The multi-mass analysis models of story isolation and general structure are established, major parameters are optimized on story isolation structure, with time-history analysis for isolation layers in different position, the contributions of modes and damping effects of absolute acceleration and story drift about story-isolation structure are analysed. Result indicates that after the parametric optimization, the natural period of story-isolation structure is longer than that of general structure; no matter where the position of isolation layer is, the damping effects are always obviously; the position of isolation layer has direct influence on the damping effect, damping effect gradually reduce when the isolation layer raised, and the contributions of high modes to dynamic responses increase; it's worth noting that infrastructure absolute acceleration magnify to some extent compared with general structure.
The shaking table tests are conducted on a 5-floor steel frame model with a scale down of 1:6. The traditional anti-seismic structure and isolation structures with isolation layer in different position are adopted. The results indicate that the natural vibration periods of isolation structure are longer than anti-seismic structure, and when the isolation layer is located in a lower position, the period becomes longer and the damping effect is better.
Crowdsourcing learning (Bonald and Combes 2016 ; Dawid and Skene, J R Stat Soc: Series C (Appl Stat), 28(1):20–28 1979 ; Karger et al. 2011 ; Li et al, IEEE Trans Knowl Data Eng, 28(9):2296–2319 2016 ; Liu et al. 2012 ; Schlagwein and Bjorn-Andersen, J Assoc Inform Syst, 15(11):3 2014 ; Zhang et al. 2014 ) plays an increasingly important role in the era of big data (Liu et al., IEEE Trans Syst Man Cybern: Syst, 48(12): 451–2461, 2017 ; Zhang et al. 2014 ) due to its ability to easily solve large-scale data annotations (Musen et al., J Amer Med Informs Assoc, 22(6):1148–1152 2015 ). However, in the process of crowdsourcing learning, the uneven knowledge level of workers often leads to low accuracy of the label after marking, which brings difficulties to the subsequent processing (Edwards and Teddy 2013 ) and analysis of crowdsourcing data. In order to solve this problem, this paper proposes a two-step learning crowdsourced data classification algorithm, which optimizes the original label data by simultaneously considering the two issues of different worker abilities and the similarity between crowdsourced data (Kasikci et al. 2013 ) samples, so as to get more accurate label data. The two-step learning algorithm mainly includes two steps. Firstly, the worker’s ability to label different samples is obtained by constructing and training the worker’s ability model, and then the similarity between samples is calculated by the cosine measurement method (Muflikhah and Baharudin 2009 ), and finally the original label data is optimized by combining the above two results. The experimental results also show that the two-step learning classification algorithm proposed in this article has achieved better experimental results than the comparison algorithm.
In Alzheimer's disease, the researchers found that if the patients were treated at the early stage of the disease, it could effectively delay the development of the disease. At present, multi-modal feature selection is widely used in the early diagnosis of Alzheimer's disease. However, existing multi-modal feature selection algorithms focus on learning the internal information of multiple modalities. They ignore the relationship between modalities, the importance of each modality and the local structure in the multi-modal data. In this paper, we propose a multi-modal feature selection algorithm with anchor graph for Alzheimer's disease. Specifically, we first use the least square loss and l2,1−norm to obtain the weight of the feature under each modality. Then we embed a modal weight factor into the objective function to obtain the importance of each modality. Finally, we use anchor graph to quickly learn the local structure information in multi-modal data. In addition, we also verify the validity of the proposed algorithm on the published ADNI dataset.
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