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RISS 인기검색어
- 검색키워드
- 저자 : Yanjie Xiao (검색결과 4 건)
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Aseismic Optimization of Mega-sub Controlled Structures Based on Gaussian Process Surrogate Model
Yanjie Xiao,Feng Yue,Xun'an Zhang,Muhammad Moman Shahzad 대한토목학회 2022 KSCE JOURNAL OF CIVIL ENGINEERING Vol.26 No.5
Due to the complex seismic characteristics of mega-sub controlled structures (MSCS), it is difficult to give full play to their advantages in earthquake resistance by traditional design methods. Meta-heuristic optimization algorithms can be used to improve the seismic performance, but the structural response needs to be calculated repeatedly, which results in high computation cost. To overcome these challenges, an efficient aseismic optimization design procedure for engineering application is developed. In this procedure, the model of optimization problem is established based on time history analysis (THA). Gaussian process regression (GPR) surrogate models are employed to predict the values of the objective and constraint functions. The expected improvement (EI) and constrained expected improvement (CEI) criteria are adopted to update the training sample set and obtain the optimal solution. Then, two examples are presented to validate the effectiveness and efficiency of this method in optimization problems of structures under earthquake loads. Finally, it is applied to optimizations of a MSCS and a mega frame structure (MFS), respectively. The response and cost of the optimized structures are reduced, and the MSCS shows better earthquake resistant capacities.
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Li, Chen,Zhao, Shuai,Xiao, Ke,Wang, Yanjie Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.1
To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.
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( Chen Li ),( Shuai Zhao ),( Ke Xiao ),( Yanjie Wang ) 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.1
To combat the adverse impact imposed by illumination variation in the face recognition process, an effective and feasible algorithm is proposed in this paper. Firstly, an enhanced local texture feature is presented by applying the central symmetric encode principle on the fused component images acquired from the wavelet decomposition. Then the proposed local texture features are combined with Deep Belief Network (DBN) to gain robust deep features of face images under severe illumination conditions. Abundant experiments with different test schemes are conducted on both CMU-PIE and Extended Yale-B databases which contain face images under various illumination condition. Compared with the DBN, LBP combined with DBN and CSLBP combined with DBN, our proposed method achieves the most satisfying recognition rate regardless of the database used, the test scheme adopted or the illumination condition encountered, especially for the face recognition under severe illumination variation.
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Differentiation potential of neural stem cells derived from fetal sheep
Qian Li,Minghai Zhang,Wei Jun Guan,Shuang Zhang,Yanjie Zheng,Hebao Wen,Xiao Han 한국통합생물학회 2017 Animal cells and systems Vol.21 No.4
Neural stem cells (NSCs) are multipotent stem cells that can differentiate into many cell types in vitro. In this study, we isolated and established an NSC line from fetal Ovis aries. Based on the results of immunofluorescence staining, NSCs expressed Nestin, Pax6 and MAP2. Moreover, a reverse transcription–polymerase chain reaction assay was used to biologically characterize the cell line. NSCs were induced to differentiate into neurogenic cells in vitro. They expressed MAP2, glial fibrillary acidic protein (GFAP) and myelin basic protein (MBP). In this study, we successfully isolated and cultivated NSCs from the hippocampal tissue of fetal sheep. NSCs not only displayed a self-renewal capacity but also had the potential to differentiate into neurons and glial cells. This study provided valuable experimental data for NSC transplant research.
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