In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intr...In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intra-pulse modulation signal type based on deep residual network.The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform(STFT),and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition.In addition,in order to improve the generalization ability of the proposed method,label smoothing and L2 regularization are introduced.The simulation results show that the proposed method has a recognition accuracy of more than 95%for complex radar intra-pulse modulation sig-nal types under low SNR(2 dB).展开更多
Forests play a crucial role in ecosystems.This study focused on five common tree species in Northeast China:pine,elm,poplar,cedar,and ash.An improved YOLOv8n-based network structure was constructed,and a UAV image dat...Forests play a crucial role in ecosystems.This study focused on five common tree species in Northeast China:pine,elm,poplar,cedar,and ash.An improved YOLOv8n-based network structure was constructed,and a UAV image dataset was developed for analysis.The results showed that the improved YOLOv8 algorithm achieved a 4.9%increase in accuracy compared with the original version,and the average precision increased from 88.0%(original YOLOv8n)to 92.1%.展开更多
Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint featur...Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and fur-ther reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power ampli-fiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and ampli-tude of the signal kurtosis (FA-SK) is further proposed. Simula-tion and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.展开更多
Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base functi...Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base function to study the change characteristics of Gauss linear frequency modulation wavelet transform with difference wavelet and signal parameters, analyzes the error origin of seismic phases identification on the basis of Gauss linear frequency modulation wavelet transform, puts forward a kind of new method identifying gradual change style seismic phases with background noise which is called fixed scale wavelet transform ratio, and presents application examples about simulation digital signal and actual seismic phases recording onsets identification.展开更多
Recently, many studies propose the use of ultra-wideband technology for passive and active radio frequency identification systems as well as for wireless sensor networks due to its numerous advantages. By harvesting t...Recently, many studies propose the use of ultra-wideband technology for passive and active radio frequency identification systems as well as for wireless sensor networks due to its numerous advantages. By harvesting these advantages of IR-UWB technology at the physical-layer design, this paper proposes that a cross layer architecture platform can be considered as a good integrator for different wireless short-ranges indoor protocols into a universal smart wireless-tagged architecture with new promising applications in cognitive radio for future applications. Adaptive transmission algorithms have been studied to show the trade-off between different specific QoS requirements, transmission rates and distances at the physical layer level and this type of dynamic optimization and reconfiguration leads to the cross-layer design proposal in the paper. Studies from both theoretical simulation and statistical indoor environments experiments are considered as a proof of concept for the proposed architecture.展开更多
Based on the multivariate continuous time autoregressive （CAR） model, this paper presents a new time-domain modal identification method of linear time-invariant system driven by the uniformly modulated Gaussian rand...Based on the multivariate continuous time autoregressive （CAR） model, this paper presents a new time-domain modal identification method of linear time-invariant system driven by the uniformly modulated Gaussian random excitation. The method can identify the physical parameters of the system from the response data. First, the structural dynamic equation is transformed into a continuous time autoregressive model （CAR） of order 3. Second, based on the assumption that the uniformly modulated function is approximately equal to a constant matrix in a very short period of time and on the property of the strong solution of the stochastic differential equation, the uniformly modulated function is identified piecewise. Two special situations are discussed. Finally, by virtue of the Girsanov theorem, we introduce a likelihood function, which is just a con- ditional density function. Maximizing the likelihood function gives the exact maximum likelihood estimators of model parameters. Numerical results show that the method has high precision and the computation is efficient.展开更多
As the competition for marine resources is increasingly fierce,the security of underwater acoustic communication has attracted a great deal of attention.The information and location of the communicating platform can b...As the competition for marine resources is increasingly fierce,the security of underwater acoustic communication has attracted a great deal of attention.The information and location of the communicating platform can be leaked during the traditional underwater acoustic communication technology.According to the unique advantages of chaos communication,we put forward a novel communication scheme using complex parameter modulation and the complex Lorenz system.Firstly,we design a feedback controller and parameter update laws in a complex-variable form with rigorous mathematical proofs(while many previous references on the real-variable form were only special cases in which the imaginary part was zero),which can be realized in practical engineering;then we design a new communication scheme employing parameter modulation.The main parameter spaces of the complex Lorenz system are discussed,then they are adopted in our communication scheme.We also find that there exist parametric attractors in the complex Lorenz system.We make numerical simulations in two channels for digital signals and the simulations verify our conclusions.展开更多
针对电池电气特性与热特性之间复杂的耦合关系、温度对电池功率性能的影响以及荷电状态(state of charge,SOC)、温度状态(stateoftemperature,SOT)与峰值功率状态(state of power,SOP)之间的复杂关联等问题,该文提出一种考虑电热耦合特...针对电池电气特性与热特性之间复杂的耦合关系、温度对电池功率性能的影响以及荷电状态(state of charge,SOC)、温度状态(stateoftemperature,SOT)与峰值功率状态(state of power,SOP)之间的复杂关联等问题,该文提出一种考虑电热耦合特性的电池模组多状态协同估计方法。首先,分析电池电气特性与热特性之间的耦合关系,将分数阶等效电路模型与集总参数双态热模型结合,构建电池模组电热耦合模型。其次,针对电热耦合关系需要准确的SOC与SOT来维持的问题,采用自适应扩展卡尔曼算法(adaptive extended Kalman filter,AEKF)实现电池模组SOC与SOT估计。最后,分析不同状态之间的关联特性,将电池的SOC、SOT引入到多约束条件下的峰值SOP估计中,实现电池模组多状态协同估计,提高电池状态估计的准确性。仿真结果表明,所提方法在SOC初始误差为20%情况下,能够快速收敛至真实值,且均方根误差在0.52%以内,核心温度与表面温度估计误差分别在0.36和0.31℃以内。在40℃时,核心温度约束起作用,峰值功率估计结果显著降低,为动力电池的实时安全监控提供了有力保障。展开更多
With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is...With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is closely related to the final yield of apples.A multiscale fusion dense network combined with an efficient multiscale attention(EMA)mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images.Incept_EMA_DenseNet consists of three crucial parts:the inception module,which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer;the EMA mechanism,which is used for obtaining appropriate weights of different dense blocks;and the improved DenseNet based on DenseNet_121.Specifically,to find appropriate multiscale fusion methods,the residual module and inception module were compared to determine the performance of each technique,and Incept_EMA_DenseNet achieved an accuracy of 95.38%.Second,this work used three attention mechanisms,and the efficient multiscale attention mechanism obtained the best performance.Third,the convolution layers and bottlenecks were modified without performance degradation,reducing half of the computational load compared with the original models.Incept_EMA_DenseNet,as proposed in this paper,has an accuracy of 96.76%,being 2.93%,3.44%,and 4.16%better than Resnet50,DenseNet_121 and GoogLeNet,respectively,proved to be reliable and beneficial,and can effectively and conveniently assist apple growers with leaf disease identification in the field.展开更多
文摘In view of low recognition rate of complex radar intra-pulse modulation signal type by traditional methods under low signal-to-noise ratio(SNR),the paper proposes an automatic recog-nition method of complex radar intra-pulse modulation signal type based on deep residual network.The basic principle of the recognition method is to obtain the transformation relationship between the time and frequency of complex radar intra-pulse modulation signal through short-time Fourier transform(STFT),and then design an appropriate deep residual network to extract the features of the time-frequency map and complete a variety of complex intra-pulse modulation signal type recognition.In addition,in order to improve the generalization ability of the proposed method,label smoothing and L2 regularization are introduced.The simulation results show that the proposed method has a recognition accuracy of more than 95%for complex radar intra-pulse modulation sig-nal types under low SNR(2 dB).
文摘Forests play a crucial role in ecosystems.This study focused on five common tree species in Northeast China:pine,elm,poplar,cedar,and ash.An improved YOLOv8n-based network structure was constructed,and a UAV image dataset was developed for analysis.The results showed that the improved YOLOv8 algorithm achieved a 4.9%increase in accuracy compared with the original version,and the average precision increased from 88.0%(original YOLOv8n)to 92.1%.
基金supported by the Youth Science and Technology Innovation Award of National University of Defense Technology (18/19-QNCXJ)the National Science Foundation of China (62271494)
文摘Extensive experiments suggest that kurtosis-based fingerprint features are effective for specific emitter identification (SEI). Nevertheless, the lack of mechanistic explanation restricts the use of fingerprint features to a data-driven technique and fur-ther reduces the adaptability of the technique to other datasets. To address this issue, the mechanism how the phase noise of high-frequency oscillators and the nonlinearity of power ampli-fiers affect the kurtosis of communication signals is investigated. Mathematical models are derived for intentional modulation (IM) and unintentional modulation (UIM). Analysis indicates that the phase noise of high-frequency oscillators and the nonlinearity of power amplifiers affect the kurtosis frequency and amplitude, respectively. A novel SEI method based on frequency and ampli-tude of the signal kurtosis (FA-SK) is further proposed. Simula-tion and real-world experiments validate theoretical analysis and also confirm the efficiency and effectiveness of the proposed method.
基金State Natural Science Foundation of China (40074007) Science and Technology Key Project during the Ten-Year Plan(2001BA601B02-03-06) and the Natural Science Foundation of Shandong Province (Y2000E08).
文摘Based on the characteristics of gradual change style seismic signal onset which has more high frequency signal components but less magnitude, this paper selects Gauss linear frequency modulation wavelet as base function to study the change characteristics of Gauss linear frequency modulation wavelet transform with difference wavelet and signal parameters, analyzes the error origin of seismic phases identification on the basis of Gauss linear frequency modulation wavelet transform, puts forward a kind of new method identifying gradual change style seismic phases with background noise which is called fixed scale wavelet transform ratio, and presents application examples about simulation digital signal and actual seismic phases recording onsets identification.
文摘Recently, many studies propose the use of ultra-wideband technology for passive and active radio frequency identification systems as well as for wireless sensor networks due to its numerous advantages. By harvesting these advantages of IR-UWB technology at the physical-layer design, this paper proposes that a cross layer architecture platform can be considered as a good integrator for different wireless short-ranges indoor protocols into a universal smart wireless-tagged architecture with new promising applications in cognitive radio for future applications. Adaptive transmission algorithms have been studied to show the trade-off between different specific QoS requirements, transmission rates and distances at the physical layer level and this type of dynamic optimization and reconfiguration leads to the cross-layer design proposal in the paper. Studies from both theoretical simulation and statistical indoor environments experiments are considered as a proof of concept for the proposed architecture.
基金supported by the National Natural Science Foundation of China (No. 50278017)
文摘Based on the multivariate continuous time autoregressive （CAR） model, this paper presents a new time-domain modal identification method of linear time-invariant system driven by the uniformly modulated Gaussian random excitation. The method can identify the physical parameters of the system from the response data. First, the structural dynamic equation is transformed into a continuous time autoregressive model （CAR） of order 3. Second, based on the assumption that the uniformly modulated function is approximately equal to a constant matrix in a very short period of time and on the property of the strong solution of the stochastic differential equation, the uniformly modulated function is identified piecewise. Two special situations are discussed. Finally, by virtue of the Girsanov theorem, we introduce a likelihood function, which is just a con- ditional density function. Maximizing the likelihood function gives the exact maximum likelihood estimators of model parameters. Numerical results show that the method has high precision and the computation is efficient.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.U1806202,61773010,and 61903207)the International Collaborative Research Project of Qilu University of Technology(Grant No.QLUTGJHZ2018020)Major Scientific and Technological Innovation Projects of Shandong Province,China(Grant Nos.2019JZZY010731 and 2020CXGC010901).
文摘As the competition for marine resources is increasingly fierce,the security of underwater acoustic communication has attracted a great deal of attention.The information and location of the communicating platform can be leaked during the traditional underwater acoustic communication technology.According to the unique advantages of chaos communication,we put forward a novel communication scheme using complex parameter modulation and the complex Lorenz system.Firstly,we design a feedback controller and parameter update laws in a complex-variable form with rigorous mathematical proofs(while many previous references on the real-variable form were only special cases in which the imaginary part was zero),which can be realized in practical engineering;then we design a new communication scheme employing parameter modulation.The main parameter spaces of the complex Lorenz system are discussed,then they are adopted in our communication scheme.We also find that there exist parametric attractors in the complex Lorenz system.We make numerical simulations in two channels for digital signals and the simulations verify our conclusions.
文摘针对电池电气特性与热特性之间复杂的耦合关系、温度对电池功率性能的影响以及荷电状态(state of charge,SOC)、温度状态(stateoftemperature,SOT)与峰值功率状态(state of power,SOP)之间的复杂关联等问题,该文提出一种考虑电热耦合特性的电池模组多状态协同估计方法。首先,分析电池电气特性与热特性之间的耦合关系,将分数阶等效电路模型与集总参数双态热模型结合,构建电池模组电热耦合模型。其次,针对电热耦合关系需要准确的SOC与SOT来维持的问题,采用自适应扩展卡尔曼算法(adaptive extended Kalman filter,AEKF)实现电池模组SOC与SOT估计。最后,分析不同状态之间的关联特性,将电池的SOC、SOT引入到多约束条件下的峰值SOP估计中,实现电池模组多状态协同估计,提高电池状态估计的准确性。仿真结果表明,所提方法在SOC初始误差为20%情况下,能够快速收敛至真实值,且均方根误差在0.52%以内,核心温度与表面温度估计误差分别在0.36和0.31℃以内。在40℃时,核心温度约束起作用,峰值功率估计结果显著降低,为动力电池的实时安全监控提供了有力保障。
基金fully supported by the National Natural Science Foundation of China(52072412)。
文摘With the development of smart agriculture,accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge.This study focused on apple leaf disease,which is closely related to the final yield of apples.A multiscale fusion dense network combined with an efficient multiscale attention(EMA)mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images.Incept_EMA_DenseNet consists of three crucial parts:the inception module,which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer;the EMA mechanism,which is used for obtaining appropriate weights of different dense blocks;and the improved DenseNet based on DenseNet_121.Specifically,to find appropriate multiscale fusion methods,the residual module and inception module were compared to determine the performance of each technique,and Incept_EMA_DenseNet achieved an accuracy of 95.38%.Second,this work used three attention mechanisms,and the efficient multiscale attention mechanism obtained the best performance.Third,the convolution layers and bottlenecks were modified without performance degradation,reducing half of the computational load compared with the original models.Incept_EMA_DenseNet,as proposed in this paper,has an accuracy of 96.76%,being 2.93%,3.44%,and 4.16%better than Resnet50,DenseNet_121 and GoogLeNet,respectively,proved to be reliable and beneficial,and can effectively and conveniently assist apple growers with leaf disease identification in the field.
