Networks are used to represent interactions in a wide variety of fields, like biology, sociology, chemistry, and more. They have a great deal of salient information contained in their structures, which have a variety ...Networks are used to represent interactions in a wide variety of fields, like biology, sociology, chemistry, and more. They have a great deal of salient information contained in their structures, which have a variety of applications. One of the important topics of network analysis is finding influential nodes. These nodes are of two kinds —leader nodes and bridge nodes. In this study, we propose an algorithm to find strong leaders in a network based on a revision of neighborhood similarity. This leadership detection is combined with a neighborhood intersection clustering algorithm to produce high quality communities for various networks. We also delve into the structure of a new network, the Houghton College Twitter network, and examine the discovered leaders and their respective followers in more depth than which is frequently attempted for a network of its size. The results of the observations on this and other networks demonstrate that the community partitions found by this algorithm are very similar to those of ground truth communities.展开更多
高光谱图像在采集过程中极易产生高斯、椒盐、条纹等噪声,从而对后续的地物空间识别工作产生影响.因此有效的噪声去除工作在高光谱图像处理中是不可缺少的一步.鲁棒主成分分析(Robust Principal Component Analysis,RPCA)是能将受稀疏...高光谱图像在采集过程中极易产生高斯、椒盐、条纹等噪声,从而对后续的地物空间识别工作产生影响.因此有效的噪声去除工作在高光谱图像处理中是不可缺少的一步.鲁棒主成分分析(Robust Principal Component Analysis,RPCA)是能将受稀疏噪声干扰的低秩矩阵进行有效恢复的模型.高光谱图像由于其光谱特征之间存在很高的相关性,即每个光谱特征可以用光谱端元的线性组合来表示,因此高光谱图像具有高度低秩性,从而RPCA算法能在高光谱图像去噪中取得显著的效果.结合高光谱图像空间邻域相似性和改进RPCA(Spatial Neighboring Similarity and Improve RPCA,S_IRPCA),提出一种新的高光谱图像去噪算法.算法在去除噪声的同时,更好的保留了细节信息.实验表明,算法与主流的低秩恢复算法相比,无论在主观视觉上还是在客观评价指标上,都做到了显著提升.展开更多
联合解密与指纹嵌入(joint fingerprinting and decryption,JFD)框架能为遥感影像的安全分发提供完整的安全保护,且效率较高。但这种方案不仅导致指纹嵌入后的图像质量下降较为明显,且不能满足遥感影像的近无损性的要求。针对JFD框架的...联合解密与指纹嵌入(joint fingerprinting and decryption,JFD)框架能为遥感影像的安全分发提供完整的安全保护,且效率较高。但这种方案不仅导致指纹嵌入后的图像质量下降较为明显,且不能满足遥感影像的近无损性的要求。针对JFD框架的这种缺陷,本文提出了一种基于内容的遥感影像安全分发方法,对遥感影像根据内容进行分类,选取对遥感影像内容影响较小的区域嵌入指纹;提出邻域相似度的定义,选取加密后邻域相关性保持较好的区域进行部分解密,嵌入指纹。实验结果表明,利用本文提出的方案获得的嵌入指纹后的图像质量较好,且不影响遥感影像的后续应用。展开更多
Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode che...Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.展开更多
文摘Networks are used to represent interactions in a wide variety of fields, like biology, sociology, chemistry, and more. They have a great deal of salient information contained in their structures, which have a variety of applications. One of the important topics of network analysis is finding influential nodes. These nodes are of two kinds —leader nodes and bridge nodes. In this study, we propose an algorithm to find strong leaders in a network based on a revision of neighborhood similarity. This leadership detection is combined with a neighborhood intersection clustering algorithm to produce high quality communities for various networks. We also delve into the structure of a new network, the Houghton College Twitter network, and examine the discovered leaders and their respective followers in more depth than which is frequently attempted for a network of its size. The results of the observations on this and other networks demonstrate that the community partitions found by this algorithm are very similar to those of ground truth communities.
文摘高光谱图像在采集过程中极易产生高斯、椒盐、条纹等噪声,从而对后续的地物空间识别工作产生影响.因此有效的噪声去除工作在高光谱图像处理中是不可缺少的一步.鲁棒主成分分析(Robust Principal Component Analysis,RPCA)是能将受稀疏噪声干扰的低秩矩阵进行有效恢复的模型.高光谱图像由于其光谱特征之间存在很高的相关性,即每个光谱特征可以用光谱端元的线性组合来表示,因此高光谱图像具有高度低秩性,从而RPCA算法能在高光谱图像去噪中取得显著的效果.结合高光谱图像空间邻域相似性和改进RPCA(Spatial Neighboring Similarity and Improve RPCA,S_IRPCA),提出一种新的高光谱图像去噪算法.算法在去除噪声的同时,更好的保留了细节信息.实验表明,算法与主流的低秩恢复算法相比,无论在主观视觉上还是在客观评价指标上,都做到了显著提升.
文摘联合解密与指纹嵌入(joint fingerprinting and decryption,JFD)框架能为遥感影像的安全分发提供完整的安全保护,且效率较高。但这种方案不仅导致指纹嵌入后的图像质量下降较为明显,且不能满足遥感影像的近无损性的要求。针对JFD框架的这种缺陷,本文提出了一种基于内容的遥感影像安全分发方法,对遥感影像根据内容进行分类,选取对遥感影像内容影响较小的区域嵌入指纹;提出邻域相似度的定义,选取加密后邻域相关性保持较好的区域进行部分解密,嵌入指纹。实验结果表明,利用本文提出的方案获得的嵌入指纹后的图像质量较好,且不影响遥感影像的后续应用。
基金Supported by the National Natural Science Foundation of China(61273160,61403418)the Natural Science Foundation of Shandong Province(ZR2011FM014)+1 种基金the Fundamental Research Funds for the Central Universities(10CX04046A)the Doctoral Fund of Shandong Province(BS2012ZZ011)
文摘Traditional data driven fault detection methods assume unimodal distribution of process data so that they often perform not well in chemical process with multiple operating modes. In order to monitor the multimode chemical process effectively, this paper presents a novel fault detection method based on local neighborhood similarity analysis(LNSA). In the proposed method, prior process knowledge is not required and only the multimode normal operation data are used to construct a reference dataset. For online monitoring of process state, LNSA applies moving window technique to obtain a current snapshot data window. Then neighborhood searching technique is used to acquire the corresponding local neighborhood data window from the reference dataset. Similarity analysis between snapshot and neighborhood data windows is performed, which includes the calculation of principal component analysis(PCA) similarity factor and distance similarity factor. The PCA similarity factor is to capture the change of data direction while the distance similarity factor is used for monitoring the shift of data center position. Based on these similarity factors, two monitoring statistics are built for multimode process fault detection. Finally a simulated continuous stirred tank system is used to demonstrate the effectiveness of the proposed method. The simulation results show that LNSA can detect multimode process changes effectively and performs better than traditional fault detection methods.
