Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The struc...Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.展开更多
Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full networ...Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.展开更多
Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine ...Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.展开更多
Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware reso...Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.展开更多
To solve the load balancing problem in a triplet-based hierarchical interconnection network(THIN) system, a dynamic load balancing (DLB)algorithm--THINDLBA, which adopts multicast tree (MT)technology to improve ...To solve the load balancing problem in a triplet-based hierarchical interconnection network(THIN) system, a dynamic load balancing (DLB)algorithm--THINDLBA, which adopts multicast tree (MT)technology to improve the efficiency of interchanging load information, is presented. To support the algorithm, a complete set of DLB messages and a schema of maintaining DLB information in each processing node are designed. The load migration request messages from the heavily loaded node (HLN)are spread along an MT whose root is the HLN. And the lightly loaded nodes(LLNs) covered by the MT are the candidate destinations of load migration; the load information interchanged between the LLNs and the HLN can be transmitted along the MT. So the HLN can migrate excess loads out as many as possible during a one time execution of the THINDLBA, and its load state can be improved as quickly as possible. To avoid wrongly transmitted or redundant DLB messages due to MT overlapping, the MT construction is restricted in the design of the THINDLBA. Through experiments, the effectiveness of four DLB algorithms are compared, and the results show that the THINDLBA can effectively decrease the time costs of THIN systems in dealing with large scale computeintensive tasks more than others.展开更多
In order to simulate the real growing process, a new type of knowledge network growth mechanism based on local world connectivity is constructed. By the mean-field method, theoretical prediction of the degree distribu...In order to simulate the real growing process, a new type of knowledge network growth mechanism based on local world connectivity is constructed. By the mean-field method, theoretical prediction of the degree distribution of the knowledge network is given, which is verified by Matlab simulations. When the new added node's local world size is very small, the degree distribution of the knowledge network approximately has the property of scale-free. When the new added node's local world size is not very small, the degree distribution transforms from pure power-law to the power-law with an exponential tailing. And the scale-free index increases as the number of new added edges decreases and the tunable parameters increase. Finally, comparisons of some knowledge indices in knowledge networks generated by the local world mechanism and the global mechanism are given. In the long run, compared with the global mechanism, the local world mechanism leads the average knowledge levels to slower growth and brings homogenous phenomena.展开更多
A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence da...A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.展开更多
Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasonin...Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers’ choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large_scale traffic networks.展开更多
In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific clu...In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.展开更多
Hierarchical magnetic-dielectric composites are promising functional materials with prospective applications in microwave absorption(MA)field.Herein,a three-dimension hierarchical“nanotubes on microrods,”core–shell...Hierarchical magnetic-dielectric composites are promising functional materials with prospective applications in microwave absorption(MA)field.Herein,a three-dimension hierarchical“nanotubes on microrods,”core–shell magnetic metal–carbon composite is rationally constructed for the first time via a fast metal–organic frameworksbased ligand exchange strategy followed by a carbonization treatment with melamine.Abundant magnetic CoFe nanoparticles are embedded within one-dimensional graphitized carbon/carbon nanotubes supported on micro-scale Mo2N rod(Mo2N@CoFe@C/CNT),constructing a special multi-dimension hierarchical MA material.Ligand exchange reaction is found to determine the formation of hierarchical magnetic-dielectric composite,which is assembled by dielectric Mo2N as core and spatially dispersed CoFe nanoparticles within C/CNTs as shell.Mo2N@CoFe@C/CNT composites exhibit superior MA performance with maximum reflection loss of−53.5 dB at 2 mm thickness and show a broad effective absorption bandwidth of 5.0 GHz.The Mo2N@CoFe@C/CNT composites hold the following advantages:(1)hierarchical core–shell structure offers plentiful of heterojunction interfaces and triggers interfacial polarization,(2)unique electronic migration/hop paths in the graphitized C/CNTs and Mo2N rod facilitate conductive loss,(3)highly dispersed magnetic CoFe nanoparticles within“tubes on rods”matrix build multi-scale magnetic coupling network and reinforce magnetic response capability,confirmed by the off-axis electron holography.展开更多
Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that ...Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks. Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.展开更多
Caching and virtualization have been considered as the promising techniques in 5G Networks. In 5G networks with virtualization, the caching resources deployed by infrastructure providers (InPs) can be abstracted into ...Caching and virtualization have been considered as the promising techniques in 5G Networks. In 5G networks with virtualization, the caching resources deployed by infrastructure providers (InPs) can be abstracted into isolated slices and transparently shared by mobile virtual network operators (MVNOs). In this case, one of the most important issues is how the MVNOs to share the caching resource. To solve this issue, different from previous works, a hierarchical caching architecture that core network and radio access network (RAN) have the caching capability in 5G networks with virtualization is first considered in this paper. Then, we study the problem of hierarchical caching resource sharing for MVNOs, and a competitive game to maximize their expectation revenue based on the oligopoly market model is formulated. As it is a hard problem to find the optimal solution in the hierarchical caching resource sharing problem, we decompose the optimization problem into two independent caching resource sharing problems in RAN and core network, respectively. Then the local optimal solutions are solved and the global Nash equilibrium solution is achieved. Finally, simulation results are illustrated to demonstrate the performance of the proposed scheme.展开更多
Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are we...Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.展开更多
Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks (HWSNs), espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink...Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks (HWSNs), espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs' utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.展开更多
In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile sta...In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow.Then, according to the mobility classification,network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.展开更多
In this paper, we study the epidemic spreading in scale-flee networks and propose a new susceptible-infected- recovered (SIR) model that includes the effect of individual vigilance. In our model, the effective sprea...In this paper, we study the epidemic spreading in scale-flee networks and propose a new susceptible-infected- recovered (SIR) model that includes the effect of individual vigilance. In our model, the effective spreading rate is dynamically adjusted with the time evolution at the vigilance period. Using the mean-field theory, an analytical result is derived. It shows that individual vigilance has no effect on the epidemic threshold. The numerical simulations agree well with the analytical result. Purthermore, we investigate the effect of individual vigilance on the epidemic spreading speed. It is shown that individual vigilance can slow the epidemic spreading speed effectively and delay the arrival of peak epidemic infection.展开更多
In the practical wireless sensor networks (WSNs), the cascading failure caused by a failure node has serious impact on the network performance. In this paper, we deeply research the cascading failure of scale-free t...In the practical wireless sensor networks (WSNs), the cascading failure caused by a failure node has serious impact on the network performance. In this paper, we deeply research the cascading failure of scale-free topology in WSNs. Firstly, a cascading failure model for scale-free topology in WSNs is studied. Through analyzing the influence of the node load on cascading failure, the critical load triggering large-scale cascading failure is obtained. Then based on the critical load, a control method for cascading failure is presented. In addition, the simulation experiments are performed to validate the effectiveness of the control method. The results show that the control method can effectively prevent cascading failure.展开更多
In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid...In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network(FPN)and deconvolutional single shot detector(DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4%to 5%higher mAP(mean average precision)than SSD,and 2%to 3%higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300×300 input reaches 79.4%mAP at 34.6 frame/s,and the mAP can raise to 82.9%after using the multi-scale testing strategy.展开更多
For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer (P2P) network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P...For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer (P2P) network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.展开更多
With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the o...With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.展开更多
基金supported in part by the National Natural Science Foundation of China(62225306,U2141235,52188102,and 62003145)the National Key Research and Development Program of China(2022ZD0119601)+1 种基金Guangdong Basic and Applied Research Foundation(2022B1515120069)the Science and Technology Project of State Grid Corporation of China(5100-202199557A-0-5-ZN).
文摘Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so forth.The structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social group.This study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific paths.More surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved subnetwork.The overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization processes.This finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these networks.Our study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.12271394 and 12071336)the Key Research and Development Program of Shanxi Province(Grant No.202102010101004)。
文摘Recently, a class of innovative notions on quantum network nonlocality(QNN), called full quantum network nonlocality(FQNN), have been proposed in Phys. Rev. Lett. 128 010403(2022). As the generalization of full network nonlocality(FNN), l-level quantum network nonlocality(l-QNN) was defined in arxiv. 2306.15717 quant-ph(2024). FQNN is a NN that can be generated only from a network with all sources being non-classical. This is beyond the existing standard network nonlocality, which may be generated from a network with only a non-classical source. One of the challenging tasks is to establish corresponding Bell-like inequalities to demonstrate the FQNN or l-QNN. Up to now, the inequality criteria for FQNN and l-QNN have only been established for star and chain networks. In this paper, we devote ourselves to establishing Bell-like inequalities for networks with more complex structures. Note that star and chain networks are special kinds of tree-shaped networks. We first establish the Bell-like inequalities for verifying l-QNN in k-forked tree-shaped networks. Such results generalize the existing inequalities for star and chain networks. Furthermore, we find the Bell-like inequality criteria for l-QNN for general acyclic and cyclic networks. Finally, we discuss the demonstration of l-QNN in the well-known butterfly networks.
基金supported in part by the National Natural Foundation of China(No.62176147)。
文摘Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.
文摘Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.
基金The National Natural Science Foundation of China(No.69973007).
文摘To solve the load balancing problem in a triplet-based hierarchical interconnection network(THIN) system, a dynamic load balancing (DLB)algorithm--THINDLBA, which adopts multicast tree (MT)technology to improve the efficiency of interchanging load information, is presented. To support the algorithm, a complete set of DLB messages and a schema of maintaining DLB information in each processing node are designed. The load migration request messages from the heavily loaded node (HLN)are spread along an MT whose root is the HLN. And the lightly loaded nodes(LLNs) covered by the MT are the candidate destinations of load migration; the load information interchanged between the LLNs and the HLN can be transmitted along the MT. So the HLN can migrate excess loads out as many as possible during a one time execution of the THINDLBA, and its load state can be improved as quickly as possible. To avoid wrongly transmitted or redundant DLB messages due to MT overlapping, the MT construction is restricted in the design of the THINDLBA. Through experiments, the effectiveness of four DLB algorithms are compared, and the results show that the THINDLBA can effectively decrease the time costs of THIN systems in dealing with large scale computeintensive tasks more than others.
基金The National Natural Science Foundation of China(No70571013,70973017)Program for New Century Excellent Talentsin University (NoNCET-06-0471)Human Social Science Fund Project ofMinistry of Education (No09YJA630020)
文摘In order to simulate the real growing process, a new type of knowledge network growth mechanism based on local world connectivity is constructed. By the mean-field method, theoretical prediction of the degree distribution of the knowledge network is given, which is verified by Matlab simulations. When the new added node's local world size is very small, the degree distribution of the knowledge network approximately has the property of scale-free. When the new added node's local world size is not very small, the degree distribution transforms from pure power-law to the power-law with an exponential tailing. And the scale-free index increases as the number of new added edges decreases and the tunable parameters increase. Finally, comparisons of some knowledge indices in knowledge networks generated by the local world mechanism and the global mechanism are given. In the long run, compared with the global mechanism, the local world mechanism leads the average knowledge levels to slower growth and brings homogenous phenomena.
文摘A kind of predictive control based on the neural network(NN) for nonlinear systems with time delay is addressed.The off line NN model is obtained by using hierarchical genetic algorithms (HGA) to train a sequence data of input and output.Output predictions are obtained by recursively mapping the NN model.The error rectification term is introduced into a performance function that is directly optimized while on line control so that it overcomes influences of the mismatched model and disturbances,etc.Simulations show the system has good dynamic responses and robustness.
文摘Human beings’ intellection is the characteristic of a distinct hierarchy and can be taken to construct a heuristic in the shortest path algorithms.It is detailed in this paper how to utilize the hierarchical reasoning on the basis of greedy and directional strategy to establish a spatial heuristic,so as to improve running efficiency and suitability of shortest path algorithm for traffic network.The authors divide urban traffic network into three hierarchies and set forward a new node hierarchy division rule to avoid the unreliable solution of shortest path.It is argued that the shortest path,no matter distance shortest or time shortest,is usually not the favorite of drivers in practice.Some factors difficult to expect or quantify influence the drivers’ choice greatly.It makes the drivers prefer choosing a less shortest,but more reliable or flexible path to travel on.The presented optimum path algorithm,in addition to the improvement of the running efficiency of shortest path algorithms up to several times,reduces the emergence of those factors,conforms to the intellection characteristic of human beings,and is more easily accepted by drivers.Moreover,it does not require the completeness of networks in the lowest hierarchy and the applicability and fault tolerance of the algorithm have improved.The experiment result shows the advantages of the presented algorithm.The authors argued that the algorithm has great potential application for navigation systems of large_scale traffic networks.
基金supported in part by the National Natural Science Foundation of China (Grant No.61361166005)the State Major Science and Technology Special Projects (Grant No.2016ZX03001020006)the National Program for Support of Top-notch Young Professionals
文摘In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.
基金This work was supported by the Ministry of Science and Technology of China(973 Project No.2018YFA0209102)the National Natural Science Foundation of China(11727807,51725101,51672050,61790581).
文摘Hierarchical magnetic-dielectric composites are promising functional materials with prospective applications in microwave absorption(MA)field.Herein,a three-dimension hierarchical“nanotubes on microrods,”core–shell magnetic metal–carbon composite is rationally constructed for the first time via a fast metal–organic frameworksbased ligand exchange strategy followed by a carbonization treatment with melamine.Abundant magnetic CoFe nanoparticles are embedded within one-dimensional graphitized carbon/carbon nanotubes supported on micro-scale Mo2N rod(Mo2N@CoFe@C/CNT),constructing a special multi-dimension hierarchical MA material.Ligand exchange reaction is found to determine the formation of hierarchical magnetic-dielectric composite,which is assembled by dielectric Mo2N as core and spatially dispersed CoFe nanoparticles within C/CNTs as shell.Mo2N@CoFe@C/CNT composites exhibit superior MA performance with maximum reflection loss of−53.5 dB at 2 mm thickness and show a broad effective absorption bandwidth of 5.0 GHz.The Mo2N@CoFe@C/CNT composites hold the following advantages:(1)hierarchical core–shell structure offers plentiful of heterojunction interfaces and triggers interfacial polarization,(2)unique electronic migration/hop paths in the graphitized C/CNTs and Mo2N rod facilitate conductive loss,(3)highly dispersed magnetic CoFe nanoparticles within“tubes on rods”matrix build multi-scale magnetic coupling network and reinforce magnetic response capability,confirmed by the off-axis electron holography.
文摘Based on the random walk and the intentional random walk, we propose two types of immunization strategies which require only local connectivity information. On several typical scale-free networks, we demonstrate that these strategies can lead to the eradication of the epidemic by immunizing a small fraction of the nodes in the networks. Particularly, the immunization strategy based on the intentional random walk is extremely efficient for the assortatively mixed networks.
基金support by the Major National Science and Technology Projects (No. 2018ZX03001019-003, 2018ZX03001014-003)
文摘Caching and virtualization have been considered as the promising techniques in 5G Networks. In 5G networks with virtualization, the caching resources deployed by infrastructure providers (InPs) can be abstracted into isolated slices and transparently shared by mobile virtual network operators (MVNOs). In this case, one of the most important issues is how the MVNOs to share the caching resource. To solve this issue, different from previous works, a hierarchical caching architecture that core network and radio access network (RAN) have the caching capability in 5G networks with virtualization is first considered in this paper. Then, we study the problem of hierarchical caching resource sharing for MVNOs, and a competitive game to maximize their expectation revenue based on the oligopoly market model is formulated. As it is a hard problem to find the optimal solution in the hierarchical caching resource sharing problem, we decompose the optimization problem into two independent caching resource sharing problems in RAN and core network, respectively. Then the local optimal solutions are solved and the global Nash equilibrium solution is achieved. Finally, simulation results are illustrated to demonstrate the performance of the proposed scheme.
基金This work was partially supported by the National Natural Science Foundation of China(62073173,61833011)the Natural Science Foundation of Jiangsu Province,China(BK20191376)the Nanjing University of Posts and Telecommunications(NY220193,NY220145)。
文摘Some recent research reports that a dendritic neuron model(DNM)can achieve better performance than traditional artificial neuron networks(ANNs)on classification,prediction,and other problems when its parameters are well-tuned by a learning algorithm.However,the back-propagation algorithm(BP),as a mostly used learning algorithm,intrinsically suffers from defects of slow convergence and easily dropping into local minima.Therefore,more and more research adopts non-BP learning algorithms to train ANNs.In this paper,a dynamic scale-free network-based differential evolution(DSNDE)is developed by considering the demands of convergent speed and the ability to jump out of local minima.The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem.Nine meta-heuristic algorithms are applied into comparison,including the champion of the 2017 IEEE Congress on Evolutionary Computation(CEC2017)benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase(EBOwithCMAR).The experimental results reveal that DSNDE achieves better performance than its peers.
基金supported by the National Natural Science Foundation of China (7070102571071105)+2 种基金the Program for New Century Excellent Talents in Universities of China (NCET-08-0396)the National Science Fund for Distinguished Young Scholars of China (70925005)the Program for Changjiang Scholars and Innovative Research Team in University (IRT/028)
文摘Power efficiency and link reliability are of great impor- tance in hierarchical wireless sensor networks (HWSNs), espe- cially at the key level, which consists of sensor nodes located only one hop away from the sink node called OHS. The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability. This problem is modeled as a non-cooperative game in which the active OHSs are con- sidered as players. By applying a double-pricing scheme in the definition of OHSs' utility function, a Nash Equilibrium solution with network properties is derived. Besides, a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium. Finally, the simulation results demonstrate the effec- tiveness of the proposed algorithm.
基金supported by Natural Science Foundation of China(61372125)973 project(2013CB329104)+1 种基金the National High-Tech R&D Program(863 Program 2015AA01A705)the open research fund of National Mobile Communications Research Laboratory,Southeast University(2013D01)
文摘In this paper, we propose a novel speed and service-sensitive handoff algorithm and analytical model for hierarchical cellular networks.First, we use the Gauss-Markov mobility model to predict the speeds of mobile stations, and divide mobile stations into three classes based on the predicted speeds: fast, medium-speed, and slow.Then, according to the mobility classification,network conditions, and service types, mobile stations will be handoff to the proper target networks prior to the deterioration of the currently operating channel. We further develop an analytical model to evaluate the performance of such a hierarchical system with different speed classes and service types. Simulations and analytical results show that the proposed handoff algorithm can significantly improve the network performance in terms of the handoff failure probability, unnecessary handoff probability, and network throughput, comparing with the traditional algorithms.
基金Project supported by the National Natural Science Foundation of China(Grant No.60874091)the Six Projects Sponsoring Talent Summits of Jiangsu Province,China(Grant No.SJ209006)+1 种基金the Natural Science Foundation of Jiangsu Province,China(Grant No.BK2010526)the Graduate Student Innovation Research Project of Jiangsu Province,China(Grant No.CXLX110417)
文摘In this paper, we study the epidemic spreading in scale-flee networks and propose a new susceptible-infected- recovered (SIR) model that includes the effect of individual vigilance. In our model, the effective spreading rate is dynamically adjusted with the time evolution at the vigilance period. Using the mean-field theory, an analytical result is derived. It shows that individual vigilance has no effect on the epidemic threshold. The numerical simulations agree well with the analytical result. Purthermore, we investigate the effect of individual vigilance on the epidemic spreading speed. It is shown that individual vigilance can slow the epidemic spreading speed effectively and delay the arrival of peak epidemic infection.
基金supported by the Natural Science Foundation of Hebei Province,China(Grant No.F2014203239)the Autonomous Research Fund of Young Teacher in Yanshan University(Grant No.14LGB017)Yanshan University Doctoral Foundation,China(Grant No.B867)
文摘In the practical wireless sensor networks (WSNs), the cascading failure caused by a failure node has serious impact on the network performance. In this paper, we deeply research the cascading failure of scale-free topology in WSNs. Firstly, a cascading failure model for scale-free topology in WSNs is studied. Through analyzing the influence of the node load on cascading failure, the critical load triggering large-scale cascading failure is obtained. Then based on the critical load, a control method for cascading failure is presented. In addition, the simulation experiments are performed to validate the effectiveness of the control method. The results show that the control method can effectively prevent cascading failure.
基金The National Natural Science Foundation of China(No.61603091)。
文摘In order to improve the detection accuracy of small objects,a neighborhood fusion-based hierarchical parallel feature pyramid network(NFPN)is proposed.Unlike the layer-by-layer structure adopted in the feature pyramid network(FPN)and deconvolutional single shot detector(DSSD),where the bottom layer of the feature pyramid network relies on the top layer,NFPN builds the feature pyramid network with no connections between the upper and lower layers.That is,it only fuses shallow features on similar scales.NFPN is highly portable and can be embedded in many models to further boost performance.Extensive experiments on PASCAL VOC 2007,2012,and COCO datasets demonstrate that the NFPN-based SSD without intricate tricks can exceed the DSSD model in terms of detection accuracy and inference speed,especially for small objects,e.g.,4%to 5%higher mAP(mean average precision)than SSD,and 2%to 3%higher mAP than DSSD.On VOC 2007 test set,the NFPN-based SSD with 300×300 input reaches 79.4%mAP at 34.6 frame/s,and the mAP can raise to 82.9%after using the multi-scale testing strategy.
文摘For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer (P2P) network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.
基金Project(61102039)supported by the National Natural Science Foundation of ChinaProject(2014AA052600)supported by National Hi-tech Research and Development Plan,China
文摘With the development of automation in smart grids,network reconfiguration is becoming a feasible approach for improving the operation of distribution systems.A novel reconfiguration strategy was presented to get the optimal configuration of improving economy of the system,and then identifying the important nodes.In this strategy,the objectives increase the node importance degree and decrease the active power loss subjected to operational constraints.A compound objective function with weight coefficients is formulated to balance the conflict of the objectives.Then a novel quantum particle swarm optimization based on loop switches hierarchical encoded was employed to address the compound objective reconfiguration problem.Its main contribution is the presentation of the hierarchical encoded scheme which is used to generate the population swarm particles of representing only radial connected solutions.Because the candidate solutions are feasible,the search efficiency would improve dramatically during the optimization process without tedious topology verification.To validate the proposed strategy,simulations are carried out on the test systems.The results are compared with other techniques in order to evaluate the performance of the proposed method.