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
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
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
For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is p...For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is proposed.This method approximately simulates the track correlation determination process using artificial data,and integrally matches the relative position relation between multiple targets in the common measuring space of various sensors in order to fulfill the purpose of multi-target track correlation.The simulation results show that this method has high correlation accuracy and robustness.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
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
Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with ot...Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.展开更多
At present,research on multi-label image classification mainly focuses on exploring the correlation between labels to improve the classification accuracy of multi-label images.However,in existing methods,label correla...At present,research on multi-label image classification mainly focuses on exploring the correlation between labels to improve the classification accuracy of multi-label images.However,in existing methods,label correlation is calculated based on the statistical information of the data.This label correlation is global and depends on the dataset,not suitable for all samples.In the process of extracting image features,the characteristic information of small objects in the image is easily lost,resulting in a low classification accuracy of small objects.To this end,this paper proposes a multi-label image classification model based on multiscale fusion and adaptive label correlation.The main idea is:first,the feature maps of multiple scales are fused to enhance the feature information of small objects.Semantic guidance decomposes the fusion feature map into feature vectors of each category,then adaptively mines the correlation between categories in the image through the self-attention mechanism of graph attention network,and obtains feature vectors containing category-related information for the final classification.The mean average precision of the model on the two public datasets of VOC 2007 and MS COCO 2014 reached 95.6% and 83.6%,respectively,and most of the indicators are better than those of the existing latest methods.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
The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs.However,it suffers from a small amount of training data and expensive computing time.In this work,we propose t...The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs.However,it suffers from a small amount of training data and expensive computing time.In this work,we propose the Attention Based Dense Network(ASDNet)for the stratigraphic correlation of well logs.To implement the suggested model,we first employ the attention mechanism to the input well logs,which can effectively generate the weighted well logs to serve for further feature extraction.Subsequently,the DenseNet is utilized to achieve good feature reuse and avoid gradient vanishing.After model training,we employ the ASDNet to the testing data set and evaluate its performance based on the well log data set from Northwest China.Finally,the numerical results demonstrate that the suggested ASDNet provides higher prediction accuracy for automated stratigraphic correlation of well logs than state-of-the-art contrastive UNet and SegNet.展开更多
Establishing the structure-property relationship in amorphous materials has been a long-term grand challenge due to the lack of a unified description of the degree of disorder.In this work,we develop SPRamNet,a neural...Establishing the structure-property relationship in amorphous materials has been a long-term grand challenge due to the lack of a unified description of the degree of disorder.In this work,we develop SPRamNet,a neural network based machine-learning pipeline that effectively predicts structure-property relationship of amorphous material via global descriptors.Applying SPRamNet on the recently discovered amorphous monolayer carbon,we successfully predict the thermal and electronic properties.More importantly,we reveal that a short range of pair correlation function can readily encode sufficiently rich information of the structure of amorphous material.Utilizing powerful machine learning architectures,the encoded information can be decoded to reconstruct macroscopic properties involving many-body and long-range interactions.Establishing this hidden relationship offers a unified description of the degree of disorder and eliminates the heavy burden of measuring atomic structure,opening a new avenue in studying amorphous materials.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
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 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 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.
基金Supported by the National Basic Research Program of China (973 Program) (2006CB303000)
文摘For the problem of track correlation failure under the influence of sensor system deviation in wireless sensor networks,a new track correlation method which is based on relative positional relation chart matching is proposed.This method approximately simulates the track correlation determination process using artificial data,and integrally matches the relative position relation between multiple targets in the common measuring space of various sensors in order to fulfill the purpose of multi-target track correlation.The simulation results show that this method has high correlation accuracy and robustness.
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Project supported by the National Natural Science Foundation of China (Grant Nos. 11875042 and 11505114)the Shanghai Project for Construction of Top Disciplines (Grant No. USST-SYS-01)。
文摘Detecting coupling pattern between elements in a complex system is a basic task in data-driven analysis. The trajectory for each specific element is a cooperative result of its intrinsic dynamic, its couplings with other elements, and the environment. It is subsequently composed of many components, only some of which take part in the couplings. In this paper we present a framework to detect the component correlation pattern. Firstly, the interested trajectories are decomposed into components by using decomposing methods such as the Fourier expansion and the Wavelet transformation. Secondly, the cross-correlations between the components are calculated, resulting into a component cross-correlation matrix(network).Finally, the dominant structure in the network is identified to characterize the coupling pattern in the system. Several deterministic dynamical models turn out to be characterized with rich structures such as the clustering of the components. The pattern of correlation between respiratory(RESP) and ECG signals is composed of five sub-clusters that are mainly formed by the components in ECG signal. Interestingly, only 7 components from RESP(scattered in four sub-clusters) take part in the realization of coupling between the two signals.
基金the National Natural Science Foundation of China(Nos.62167005 and 61966018)the Key Research Projects of Jiangxi Provincial Department of Education(No.GJJ200302)。
文摘At present,research on multi-label image classification mainly focuses on exploring the correlation between labels to improve the classification accuracy of multi-label images.However,in existing methods,label correlation is calculated based on the statistical information of the data.This label correlation is global and depends on the dataset,not suitable for all samples.In the process of extracting image features,the characteristic information of small objects in the image is easily lost,resulting in a low classification accuracy of small objects.To this end,this paper proposes a multi-label image classification model based on multiscale fusion and adaptive label correlation.The main idea is:first,the feature maps of multiple scales are fused to enhance the feature information of small objects.Semantic guidance decomposes the fusion feature map into feature vectors of each category,then adaptively mines the correlation between categories in the image through the self-attention mechanism of graph attention network,and obtains feature vectors containing category-related information for the final classification.The mean average precision of the model on the two public datasets of VOC 2007 and MS COCO 2014 reached 95.6% and 83.6%,respectively,and most of the indicators are better than those of the existing latest methods.
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.
基金supported by the Key Research and Development Program of Shaanxi,China under Grant 2023-YBGY-076the Fundamental Research Funds for the Central Universities,China under Grant XZY012022086the China Postdoctoral Science Foundation Project under Grant 2022M712509.
文摘The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs.However,it suffers from a small amount of training data and expensive computing time.In this work,we propose the Attention Based Dense Network(ASDNet)for the stratigraphic correlation of well logs.To implement the suggested model,we first employ the attention mechanism to the input well logs,which can effectively generate the weighted well logs to serve for further feature extraction.Subsequently,the DenseNet is utilized to achieve good feature reuse and avoid gradient vanishing.After model training,we employ the ASDNet to the testing data set and evaluate its performance based on the well log data set from Northwest China.Finally,the numerical results demonstrate that the suggested ASDNet provides higher prediction accuracy for automated stratigraphic correlation of well logs than state-of-the-art contrastive UNet and SegNet.
基金supported by the National Key R&D Program of China under Grant No.2021YFA1400500the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No.XDB33000000+1 种基金the National Natural Science Foundation of China under Grant No.12334003the Beijing Municipal Natural Science Foundation under Grant Nos.JQ22001 and QY23014。
文摘Establishing the structure-property relationship in amorphous materials has been a long-term grand challenge due to the lack of a unified description of the degree of disorder.In this work,we develop SPRamNet,a neural network based machine-learning pipeline that effectively predicts structure-property relationship of amorphous material via global descriptors.Applying SPRamNet on the recently discovered amorphous monolayer carbon,we successfully predict the thermal and electronic properties.More importantly,we reveal that a short range of pair correlation function can readily encode sufficiently rich information of the structure of amorphous material.Utilizing powerful machine learning architectures,the encoded information can be decoded to reconstruct macroscopic properties involving many-body and long-range interactions.Establishing this hidden relationship offers a unified description of the degree of disorder and eliminates the heavy burden of measuring atomic structure,opening a new avenue in studying amorphous materials.
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.
