We propose a generalized Lanczos method to generate the many-body basis states of quantum lattice models using tensor-network states (TNS). The ground-state wave function is represented as a linear superposition com...We propose a generalized Lanczos method to generate the many-body basis states of quantum lattice models using tensor-network states (TNS). The ground-state wave function is represented as a linear superposition composed from a set of TNS generated by Lanczos iteration. This method improves significantly the accuracy of the tensor-network algorithm and provides an effective way to enlarge the maximal bond dimension of TNS. The ground state such obtained contains significantly more entanglement than each individual TNS, reproducing correctly the logarithmic size dependence of the entanglement entropy in a critical system. The method can be generalized to non-Hamiltonian systems and to the calculation of low-lying excited states, dynamical correlation functions, and other physical properties of strongly correlated systems.展开更多
Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is c...Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world tasks.This work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness guaranteed.Specifically,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the PRESN.Furthermore,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical analyses.Finally,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN.The source code is publicly available at https://github.com/LongJinlab/probabilistic-regularized-echo-state-network.展开更多
The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI ca...The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits.However,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold calculation.The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)performance.In contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using ESN.The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation.With this approach,the calculation complexity is reduced compared to the previous ESN-based approach.The proposed method achieves better BER performance compared to the previous method.The reason for this superior result is twofold.First,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available.Second,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information estimation.Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.展开更多
The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and...The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.展开更多
In the forward channel of a networked control system (NCS), by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time in...In the forward channel of a networked control system (NCS), by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time interval, the relation between the network states and the network-induced delays is modelled as a discrete-time hidden Markov model (DTHMM). The expectation maximization (EM) algorithm is introduced to derive the maximumlikelihood estimation (MLE) of the parameters of the DTHMM. Based on the derived DTHMM, the Viterbi algorithm is introduced to predict the controller-to-actuator (C-A) delay during the current sampling period. The simulation experiments demonstrate the effectiveness of the modelling and predicting methods proposed.展开更多
Objective:Cervical disease,a type of chronic pain,can greatly impact quality of life.Traditional Chinese tuina,a form of therapeutic massage and manipulation,has been shown to be effective in relieving pain and other ...Objective:Cervical disease,a type of chronic pain,can greatly impact quality of life.Traditional Chinese tuina,a form of therapeutic massage and manipulation,has been shown to be effective in relieving pain and other symptoms in patients with chronic neck pain.This study applied functional magnetic resonance imaging (fMRI) to explore the features of the resting state network of patients with chronic neck pain caused by cervical radiculopathy,and how tuina affects the causality between intrinsic brain networks.Methods:Using Granger causality analysis,effective connectivity of brain networks of 10 patients with chronic neck pain was compared with 10 healthy control subjects.Resting state fMRI data were using magnetic resonance scanning.Cervical spondylosis symptom scores were evaluated before and after 4 weeks of tuina therapy.Independent component analysis was applied to extract the specific networks related to sensation,execution,and cognition,including sensorimotor network (SMN),visual network (VN),auditory network (AN),anterior and posterior default mode network (aDMN,pDMN),left frontoparietal network and right frontoparietal network.Results:Compared with the control group,data from the treatment group revealed two major findings:before tuina therapy,SMN had a profound influence on aDMN and AN greatly affected pDMN;however,after 4 weeks of tuina therapy,aDMN and SMN showed reversed causality.Conclusion:Chronic neck pain caused by cervical radiculopathy may influence the DMN,which plays an important role in emotion,cognition,and memory,by stimulating the sensory afferent network.Tuina not only significantly relieves pain and discomfort,but also reverses the causality between aDMN and SMN.展开更多
Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, w...Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is-36 dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.展开更多
Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro...Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro-blems.We compared our ESN with a long short-term memory(LSTM)network by forecasting the stock data of Kweichow Moutai,a leading enterprise in China’s liquor industry.By analyzing data for 120,240,and 300 days,we generated fore-cast data for the next 40,80,and 100 days,respectively,using both ESN and LSTM.In terms of accuracy,ESN had the unique advantage of capturing non-linear data.Mean absolute error(MAE)was used to present the accuracy results.The MAEs of the data forecast by ESN were 0.024,0.024,and 0.025,which were,respectively,0.065,0.007,and 0.009 less than those of LSTM.In terms of con-vergence,ESN has a reservoir state-space structure,which makes it perform faster than other models.Root-mean-square error(RMSE)was used to present the con-vergence time.In our experiment,the RMSEs of ESN were 0.22,0.27,and 0.26,which were,respectively,0.08,0.01,and 0.12 less than those of LSTM.In terms of network structure,ESN consists only of input,reservoir,and output spaces,making it a much simpler model than the others.The proposed ESN was found to be an effective model that,compared to others,converges faster,forecasts more accurately,and builds time-series analyses more easily.展开更多
<div style="text-align:justify;"> This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservo...<div style="text-align:justify;"> This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series. </div>展开更多
Quantum many-body problem(QMBP)has become a hot topic in high energy physics and condensed matter physics.With the exponential increasing of the dimension of the Hilbert space,it becomes a big challenge to solve the Q...Quantum many-body problem(QMBP)has become a hot topic in high energy physics and condensed matter physics.With the exponential increasing of the dimension of the Hilbert space,it becomes a big challenge to solve the QMBP even with the most powerful computers.With the rapid development of machine learning,artificial neural networks provide a powerful tool to represent or approximate quantum many-body states.In this paper,we aim to construct explicitly the neural network representations of graph states,without stochastic optimization of the network parameters.Our method shows constructively that all graph states can be represented precisely by proper neural networks originated from[Science,355,602(2017)]and formulated in[Sci.China-Phys.Mech.Astron.,63,210312(2020)].展开更多
Age-related changes in the brain connectivity of healthy older adults have been widely studied in recent years,with some differences in the obtained results.Most of these studies showed decreases in general functional...Age-related changes in the brain connectivity of healthy older adults have been widely studied in recent years,with some differences in the obtained results.Most of these studies showed decreases in general functional connectivity,but they also found increases in some particular regions and areas.Frequently,these studies compared young individuals with older subjects,but few studies compared different age groups only in older populations.The purpose of this study is to analyze whole-brain functional connectivity in healthy older adult groups and its network characteristics through functional segregation.A total of 114 individuals,48 to 89 years old,were scanned using resting-state functional magnetic resonance imaging in a resting state paradigm and were divided into six different age groups(<60,60–64,65–69,70–74,75–79,≥80 years old).A partial correlation analysis,a pooled correlation analysis and a study of 3-cycle regions with prominent connectivity were conducted.Our results showed progressive diminution in the functional connectivity among different age groups and this was particularly pronounced between 75 and 79 years old.The oldest group(≥80 years old)showed a slight increase in functional connectivity compared to the other groups.This occurred possibly because of compensatory mechanism in brain functioning.This study provides information on the brain functional characteristics of every age group,with more specific information on the functional progressive decline,and supplies methodological tools to study functional connectivity characteristics.Approval for the study was obtained from the ethics committee of the Comision de Bioetica de la Universidad de Barcelona(approval No.PSI2012-38257)on June 5,2012,and from the ethics committee of the Barcelona’s Hospital Clinic(approval No.2009-5306 and 2011-6604)on October 22,2009 and April 7,2011 respectively.展开更多
The quantum many-body problem(QMBP) has become a hot topic in high-energy physics and condensed-matter physics. With an exponential increase in the dimensions of Hilbert space, it becomes very challenging to solve the...The quantum many-body problem(QMBP) has become a hot topic in high-energy physics and condensed-matter physics. With an exponential increase in the dimensions of Hilbert space, it becomes very challenging to solve the QMBP, even with the most powerful computers. With the rapid development of machine learning, artificial neural networks provide a powerful tool that can represent or approximate quantum many-body states. In this paper, we aim to explicitly construct the neural network representations of hypergraph states. We construct the neural network representations for any k-uniform hypergraph state and any hypergraph state,respectively, without stochastic optimization of the network parameters. Our method constructively shows that all hypergraph states can be represented precisely by the appropriate neural networks introduced in [Science 355(2017) 602] and formulated in [Sci. China-Phys.Mech. Astron. 63(2020) 210312].展开更多
Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propos...Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.展开更多
In this work,we sought to investigate constrained docking control during shipborne SideArm recovery of an Unmanned Aerial Vehicle(UAV)under preassigned safe docking constraints,rough ocean environments,and different i...In this work,we sought to investigate constrained docking control during shipborne SideArm recovery of an Unmanned Aerial Vehicle(UAV)under preassigned safe docking constraints,rough ocean environments,and different initial positions.The aim was to solve the UAV tracking-lag problem that manifests when attempting to dock with a rapidly moving SideArm and to improve the accuracy and rapidity of docking.First,together with the formulations of the shipborne SideArm system and environmental airflows,the affine nonlinear dynamics of the hook was established to reduce tracking lag.Then,echo state network approximators with good approximation capacity and low computational consumption were designed to accurately approximate the UAV’s unknown nonlinear dynamics.With feedforward compensation provided by these approximators,a nonlinear-mapping-based constrained docking control law was developed for shipborne SideArm recovery of UAVs.This approach to controlling the docking trajectory and the forward docking speed of the UAV can achieve rapid and exact docking with a moving SideArm,without violating the preassigned safe docking-constraint envelopes.Simulations under different docking scenarios were used to validate the effectiveness and advantages of the proposed docking-control algorithm.展开更多
To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. Th...To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.展开更多
This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation com...This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation component and detail components of time-delay sequences are fgured out.Next,one step prediction of time-delay is obtained through echo state network(ESN)model and auto-regressive integrated moving average model(ARIMA)according to the diferent characteristics of approximate component and detail components.Then,the fnal predictive value of time-delay is obtained by summation.Meanwhile,the parameters of echo state network is optimized by genetic algorithm.The simulation results indicate that higher accuracy can be achieved through this prediction method.展开更多
Echo state network (ESN) has become one of the most popular recurrent neural networks (RNN) for its good prediction performance of non-linear time series and simple training process. But several problems still pre...Echo state network (ESN) has become one of the most popular recurrent neural networks (RNN) for its good prediction performance of non-linear time series and simple training process. But several problems still prevent ESN from becoming a widely used tool. The most prominent problem is its high complexity with lots of random parameters. Aiming at this problem, a minimum complexity ESN model (MCESN) was proposed. In this paper, we proposed a new wavelet minimum complexity ESN model (WMCESN) to improve the prediction accuracy and increase the practical applicability. Our new model inherits the characters of minimum complexity ESN model using the fixed parameters and simple circle topology. We injected wavelet neurons to replace the original neurons in internal reservoir and designed a wavelet parameter matrix to reduce the computing time. By using different datasets, our new model performed better than the minimum complexity ESN model with normal neurons, but only utilized tiny time cost. We also used our own packets of transmission control protocol (TCP) and user datagram protocol (UDP) dataset to prove that our model can deal with the data packet bit prediction problem well.展开更多
High rigidity twenty-high Sendzimir mills (ZRMs) are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a s...High rigidity twenty-high Sendzimir mills (ZRMs) are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls are generally used for shape: recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron (MLP) is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition per- formance of ZRM control systems, echo state networks (ESNs) are proposed to be used. Through simulation re- sults, it is found that shape recognition performance could be improved using the proposed ESN method.展开更多
Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial con...Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.展开更多
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11190024 and 11474331)
文摘We propose a generalized Lanczos method to generate the many-body basis states of quantum lattice models using tensor-network states (TNS). The ground-state wave function is represented as a linear superposition composed from a set of TNS generated by Lanczos iteration. This method improves significantly the accuracy of the tensor-network algorithm and provides an effective way to enlarge the maximal bond dimension of TNS. The ground state such obtained contains significantly more entanglement than each individual TNS, reproducing correctly the logarithmic size dependence of the entanglement entropy in a critical system. The method can be generalized to non-Hamiltonian systems and to the calculation of low-lying excited states, dynamical correlation functions, and other physical properties of strongly correlated systems.
基金supported in part by the National Natural Science Foundation of China(62176109)the CAAI-Huawei MindSpore Open Fund(CAAIXSJLJJ-2022-020A)+3 种基金the Natural Science Foundation of Gansu Province(21JR7RA531,22JR5RA427,22JR5RA487)the Fundamental Research Funds for the Central Universities(lzujbky-2022-kb12,lzujbky-2022-23)the Science and Technology Project of Chengguan Discrict of Lanzhou(2021-1-2)the Supercomputing Center of Lanzhou University。
文摘Recent decades have witnessed a trend that the echo state network(ESN)is widely utilized in field of time series prediction due to its powerful computational abilities.However,most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise,which lacks robustness or even fails to solve real-world tasks.This work handles this issue by proposing a probabilistic regularized ESN(PRESN)with robustness guaranteed.Specifically,we design a novel objective function for minimizing both the mean and variance of modeling error,and then a scheme is derived for getting output weights of the PRESN.Furthermore,generalization performance,robustness,and unbiased estimation abilities of the PRESN are revealed by theoretical analyses.Finally,experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN.The source code is publicly available at https://github.com/LongJinlab/probabilistic-regularized-echo-state-network.
文摘The Chaotic Baseband Wireless Communication System(CBWCS)is expected to eliminate the Inter-Symbol Interference(ISI)caused by multipath propagation by using the optimal decoding threshold that is the sum of the ISI caused by past decoded bits and the ISI caused by future transmitting bits.However,the current technique is only capable of removing partial effects of the ISI,because only past decoded bits are available for the suboptimal decoding threshold calculation.The unavailability of the future information needed for the optimal decoding threshold is an obstacle to further improve the Bit Error Rate(BER)performance.In contrast to the previous method using Echo State Network(ESN)to predict one future bit,the proposed method in this paper predicts the optimal decoding threshold directly using ESN.The proposed ESN-based threshold prediction method simplifies the symbol decoding operation by avoiding the iterative prediction of the output waveform points using ESN and accumulated error caused by the iterative operation.With this approach,the calculation complexity is reduced compared to the previous ESN-based approach.The proposed method achieves better BER performance compared to the previous method.The reason for this superior result is twofold.First,the proposed ESN is capable of using more future symbols information conveyed by the ESN input to obtain more accurate threshold rather than the previous method in which only one future symbol was available.Second,the proposed method here does not need to estimate the channel information using Least Squared(LS)method,which avoids the extra error caused by inaccurate channel information estimation.Simulation results and experiment based on a wireless open-access research platform under a practical wireless channel show the effectiveness and superiority of the proposed method.
基金supported in part by the Open Research Fund of Joint Laboratory on Cyberspace Security,China Southern Power Grid(Grant No.CSS2022KF03)the Science and Technology Planning Project of Guangzhou,China(GrantNo.202201010388)the Fundamental Research Funds for the Central Universities.
文摘The blockchain-empowered Internet of Vehicles(IoV)enables various services and achieves data security and privacy,significantly advancing modern vehicle systems.However,the increased frequency of data transmission and complex network connections among nodes also make them more susceptible to adversarial attacks.As a result,an efficient intrusion detection system(IDS)becomes crucial for securing the IoV environment.Existing IDSs based on convolutional neural networks(CNN)often suffer from high training time and storage requirements.In this paper,we propose a lightweight IDS solution to protect IoV against both intra-vehicle and external threats.Our approach achieves superior performance,as demonstrated by key metrics such as accuracy and precision.Specifically,our method achieves accuracy rates ranging from 99.08% to 100% on the Car-Hacking dataset,with a remarkably short training time.
基金supported in part by the National Natural Science Foundation of China (60774098 60843003+3 种基金 50905172)the Science Foundation of Anhui Province (090412071 090412040)the University of Science and Technology of China Initiative Foundation
文摘In the forward channel of a networked control system (NCS), by defining the network states as a hidden Markov chain and quantizing the network-induced delays to a discrete sequence distributing over a finite time interval, the relation between the network states and the network-induced delays is modelled as a discrete-time hidden Markov model (DTHMM). The expectation maximization (EM) algorithm is introduced to derive the maximumlikelihood estimation (MLE) of the parameters of the DTHMM. Based on the derived DTHMM, the Viterbi algorithm is introduced to predict the controller-to-actuator (C-A) delay during the current sampling period. The simulation experiments demonstrate the effectiveness of the modelling and predicting methods proposed.
基金This research was supported by the Young Teachers Foundation of Beijing University of Chinese Medicine,No.2013-JYBZZ-JS-047The funders had no role in study design,data collection and analysis,decision to publish,or preparation of the manuscript.
文摘Objective:Cervical disease,a type of chronic pain,can greatly impact quality of life.Traditional Chinese tuina,a form of therapeutic massage and manipulation,has been shown to be effective in relieving pain and other symptoms in patients with chronic neck pain.This study applied functional magnetic resonance imaging (fMRI) to explore the features of the resting state network of patients with chronic neck pain caused by cervical radiculopathy,and how tuina affects the causality between intrinsic brain networks.Methods:Using Granger causality analysis,effective connectivity of brain networks of 10 patients with chronic neck pain was compared with 10 healthy control subjects.Resting state fMRI data were using magnetic resonance scanning.Cervical spondylosis symptom scores were evaluated before and after 4 weeks of tuina therapy.Independent component analysis was applied to extract the specific networks related to sensation,execution,and cognition,including sensorimotor network (SMN),visual network (VN),auditory network (AN),anterior and posterior default mode network (aDMN,pDMN),left frontoparietal network and right frontoparietal network.Results:Compared with the control group,data from the treatment group revealed two major findings:before tuina therapy,SMN had a profound influence on aDMN and AN greatly affected pDMN;however,after 4 weeks of tuina therapy,aDMN and SMN showed reversed causality.Conclusion:Chronic neck pain caused by cervical radiculopathy may influence the DMN,which plays an important role in emotion,cognition,and memory,by stimulating the sensory afferent network.Tuina not only significantly relieves pain and discomfort,but also reverses the causality between aDMN and SMN.
基金supported by the National Natural Science Foundation of China under Grant 62071364in part by the Aeronautical Science Foundation of China under Grant 2020Z073081001+2 种基金in part by the Fundamental Research Funds for the Central Universities under Grant JB210104in part by the Shaanxi Provincial Key Research and Development Program under Grant 2019GY-043in part by the 111 Project under Grant B08038。
文摘Passive detection of moving target is an important part of intelligent surveillance. Satellite has the potential to play a key role in many applications of space-air-ground integrated networks(SAGIN). In this paper, we propose a novel intelligent passive detection method for aerial target based on reservoir computing networks. Specifically, delayed feedback networks are utilized to refine the direct signals from the satellite in the reference channels. In addition, the satellite direct wave interference in the monitoring channels adopts adaptive interference suppression using the minimum mean square error filter. Furthermore, we employ decoupling echo state networks to predict the clutter interference in the monitoring channels and construct the detection statistics accordingly. Finally, a multilayer perceptron is adopted to detect the echo signal after interference suppression. Extensive simulations is conducted to evaluate the performance of our proposed method. Results show that the detection probability is almost 100% when the signal-to-interference ratio of echo signal is-36 dB, which demonstrates that our proposed method achieves efficient passive detection for aerial targets in typical SAGIN scenarios.
基金supported by the National Natural Science Foundation of China(No.72073041)Open Foundation for the University Innovation Platform in Hunan Province(No.18K103)+2 种基金2011 Collaborative Innovation Center for Development and Utilization of Finance and Economics Big Data Property,Universities of Hunan Province,Open Project(Nos.20181901CRP03,20181901CRP04,20181901CRP05)2020 Hunan Provincial Higher Education Teaching Reform Research Project(Nos.HNJG-2020-1130,HNJG-2020-1124)2020 General Project of Hunan Social Science Fund(No.20B16).
文摘Forecasting stock prices using deep learning models suffers from pro-blems such as low accuracy,slow convergence,and complex network structures.This study developed an echo state network(ESN)model to mitigate such pro-blems.We compared our ESN with a long short-term memory(LSTM)network by forecasting the stock data of Kweichow Moutai,a leading enterprise in China’s liquor industry.By analyzing data for 120,240,and 300 days,we generated fore-cast data for the next 40,80,and 100 days,respectively,using both ESN and LSTM.In terms of accuracy,ESN had the unique advantage of capturing non-linear data.Mean absolute error(MAE)was used to present the accuracy results.The MAEs of the data forecast by ESN were 0.024,0.024,and 0.025,which were,respectively,0.065,0.007,and 0.009 less than those of LSTM.In terms of con-vergence,ESN has a reservoir state-space structure,which makes it perform faster than other models.Root-mean-square error(RMSE)was used to present the con-vergence time.In our experiment,the RMSEs of ESN were 0.22,0.27,and 0.26,which were,respectively,0.08,0.01,and 0.12 less than those of LSTM.In terms of network structure,ESN consists only of input,reservoir,and output spaces,making it a much simpler model than the others.The proposed ESN was found to be an effective model that,compared to others,converges faster,forecasts more accurately,and builds time-series analyses more easily.
文摘<div style="text-align:justify;"> This paper proposes a prediction method based on improved Echo State Network for COVID-19 nonlinear time series, which improves the Echo State Network from the reservoir topology and the output weight matrix, and adopt the ABC (Artificial Bee Colony) algorithm based on crossover and crowding strategy to optimize the parameters. Finally, the proposed method is simulated and the results show that it has stronger prediction ability for COVID-19 nonlinear time series. </div>
基金Supported by the National Natural Science Foundation of China (Grant Nos.12001480,11871318)Applied Basic Research Program of Shanxi Province (Grant No.201901D211461)+2 种基金Scientific and Technologial Innovation Programs of Higher Education Institutions in Shanxi (Grant No.2020L0554)Excellent Doctoral Research Pro ject of Shanxi Province (Grant No.QZX-2020001)PhD Start-up Pro ject of Yuncheng University (Grant No.YQ-2019021)。
文摘Quantum many-body problem(QMBP)has become a hot topic in high energy physics and condensed matter physics.With the exponential increasing of the dimension of the Hilbert space,it becomes a big challenge to solve the QMBP even with the most powerful computers.With the rapid development of machine learning,artificial neural networks provide a powerful tool to represent or approximate quantum many-body states.In this paper,we aim to construct explicitly the neural network representations of graph states,without stochastic optimization of the network parameters.Our method shows constructively that all graph states can be represented precisely by proper neural networks originated from[Science,355,602(2017)]and formulated in[Sci.China-Phys.Mech.Astron.,63,210312(2020)].
基金the Grup de Recerca en Tecniques Estadistiques Avancades Aplicades a la Psicologia(GTEAAP)members of the Generalitat de Catalunya’s 2014 SGR 326 Consolidated Research Group(GRC)the PSI2013-41400-P project of Ministerio de Economia y Competitividad of the Spanish Government
文摘Age-related changes in the brain connectivity of healthy older adults have been widely studied in recent years,with some differences in the obtained results.Most of these studies showed decreases in general functional connectivity,but they also found increases in some particular regions and areas.Frequently,these studies compared young individuals with older subjects,but few studies compared different age groups only in older populations.The purpose of this study is to analyze whole-brain functional connectivity in healthy older adult groups and its network characteristics through functional segregation.A total of 114 individuals,48 to 89 years old,were scanned using resting-state functional magnetic resonance imaging in a resting state paradigm and were divided into six different age groups(<60,60–64,65–69,70–74,75–79,≥80 years old).A partial correlation analysis,a pooled correlation analysis and a study of 3-cycle regions with prominent connectivity were conducted.Our results showed progressive diminution in the functional connectivity among different age groups and this was particularly pronounced between 75 and 79 years old.The oldest group(≥80 years old)showed a slight increase in functional connectivity compared to the other groups.This occurred possibly because of compensatory mechanism in brain functioning.This study provides information on the brain functional characteristics of every age group,with more specific information on the functional progressive decline,and supplies methodological tools to study functional connectivity characteristics.Approval for the study was obtained from the ethics committee of the Comision de Bioetica de la Universidad de Barcelona(approval No.PSI2012-38257)on June 5,2012,and from the ethics committee of the Barcelona’s Hospital Clinic(approval No.2009-5306 and 2011-6604)on October 22,2009 and April 7,2011 respectively.
基金Supported by the National Natural Science Foundation of China(Nos.12001480,11871318)the Applied Basic Research Program of Shanxi Province(No.201901D211461)+2 种基金the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi(No.2020L0554)the Excellent Doctoral Research Project of Shanxi Province(No.QZX-2020001)the PhD Start-up Project of Yuncheng University(No.YQ-2019021)。
文摘The quantum many-body problem(QMBP) has become a hot topic in high-energy physics and condensed-matter physics. With an exponential increase in the dimensions of Hilbert space, it becomes very challenging to solve the QMBP, even with the most powerful computers. With the rapid development of machine learning, artificial neural networks provide a powerful tool that can represent or approximate quantum many-body states. In this paper, we aim to explicitly construct the neural network representations of hypergraph states. We construct the neural network representations for any k-uniform hypergraph state and any hypergraph state,respectively, without stochastic optimization of the network parameters. Our method constructively shows that all hypergraph states can be represented precisely by the appropriate neural networks introduced in [Science 355(2017) 602] and formulated in [Sci. China-Phys.Mech. Astron. 63(2020) 210312].
基金supported by the National Natural Science Foundation of China(Grant Nos.11871318,11771009,11571213,and 11601300)the Fundamental Research Funds for the Central Universities(Grant Nos.GK201703093,and GK201801011)+2 种基金the Natural Science Basic Research Plan in Shaanxi Province of China(Grant No.2018JM1020)the Shaanxi Province Innovation Ability Support Program(Grant No.2018KJXX-054)the Subject Research Project of Yuncheng University(Grant No.XK-2018032)
文摘Machine learning is currently the most active interdisciplinary field having numerous applications;additionally,machine-learning techniques are used to research quantum many-body problems.In this study,we first propose neural network quantum states(NNQSs)with general input observables and explore a few related properties,such as the tensor product and local unitary operation.Second,we determine the necessary and sufficient conditions for the representability of a general graph state using normalized NNQS.Finally,to quantify the approximation degree of a given pure state,we define the best approximation degree using normalized NNQSs.Furthermore,we observe that some 7V-qubit states can be represented by a normalized NNQS,such as separable pure states,Bell states and GHZ states.
基金This study was supported by the National Key Laboratory of Science and Technology on UAV in NWPU,China(No.2022-JCJQ-LB-071)the National Natural Science Foundations of China(No.61903190)+2 种基金the Aeronautical Science Foundation(N2022Z023052003)the Fundamental Research Funds for the Central Universities,China(No.NS2023016)the Postgraduate Research&Practice Innovation Program of NUAA,China(No.xcxjh20230311).
文摘In this work,we sought to investigate constrained docking control during shipborne SideArm recovery of an Unmanned Aerial Vehicle(UAV)under preassigned safe docking constraints,rough ocean environments,and different initial positions.The aim was to solve the UAV tracking-lag problem that manifests when attempting to dock with a rapidly moving SideArm and to improve the accuracy and rapidity of docking.First,together with the formulations of the shipborne SideArm system and environmental airflows,the affine nonlinear dynamics of the hook was established to reduce tracking lag.Then,echo state network approximators with good approximation capacity and low computational consumption were designed to accurately approximate the UAV’s unknown nonlinear dynamics.With feedforward compensation provided by these approximators,a nonlinear-mapping-based constrained docking control law was developed for shipborne SideArm recovery of UAVs.This approach to controlling the docking trajectory and the forward docking speed of the UAV can achieve rapid and exact docking with a moving SideArm,without violating the preassigned safe docking-constraint envelopes.Simulations under different docking scenarios were used to validate the effectiveness and advantages of the proposed docking-control algorithm.
基金supported by the Key Project of National Defense Basic Research Program of China (No.B1120132031)supported by the Cultivation and Development Program for Technology Innovation Base of Beijing Municipal Science and Technology Commission (No.Z151100001615034)
文摘To improve the efficiency and coverage of stateful network protocol fuzzing, this paper proposes a new method, using a rule-based state machine and a stateful rule tree to guide the generation of fuzz testing data. The method first builds a rule-based state machine model as a formal description of the states of a network protocol. This removes safety paths, to cut down the scale of the state space. Then it uses a stateful rule tree to describe the relationship between states and messages, and then remove useless items from it. According to the message sequence obtained by the analysis of paths using the stateful rule tree and the protocol specification, an abstract data model of test case generation is defined. The fuzz testing data is produced by various generation algorithms through filling data in the fields of the data model. Using the rule-based state machine and the stateful rule tree, the quantity of test data can be reduced. Experimental results indicate that our method can discover the same vulnerabilities as traditional approaches, using less test data, while optimizing test data generation and improving test efficiency.
基金supported by National Natural Science Foundation of China(No.61034005)
文摘This paper presents an Ethernet based hybrid method for predicting random time-delay in the networked control system.First,db3 wavelet is used to decompose and reconstruct time-delay sequence,and the approximation component and detail components of time-delay sequences are fgured out.Next,one step prediction of time-delay is obtained through echo state network(ESN)model and auto-regressive integrated moving average model(ARIMA)according to the diferent characteristics of approximate component and detail components.Then,the fnal predictive value of time-delay is obtained by summation.Meanwhile,the parameters of echo state network is optimized by genetic algorithm.The simulation results indicate that higher accuracy can be achieved through this prediction method.
基金supported by the National Natural Science Foundation of China (61201153)the National Basic Research Program of China (2012CB315805)the National Key Science and Technology Projects (2010ZX03004-002-02)
文摘Echo state network (ESN) has become one of the most popular recurrent neural networks (RNN) for its good prediction performance of non-linear time series and simple training process. But several problems still prevent ESN from becoming a widely used tool. The most prominent problem is its high complexity with lots of random parameters. Aiming at this problem, a minimum complexity ESN model (MCESN) was proposed. In this paper, we proposed a new wavelet minimum complexity ESN model (WMCESN) to improve the prediction accuracy and increase the practical applicability. Our new model inherits the characters of minimum complexity ESN model using the fixed parameters and simple circle topology. We injected wavelet neurons to replace the original neurons in internal reservoir and designed a wavelet parameter matrix to reduce the computing time. By using different datasets, our new model performed better than the minimum complexity ESN model with normal neurons, but only utilized tiny time cost. We also used our own packets of transmission control protocol (TCP) and user datagram protocol (UDP) dataset to prove that our model can deal with the data packet bit prediction problem well.
基金Sponsored by Korea Science and Engineering Foundation(KOSEF)Funded by Korea Government(MEST)(2010-0022521)
文摘High rigidity twenty-high Sendzimir mills (ZRMs) are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls are generally used for shape: recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron (MLP) is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition per- formance of ZRM control systems, echo state networks (ESNs) are proposed to be used. Through simulation re- sults, it is found that shape recognition performance could be improved using the proposed ESN method.
基金supported by National Natural Science Foundation of China(Nos.61374102,61433002 and 61120106009)High Education Science&Technology Fund Planning Project of Shandong Province of China(No.J14LN30)
文摘Terminal iterative learning control(TILC) is developed to reduce the error between system output and a fixed desired point at the terminal end of operation interval over iterations under strictly identical initial conditions. In this work, the initial states are not required to be identical further but can be varying from iteration to iteration. In addition, the desired terminal point is not fixed any more but is allowed to change run-to-run. Consequently, a new adaptive TILC is proposed with a neural network initial state learning mechanism to achieve the learning objective over iterations. The neural network is used to approximate the effect of iteration-varying initial states on the terminal output and the neural network weights are identified iteratively along the iteration axis.A dead-zone scheme is developed such that both learning and adaptation are performed only if the terminal tracking error is outside a designated error bound. It is shown that the proposed approach is able to track run-varying terminal desired points fast with a specified tracking accuracy beyond the initial state variance.