Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time...Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time-delayed CNNs with input saturation.Based on the Lyapunov theory and the Schur complement principle,a bilinear matrix inequality(BMI)criterion is designed to stabilize the system with input saturation.By matrix congruent transformation,the BMI control criterion can be changed into linear matrix inequality(LMI)criterion,then it can be easily solved by the computer.It is a one-step AW strategy that the feedback compensator and the AW compensator can be determined simultaneously.The attraction domain and its optimization are also discussed.The structure of CNNs with both constant timedelays and distribute time-delays is more general.This method is simple and systematic,allowing dealing with a large class of such systems whose excitation satisfies the Lipschitz condition.The simulation results verify the effectiveness and feasibility of the proposed method.展开更多
Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application t...Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.展开更多
A new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks （DCNNs） is presented. It is shown that the use of a more general type of Lyapunov-Krasov...A new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks （DCNNs） is presented. It is shown that the use of a more general type of Lyapunov-Krasovskii function enables the derivation of new results for an exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.展开更多
Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on del...Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.展开更多
The global asymptotic stability of cellular neural networks with delays is investigated. Three kinds of time delays have been considered. New delay-dependent stability criteria are proposed and are formulated as the f...The global asymptotic stability of cellular neural networks with delays is investigated. Three kinds of time delays have been considered. New delay-dependent stability criteria are proposed and are formulated as the feasibility of some linear matrix inequalities, which can be checked easily by resorting to the recently developed interior-point algorithms. Based on the Finsler Lemma, it is theoretically proved that the proposed stability criteria are less conservative than some existing results.展开更多
The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is n...The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is not a convergent point. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. The attraction basin of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are presented. The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN.展开更多
业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据...业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据历史事件日志中的轨迹提取轨迹前缀,构造带拟合情况标记的数据集;其次,使用融合相对上下文关系的BERT模型完成轨迹特征向量的表示;最后,使用轻量化CNN模型构建合规性检查分类器,完成在线业务流程合规性检查,有效提高合规性检查的准确率。在5个真实事件日志数据集上进行实验,结果表明,该方法相比Word2Vec+CNN模型、Transformer模型、BERT分类模型在准确率方面有较大提升,且与传统BERT+CNN相比,所提方法的准确率最高可提升2.61%。展开更多
How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classif...How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.展开更多
针对齿轮箱振动信号复杂多变,导致现有的齿轮箱故障诊断方法诊断精度不高、较弱故障特征容易被噪声淹没等问题,提出了一种基于向量加权平均优化算法(weighted mean of vectors,INFO)、变分模态分解(variational mode decomposition,VMD...针对齿轮箱振动信号复杂多变,导致现有的齿轮箱故障诊断方法诊断精度不高、较弱故障特征容易被噪声淹没等问题,提出了一种基于向量加权平均优化算法(weighted mean of vectors,INFO)、变分模态分解(variational mode decomposition,VMD)和卷积神经网络(convolutional neural network,CNN)的齿轮故障诊断方法。该方法首先采用熵权法将不同位置的振动传感器信号信息进行融合,利用INFO对VMD算法中参数进行优化,并设计一个复合评价指标作为参数优化的评价标准,使用奇异峭度差分谱的方法对敏感分量进行重构;其次,从重构的信号中提取时域、频域特征并输入到CNN模型中进行分类;最后通过Shap(Shapley additive explanations)值法对模型输入特征的重要性进行排序,分析不同特征组合对模型分类和特定故障识别的影响。在东南大学行星齿轮数据集上进行验证,结果表明,利用所提特征组合进行故障诊断,CNN模型故障诊断准确率为98.24%,高于其他特征组合,为行星齿轮箱的故障诊断提供了一组有效的特征指标。展开更多
Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a de...Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.展开更多
Non-orthogonal multiple access(NOMA), featuring high spectrum efficiency, massive connectivity and low latency, holds immense potential to be a novel multi-access technique in fifth-generation(5G) communication. Succe...Non-orthogonal multiple access(NOMA), featuring high spectrum efficiency, massive connectivity and low latency, holds immense potential to be a novel multi-access technique in fifth-generation(5G) communication. Successive interference cancellation(SIC) is proved to be an effective method to detect the NOMA signal by ordering the power of received signals and then decoding them. However, the error accumulation effect referred to as error propagation is an inevitable problem. In this paper,we propose a convolutional neural networks(CNNs) approach to restore the desired signal impaired by the multiple input multiple output(MIMO) channel. Especially in the uplink NOMA scenario,the proposed method can decode multiple users' information in a cluster instantaneously without any traditional communication signal processing steps. Simulation experiments are conducted in the Rayleigh channel and the results demonstrate that the error performance of the proposed learning system outperforms that of the classic SIC detection. Consequently, deep learning has disruptive potential to replace the conventional signal detection method.展开更多
当前推特等国外社交平台,已成为从事网络黑灰产犯罪不可或缺的工具,对推特上黑灰产账号进行发现、检测和分类对于打击网络犯罪、维护社会稳定具有重大意义。现有的推文分类模型双向长短时记忆网络(bi-directional long short-term memor...当前推特等国外社交平台,已成为从事网络黑灰产犯罪不可或缺的工具,对推特上黑灰产账号进行发现、检测和分类对于打击网络犯罪、维护社会稳定具有重大意义。现有的推文分类模型双向长短时记忆网络(bi-directional long short-term memory,BiLSTM)可以学习推文的上下文信息,却无法学习局部关键信息,卷积神经网络(convolution neural network,CNN)模型可以学习推文的局部关键信息,却无法学习推文的上下文信息。结合BiLSTM与CNN两种模型的优势,提出了BiLSTM-CNN推文分类模型,该模型将推文进行向量化后,输入BiLSTM模型学习推文的上下文信息,再在BiLSTM模型后引入CNN层,进行局部特征的提取,最后使用全连接层将经过池化的特征连接在一起,并应用softmax函数进行四分类。模型在自主构建的中文推特黑灰产推文数据集上进行实验,并使用TextCNN、TextRNN、TextRCNN三种分类模型作为对比实验,实验结果显示,所提的BiLSTM-CNN推文分类模型在对四类推文进行分类的宏准确率为98.32%,明显高于TextCNN、TextRNN和TextRCNN三种模型的准确率。展开更多
基金supported by the National Natural Science Foundation of China(61374003 41631072)the Academic Foundation of Naval University of Engineering(20161475)
文摘Both time-delays and anti-windup(AW)problems are conventional problems in system design,which are scarcely discussed in cellular neural networks(CNNs).This paper discusses stabilization for a class of distributed time-delayed CNNs with input saturation.Based on the Lyapunov theory and the Schur complement principle,a bilinear matrix inequality(BMI)criterion is designed to stabilize the system with input saturation.By matrix congruent transformation,the BMI control criterion can be changed into linear matrix inequality(LMI)criterion,then it can be easily solved by the computer.It is a one-step AW strategy that the feedback compensator and the AW compensator can be determined simultaneously.The attraction domain and its optimization are also discussed.The structure of CNNs with both constant timedelays and distribute time-delays is more general.This method is simple and systematic,allowing dealing with a large class of such systems whose excitation satisfies the Lipschitz condition.The simulation results verify the effectiveness and feasibility of the proposed method.
基金supported by the Research Fund for International Young Scientists of the National Natural Science Foundation of China(61550110248)the Research on Fundamental Theory of Shared Intelligent Street Lamp for New Scene Service(H04W200495)+1 种基金Sichuan Science and Technology Program(2019YFG0190)the Research on Sino-Tibetan Multi-source Information Acquisition,Fusion,Data Mining and its Application(H04W170186).
文摘Memristor with memory properties can be applied to connection points(synapses)between cells in a cellular neural network(CNN).This paper highlights memristor crossbar-based multilayer CNN(MCM-CNN)and its application to edge detection.An MCM-CNN is designed by adopting a memristor crossbar composed of a pair of memristors.MCM-CNN based on the memristor crossbar with changeable weight is suitable for edge detection of a binary image and a color image considering its characteristics of programmablization and compactation.Figure of merit(FOM)is introduced to evaluate the proposed structure and several traditional edge detection operators for edge detection results.Experiment results show that the FOM of MCM-CNN is three times more than that of the traditional edge detection operators.
基金This project was supported in part by the National Natural Science Foundation of China (60404022, 60604004)the Key Scientific Research project of Education Ministry of China (204014)the National Natural Science Foundation of China for Distinguished Young Scholars (60525303).
文摘A new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks （DCNNs） is presented. It is shown that the use of a more general type of Lyapunov-Krasovskii function enables the derivation of new results for an exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.
文摘Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.
基金supported by the National Natural Science Foundation of China (60604017) the Natural Science Foundation of Zhejiang Province (Y107657).
文摘The global asymptotic stability of cellular neural networks with delays is investigated. Three kinds of time delays have been considered. New delay-dependent stability criteria are proposed and are formulated as the feasibility of some linear matrix inequalities, which can be checked easily by resorting to the recently developed interior-point algorithms. Based on the Finsler Lemma, it is theoretically proved that the proposed stability criteria are less conservative than some existing results.
文摘The dynamic behavior of discrete-time cellular neural networks(DTCNN), which is strict with zero threshold value, is mainly studied in asynchronous mode and in synchronous mode. In general, a k-attractor of DTCNN is not a convergent point. But in this paper, it is proved that a k-attractor is a convergent point if the strict DTCNN satisfies some conditions. The attraction basin of the strict DTCNN is studied, one example is given to illustrate the previous conclusions to be wrong, and several results are presented. The obtained results on k-attractor and attraction basin not only correct the previous results, but also provide a theoretical foundation of performance analysis and new applications of the DTCNN.
文摘业务流程合规性检查可以帮助企业及早发现潜在问题,保证业务流程的正常运行和安全性。提出一种基于改进BERT(Bidirectional Encoder Representations from Transformers)和轻量化卷积神经网络(CNN)的业务流程合规性检查方法。首先,根据历史事件日志中的轨迹提取轨迹前缀,构造带拟合情况标记的数据集;其次,使用融合相对上下文关系的BERT模型完成轨迹特征向量的表示;最后,使用轻量化CNN模型构建合规性检查分类器,完成在线业务流程合规性检查,有效提高合规性检查的准确率。在5个真实事件日志数据集上进行实验,结果表明,该方法相比Word2Vec+CNN模型、Transformer模型、BERT分类模型在准确率方面有较大提升,且与传统BERT+CNN相比,所提方法的准确率最高可提升2.61%。
基金supported by the National Natural Science Foundation of China(U1435220)
文摘How to recognize targets with similar appearances from remote sensing images(RSIs) effectively and efficiently has become a big challenge. Recently, convolutional neural network(CNN) is preferred in the target classification due to the powerful feature representation ability and better performance. However,the training and testing of CNN mainly rely on single machine.Single machine has its natural limitation and bottleneck in processing RSIs due to limited hardware resources and huge time consuming. Besides, overfitting is a challenge for the CNN model due to the unbalance between RSIs data and the model structure.When a model is complex or the training data is relatively small,overfitting occurs and leads to a poor predictive performance. To address these problems, a distributed CNN architecture for RSIs target classification is proposed, which dramatically increases the training speed of CNN and system scalability. It improves the storage ability and processing efficiency of RSIs. Furthermore,Bayesian regularization approach is utilized in order to initialize the weights of the CNN extractor, which increases the robustness and flexibility of the CNN model. It helps prevent the overfitting and avoid the local optima caused by limited RSI training images or the inappropriate CNN structure. In addition, considering the efficiency of the Na¨?ve Bayes classifier, a distributed Na¨?ve Bayes classifier is designed to reduce the training cost. Compared with other algorithms, the proposed system and method perform the best and increase the recognition accuracy. The results show that the distributed system framework and the proposed algorithms are suitable for RSIs target classification tasks.
文摘针对齿轮箱振动信号复杂多变,导致现有的齿轮箱故障诊断方法诊断精度不高、较弱故障特征容易被噪声淹没等问题,提出了一种基于向量加权平均优化算法(weighted mean of vectors,INFO)、变分模态分解(variational mode decomposition,VMD)和卷积神经网络(convolutional neural network,CNN)的齿轮故障诊断方法。该方法首先采用熵权法将不同位置的振动传感器信号信息进行融合,利用INFO对VMD算法中参数进行优化,并设计一个复合评价指标作为参数优化的评价标准,使用奇异峭度差分谱的方法对敏感分量进行重构;其次,从重构的信号中提取时域、频域特征并输入到CNN模型中进行分类;最后通过Shap(Shapley additive explanations)值法对模型输入特征的重要性进行排序,分析不同特征组合对模型分类和特定故障识别的影响。在东南大学行星齿轮数据集上进行验证,结果表明,利用所提特征组合进行故障诊断,CNN模型故障诊断准确率为98.24%,高于其他特征组合,为行星齿轮箱的故障诊断提供了一组有效的特征指标。
基金supported by the China Ministry of Industry and Information Technology Foundation and Aeronautical Science Foundation of China(ASFC-201920007002)the National Key Research and Development Plan(2021YFB1600603)the Open Fund of Key Laboratory of Civil Aircraft Airworthiness Technology,Civil Aviation University of China.
文摘Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.
基金supported by the National Natural Science Foundation of China (61471021)。
文摘Non-orthogonal multiple access(NOMA), featuring high spectrum efficiency, massive connectivity and low latency, holds immense potential to be a novel multi-access technique in fifth-generation(5G) communication. Successive interference cancellation(SIC) is proved to be an effective method to detect the NOMA signal by ordering the power of received signals and then decoding them. However, the error accumulation effect referred to as error propagation is an inevitable problem. In this paper,we propose a convolutional neural networks(CNNs) approach to restore the desired signal impaired by the multiple input multiple output(MIMO) channel. Especially in the uplink NOMA scenario,the proposed method can decode multiple users' information in a cluster instantaneously without any traditional communication signal processing steps. Simulation experiments are conducted in the Rayleigh channel and the results demonstrate that the error performance of the proposed learning system outperforms that of the classic SIC detection. Consequently, deep learning has disruptive potential to replace the conventional signal detection method.
