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Guy Austern, Tanya Bloch and Yael Abulafia
The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of s...
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Jiayao Liang and Mengxiao Yin
With the rapid advancement of deep learning, 3D human pose estimation has largely freed itself from reliance on manually annotated methods. The effective utilization of joint features has become significant. Utilizing 2D human joint information to predic...
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Jingjing Liu, Xinli Yang, Denghui Zhang, Ping Xu, Zhuolin Li and Fengjun Hu
Multi-node wind speed forecasting is greatly important for offshore wind power. It is a challenging task due to unknown complex spatial dependencies. Recently, graph neural networks (GNN) have been applied to wind forecasting because of their capability ...
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Enyu Yu, Yan Fu, Junlin Zhou, Hongliang Sun and Duanbing Chen
Many real-world systems can be expressed in temporal networks with nodes playing different roles in structure and function, and edges representing the relationships between nodes. Identifying critical nodes can help us control the spread of public opinio...
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Lu Zhang, Hongyu Yang and Xiping Wu
Air traffic management (ATM) relies on the running condition of the air traffic control sector (ATCS), and assessing whether it is overloaded is crucial for efficiency and safety for the entire aviation industry. Previous approaches to evaluating air tra...
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Rui-Yu Li, Yu Guo and Bin Zhang
Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized extens...
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Shumin Lai, Longjun Huang, Ping Li, Zhenzhen Luo, Jianzhong Wang and Yugen Yi
In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve the p...
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Xinyue Lan, Liyue Wang, Cong Wang, Gang Sun, Jinzhang Feng and Miao Zhang
In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed ...
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Chunwei Hu, Xianfeng Liu, Sheng Wu, Fei Yu, Yongkun Song and Jin Zhang
Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic g...
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Jie Long, Zihan Li, Qi Xuan, Chenbo Fu, Songtao Peng and Yong Min
The opinion recognition for comments in Internet media is a new task in text analysis. It takes comment statements as the research object, by learning the opinion tendency in the original text with annotation, and then performing opinion tendency recogni...
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