The growing correlation length prior to the moderate-great earthquakes occurred in Gansu Province and its nearby area since 1986 has been studied using the method of single-link cluster analysis (SLC). According to di...The growing correlation length prior to the moderate-great earthquakes occurred in Gansu Province and its nearby area since 1986 has been studied using the method of single-link cluster analysis (SLC). According to different conditions in the source area, the circular spatial window centered in the epicenter and the parallelgrammic spatial window along the fault belt have been selected. The results show that the phenomena of growing correlation length have been observed before the earthquakes studied in the paper.展开更多
链路预测作为复杂网络分析的一个重要分支,在不同领域中有着广泛的应用,而且通过进一步提取网络结构信息可以提高链路预测的精度。提出了一种基于结构深度网络嵌入和关联相似性的链路预测算法(Structural Deep Correlation Similarity N...链路预测作为复杂网络分析的一个重要分支,在不同领域中有着广泛的应用,而且通过进一步提取网络结构信息可以提高链路预测的精度。提出了一种基于结构深度网络嵌入和关联相似性的链路预测算法(Structural Deep Correlation Similarity Network Embedding,SDCSNE)。SDCSNE算法结合了网络嵌入捕捉高维非线性网络结构的特征,将网络映射到向量空间中,这些映射向量的内积即为对应节点的相似性,并保持了全局和局部的网络结构,获得了更加稳定的网络结构信息;SDCSNE算法还融入了节点的关联性,以提高预测的准确性。实际结果表明,在链路预测任务中,SDCSNE算法具有良好的性能。展开更多
This paper investigates the cross-correlation characteristics of large-scale parameters(LSPs) and small-scale fading(SSF) for high-speed railway(HSR) multilink propagation scenarios, based on realistic measurements co...This paper investigates the cross-correlation characteristics of large-scale parameters(LSPs) and small-scale fading(SSF) for high-speed railway(HSR) multilink propagation scenarios, based on realistic measurements conducted on Beijing to Tianjin HSR line in China. A long-term evolution-based channel sounding system is utilized in the measurements to obtain the channel data. By applying a proposed time-delay based dynamic partition method, multi-link channel impulse responses are extracted from the raw channel data. Then, the statistical results of LSPs, including shadow fading, K-factor, and root-mean-square delay spread are derived and the cross-correlation coefficients of these LPSs are calculated. Moreover, the SSF spatial correlation and cross-correlation of SSF are analyzed. These results can be used to exploit multi-link channel model and to optimize the next-generation HSR communication system.展开更多
As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image...As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.展开更多
基金Joint Seismological Science Foundation of China (95-07-436)
文摘The growing correlation length prior to the moderate-great earthquakes occurred in Gansu Province and its nearby area since 1986 has been studied using the method of single-link cluster analysis (SLC). According to different conditions in the source area, the circular spatial window centered in the epicenter and the parallelgrammic spatial window along the fault belt have been selected. The results show that the phenomena of growing correlation length have been observed before the earthquakes studied in the paper.
基金supported by the Beijing Municipal Natural Science Foundation under Grant 4174102the National Natural Science Foundation of China under Grant 61701017+1 种基金the Open Research Fund through the National Mobile Communications Research Laboratory, Southeast University, under Grant 2018D11the Fundamental Research Funds for the Central Universities under Grant 2018JBM003
文摘This paper investigates the cross-correlation characteristics of large-scale parameters(LSPs) and small-scale fading(SSF) for high-speed railway(HSR) multilink propagation scenarios, based on realistic measurements conducted on Beijing to Tianjin HSR line in China. A long-term evolution-based channel sounding system is utilized in the measurements to obtain the channel data. By applying a proposed time-delay based dynamic partition method, multi-link channel impulse responses are extracted from the raw channel data. Then, the statistical results of LSPs, including shadow fading, K-factor, and root-mean-square delay spread are derived and the cross-correlation coefficients of these LPSs are calculated. Moreover, the SSF spatial correlation and cross-correlation of SSF are analyzed. These results can be used to exploit multi-link channel model and to optimize the next-generation HSR communication system.
基金Project supported by the Hi-Tech Research and Development Pro-gram (863) of China (No. 2003AA119010), and China-American Digital Academic Library (CADAL) Project (No. CADAL2004002)
文摘As the popularity of digital images is rapidly increasing on the Internet, research on technologies for semantic image classification has become an important research topic. However, the well-known content-based image classification methods do not overcome the so-called semantic gap problem in which low-level visual features cannot represent the high-level semantic content of images. Image classification using visual and textual information often performs poorly since the extracted textual features are often too limited to accurately represent the images. In this paper, we propose a semantic image classification ap- proach using multi-context analysis. For a given image, we model the relevant textual information as its multi-modal context, and regard the related images connected by hyperlinks as its link context. Two kinds of context analysis models, i.e., cross-modal correlation analysis and link-based correlation model, are used to capture the correlation among different modals of features and the topical dependency among images induced by the link structure. We propose a new collective classification model called relational support vector classifier (RSVC) based on the well-known Support Vector Machines (SVMs) and the link-based cor- relation model. Experiments showed that the proposed approach significantly improved classification accuracy over that of SVM classifiers using visual and/or textual features.
