As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network （DLSN） with MEO satellites and LEO satellites is proposed. The Inter-Orb...As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network （DLSN） with MEO satellites and LEO satellites is proposed. The Inter-Orbit-Links （IOLs） between layers is an essential factor, which affects the performances of the DLSN systems. Considering certain constellation parameters, the geometric characteristics of IOLs are described and the connectivity of MEO satellites and LEO satellites in the DLSN is analyzed. By computer simulation, the results show that IOLs should be selectively established according to certain parameters rather than the simple in-sight principle.展开更多
Nowadays,carbon frameworks derived from natural biomaterials have attracted extensive attention for electromagnetic interference(EMI)shielding due to their renewability and affordability.However,it is critical and cha...Nowadays,carbon frameworks derived from natural biomaterials have attracted extensive attention for electromagnetic interference(EMI)shielding due to their renewability and affordability.However,it is critical and challenging to achieve effective regulation of shielding effectiveness(SE)as well as weaken the strong EM reflection of highly conductive biomass-based carbon materials.Herein,commercial cotton pads with oriented structure were selected as carbonaceous precursor to fabricate aligned carbon networks by pyrolysis,and the EMI SE of the samples with increased temperature of 800-1000℃ can be accurately controlled in the effective range of~21.7-29.1,~27.7-37.1 and~32.7-43.3 d B with high reflection coefficient of>0.8 by changing the cross-angle between the electric-field direction of incident EM waves and the fiber-orientation direction due to the occurrence of opposite internal electric field.Moreover,the further construction of Salisbury absorber-liked double-layer structure could result in an ultralow reflection coefficient of only~0.06 but enhanced SE variation range up to~38.7-49.3 d B during the adjustment of cross-angle,possibly due to the destructive interference of EM waves in the double-layer carbon networks.This work would provide a simple and effective way for constructing high-performance biomass carbon materials with adjustable EMI shielding and ultra-low reflectivity.展开更多
Quadratic programming models for integrated space-time interference suppression in CDMA systems are proposed in this paper. The models integrate the advantages of smart antenna and RAKE receiver, mitigate multiuser ac...Quadratic programming models for integrated space-time interference suppression in CDMA systems are proposed in this paper. The models integrate the advantages of smart antenna and RAKE receiver, mitigate multiuser access interference (MAI) and interchip interference (ICI),and combine multipath components. The zero-forcing conditions are derived. Neural network implementation of the models is also studied.展开更多
The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some d...The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some degree of directionality. So multiplex directed networks can better explain the synchronizability phenomenon. Here, based on the theory of master stability function (MSF), we study the eigenvalue spectrum and synchronizability of double-layer inter-layer directed ring networks (Networks-A) and double-layer intra-layer directed ring networks (Networks-B). The eigenvalue spectrum of the supra-Laplacian matrix of the networks is rigorously derived, and the influence of the networks structure parameters on the network’s synchronizability is analyzed. The correctness of the theory is further verified by numerical simulation analysis. Finally, the synchronizability of four kinds of double-layer ring networks with different coupling modes, namely, Networks-A, Networks-B, Networks-C (double-layer undirected ring networks), and Networks-D (double-layer undirected inter-layer random-added-edge ring networks), is compared and the results can provide guidance for constructing the optimal synchronization network.展开更多
The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention R...The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.展开更多
In this article,we introduce a new bi-directional dual-relay selection strategy with its bit error rate(BER)performance analysis.During the first step of the proposed strategy,two relays out of a set of N relay-nodes ...In this article,we introduce a new bi-directional dual-relay selection strategy with its bit error rate(BER)performance analysis.During the first step of the proposed strategy,two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER,based on the suggested algorithm which checks if the selected relays using the maxmin criterion are the best ones.In the second step,the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals,leading to a significant improvement in the achievable coding and diversity gain.To further improve the overall system performance,the selected relay-nodes apply also a digital network coding scheme.Furthermore,this paper discusses the analytical approximation of the BER performance of the proposed strategy,where we prove that the analytical results match almost perfectly the simulated ones.Finally,our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.展开更多
Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data ana...Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.展开更多
Multiple access interference (MAI) and near-far problem are two major obstacles in DS-CDMA systems. Combining wavelet neural networks and two matched filters, the novel multiuser detector, which is based on multiple v...Multiple access interference (MAI) and near-far problem are two major obstacles in DS-CDMA systems. Combining wavelet neural networks and two matched filters, the novel multiuser detector, which is based on multiple variable function estimation wavelet networks over single path asynchronous channel and space-time channel respectively is presented. Excellent localization characteristics of wavelet functions in both time and frequency domains allowed hierarchical multiple resolution learning of input-output data mapping. The ma thematic frame of the neural networks and error back ward propagation algorithm are introduced. The complexity of the multiuser detector only depends on that of wavelet networks. With numerical simulations and performance analysis, it indicates that the multiuser detector has excellent performance in eliminating MAI and near-far resistance.展开更多
The phenomenon of activity synchronization in biological neural network is considered. Simulation of neurons dynamics in the 6-layer neural network with 110 elements in different regimes： regular spikes, chaotic spik...The phenomenon of activity synchronization in biological neural network is considered. Simulation of neurons dynamics in the 6-layer neural network with 110 elements in different regimes： regular spikes, chaotic spikes, regular and chaotic bursting, etc was performed. Izhykevich＇s phenomenological model that displays different types of activity inherent for real biological neurons was used for simulation. Space-time diagram for the entire network and raster plots for the whole structure and for each layer separately were built for visual inspection of neural network activity synchronization. Synchronization coefficients based on cross-correlation times of action potentials for all neurons pairs were calculated for the whole neural system and for each layer separately.展开更多
In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to t...In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.展开更多
Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics o...Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves.On the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment.Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading dynamics.Simulation results show that the increasing of information transmission rate can reduce the scale of disease spreading in some degree.Shortening infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information dissemination.In addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.展开更多
Deep Learning(DL)has important applications to both commercial and military communications,such as software-defined radio,cognitive radio and spectrum surveillance.While DL has been intensively studied for modulation ...Deep Learning(DL)has important applications to both commercial and military communications,such as software-defined radio,cognitive radio and spectrum surveillance.While DL has been intensively studied for modulation recognition,there are very few investigations for blind identification of Space-Time Block Codes(STBCs).This paper proposes a Residual Network(RN)-based model for identifying 6 kinds of STBC signals with a single receiving antenna,including the same length of coding matrix.In our work,we use the frequency-domain correlation function of a single time delay as the training data of DL model.Then,we explore the suitable RN structure for blind identification of STBCs.Finally,we compare the RN model with convolutional neural network and traditional method,and test the performance of RN model.Simulation results show that our RN-based model provides good performance with low sensitivity to decay of the dataset,such as sample length and data size.At the same time,better identification accuracy can be achieved under the condition of different modulation types and channel fading parameters at low Signal to Noise Ratio(SNR).展开更多
Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the exis...Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.展开更多
With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study c...With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission.We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission.We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission.The simulation results showed that the higher the information dissemination rate,the larger the scale of information dissemination and the smaller the scale of epidemic transmission.In addition,the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic,the smaller the scale of information dissemination and the smaller the scale of epidemic transmission.Moreover,the greater the probability of individuals moving across regions,the larger the spread of the epidemic and information.Finally,the higher the probability of an individual taking preventive behavior,the smaller the spread of the epidemic and information.Therefore,it is possible to suppress epidemic spread by increasing the information dissemination rate,epidemic recovery rate,and probability of individuals taking preventive behavior,while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.展开更多
Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling.However,the simulation outcomes have be...Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling.However,the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget.Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority,and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required.Building on the Coupled Model Intercomparison Project Phase 6(CMIP6),we have transplanted a conventional ensemble learning approach,and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset,both of which have realised outstanding performances(R2>0.95,RMSE<2.12 ppbv).The conventional ensemble learning approach is computationally cheaper and results in higher overall performance,but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable.The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability,but induces heavier computational burdens.Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.展开更多
Track utilization is the most important technical operation in high-speed railway stations.It is an effective way to take flexible man-agement based on dispatchers’decision preferences into consideration for making t...Track utilization is the most important technical operation in high-speed railway stations.It is an effective way to take flexible man-agement based on dispatchers’decision preferences into consideration for making track utilization plans to relieve the influence caused by unmeasurable unstructured factors.Thus,based on the flexible management concept and taking the flexible optimal for track utilization in high-speed railway stations as the object,time and space occupation safety trajectories of arrival routes,departure routes and tracks are all analysed.Then,taking the following constraints into consideration-minimum safety time intervals for var-ious routes and tracks occupation,space-time arc occupation and decision-makers’preferences-a flexible optimal model for track utilization in high-speed railway stations is established to maximize its balance and robustness and to minimize its volatility at the same time.Further,a flexible optimal solution based on a simulated annealing algorithm is designed to make a safety track utilization plan in high-speed railway stations integrating the dispatchers’decision preference.The results from the experiments show that the proposed methodology can effectively make satisfied safety track utilization plans based on decision-makers’preferences,which can improve its balance and robustness level significantly.Meanwhile,its volatility can be reduced as much as possible caused by flexible management based on artificial intervention to ensure the relative stability of the plan.展开更多
基金National Natural Science Foundation of China(60532030)
文摘As an important scheme of future global mobile satellite communication systems to provide multimedia service, a Double-Layer Satellite Network （DLSN） with MEO satellites and LEO satellites is proposed. The Inter-Orbit-Links （IOLs） between layers is an essential factor, which affects the performances of the DLSN systems. Considering certain constellation parameters, the geometric characteristics of IOLs are described and the connectivity of MEO satellites and LEO satellites in the DLSN is analyzed. By computer simulation, the results show that IOLs should be selectively established according to certain parameters rather than the simple in-sight principle.
基金financial supports from Natural Science Foundation of Ningbo(202003N4026)S&T Innovation 2025 Major Special Programme of Ningbo(2018B10054)National Natural Science Foundation of China(62001065 and 51603218)。
文摘Nowadays,carbon frameworks derived from natural biomaterials have attracted extensive attention for electromagnetic interference(EMI)shielding due to their renewability and affordability.However,it is critical and challenging to achieve effective regulation of shielding effectiveness(SE)as well as weaken the strong EM reflection of highly conductive biomass-based carbon materials.Herein,commercial cotton pads with oriented structure were selected as carbonaceous precursor to fabricate aligned carbon networks by pyrolysis,and the EMI SE of the samples with increased temperature of 800-1000℃ can be accurately controlled in the effective range of~21.7-29.1,~27.7-37.1 and~32.7-43.3 d B with high reflection coefficient of>0.8 by changing the cross-angle between the electric-field direction of incident EM waves and the fiber-orientation direction due to the occurrence of opposite internal electric field.Moreover,the further construction of Salisbury absorber-liked double-layer structure could result in an ultralow reflection coefficient of only~0.06 but enhanced SE variation range up to~38.7-49.3 d B during the adjustment of cross-angle,possibly due to the destructive interference of EM waves in the double-layer carbon networks.This work would provide a simple and effective way for constructing high-performance biomass carbon materials with adjustable EMI shielding and ultra-low reflectivity.
基金Supported by the National Natural Science Foundation of China under Grant 69882004 and MPT Project
文摘Quadratic programming models for integrated space-time interference suppression in CDMA systems are proposed in this paper. The models integrate the advantages of smart antenna and RAKE receiver, mitigate multiuser access interference (MAI) and interchip interference (ICI),and combine multipath components. The zero-forcing conditions are derived. Neural network implementation of the models is also studied.
文摘The synchronizability of multiplex undirected regular networks has been intensively studied based on the study of the synchronizability of single-layer networks. However, most real networks are characterized by some degree of directionality. So multiplex directed networks can better explain the synchronizability phenomenon. Here, based on the theory of master stability function (MSF), we study the eigenvalue spectrum and synchronizability of double-layer inter-layer directed ring networks (Networks-A) and double-layer intra-layer directed ring networks (Networks-B). The eigenvalue spectrum of the supra-Laplacian matrix of the networks is rigorously derived, and the influence of the networks structure parameters on the network’s synchronizability is analyzed. The correctness of the theory is further verified by numerical simulation analysis. Finally, the synchronizability of four kinds of double-layer ring networks with different coupling modes, namely, Networks-A, Networks-B, Networks-C (double-layer undirected ring networks), and Networks-D (double-layer undirected inter-layer random-added-edge ring networks), is compared and the results can provide guidance for constructing the optimal synchronization network.
基金supported by the National Natural Science Foundation of China(Nos.62106283 and 72001214)。
文摘The battlefield environment is changing rapidly,and fast and accurate identification of the tactical intention of enemy targets is an important condition for gaining a decision-making advantage.The current Intention Recognition(IR)method for air targets has shortcomings in temporality,interpretability and back-and-forth dependency of intentions.To address these problems,this paper designs a novel air target intention recognition method named STABC-IR,which is based on Bidirectional Gated Recurrent Unit(Bi GRU)and Conditional Random Field(CRF)with Space-Time Attention mechanism(STA).First,the problem of intention recognition of air targets is described and analyzed in detail.Then,a temporal network based on Bi GRU is constructed to achieve the temporal requirement.Subsequently,STA is proposed to focus on the key parts of the features and timing information to meet certain interpretability requirements while strengthening the timing requirements.Finally,an intention transformation network based on CRF is proposed to solve the back-and-forth dependency and transformation problem by jointly modeling the tactical intention of the target at each moment.The experimental results show that the recognition accuracy of the jointly trained STABC-IR model can reach 95.7%,which is higher than other latest intention recognition methods.STABC-IR solves the problem of intention transformation for the first time and considers both temporality and interpretability,which is important for improving the tactical intention recognition capability and has reference value for the construction of command and control auxiliary decision-making system.
基金This work was supported by College of Engineering and Technology,the American University of the Middle East,Kuwait.Homepage:https://www.aum.edu.kw.
文摘In this article,we introduce a new bi-directional dual-relay selection strategy with its bit error rate(BER)performance analysis.During the first step of the proposed strategy,two relays out of a set of N relay-nodes are selected in a way to optimize the system performance in terms of BER,based on the suggested algorithm which checks if the selected relays using the maxmin criterion are the best ones.In the second step,the chosen relay-nodes perform an orthogonal space-time coding scheme using the two-phase relaying protocol to establish a bi-directional communication between the communicating terminals,leading to a significant improvement in the achievable coding and diversity gain.To further improve the overall system performance,the selected relay-nodes apply also a digital network coding scheme.Furthermore,this paper discusses the analytical approximation of the BER performance of the proposed strategy,where we prove that the analytical results match almost perfectly the simulated ones.Finally,our simulation results show that the proposed strategy outperforms the current state-of-the-art ones.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. U20A20227,62076208, and 62076207)Chongqing Talent Plan “Contract System” Project (Grant No. CQYC20210302257)+3 种基金National Key Laboratory of Smart Vehicle Safety Technology Open Fund Project (Grant No. IVSTSKL-202309)the Chongqing Technology Innovation and Application Development Special Major Project (Grant No. CSTB2023TIAD-STX0020)College of Artificial Intelligence, Southwest UniversityState Key Laboratory of Intelligent Vehicle Safety Technology
文摘Neuromorphic computing,inspired by the human brain,uses memristor devices for complex tasks.Recent studies show that self-organizing random nanowires can implement neuromorphic information processing,enabling data analysis.This paper presents a model based on these nanowire networks,with an improved conductance variation profile.We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses.The nanowire network layer generates dynamic behaviors for pulse voltages,allowing time series prediction analysis.Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals,outperforming traditional reservoir computing in terms of fewer nodes,enriched dynamics and improved prediction accuracy.Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets,making neuromorphic nanowire networks promising for physical implementation of reservoir computing.
基金This project was supported by the National Natural Science Foundation of China (60073053 60133010).
文摘Multiple access interference (MAI) and near-far problem are two major obstacles in DS-CDMA systems. Combining wavelet neural networks and two matched filters, the novel multiuser detector, which is based on multiple variable function estimation wavelet networks over single path asynchronous channel and space-time channel respectively is presented. Excellent localization characteristics of wavelet functions in both time and frequency domains allowed hierarchical multiple resolution learning of input-output data mapping. The ma thematic frame of the neural networks and error back ward propagation algorithm are introduced. The complexity of the multiuser detector only depends on that of wavelet networks. With numerical simulations and performance analysis, it indicates that the multiuser detector has excellent performance in eliminating MAI and near-far resistance.
文摘The phenomenon of activity synchronization in biological neural network is considered. Simulation of neurons dynamics in the 6-layer neural network with 110 elements in different regimes： regular spikes, chaotic spikes, regular and chaotic bursting, etc was performed. Izhykevich＇s phenomenological model that displays different types of activity inherent for real biological neurons was used for simulation. Space-time diagram for the entire network and raster plots for the whole structure and for each layer separately were built for visual inspection of neural network activity synchronization. Synchronization coefficients based on cross-correlation times of action potentials for all neurons pairs were calculated for the whole neural system and for each layer separately.
文摘In this paper, Hailin City of Heilongjiang Province, China is taken as the research area. As an important city in Heilongjiang Province, China, the sustainable development of its ecological environment is related to the opening up, economic prosperity and social stability of Northeast China. In this paper, the remote sensing ecological index (RSEI) of Hailin City in recent 20 years was calculated by using Landsat 5/8/9 series satellite images, and the temporal and spatial changes of the ecological environment in Hailin City were further analyzed and the influencing factors were discussed. From 2003 to 2023, the mean value of RSEI in Hailin City decreased and increased, and the ecological environment decreased slightly as a whole. RSEI declined most significantly from 2003 to 2008, and it increased from 2008 to 2013, decreased from 2013 to 2018, and increased from 2018 to 2023 again, with higher RSEI value in the south and lower RSEI value in the northwest. It is suggested to appropriately increase vegetation coverage in the northwest to improve ecological quality. As a result, the predicted value of Elman dynamic recurrent neural network model is consistent with the change trend of the mean value, and the prediction error converges quickly, which can accurately predict the ecological environment quality in the future study area.
基金This research has been supported by the National Natural Science Foundation of China(Grant No.61672298 and 61373136)the National Social Science Foundation of China(Grant No.13BTQ046)+3 种基金the High-level Introduction of Talent Scientific Research Start-up Fund of Jiangsu Police Institute(Grant No.JSPI17GKZL403)the Scientific Research Program of Jiangsu Police Institute(Grant No.2017SJYZQ01)the Science and Technology Plan Projects of Jiangsu Province(Grant No.BE2017067)and the Research Foundation for Humanities and Social Sciences of Ministry of Education of China(Grant No.15YJAZH016).
文摘Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves.On the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment.Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading dynamics.Simulation results show that the increasing of information transmission rate can reduce the scale of disease spreading in some degree.Shortening infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information dissemination.In addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.
基金supported by the Taishan Scholar Special Foundation of China(No.ts201511020).
文摘Deep Learning(DL)has important applications to both commercial and military communications,such as software-defined radio,cognitive radio and spectrum surveillance.While DL has been intensively studied for modulation recognition,there are very few investigations for blind identification of Space-Time Block Codes(STBCs).This paper proposes a Residual Network(RN)-based model for identifying 6 kinds of STBC signals with a single receiving antenna,including the same length of coding matrix.In our work,we use the frequency-domain correlation function of a single time delay as the training data of DL model.Then,we explore the suitable RN structure for blind identification of STBCs.Finally,we compare the RN model with convolutional neural network and traditional method,and test the performance of RN model.Simulation results show that our RN-based model provides good performance with low sensitivity to decay of the dataset,such as sample length and data size.At the same time,better identification accuracy can be achieved under the condition of different modulation types and channel fading parameters at low Signal to Noise Ratio(SNR).
基金National Natural Science Foundation of China(62073212).
文摘Improving the cooperative scheduling efficiency of equipment is the key for automated container terminals to copewith the development trend of large-scale ships. In order to improve the solution efficiency of the existing spacetimenetwork (STN) model for the cooperative scheduling problem of yard cranes (YCs) and automated guidedvehicles (AGVs) and extend its application scenarios, two improved STN models are proposed. The flow balanceconstraints in the original model are decomposed, and the trajectory constraints of YCs and AGVs are added toacquire the model STN_A. The coupling constraint in STN_A is updated, and buffer constraints are added toSTN_A so that themodel STN_B is built.As the size of the problem increases, the solution speed of CPLEX becomesthe bottleneck. So a heuristic method containing three groups of heuristic rules is designed to obtain a near-optimalsolution quickly. Experimental results showthat the computation time of STN_A is shortened by 49.47% on averageand the gap is reduced by 1.69% on average compared with the original model. The gap between the solution ofthe heuristic rules and the solution of CPLEX is less than 3.50%, and the solution time of the heuristic rules is onaverage 99.85% less than the solution time of CPLEX. Compared with STN_A, the computation time for solvingSTN_B increases by 58.93% on average.
基金supported by National Natural Science Foundation of China(Grant No.71673256).
文摘With COVID-19 continuing to rage around the world,there is a spread of epidemic-related information on social networking platforms.This phenomenon may inhibit or promote the scale of epidemic transmission.This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission.We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission.We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission.The simulation results showed that the higher the information dissemination rate,the larger the scale of information dissemination and the smaller the scale of epidemic transmission.In addition,the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic,the smaller the scale of information dissemination and the smaller the scale of epidemic transmission.Moreover,the greater the probability of individuals moving across regions,the larger the spread of the epidemic and information.Finally,the higher the probability of an individual taking preventive behavior,the smaller the spread of the epidemic and information.Therefore,it is possible to suppress epidemic spread by increasing the information dissemination rate,epidemic recovery rate,and probability of individuals taking preventive behavior,while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.
文摘Accurately simulating the geographical distribution and temporal variability of global surface ozone has long been one of the principal components of chemistry-climate modelling.However,the simulation outcomes have been reported to vary significantly as a result of the complex mixture of uncertain factors that control the tropospheric ozone budget.Settling the cross-model discrepancies to achieve higher accuracy predictions of surface ozone is thus a task of priority,and methods that overcome structural biases in models going beyond naïve averaging of model simulations are urgently required.Building on the Coupled Model Intercomparison Project Phase 6(CMIP6),we have transplanted a conventional ensemble learning approach,and also constructed an innovative 2-stage enhanced space-time Bayesian neural network to fuse an ensemble of 57 simulations together with a prescribed ozone dataset,both of which have realised outstanding performances(R2>0.95,RMSE<2.12 ppbv).The conventional ensemble learning approach is computationally cheaper and results in higher overall performance,but at the expense of oceanic ozone being overestimated and the learning process being uninterpretable.The Bayesian approach performs better in spatial generalisation and enables perceivable interpretability,but induces heavier computational burdens.Both of these multi-stage machine learning-based approaches provide frameworks for improving the fidelity of composition-climate model outputs for uses in future impact studies.
基金This research is supported by the Natural Science Foundation of China(Grants No.71971220 and 71901093)Hunan Provincial Natural Science Foundation of China(Grants No.2023JJ30710 and 2022JJ31020).
文摘Track utilization is the most important technical operation in high-speed railway stations.It is an effective way to take flexible man-agement based on dispatchers’decision preferences into consideration for making track utilization plans to relieve the influence caused by unmeasurable unstructured factors.Thus,based on the flexible management concept and taking the flexible optimal for track utilization in high-speed railway stations as the object,time and space occupation safety trajectories of arrival routes,departure routes and tracks are all analysed.Then,taking the following constraints into consideration-minimum safety time intervals for var-ious routes and tracks occupation,space-time arc occupation and decision-makers’preferences-a flexible optimal model for track utilization in high-speed railway stations is established to maximize its balance and robustness and to minimize its volatility at the same time.Further,a flexible optimal solution based on a simulated annealing algorithm is designed to make a safety track utilization plan in high-speed railway stations integrating the dispatchers’decision preference.The results from the experiments show that the proposed methodology can effectively make satisfied safety track utilization plans based on decision-makers’preferences,which can improve its balance and robustness level significantly.Meanwhile,its volatility can be reduced as much as possible caused by flexible management based on artificial intervention to ensure the relative stability of the plan.
