To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on th...To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.展开更多
Nonpolar(11–20) a-plane p-type GaN films were successfully grown on r-plane sapphire substrate with the metal–organic chemical vapor deposition(MOCVD) system. The effects of Mg-doping temperature on the structural a...Nonpolar(11–20) a-plane p-type GaN films were successfully grown on r-plane sapphire substrate with the metal–organic chemical vapor deposition(MOCVD) system. The effects of Mg-doping temperature on the structural and electrical properties of nonpolar p-type GaN films were investigated in detail. It is found that all the surface morphology, crystalline quality, strains, and electrical properties of nonpolar a-plane p-type GaN films are interconnected, and are closely related to the Mg-doping temperature. This means that a proper performance of nonpolar p-type GaN can be expected by optimizing the Mg-doping temperature. In fact, a hole concentration of 1.3×10^(18)cm^(-3), a high Mg activation efficiency of 6.5%,an activation energy of 114 me V for Mg acceptor, and a low anisotropy of 8.3% in crystalline quality were achieved with a growth temperature of 990℃. This approach to optimizing the Mg-doping temperature of the nonpolar a-plane p-type GaN film provides an effective way to fabricate high-efficiency optoelectronic devices in the future.展开更多
Defect engineering can give birth to novel properties for adsorption and photocatalysis in the control of antibiotics and heavy metal combined pollution with photocatalytic composites.However,the role of defects and t...Defect engineering can give birth to novel properties for adsorption and photocatalysis in the control of antibiotics and heavy metal combined pollution with photocatalytic composites.However,the role of defects and the process mechanism are complicated and indefinable.Herein,TiO_(2)/CN/3DC was fabricated and defects were introduced into the tripartite structure with separate O_(2)plasma treatment for the single component.We find that defect engineering can improve the photocatalytic activity,attributing to the increase of the contribution from h^(+)and OH.In contrast to TiO_(2)/CN/3DC with a photocatalytic tetracycline removal rate of 75.2%,the removal rate of TC with D-TiO_(2)/CN/3DC has increased to 88.5%.Moreover,the reactive sites of tetracycline can be increased by adsorbing on the defective composites.The defect construction on TiO_(2)shows the advantages in tetracycline degradation and Cu^(2+)adsorption,but also suffers significant inhibition for the tetracycline degradation in a tetracycline/Cu^(2+)combined system.In contrast,the defect construction on graphene can achieve the cooperative removal of tetracycline and Cu^(2+).These findings can provide new insights into water treatment strategies with defect engineering.展开更多
The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is w...The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.展开更多
This study underscores the significance of online monitoring of standard substances for bituminous coal and anthracite,two commonly used fossil fuels.Terahertz technology emerges as a powerful non-destructive detectio...This study underscores the significance of online monitoring of standard substances for bituminous coal and anthracite,two commonly used fossil fuels.Terahertz technology emerges as a powerful non-destructive detection method capable of revealing the physical and chemical properties of measured objects.In this research,terahertz time-domain spectroscopy technology was employed to investigate the spectral characteristics of four distinct types of bituminous coal and anthracite samples.The refractive index and absorption coefficient spectra of these samples were calculated across a frequency range of 0.5 THz to 2.5 THz.Furthermore,principal component analysis was conducted using all refractive index and absorption coefficient data within this frequency band.Through the analysis and comparison with known parameters of coal standard materials,it was established that carbon content primarily influences the refractive index of bituminous coal and anthracite,while ash content predominantly affects the absorption effect.These findings underscore the potential of terahertz spectroscopy in conjunction with principal component analysis to qualitatively assess the similarities and differences between coal samples,thus offering novel insights for the online monitoring of diverse coal types and qualities.展开更多
We investigate the quasi-synchronization of fractional-order complex networks(FCNs) with random coupling via quantized control. Firstly, based on the logarithmic quantizer theory and the Lyapunov stability theory, a n...We investigate the quasi-synchronization of fractional-order complex networks(FCNs) with random coupling via quantized control. Firstly, based on the logarithmic quantizer theory and the Lyapunov stability theory, a new quantized feedback controller, which can make all nodes of complex networks quasi-synchronization and eliminate the disturbance of random coupling in the system state, is designed under non-delay conditions. Secondly, we extend the theoretical results under non-delay conditions to time-varying delay conditions and design another form of quantization feedback controller to ensure that the network achieves quasi-synchronization. Furthermore, the error bound of quasi-synchronization is obtained.Finally, we verify the accuracy of our results using two numerical simulation examples.展开更多
Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/...Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/TaO_(x) structure,which is facilitated by a wedge-shaped HfO_(2)buffer layer.The field-free switching ratio varies with HfO_(2)thickness,reaching optimal performance at 25 nm.This phenomenon is attributed to the lateral anisotropy gradient of the Co layer,which is induced by the wedge-shaped HfO_(2)buffer layer.The thickness gradient of HfO_(2)along the wedge creates a corresponding lateral anisotropy gradient in the Co layer,correlating with the switching ratio.These findings indicate that field-free SOT switching can be achieved through designing buffer layer,offering a novel approach to innovating spin-orbit device.展开更多
Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have mac...Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network(CNN) and convolutional long short-term memory(ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally,to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particle swarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System(PeMS)are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this research is superior to those of the prevalent methods.展开更多
Current induced spin-orbit torque(SOT)switching of magnetization is a promising technology for nonvolatile spintronic memory and logic applications.In this work,we systematically investigated the effect of Ta thicknes...Current induced spin-orbit torque(SOT)switching of magnetization is a promising technology for nonvolatile spintronic memory and logic applications.In this work,we systematically investigated the effect of Ta thickness on the magnetic properties,field-free switching and SOT efficiency in a ferromagnetically coupled Co/Ta/Co Fe B trilayer with perpendicular magnetic anisotropy.We found that both the anisotropy field and coercivity increase with increasing Ta thickness from0.15 nm to 0.4 nm.With further increase of Ta thickness to 0.5 nm,two-step switching is observed,indicating that the two magnetic layers are magnetically decoupled.Measurements of pulse-current induced magnetization switching and harmonic Hall voltages show that the critical switching current density increases while the field-free switching ratio and SOT efficiency decrease with increasing Ta thickness.Both the enhanced spin memory loss and reduced interlayer exchange coupling might be responsible for theβ_(DL)decrease as the Ta spacer thickness increases.The studied structure with the incorporation of a Co Fe B layer is able to realize field-free switching in the strong ferromagnetic coupling region,which may contribute to the further development of magnetic tunnel junctions for better memory applications.展开更多
For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of sing...For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.展开更多
This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates...This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams,Lyapunov exponents(LEs),and bifurcation diagrams.Secondly,the parameter related firing behaviors are described through two-parameter bifurcation diagrams.Subsequently,local attraction basins reveal multi-stability phenomena related to initial values.Moreover,the proposed model is implemented on a microcomputer-based ARM platform,and the experimental results correspond to the numerical simulations.Finally,the article explores the application of digital watermarking for medical images,illustrating its features of excellent imperceptibility,extensive key space,and robustness against attacks including noise and cropping.展开更多
The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition...The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.展开更多
As the world transitions to green energy, there is a growing focus among many researchers on the requirement for high-efficient and safe batteries. Solid-state lithium metal batteries(SSLMBs) have emerged as a promisi...As the world transitions to green energy, there is a growing focus among many researchers on the requirement for high-efficient and safe batteries. Solid-state lithium metal batteries(SSLMBs) have emerged as a promising alternative to traditional liquid lithium-ion batteries(LIBs), offering higher energy density, enhanced safety, and longer lifespan. The rise of SSLMBs has brought about a transformation in energy storage, with aluminum(Al)-based material dopants playing a crucial role in advancing the next generation of batteries. The review highlights the significance of Al-based material dopants in SSLMBs applications, particularly its contributions to solid-state electrolytes(SSEs), cathodes, anodes,and other components of SSLMBs. Some studies have also shown that Al-based material dopants effectively enhance SSE ion conductivity, stabilize electrode and SSE interfaces, and suppress lithium dendrite growth, thereby enhancing the electrochemical performance of SSLMBs. Despite the above mentioned progresses, there are still problems and challenges need to be addressed. The review offers a comprehensive insight into the important role of Al in SSLMBs and addresses some of the issues related to its applications, endowing valuable support for the practical implementation of SSLMBs.展开更多
This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmi...This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism,instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.展开更多
Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In exist...Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.展开更多
Achieving high‐precision extraction of sea islands from high‐resolution satellite remote sensing images is crucial for effective resource development and sustainable management.Unfortunately,achieving such accuracy ...Achieving high‐precision extraction of sea islands from high‐resolution satellite remote sensing images is crucial for effective resource development and sustainable management.Unfortunately,achieving such accuracy for sea island extraction presents significant challenges due to the presence of extensive background interference.A more widely applicable noise‐tolerant matched filter(NTMF)scheme is proposed for sea island extraction based on the MF scheme.The NTMF scheme effectively suppresses the background interference,leading to more accurate and robust sea island extraction.To further enhance the accuracy and robustness of the NTMF scheme,a neural dynamics algorithm is supplemented that adds an error integration feedback term to counter noise interference during internal computer operations in practical applications.Several comparative experiments were conducted on various remote sensing images of sea islands under different noisy working conditions to demonstrate the superiority of the proposed neural dynamics algorithm‐assisted NTMF scheme.These experiments confirm the ad-vantages of using the NTMF scheme for sea island extraction with the assistance of neural dynamics algorithm.展开更多
In the model of the vehicle recognition algorithm implemented by the convolutional neural network,the model needs to compute and store a lot of parameters.Too many parameters occupy a lot of computational resources ma...In the model of the vehicle recognition algorithm implemented by the convolutional neural network,the model needs to compute and store a lot of parameters.Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance.Therefore,obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research.In this paper,a lightweight densely connected,and deeply separable convolutional network(DCDSNet)algorithmis proposed to achieve this goal.Visual Geometry Group(VGG)model is improved by utilizing the convolution instead of the fully connected module,the deeply separable convolution module,and the densely connected network module,with the first two modules reducing the parameters and the third module allowing the algorithm to have more features in a limited number of parameters.The algorithm achieves better results in the mine vehicle recognition dataset.Experiments show that the recognition accuracy is improved by 4.41% compared to VGG19 and the amount of parameters is reduced by 71% compared to VGG19.展开更多
The warhead of a ballistic missile may precess due to lateral moments during release. The resulting micro-Doppler effect is determined by parameters such as the target's motion state and size. A three-dimensional ...The warhead of a ballistic missile may precess due to lateral moments during release. The resulting micro-Doppler effect is determined by parameters such as the target's motion state and size. A three-dimensional reconstruction method for the precession warhead via the micro-Doppler analysis and inverse Radon transform(IRT) is proposed in this paper. The precession parameters are extracted by the micro-Doppler analysis from three radars, and the IRT is used to estimate the size of targe. The scatterers of the target can be reconstructed based on the above parameters. Simulation experimental results illustrate the effectiveness of the proposed method in this paper.展开更多
With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the netw...With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.展开更多
The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering fields.Although the original zeroing neural network is powerful for solving the DMSR,it cannot vanis...The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering fields.Although the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence speed.Therefore,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed simultaneously.Then,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed circumstances.Furthermore,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution.Compared with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.展开更多
基金supported by the National Natural Science Foundation of China(61701140).
文摘To analyze the influence of time synchronization error,phase synchronization error,frequency synchronization error,internal delay of the transceiver system,and range error and angle error between the unit radars on the target detection performance,firstly,a spatial detection model of distributed high-frequency surface wave radar(distributed-HFSWR)is established in this paper.In this model,a method for accurate extraction of direct wave spectrum based on curve fitting is proposed to obtain accurate system internal delay and frequency synchronization error under complex electromagnetic environment background and low signal to noise ratio(SNR),and to compensate for the shift of range and Doppler frequency caused by time-frequency synchronization error.The direct wave component is extracted from the spectrum,the range estimation error and Doppler estimation error are reduced by the method of curve fitting,and the fitting accuracy of the parameters is improved.Then,the influence of frequency synchronization error on target range and radial Doppler velocity is quantitatively analyzed.The relationship between frequency synchronization error and radial Doppler velocity shift and range shift is given.Finally,the system synchronization parameters of the trial distributed-HFSWR are obtained by the proposed spectrum extraction method based on curve fitting,the experimental data is compensated to correct the shift of the target,and finally the correct target parameter information is obtained.Simulations and experimental results demonstrate the superiority and correctness of the proposed method,theoretical derivation and detection model proposed in this paper.
基金Project supported by the National Key Research and Development Program of China (Grant Nos.2021YFB3601000 and 2021YFB3601002)the National Natural Science Foundation of China (Grant Nos.62074077,61921005,61974062,62204121,and 61904082)+1 种基金Leading-edge Technology Program of Jiangsu Natural Science Foundation (Grant No.BE2021008-2)the China Postdoctoral Science Foundation (Grant No.2020M671441)。
文摘Nonpolar(11–20) a-plane p-type GaN films were successfully grown on r-plane sapphire substrate with the metal–organic chemical vapor deposition(MOCVD) system. The effects of Mg-doping temperature on the structural and electrical properties of nonpolar p-type GaN films were investigated in detail. It is found that all the surface morphology, crystalline quality, strains, and electrical properties of nonpolar a-plane p-type GaN films are interconnected, and are closely related to the Mg-doping temperature. This means that a proper performance of nonpolar p-type GaN can be expected by optimizing the Mg-doping temperature. In fact, a hole concentration of 1.3×10^(18)cm^(-3), a high Mg activation efficiency of 6.5%,an activation energy of 114 me V for Mg acceptor, and a low anisotropy of 8.3% in crystalline quality were achieved with a growth temperature of 990℃. This approach to optimizing the Mg-doping temperature of the nonpolar a-plane p-type GaN film provides an effective way to fabricate high-efficiency optoelectronic devices in the future.
基金support of this research by the National Natural Science Foundation of China(Grant No.51909165,42177438)the Start-up Research Funding of Southwest Jiaotong University(YH1100312372222)+4 种基金the Fundamental Research Funds for the Central Universities(XJ2022003201)Science and Technology Program of Guangzhou(2019050001)National Key Research and Development Program of China(2019YFE0198000)the High-End Foreign Experts Project(G2021030016L)Pearl River Talent Program(2019QN01L951)
文摘Defect engineering can give birth to novel properties for adsorption and photocatalysis in the control of antibiotics and heavy metal combined pollution with photocatalytic composites.However,the role of defects and the process mechanism are complicated and indefinable.Herein,TiO_(2)/CN/3DC was fabricated and defects were introduced into the tripartite structure with separate O_(2)plasma treatment for the single component.We find that defect engineering can improve the photocatalytic activity,attributing to the increase of the contribution from h^(+)and OH.In contrast to TiO_(2)/CN/3DC with a photocatalytic tetracycline removal rate of 75.2%,the removal rate of TC with D-TiO_(2)/CN/3DC has increased to 88.5%.Moreover,the reactive sites of tetracycline can be increased by adsorbing on the defective composites.The defect construction on TiO_(2)shows the advantages in tetracycline degradation and Cu^(2+)adsorption,but also suffers significant inhibition for the tetracycline degradation in a tetracycline/Cu^(2+)combined system.In contrast,the defect construction on graphene can achieve the cooperative removal of tetracycline and Cu^(2+).These findings can provide new insights into water treatment strategies with defect engineering.
文摘The Internet of Things(IoT)connects objects to Internet through sensor devices,radio frequency identification devices and other information collection and processing devices to realize information interaction.IoT is widely used in many fields,including intelligent transportation,intelligent healthcare,intelligent home and industry.In these fields,IoT devices connected via high-speed internet for efficient and reliable communications and faster response times.
基金Anhui Province Natural Science Research Project for Universities(2022AH052272)。
文摘This study underscores the significance of online monitoring of standard substances for bituminous coal and anthracite,two commonly used fossil fuels.Terahertz technology emerges as a powerful non-destructive detection method capable of revealing the physical and chemical properties of measured objects.In this research,terahertz time-domain spectroscopy technology was employed to investigate the spectral characteristics of four distinct types of bituminous coal and anthracite samples.The refractive index and absorption coefficient spectra of these samples were calculated across a frequency range of 0.5 THz to 2.5 THz.Furthermore,principal component analysis was conducted using all refractive index and absorption coefficient data within this frequency band.Through the analysis and comparison with known parameters of coal standard materials,it was established that carbon content primarily influences the refractive index of bituminous coal and anthracite,while ash content predominantly affects the absorption effect.These findings underscore the potential of terahertz spectroscopy in conjunction with principal component analysis to qualitatively assess the similarities and differences between coal samples,thus offering novel insights for the online monitoring of diverse coal types and qualities.
基金supported by the Anhui Provincial Development and Reform Commission New Energy Vehicles and Intelligent Connected Automobile Industry Technology Innovation Project。
文摘We investigate the quasi-synchronization of fractional-order complex networks(FCNs) with random coupling via quantized control. Firstly, based on the logarithmic quantizer theory and the Lyapunov stability theory, a new quantized feedback controller, which can make all nodes of complex networks quasi-synchronization and eliminate the disturbance of random coupling in the system state, is designed under non-delay conditions. Secondly, we extend the theoretical results under non-delay conditions to time-varying delay conditions and design another form of quantization feedback controller to ensure that the network achieves quasi-synchronization. Furthermore, the error bound of quasi-synchronization is obtained.Finally, we verify the accuracy of our results using two numerical simulation examples.
基金Project supported by the National Natural Science Foundation of China (Grant No.12274108)the Natural Science Foundation of Zhejiang Province,China (Grant Nos.LY23A040008 and LY23A040008)the Basic Scientific Research Project of Wenzhou,China (Grant No.G20220025)。
文摘Field-free spin-orbit torque(SOT)switching of perpendicular magnetization is essential for future spintronic devices.This study demonstrates the field-free switching of perpendicular magnetization in an HfO_(2)/Pt/Co/TaO_(x) structure,which is facilitated by a wedge-shaped HfO_(2)buffer layer.The field-free switching ratio varies with HfO_(2)thickness,reaching optimal performance at 25 nm.This phenomenon is attributed to the lateral anisotropy gradient of the Co layer,which is induced by the wedge-shaped HfO_(2)buffer layer.The thickness gradient of HfO_(2)along the wedge creates a corresponding lateral anisotropy gradient in the Co layer,correlating with the switching ratio.These findings indicate that field-free SOT switching can be achieved through designing buffer layer,offering a novel approach to innovating spin-orbit device.
基金Project supported by the National Natural Science Foundation of China (Grant No. 62063014)the Natural Science Foundation of Gansu Province, China (Grant No. 22JR5RA365)。
文摘Traffic flow prediction is an effective strategy to assess traffic conditions and alleviate traffic congestion. Influenced by external non-stationary factors and road network structure, traffic flow sequences have macro spatiotemporal characteristics and micro chaotic characteristics. The key to improving the model prediction accuracy is to fully extract the macro and micro characteristics of traffic flow time sequences. However, traditional prediction model by only considers time features of traffic data, ignoring spatial characteristics and nonlinear characteristics of the data itself, resulting in poor model prediction performance. In view of this, this research proposes an intelligent combination prediction model taking into account the macro and micro features of chaotic traffic data. Firstly, to address the problem of time-consuming and inefficient multivariate phase space reconstruction by iterating nodes one by one, an improved multivariate phase space reconstruction method is proposed by filtering global representative nodes to effectively realize the high-dimensional mapping of chaotic traffic flow. Secondly, to address the problem that the traditional combinatorial model is difficult to adequately learn the macro and micro characteristics of chaotic traffic data, a combination of convolutional neural network(CNN) and convolutional long short-term memory(ConvLSTM) is utilized for capturing nonlinear features of traffic flow more comprehensively. Finally,to overcome the challenge that the combined model performance degrades due to subjective empirical determined network parameters, an improved lightweight particle swarm is proposed for improving prediction accuracy by optimizing model hyperparameters. In this paper, two highway datasets collected by the Caltrans Performance Measurement System(PeMS)are taken as the research objects, and the experimental results from multiple perspectives show that the comprehensive performance of the method proposed in this research is superior to those of the prevalent methods.
基金Project supported by the‘Pioneer’and‘Leading Goose’Research and Development Program of Zhejiang Province,China(Grant No.2022C01053)the National Natural Science Foundation of China(Grant Nos.11874135,12104119+2 种基金12004090)Key Research and Development Program of Zhejiang Province,China(Grant No.2021C01039)Natural Science Foundation of Zhejiang Province,China(Grant Nos.LQ20F040005 and LQ21A050001)。
文摘Current induced spin-orbit torque(SOT)switching of magnetization is a promising technology for nonvolatile spintronic memory and logic applications.In this work,we systematically investigated the effect of Ta thickness on the magnetic properties,field-free switching and SOT efficiency in a ferromagnetically coupled Co/Ta/Co Fe B trilayer with perpendicular magnetic anisotropy.We found that both the anisotropy field and coercivity increase with increasing Ta thickness from0.15 nm to 0.4 nm.With further increase of Ta thickness to 0.5 nm,two-step switching is observed,indicating that the two magnetic layers are magnetically decoupled.Measurements of pulse-current induced magnetization switching and harmonic Hall voltages show that the critical switching current density increases while the field-free switching ratio and SOT efficiency decrease with increasing Ta thickness.Both the enhanced spin memory loss and reduced interlayer exchange coupling might be responsible for theβ_(DL)decrease as the Ta spacer thickness increases.The studied structure with the incorporation of a Co Fe B layer is able to realize field-free switching in the strong ferromagnetic coupling region,which may contribute to the further development of magnetic tunnel junctions for better memory applications.
文摘For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.
文摘This article proposes a novel fractional heterogeneous neural network by coupling a Rulkov neuron with a Hopfield neural network(FRHNN),utilizing memristors for emulating neural synapses.The study firstly demonstrates the coexistence of multiple firing patterns through phase diagrams,Lyapunov exponents(LEs),and bifurcation diagrams.Secondly,the parameter related firing behaviors are described through two-parameter bifurcation diagrams.Subsequently,local attraction basins reveal multi-stability phenomena related to initial values.Moreover,the proposed model is implemented on a microcomputer-based ARM platform,and the experimental results correspond to the numerical simulations.Finally,the article explores the application of digital watermarking for medical images,illustrating its features of excellent imperceptibility,extensive key space,and robustness against attacks including noise and cropping.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(62325104).
文摘The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.
基金Tianjin Natural Science Foundation (23JCYBJC00660)Tianjin Enterprise Science and Technology Commissioner Project (23YDTPJC00490)+4 种基金National Natural Science Foundation of China (52203066, 51973157, 61904123)China Postdoctoral Science Foundation Grant (2023M742135)National innovation and entrepreneurship training program for college students (202310058007)Tianjin Municipal college students’ innovation and entrepreneurship training program (202310058088)State Key Laboratory of Membrane and Membrane Separation, Tiangong University。
文摘As the world transitions to green energy, there is a growing focus among many researchers on the requirement for high-efficient and safe batteries. Solid-state lithium metal batteries(SSLMBs) have emerged as a promising alternative to traditional liquid lithium-ion batteries(LIBs), offering higher energy density, enhanced safety, and longer lifespan. The rise of SSLMBs has brought about a transformation in energy storage, with aluminum(Al)-based material dopants playing a crucial role in advancing the next generation of batteries. The review highlights the significance of Al-based material dopants in SSLMBs applications, particularly its contributions to solid-state electrolytes(SSEs), cathodes, anodes,and other components of SSLMBs. Some studies have also shown that Al-based material dopants effectively enhance SSE ion conductivity, stabilize electrode and SSE interfaces, and suppress lithium dendrite growth, thereby enhancing the electrochemical performance of SSLMBs. Despite the above mentioned progresses, there are still problems and challenges need to be addressed. The review offers a comprehensive insight into the important role of Al in SSLMBs and addresses some of the issues related to its applications, endowing valuable support for the practical implementation of SSLMBs.
文摘This paper addresses the issue of nonfragile state estimation for memristive recurrent neural networks with proportional delay and sensor saturations. In practical engineering, numerous unnecessary signals are transmitted to the estimator through the networks, which increases the burden of communication bandwidth. A dynamic event-triggered mechanism,instead of a static event-triggered mechanism, is employed to select useful data. By constructing a meaningful Lyapunov–Krasovskii functional, a delay-dependent criterion is derived in terms of linear matrix inequalities for ensuring the global asymptotic stability of the augmented system. In the end, two numerical simulations are employed to illustrate the feasibility and validity of the proposed theoretical results.
基金supported by National Natural Sciences Foundation of China(No.62271165,62027802,62201307)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515030297)+2 种基金the Shenzhen Science and Technology Program ZDSYS20210623091808025Stable Support Plan Program GXWD20231129102638002the Major Key Project of PCL(No.PCL2024A01)。
文摘Due to the restricted satellite payloads in LEO mega-constellation networks(LMCNs),remote sensing image analysis,online learning and other big data services desirably need onboard distributed processing(OBDP).In existing technologies,the efficiency of big data applications(BDAs)in distributed systems hinges on the stable-state and low-latency links between worker nodes.However,LMCNs with high-dynamic nodes and long-distance links can not provide the above conditions,which makes the performance of OBDP hard to be intuitively measured.To bridge this gap,a multidimensional simulation platform is indispensable that can simulate the network environment of LMCNs and put BDAs in it for performance testing.Using STK's APIs and parallel computing framework,we achieve real-time simulation for thousands of satellite nodes,which are mapped as application nodes through software defined network(SDN)and container technologies.We elaborate the architecture and mechanism of the simulation platform,and take the Starlink and Hadoop as realistic examples for simulations.The results indicate that LMCNs have dynamic end-to-end latency which fluctuates periodically with the constellation movement.Compared to ground data center networks(GDCNs),LMCNs deteriorate the computing and storage job throughput,which can be alleviated by the utilization of erasure codes and data flow scheduling of worker nodes.
基金Key projects of the Guangdong Education Department,Grant/Award Number:2023ZDZX4009National Natural Science Foundation of China,Grant/Award Number:42206187+1 种基金Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory,Grant/Award Number:GML2021GD0809National Key Research and Development Program of China,Grant/Award Number:2022YFC3103101。
文摘Achieving high‐precision extraction of sea islands from high‐resolution satellite remote sensing images is crucial for effective resource development and sustainable management.Unfortunately,achieving such accuracy for sea island extraction presents significant challenges due to the presence of extensive background interference.A more widely applicable noise‐tolerant matched filter(NTMF)scheme is proposed for sea island extraction based on the MF scheme.The NTMF scheme effectively suppresses the background interference,leading to more accurate and robust sea island extraction.To further enhance the accuracy and robustness of the NTMF scheme,a neural dynamics algorithm is supplemented that adds an error integration feedback term to counter noise interference during internal computer operations in practical applications.Several comparative experiments were conducted on various remote sensing images of sea islands under different noisy working conditions to demonstrate the superiority of the proposed neural dynamics algorithm‐assisted NTMF scheme.These experiments confirm the ad-vantages of using the NTMF scheme for sea island extraction with the assistance of neural dynamics algorithm.
基金supported by the open project of National Local Joint Engineering Research Center for Agro-Ecological Big Data Analysis and Application Technology,“Adaptive Agricultural Machinery Motion Detection and Recognition in Natural Scenes”,AE202210By the school-level key discipline of Suzhou University in China with No.2019xjzdxk12022 Anhui Province College Research Program Project of the Suzhou Vocational College of Civil Aviation,No.2022AH053155.
文摘In the model of the vehicle recognition algorithm implemented by the convolutional neural network,the model needs to compute and store a lot of parameters.Too many parameters occupy a lot of computational resources making it difficult to run on computers with poor performance.Therefore,obtaining more efficient feature information of target image or video with better accuracy on computers with limited arithmetic power becomes the main goal of this research.In this paper,a lightweight densely connected,and deeply separable convolutional network(DCDSNet)algorithmis proposed to achieve this goal.Visual Geometry Group(VGG)model is improved by utilizing the convolution instead of the fully connected module,the deeply separable convolution module,and the densely connected network module,with the first two modules reducing the parameters and the third module allowing the algorithm to have more features in a limited number of parameters.The algorithm achieves better results in the mine vehicle recognition dataset.Experiments show that the recognition accuracy is improved by 4.41% compared to VGG19 and the amount of parameters is reduced by 71% compared to VGG19.
基金supported by the National Natural Science Foundation of China (61871146)the Fundamental Research Funds for the Central Universities (FRFCU5710093720)。
文摘The warhead of a ballistic missile may precess due to lateral moments during release. The resulting micro-Doppler effect is determined by parameters such as the target's motion state and size. A three-dimensional reconstruction method for the precession warhead via the micro-Doppler analysis and inverse Radon transform(IRT) is proposed in this paper. The precession parameters are extracted by the micro-Doppler analysis from three radars, and the IRT is used to estimate the size of targe. The scatterers of the target can be reconstructed based on the above parameters. Simulation experimental results illustrate the effectiveness of the proposed method in this paper.
基金This work was supported by the Key Research and Development(R&D)Plan of Heilongjiang Province of China(JD22A001).
文摘With the continuous development of network func-tions virtualization(NFV)and software-defined networking(SDN)technologies and the explosive growth of network traffic,the requirement for computing resources in the network has risen sharply.Due to the high cost of edge computing resources,coordinating the cloud and edge computing resources to improve the utilization efficiency of edge computing resources is still a considerable challenge.In this paper,we focus on optimiz-ing the placement of network services in cloud-edge environ-ments to maximize the efficiency.It is first proved that,in cloud-edge environments,placing one service function chain(SFC)integrally in the cloud or at the edge can improve the utilization efficiency of edge resources.Then a virtual network function(VNF)performance-resource(P-R)function is proposed to repre-sent the relationship between the VNF instance computing per-formance and the allocated computing resource.To select the SFCs that are most suitable to deploy at the edge,a VNF place-ment and resource allocation model is built to configure each VNF with its particular P-R function.Moreover,a heuristic recur-sive algorithm is designed called the recursive algorithm for max edge throughput(RMET)to solve the model.Through simula-tions on two scenarios,it is verified that RMET can improve the utilization efficiency of edge computing resources.
基金Natural Science Foundation of Guangdong Province,Grant/Award Number:2021A1515011847Special Project in Key Fields of Universities in Department of Education of Guangdong Province,Grant/Award Number:2019KZDZX1036+3 种基金Demonstration Bases for Joint Training of Postgraduates of Department of Education of Guangdong Province,Grant/Award Number:202205Key Lab of Digital Signal and Image Processing of Guangdong Province,Grant/Award Number:2019GDDSIPL-01Innovation and Entrepreneurship Training Program for College Students of Guangdong Ocean University,Grant/Award Number:202210566028Postgraduate Education Innovation Plan Project of Guangdong Ocean University,Grant/Award Numbers:202214,202250,202251,202160。
文摘The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering fields.Although the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence speed.Therefore,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed simultaneously.Then,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed circumstances.Furthermore,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution.Compared with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.
