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RISS 인기검색어
- 검색키워드
- 저자 : ( Yanbin Jiao ) (검색결과 3 건)
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Static behavior of high strength friction-grip bolt shear connectors in composite beams
Ying Xing,Yanbin Liu,Caijun Shi,Zhipeng Wang,Qi Guo,Jin-feng Jiao 국제구조공학회 2022 Steel and Composite Structures, An International J Vol.42 No.3
Superior to traditional welded studs, high strength friction-grip bolted shear connectors facilitate the assembling and demounting of the composite members, which maximizes the potential for efficiency in the construction and retrofitting of new and old structures respectively. Hence, it is necessary to investigate the structural properties of high strength friction-grip bolts used in steel concrete composite beams. By means of push-out tests, an experimental study was conducted on post-installed high strength friction-grip bolts, considering the effects of different bolt size, concrete strength, bolt tensile strength and bolt pretension. The test results showed that bolt shear fracture was the dominant failure mode of all specimens. Based on the loadslip curves, uplifting curves and bolt tensile force curves between the precast concrete slab and steel beam obtained by push-out tests, the anti-slip performance of steel-concrete interface and shear behavior of bolt shank were studied, including the quantitative analysis of anti-slip load, and anti-slip stiffness, frictional coefficient, shear stiffness of bolt shank and ultimate shear capacity. Meanwhile, the interfacial anti-slip stiffness and shear stiffness of bolt shank were defined reasonably. In addition, a total of 56 push-out finite element models verified by the experimental results were also developed, and used to conduct parametric analyses for investigating the shear behavior of high-strength bolted shear connectors in steel-concrete composite beams. Finally, on ground of the test results and finite element simulation analysis, a new design formula for predicting shear capacity was proposed by nonlinear fitting, considering the bolt diameter, concrete strength and bolt tensile strength. Comparison of the calculated value from proposed formula and test results given in the relevant references indicated that the proposed formulas can give a reasonable prediction.
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Fast Face Gender Recognition by Using Local Ternary Pattern and Extreme Learning Machine
( Jucheng Yang ),( Yanbin Jiao ),( Naixue Xiong ),( Dongsun Park ) 한국인터넷정보학회 2013 KSII Transactions on Internet and Information Syst Vol.7 No.7
Human face gender recognition requires fast image processing with high accuracy. Existing face gender recognition methods used traditional local features and machine learning methods have shortcomings of low accuracy or slow speed. In this paper, a new framework for face gender recognition to reach fast face gender recognition is proposed, which is based on Local Ternary Pattern (LTP) and Extreme Learning Machine (ELM). LTP is a generalization of Local Binary Pattern (LBP) that is in the presence of monotonic illumination variations on a face image, and has high discriminative power for texture classification. It is also more discriminate and less sensitive to noise in uniform regions. On the other hand, ELM is a new learning algorithm for generalizing single hidden layer feed forward networks without tuning parameters. The main advantages of ELM are the less stringent optimization constraints, faster operations, easy implementation, and usually improved generalization performance. The experimental results on public databases show that, in comparisons with existing algorithms, the proposed method has higher precision and better generalization performance at extremely fast learning speed.
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