Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this...Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime analysis.This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.展开更多
A global asymptotic stability problem of cellular neural networks with delay is investigated. A new stability condition is presented based on the Lyapunov-Krasovskii method, which is dependent on the amount of delay. ...A global asymptotic stability problem of cellular neural networks with delay is investigated. A new stability condition is presented based on the Lyapunov-Krasovskii method, which is dependent on the amount of delay. A result is given in the form of a linear matrix inequality, and the admitted upper bound of the delay can be easily obtained. The time delay dependent and independent results can be obtained, which include some previously published results. A numerical example is given to show the effectiveness of the main results.展开更多
In this paper a sufficient condition is presented to ensure the complete stability of delayed CNNs. Such a condition establishes a relation between the time delay and the parameters of the networks. Specially, for a g...In this paper a sufficient condition is presented to ensure the complete stability of delayed CNNs. Such a condition establishes a relation between the time delay and the parameters of the networks. Specially, for a given output function f(x) = tanh(x), we address a sufficient condition to ensure absolute convergence of system state.展开更多
Purpose-Aiming at the shortcomings of EEG signals generated by brain’s sensorimotor region activated tasks,such as poor performance,low efficiency and weak robustness,this paper proposes an EEG signals classification...Purpose-Aiming at the shortcomings of EEG signals generated by brain’s sensorimotor region activated tasks,such as poor performance,low efficiency and weak robustness,this paper proposes an EEG signals classification method based on multi-dimensional fusion features.Design/methodology/approach-First,the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals.Then,the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks(3DCNNs)model.Finally,the spatial-frequency features are incorporated to the bidirectional gated recurrent units(Bi-GRUs)models to extract the spatial-frequencysequential multi-dimensional fusion features for recognition of brain’s sensorimotor region activated task.Findings-In the comparative experiments,the data sets of motor imagery(MI)/action observation(AO)/action execution(AE)tasks are selected to test the classification performance and robustness of the proposed algorithm.In addition,the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.Originality/value-The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks,so as to achieve more stable classification performance in dealing with AO/MI/AE tasks,and has the best robustness on EEGsignals of different subjects.展开更多
The global asymptotic stability problem of Cellular neural networks with delay is investigated.A new stability condition is presented based on Lyapunov-Krasovskii method,which is dependent on the size of delay.The res...The global asymptotic stability problem of Cellular neural networks with delay is investigated.A new stability condition is presented based on Lyapunov-Krasovskii method,which is dependent on the size of delay.The result is given in the form of LMI,and the admitted upper bound of the delay can be obtained easily.The time delay dependent and independent results can be obtained,which include some results in the former literature.Finally,a numerical example is given to illustrate the effectiveness of the main results.展开更多
基金This work is supported,in part,by the National Natural Science Foundation of China under grant numbers U1536206,U1405254,61772283,61602253,61672294,61502242In part,by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20150925 and BK20151530+1 种基金In part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundIn part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Currently,some photorealistic computer graphics are very similar to photographic images.Photorealistic computer generated graphics can be forged as photographic images,causing serious security problems.The aim of this work is to use a deep neural network to detect photographic images(PI)versus computer generated graphics(CG).In existing approaches,image feature classification is computationally intensive and fails to achieve realtime analysis.This paper presents an effective approach to automatically identify PI and CG based on deep convolutional neural networks(DCNNs).Compared with some existing methods,the proposed method achieves real-time forensic tasks by deepening the network structure.Experimental results show that this approach can effectively identify PI and CG with average detection accuracy of 98%.
基金Project supported by the National Natural Science Foundation of China (No.60604004)the Natural Science Foundation of Hebei Province of China (No.F2007000637)the National Natural Science Foundation for Distinguished Young Scholars (No.60525303)
文摘A global asymptotic stability problem of cellular neural networks with delay is investigated. A new stability condition is presented based on the Lyapunov-Krasovskii method, which is dependent on the amount of delay. A result is given in the form of a linear matrix inequality, and the admitted upper bound of the delay can be easily obtained. The time delay dependent and independent results can be obtained, which include some previously published results. A numerical example is given to show the effectiveness of the main results.
基金supported by the 973 Project of China(Grant No.2002cb312205)Basal Research Foundation of Tsinghua University(Grant No.JC2001029)+1 种基金HITMD(Grant No.2000-24)985 Basic Research Foundation of the School of Information Science and Technology in Tsinghua University.
文摘In this paper a sufficient condition is presented to ensure the complete stability of delayed CNNs. Such a condition establishes a relation between the time delay and the parameters of the networks. Specially, for a given output function f(x) = tanh(x), we address a sufficient condition to ensure absolute convergence of system state.
基金The education and scientific research project of young and middle-aged teachers of Fujian provincial department of education(No.JAT171070).
文摘Purpose-Aiming at the shortcomings of EEG signals generated by brain’s sensorimotor region activated tasks,such as poor performance,low efficiency and weak robustness,this paper proposes an EEG signals classification method based on multi-dimensional fusion features.Design/methodology/approach-First,the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals.Then,the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks(3DCNNs)model.Finally,the spatial-frequency features are incorporated to the bidirectional gated recurrent units(Bi-GRUs)models to extract the spatial-frequencysequential multi-dimensional fusion features for recognition of brain’s sensorimotor region activated task.Findings-In the comparative experiments,the data sets of motor imagery(MI)/action observation(AO)/action execution(AE)tasks are selected to test the classification performance and robustness of the proposed algorithm.In addition,the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.Originality/value-The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks,so as to achieve more stable classification performance in dealing with AO/MI/AE tasks,and has the best robustness on EEGsignals of different subjects.
文摘The global asymptotic stability problem of Cellular neural networks with delay is investigated.A new stability condition is presented based on Lyapunov-Krasovskii method,which is dependent on the size of delay.The result is given in the form of LMI,and the admitted upper bound of the delay can be obtained easily.The time delay dependent and independent results can be obtained,which include some results in the former literature.Finally,a numerical example is given to illustrate the effectiveness of the main results.
