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Strengthening of perforated walls in cable-stayed bridge pylons with double cable planes
Bin Cheng,Jie Wu,Jianlei Wang 국제구조공학회 2015 Steel and Composite Structures, An International J Vol.18 No.4
This paper focuses on the strengthening methods used for improving the compression behaviors of perforated box-section walls as provided in the anchorage zones of steel pylons. Rectangular plates containing double-row continuous elliptical holes are investigated by employing the boundary condition of simple supporting on four edges in the out-of-plane direction of plate. Two types of strengthening stiffeners, named flat stiffener (FS) and longitudinal stiffener (LS), are considered. Uniaxial compression tests are first conducted for 18 specimens, of which 5 are unstrengthened plates and 13 are strengthened plates. The mechanical behaviors such as stress concentration, out-of-plane deformation, failure pattern, and elasto-plastic ultimate strength are experimentally investigated. Finite element (FE) models are also developed to predict the ultimate strengths of plates with various dimensions. The results of FE analysis are validated by test data. The influences of non-dimensional parameters including plate aspect ratio, hole spacing, hole width, stiffener slenderness ratio, as well as stiffener thickness on the ultimate strengths are illustrated on the basis of numerous parametric studies. Comparison of strengthening efficiency shows that the continuous longitudinal stiffener is the best strengthening method for such perforated plates. The simplified formulas used for estimating the compression strengths of strengthened plates are finally proposed.
Data-driven prognostics method for turbofan engine degradation using hybrid deep neural network
Bin Xue,Zhong-bin Xu,Xing Huang,Peng-cheng Nie 대한기계학회 2021 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.35 No.12
Powerful sequence modeling capability for massive multi-sensor data enables deep-learning-based methods to obtain accurate remaining useful life (RUL) estimations. Hybrid neural networks, with learned representations based on various networks, have enhanced the prognostics accuracies than single networks. However, assembly strategies that are limited to either parallel or serial, and insufficient utilization of single networks restrict the development of hybrid networks for more complex problems. This paper proposes a datadriven method using hybrid multi-scale convolutional neural network (MSCNN) and bidirectional long short-term memory (BLSTM) network (namely HMCB network) for RUL estimation. The framework of the network includes two parallel paths. One is composed of MSCNN and BLSTM in serial and the other is a BLSTM path. The HMCB network integrates the merits of multi-scale spatial feature extraction of MSCNN and sequence learning capacity of BLSTM. Validated by C-MAPSS dataset, the HMCB network demonstrates noticeably higher prognostic accuracy than other state-of-the-art methods.
Cheng-Bin Jin(김성빈),Trung Dung Do,Mingjie Liu,Hakil Kim(김학일) 제어로봇시스템학회 2018 제어·로봇·시스템학회 논문지 Vol.24 No.3
When we say a person is texting, can you tell the person is walking or sitting? Emphatically, no. In order to solve this incomplete representation problem, this paper presents a sub-action descriptor for detailed action detection. The sub-action descriptor consists of three levels: posture, locomotion, and gestures. The three levels provide three sub-action categories for a single action in order to address the representation problem. The proposed action detection model simultaneously localizes and recognizes the actions of multiple individuals in video surveillance using appearance-based temporal features with multi-convolutional neural networks. The proposed approach achieved a mean average precision of 76.6% for frame-based measurement and 83.5% for video-based measurement of the ICVL video surveillance dataset. Extensive experiments on the benchmark KTH dataset demonstrate that the proposed approach achieved better performance, which in turn improves action recognition performance in comparison to the stateof-the-art methods. The action detection model can run at around 25 fps with the ICVL dataset and at more than 80 fps with the KTH dataset, which is suitable for real-time surveillance applications.
Bin Cheng,Chen Lu,Dongliang Lin,Xiaoqin Zeng 대한금속·재료학회 2014 METALS AND MATERIALS International Vol.20 No.2
A Mg95.5Y3Zn1.5 alloy processed via a two-step processing route of extrusion plus ECAP has been investigated. It was found that the ECAP processed Mg95.5Y3Zn1.5 alloy contained ultrafine grains and exhibitedexcellent mechanical properties. After ECAP, the average grain size of Mg95.5Y3Zn1.5 alloy was refined toabout 300 nm. The highest strengths, with yield strength of 444.6 MPa and ultimate tensile strength of472.7 MPa, were obtained after 1 pass at 623 K. The SAED patterns indicated that the microstructure afterECAP consisted of both high angle and low angle grain boundaries. The fraction of high-angle boundariesincreased with increasing numbers of ECAP passes. The Mg95.5Y3Zn1.5 alloy contained a high volume fractionof X-Mg12ZnY phase due to high yttrium and zinc addition. And, it accelerated the growth and coalescenceof cracks during tensile testing, resulting in premature fracture and lower elongation of alloy.
Bin Cheng,Dingjie Guan,Bingxue Jing 한국정밀공학회 2022 International Journal of Precision Engineering and Vol.23 No.2
Small and medium-sized manufacturing enterprises involve a lot of customized products. The degree of adaptability should be noted while improving product design and manufacturing digital and intelligent levels. This paper presents a process sequencing method of manufacturing features based on the node importance of a complex network. The method is based on the adjacency matrix and connected graph to analyze the process constraint semantics of the product model. The adjacency matrix expresses the positioning dimensions between features. The connected graph is applied to define the constraint relationships between features and aggregate the multi-dimensional process dimension chain in all directions. Based on the processing sequence of node importance in a complex network, most of process planning can be realized. The method also can make adaptive decisions for different structural parts and monitor the machining of key features. Examples verify the validity and feasibility of the proposed method.