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| 1 | An Image Encryption Algorithm Based on BP Neural Network and Hyperchaotic System显示文摘To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication. | Feifei Yang Jun Mou Yinghong Cao Ran Chu | 2020 | China Communications2020，17，5： | 5 |
| 2 | Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery显示文摘Aiming at the statistical sparse decomposition principle(SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, which leading to the application bound of SSDP is very finite, an improved SSDP(ISSDP) method is proposed. Based on the principle of recovering the source signals by minimizing the correlation coefficients within a fixed time interval, the selection method of mixing matrix’s column vectors used for signal recovery is modified, which enables the choose of mixing matrix’s column vectors according to the number of active source signals self-adaptively. By simulation experiments, the proposed method is validated. The proposed method is applicable to the case where the number of active signals is equal to or less than that of observed signals, which is a new way for underdetermined blind source signal recovery. | Wang Chuanchuan Zeng Yonghu Wang Liandong Fu Weihong | 2019 | The Journal of China Universities of Posts and Telecommunications2019，26，6： | 1 |
| 3 | 基于自适应字典压缩感知的欠定工作模态参数识别显示文摘针对基于稀疏成分分析和正交基压缩感知的欠定工作模态参数识别方法准确率低、鲁棒性差的问题,提出一种基于自适应字典压缩感知的欠定工作模态参数识别方法。所提方法在模态振型估计的基础上利用自适应字典压缩感知重构模态坐标响应。在压缩感知框架下,首先,所提方法利用滤波分离的方法构造字典学习的训练样本;然后,使用基于K均值奇异值分解的字典学习方法和层次耦合字典训练策略生成自适应字典,实现了无监督的字典学习;最后,利用正交匹配追踪算法得到稀疏系数分量,进而恢复源信号重构模态坐标响应。在压缩感知框架下,所提方法利用K均值奇异值分解算法学习得到的自适应字典,对于信号的分解比傅里叶基或离散余弦基等正交基具有更强的稀疏表示能力。在5自由度的仿真数据集下的欠定工作模态参数识别的结果表明,所提方法比稀疏成分分析、正交基压缩感知等方法具有更好的识别精度和鲁棒性。 | 王继争 王成 陈建伟 李海波 赖雄鸣 王鑫 何霆 | 2023 | 计算机集成制造系统2023，29，1： | 0 |
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