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您的检索式：作者名="Guohan Zhao"
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| 1 | Alkaline Intrusives at the East Foot of the Taihang-Da Hinggan Mountains:Chronology,Sr,Nd and Pb Isotopic Characteristics and Their Implications显示文摘Based on the Rb-Sr isochron dating results, this paper suggests that the alkaline intrusive belt at the east foot of the Taihang-Da Hinggan Mountains were formed between 135 and 122 Ma. And the alkaline intrusives in the north and south sections of this belt have entirely different Sr, Nd and Pb isotopic characteristics, i.e., all the rocks in the south section have positive εSr(t) and negative εNd(t) values and all those in the north have the opposite values. On the εSr(t) versus εNd(t) correlation diagram, the samples from the south are concentrated along the enriched mantle evolution trend lines and nearby, while those from the north fall along the depleted mantle trend lines and nearby. On the Pb isotope composition diagram, most of the samples from the south section fall on the mantle Pb evolution line and nearby, while those from the north lie between the Pb evolution lines of the mantle and the erogenic belt. The above-stated isotopic characteristics not only indicate that the source rocks | YAN Guohan XU Baoliang MU Baolei WANG Guanyu CHANG Zhaoshan CHEN Tingli ZHAO Yongchao WANG Xiaofang ZHANG Renhu QIAO Guangsheng CHU Zhuyin | 2000 | Acta Geologica Sinica(English Edition)2000，74，4： | 18 |
| 2 | A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data显示文摘Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled.However,multiple kernel clustering for incomplete data is a critical yet challenging task.Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task,they may fail when data has a high value-missing rate,and they may easily fall into a local optimum.To address these problems,in this paper,we propose an absent multiple kernel clustering(AMKC)method on incomplete data.The AMKC method rst clusters the initialized incomplete data.Then,it constructs a new multiple-kernel-based data space,referred to as K-space,from multiple sources to learn kernel combination coefcients.Finally,it seamlessly integrates an incomplete-kernel-imputation objective,a multiple-kernel-learning objective,and a kernel-clustering objective in order to achieve absent multiple kernel clustering.The three stages in this process are carried out simultaneously until the convergence condition is met.Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is signicantly better than state-of-the-art competitors.Meanwhile,the proposed method gains fast convergence speed. | Lingyun Xiang Guohan Zhao Qian Li Gwang-Jun Kim Osama Alfarraj Amr Tolba | 2021 | Computers, Materials & Continua2021，，4： | 1 |
| 3 | Application Example of Reinforced Concrete Frame Structure Waste Dam in the Proiect of Valley Sanitary Landfill显示文摘 | Zhao Guohan Yang Shunshen Liu Fan | 2014 | International Journal of Technology Management2014，，5： | 0 |
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