-regulation of p53 signaling pathway might rescue gynogenetic embryos from haploid syndrome via regulating cell cycle arrest, apoptosis and DNA repair. Moreover, normal diploid has more robust nervous system. This work provides novel insights into molecular mechanisms in haploid syndrome and the rescue process by gynogenetic diploidization.
基金Project supported by the National Natural Science Foundation of China (Grant Nos 70671089 and 10635040)the foundation of XM06-142
文摘In this paper, an empirical investigation is presented, which focuses on unveiling the universality of connectivity correlations in three spaces (the route space, the stop geographical space and bus-transferring space) of urban bustransport networks (BTNs) in four major cities of China. The underlying features of the connectivity correlations are shown in two statistical ways. One is the correlation between the (weighted) average degree of all the nearest neighbouring vertices with degree k, (Knn^w,(k)) Knn(k), and k, and the other is the correlations between the assortativity coefficient r and, respectively, the network size N, the network diameter D, the averaged clustering coefficient C, and the averaged distance (l). The obtained results show qualitatively the same connectivity correlations of all the considered cities under all the three spaces.
基金The project supported by National Natural Science Foundation of China under Grant Nos. 70271067 and 70401020 and the Science Foundation of the Ministry of Education of China under Grant No. 03113
文摘We analyze the correlation properties of the Erd6s-Rdnyi random graph (RG) and the Barabdsi-Albert scale-free network (SF) under the attack and repair strategy with detrended fluctuation analysis (DFA). The maximum degree kmax, representing the local property of the system, shows similar scaling behaviors for random graphs and scale-free networks. The fluctuations are quite random at short time scales but display strong anticorrelation at longer time scales under the same system size N and different repair probability pre. The average degree 〈k〉, revealing the statistical property of the system, exhibits completely different scaling behaviors for random graphs and scale-free networks. Random graphs display long-range power-law correlations. Scale-free networks are uncorrelated at short time scales; while anticorrelated at longer time scales and the anticorrelation becoming stronger with the increase of pre.
基金Project supported by the Research Foundation from Ministry of Science and Technology, China (Grant Nos 2006AA02Z317,2004CB720103, 2003CB715901 and 2006AA02312), the National High Technology Research and Development Program of China (Grant No 2006AA020805), the National Natural Science Foundation of China (Grant Nos 30500107, 30670953 and 30670574), the International Cooperation Project of Science and Technology Commission of Shanghai Municipality, China (Grant No 06RS07109), and Grant from Science and Technology Commission of Shanghai Municipality, China (Grant Nos 04DZ19850, 06PJ14072 and 04DZ 14005).Acknowledgement We thank Luis A. Nunes Amaral and Roger Guimerà for kindly providing us with the software Modul-w of computing the network modularity metric. Our gratitude must also be extended to John Doyle, Petter Holme and Lun Li for useful discussion and constructive comments.
文摘Complex networks have been applied to model numerous interactive nonlinear systems in the real world. Knowledge about network topology is crucial to an understanding of the function, performance and evolution of complex systems. In the last few years, many network metrics and models have been proposed to investigate the network topology, dynamics and evolution. Since these network metrics and models are derived from a wide range of studies, a systematic study is required to investigate the correlations among them. The present paper explores the effect of degree correlation on the other network metrics through studying an ensemble of graphs where the degree sequence (set of degrees) is fixed. We show that to some extent, the characteristic path length, clustering coefficient, modular extent and robustness of networks are directly influenced by the degree correlation.
基金Project supported in part by the National High Technology Research and Development Program of China (Grant No. 2007AA01Z480)
文摘This paper uses a correlation dimension based nonlinear analysis approach to analyse the dynamics of network traffics with three different application protocols-HTTP, FTP and SMTP. First, the phase space is reconstructed and the embedding parameters are obtained by the mutual information method. Secondly, the correlation dimensions of three different traffics are calculated and the results of analysis have demonstrated that the dynamics of the three different application protocol traffics is different from each other in nature, i.e. HTTP and FTP traffics are chaotic, furthermore, the former is more complex than the later; on the other hand, SMTP traffic is stochastic. It is shown that correlation dimension approach is an efficient method to understand and to characterize the nonlinear dynamics of HTTP, FTP and SMTP protocol network traffics. This analysis provided insight into and a more accurate understanding of nonlinear dynamics of internet traffics which have a complex mixture of chaotic and stochastic components.
基金supported in part by the Fundamental Research Funds for the Central Universities(No.21620350)in part by the National Natural Science Foundation of China(No.62102167 and No.62032025)in part by the Guangdong Basic and Applied Basic Research Foundation(2020A1515110364).
文摘Due to the openness of wireless multiuser networks,the private information transmitted in uplink or downlink is vulnerable to eavesdropping.Especially,when the downlink transmissions use nonorthogonal multiple access(NOMA)techniques,the system further encounters interior eavesdropping.In order to address these security problems,we study the secret communication in multiuser networks with both uplink and downlink transmissions.Specifically,in uplink transmissions,the private messages transmitted in each slot are correlated,so any loss of the private information at the eavesdropper will prevent the eavesdropper from decoding the private information in later time slots.In downlink transmissions,the messages are correlated to the uplink information.In this way,any unexpected users who lose the expected user’s uplink information cannot decode its downlink information.The intercept probability is used to measure security performance and we analyze it in theory.Finally,simulation results are provided to corroborate our theoretical analysis.
文摘In this paper, we use the global search characteristics of genetic algorithms to help search the weight space of the neurons in the cascade-correlation architecture. The cascade-correlation learning architecture is a technique of training and building neural networks that starts with a simple network of neurons and adds additional neurons as they are needed to suit a particular problem. In our approach, instead ofmodifying the genetic algorithm to account for convergence problems, we search the weight-space using the genetic algorithm and then apply the gradient technique of Quickprop to optimize the weights. This hybrid algorithm which is a combination of genetic algorithms and cascade-correlation is applied to the two spirals problem. We also use our algorithm in the prediction of the cyclic oxidation resistance of Ni- and Co-base superalloys.
基金supported by the National Natural Science Foundation of Hainan(2018CXTD333,617048)National Natural Science Foundation of China(61762033,61702539)+4 种基金The National Natural Science Foundation of Hunan(2018JJ3611)Social Development Project of Public Welfare Technology Application of Zhejiang Province(LGF18F020019)Hainan University Doctor Start Fund Project(kyqd1328)Hainan University Youth Fund Project(qnjj1444)State Key Laboratory of Marine Resource Utilization in South China Sea Funding.
文摘Distributed denial of service(DDoS)attacks launch more and more frequently and are more destructive.Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense.Most DDoS feature extraction methods cannot fully utilize the information of the original data,resulting in the extracted features losing useful features.In this paper,a DDoS feature representation method based on deep belief network(DBN)is proposed.We quantify the original data by the size of the network flows,the distribution of IP addresses and ports,and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified values.Two feedforward neural networks(FFNN)are initialized by the trained deep belief network,and one of the feedforward neural networks continues to be trained in a supervised manner.The canonical correlation analysis(CCA)method is used to fuse the features extracted by two feedforward neural networks per layer.Experiments show that compared with other methods,the proposed method can extract better features.
文摘Studied forecasting and controlling the blasting fragmentation by using artifi- cial neural network for multi-ingredients. At the same time, according to the characteris- tic of multi-parameters input to network model, the gray correlation theory was employed to find out key factors, which can not only save time of computation and parameters in- put, but improve the stability of the model.
基金supported by National Natural Science Foundation of China(Grant Nos.52279137,52009090).
文摘Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.
基金the financial support by the National Natural Science Foundation of China(Grant No.51706055).
文摘Fluidization of non-spherical particles is very common in petroleum engineering.Understanding the complex phenomenon of non-spherical particle flow is of great significance.In this paper,coupled with two-fluid model,the drag coefficient correlation based on artificial neural network was applied in the simulations of a bubbling fluidized bed filled with non-spherical particles.The simulation results were compared with the experimental data from the literature.Good agreement between the experimental data and the simulation results reveals that the modified drag model can accurately capture the interaction between the gas phase and solid phase.Then,several cases of different particles,including tetrahedron,cube,and sphere,together with the nylon beads used in the model validation,were employed in the simulations to study the effect of particle shape on the flow behaviors in the bubbling fluidized bed.Particle shape affects the hydrodynamics of non-spherical particles mainly on microscale.This work can be a basis and reference for the utilization of artificial neural network in the investigation of drag coefficient correlation in the dense gas-solid two-phase flow.Moreover,the proposed drag coefficient correlation provides one more option when investigating the hydrodynamics of non-spherical particles in the gas-solid fluidized bed.
基金the funding support from the National Key Research and Development Project of China(No.2018YFC1707606)National Natural Science Foundation of China(No.81904324)Youth Foundation of Sichuan Administration of Traditional Chinese Medicine(No.2016Q065).
文摘Objective To establish correlation models between various physical examination indexes and traditional Chinese medicine(TCM)constitutions,and explore their relationships based on the radial basis function(RBF)neural network.Methods The raw data of physical examination indexes and TMC constitutions of 650 subjects who underwent a physical examination were cleaned,classified and sorted,on the basis of which valid data were retrieved and categorized into a training dataset and a test dataset.Subsequently,the RBF neural network was applied to the valid samples in the training set to establish correlation models between various physical examination indexes and TCM constitutions.The accuracy and the error margin of the correlation model were then verified using the valid samples in the test set.Results Of all selected samples,the highest accuracy rates were 80% for the blood lipid index-TCM constitution model;100% for the renal function index-TCM constitution model;100% for the blood routine(male)index-TCM constitution model;88.8% for the blood routine(female)index-TCM constitution model;84.1%for the urine routine index-TCM constitution model;and 100% for the blood transfusion index-TCM constitution model.Conclusions The samples selected in this study suggested that there is a strong correlation between physical examination indexes and TCM constitutions,making it feasible to apply the established correlation models to TCM constitution identification.
基金Funding for this research is provided by the Natural Sciences and Engineering Research Council of Canada
文摘The Wireless Sensor Networks(WSNs)are widely utilized in various industrial and environmental monitoring applications.The process of data gathering within the WSN is significant in terms of reporting the environmental data.However,it might occur that certain sensor node malfunctions due to the energy draining out or unexpected damage.Therefore,the collected data may become inaccurate or incomplete.Focusing on the spatiotemporal correlation among sensor nodes,this paper proposes a novel algorithm to predict the value of the missing or inaccurate data and predict the future data in replacement of certain nonfunctional sensor nodes.The Long-Short-Term-Memory Recurrent Neural Network(LSTM RNN)helps to more accurately derive the time-series data corresponding to the sets of past collected data,making the prediction results more reliable.It is observed from the simulation results that the proposed algorithm provides an outstanding data gathering efficiency while ensuring the data accuracy.
基金supported by the National Natural Science Foundation of China(No.61871400)the Natural Science Foundation of the Jiangsu Province of China(No.BK20171401)。
文摘In wireless sensor networks(WSNs),the performance of related applications is highly dependent on the quality of data collected.Unfortunately,missing data is almost inevitable in the process of data acquisition and transmission.Existing methods often rely on prior information such as low-rank characteristics or spatiotemporal correlation when recovering missing WSNs data.However,in realistic application scenarios,it is very difficult to obtain these prior information from incomplete data sets.Therefore,we aim to recover the missing WSNs data effectively while getting rid of the perplexity of prior information.By designing the corresponding measurement matrix that can capture the position of missing data and sparse representation matrix,a compressive sensing(CS)based missing data recovery model is established.Then,we design a comparison standard to select the best sparse representation basis and introduce average cross-correlation to examine the rationality of the established model.Furthermore,an improved fast matching pursuit algorithm is proposed to solve the model.Simulation results show that the proposed method can effectively recover the missing WSNs data.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11705042 and 71874172)the China Postdoctoral Science Foundation(Grant Nos.2018T110040 and 2016M590041)+2 种基金the Fundamental Research Funds for Central Universities,China(Grant No.JZ2018HGTB0238)Curriculum Planning and Design Research Project,China(Grant No.102-033119)the Teaching Quality and Teaching Reform Project,China(Grant No.JYQZ1815)
文摘In the field of statistical mechanics and system science, it is acknowledged that the financial crisis has a profound influence on stock market. However, the influence of total asset of enterprise on stock quote was not considered in the previous studies. In this work, a modified cross-correlation matrix that focuses on the influence of total asset on stock quote is introduced into the analysis of the stocks collected from Asian and American stock markets, which is different from the previous studies. The key results are obtained as follows. Firstly, stock is more greatly correlated with big asset than with small asset. Secondly, the higher the correlation coefficient among stocks, the larger the eigenvector is. Thirdly, in different periods, like the pre-subprime crisis period and the peak of subprime crisis period, Asian stock quotes show that the component of the third eigenvector of the cross-correlation matrix decreases with the asset of the enterprise decreasing.Fourthly, by simulating the threshold network, the small network constructed by 10 stocks with large assets can show the large network state constructed by 30 stocks. In this research we intend to fully explain the physical mechanism for understanding the historical correlation between stocks and provide risk control strategies in the future.
文摘This paper analyzes the relationship between capacity and dynamics in recurrent correlation neural network, and points out that in some conditions the recurrent correlation neural network has high memory capacity. Then this paper presents several methods for improving the performance.
基金Project supported by the National Natural Science Foundation of China(Grants Nos.91436211,11374104,and 10974057)the Natural Science Foundation of Shanghai,China(Grant No.17ZR1442900)+5 种基金the Specialized Research Fund for the Doctoral Program of Higher Education,China(Grant No.20130076110011)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning,the Program for New Century Excellent Talents in University,China(Grant No.NCET-10-0383)the Shu Guang Project supported by Shanghai Municipal Education Commission and Shanghai Education Development Foundation,China(Grant No.11SG26)the Shanghai Pujiang Program,China(Grant No.09PJ1404400)the Scientific Research Foundation for the Returned Overseas Chinese Scholars,State Education Ministry,National Basic Research Program of China(Grant No.2016YFA0302103)the Program of State Key Laboratory of Advanced 207 Optical Communication Systems and Networks,China(Grant No.2016GZKF0JT003)
文摘We investigate the performances of the pairwise correlations(PCs) in different quantum networks consisting of fourwave mixers(FWMs) and beamsplitters(BSs). PCs with quantum correlation in different quantum networks can be verified by calculating the degree of relative intensity squeezing for any pair of all the output fields. More interestingly, the quantum correlation recovery and enhancement are present in the FWM+BS network and the repulsion effect phenomena(signal(idler)-frequency mode cannot be quantum correlated with the other two idler(signal)-frequency modes simultaneously)between the PCs with quantum correlation are predicted in the FWM + FWM and FWM + FWM + FWM networks. Our results presented here pave the way for the manipulation of the quantum correlation in quantum networks.