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RISS 인기검색어
- 검색키워드
- 저자 : ( Yazhou Ren ) (검색결과 4 건)
- 무료
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Experimental research on design wind loads of a large air-cooling structure
Xu Yazhou,Ren Qianqian,Bai Guoliang,Li Hongxing 한국풍공학회 2019 Wind and Structures, An International Journal (WAS Vol.28 No.4
Because of the particularity and complexity of direct air-cooling structures (ACS), wind parameters given in the general load codes are not suitable for the wind-resistant design. In order to investigate the wind loads of ACS, two 1/150 scaled three-span models were designed and fabricated, corresponding to a rigid model and an aero-elastic model, and wind tunnel tests were then carried out. The model used for testing the wind pressure distribution of the ACS was defined as the rigid model in this paper, and the stiffness of which was higher than that of the aero-elastic model. By testing the rigid model, the wind pressure distribution of the ACS model was studied, the shape coefficients of “A” shaped frame and windbreak walls, and the gust factor of the windbreak walls were determined. Through testing the aero-elastic model, the wind-induced dynamic responses of the ACS model was studied, and the wind vibration coefficients of ACS were determined based on the experimental displacement responses. The factors including wind direction angle and rotation of fan were taken into account in this test. The results indicated that the influence of running fans could be ignored in the structural design of ACS, and the wind direction angle had a certain effect on the parameters. Moreover, the shielding effect of windbreak walls induced that wind loads of the “A” shaped frame were all suction. Subsequently, based on the design formula of wind loads in accordance with the Chinese load code, the corresponding parameters were presented as a reference for wind-resistant design and wind load calculation of air-cooling structures.
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Zhiping Lei,Yazhou Li,Zhao Lei,Xue Yang,Jingchong Yan,Zhanku Li,Hengfu Shui,Shibiao Ren,Zhicai Wang,Ying Kong,Shigang Kang 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.117 No.-
Large-scale preparation of cheap and high-performance carbonaceous materials is in urgent need due tothe huge demand of carbonaceous materials-organic binder composites for Joule heating. Here, carbonbasedelectrothermal composites with high electrical conductivity were fabricated by adjusting the morphologyand structure of pitch-based carbonaceous materials (PC) through the use of graphene asstructure-directing agent to tune the orientation and carbonization of coal pitch. It is demonstrated thatthe addition of graphene can effectively promote the formation of graphitized carbon, increase the contentof sp2C, reduce defective carbon and increase the graphite interlayer spacing. 1% graphene-added PCPVDFcomposite exhibits 290% increase in the carrier concentration, 190% enhancement in mobility, and67% reduction in the volume resistivity compared to PC-PVDF composite. Molecular simulations elucidatethat the graphene edges favor pitch carbonization and improve the orientation factor and energy gap ofcarbon materials. This study provides clues for design of low-cost pitch-derived carbon materials-bindercomposites.
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Weighted Local Naive Bayes Link Prediction
( Jiehua Wu ),( Guoji Zhang ),( Yazhou Ren ),( Xiayan Zhang ),( Qiao Yang ) 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.4
Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model―local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.
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Weighted Local Naive Bayes Link Prediction
Wu, JieHua,Zhang, GuoJi,Ren, YaZhou,Zhang, XiaYan,Yang, Qiao Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.4
Weighted network link prediction is a challenge issue in complex network analysis. Unsupervised methods based on local structure are widely used to handle the predictive task. However, the results are still far from satisfied as major literatures neglect two important points: common neighbors produce different influence on potential links; weighted values associated with links in local structure are also different. In this paper, we adapt an effective link prediction model-local naive Bayes model into a weighted scenario to address this issue. Correspondingly, we propose a weighted local naive Bayes (WLNB) probabilistic link prediction framework. The main contribution here is that a weighted cluster coefficient has been incorporated, allowing our model to inference the weighted contribution in the predicting stage. In addition, WLNB can extensively be applied to several classic similarity metrics. We evaluate WLNB on different kinds of real-world weighted datasets. Experimental results show that our proposed approach performs better (by AUC and Prec) than several alternative methods for link prediction in weighted complex networks.
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