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- 저자 : Yumin Lin (검색결과 2 건)
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Xia Zhang,Yumin Wang,Chao Hou,Xiaohui Lin,Hongli Jiang,Zhenhua Wei 성균관대학교(자연과학캠퍼스) 성균나노과학기술원 2018 NANO Vol.13 No.3
We report a simple and green method for fabricating SnS2 modified moss composites for removal of heavy metal (Cr(VI)) considering the fact that the issue of wastewater caused by the discharge of heavy metal is becoming increasingly serious. More importantly, this kind of functional materials that are capable of removing effectively Cr(VI) from wastewater for water purification is quite few on the market. Hence, in the paper, such SnS2 decorated moss photocatalysts for removal of heavy metal (Cr(VI)) were designed and prepared via a simple and green method: chemical bath reaction. The as-designed materials (C@SnS2) integrate the independent properties of moss (with excellent performance in absorption of heavy metal) and SnS2 (with good photocatalytic reduction of Cr(VI) under visible light irradiation), showing a faster photocatalytic rate than those independent components. The experiment results indicate that more than 98.5% of Cr(VI) was reduced under visible light irradiation for 70 min, while only 18.2% and 84.4% of Cr(VI) were removed in the presence of C-moss and pure SnS2 at the same condition, respectively. Furthermore, the as-designed C@SnS2 has good recyclability in the removal of Cr(VI). We believe such simple and green method to prepare photocatalyst for wastewater treatment with excellent performance will provide a good platform to design new photocatalytic materials for water purification.
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Improving Recommendation Accuracy and Diversity through Cost-Awareness Probabilistic Spreading
Guoyong Cai,Dong Zhang,Yumin Lin 보안공학연구지원센터 2016 International Journal of Grid and Distributed Comp Vol.9 No.11
Recommender systems provide users with personalized suggestions for products. A key challenge is how to improve the diversity of recommendation results as much as possible, while maintaining reliably accurate suggestions. Although the bipartite graph based probabilistic spreading algorithm has its advantages of good accuracy and low computational complexity, its diversity is poor. In this paper, we introduce a cost-aware probabilistic spreading algorithm, and show how it can improve both recommendation accuracy and diversity by designing different spreading costs. Comparative experiments on widely used datasets confirm the effectiveness of the cost-aware probabilistic spreading approach in terms of accuracy, aggregate diversity and individual diversity of recommendation results. In addition, the time complexity of the proposed algorithm is also analyzed.
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