Y
Yongming Han
Researcher at Beijing University of Chemical Technology
Publications - 112
Citations - 2955
Yongming Han is an academic researcher from Beijing University of Chemical Technology. The author has contributed to research in topics: Efficient energy use & Data envelopment analysis. The author has an hindex of 24, co-authored 111 publications receiving 1761 citations. Previous affiliations of Yongming Han include Guizhou University & Chinese Ministry of Education.
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Review: Multi-objective optimization methods and application in energy saving
Yunfei Cui,Yunfei Cui,Zhiqiang Geng,Zhiqiang Geng,Qunxiong Zhu,Qunxiong Zhu,Yongming Han,Yongming Han +7 more
TL;DR: In order to get the final optimal solution in the real-world multi-objective optimization problems, trade-off methods including a priori methods, interactive methods, Pareto-dominated methods and new dominance methods are utilized.
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Semantic relation extraction using sequential and tree-structured LSTM with attention
Zhiqiang Geng,Zhiqiang Geng,Guofei Chen,Guofei Chen,Yongming Han,Yongming Han,Gang Lu,Gang Lu,Fang Li,Fang Li +9 more
TL;DR: An end-to-end method that uses bidirectional tree-structured long short-term memory (LSTM) to extract structural features based on the dependency tree of a sentence to enhance the performance of the relation extraction.
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Energy efficiency analysis method based on fuzzy DEA cross-model for ethylene production systems in chemical industry
TL;DR: In this article, the authors proposed an efficiency analysis method based on Fuzzy DEA cross-model with fuzzy data, which has better objectivity and resolving power for the decision-making.
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Carbon emission analysis and evaluation of industrial departments in China: An improved environmental DEA cross model based on information entropy.
TL;DR: An improved environmental DEA cross model based on the information entropy to analyze and evaluate the carbon emission of industrial departments in China and can obtain the potential of carbon emission reduction ofindustrial departments to improve the energy efficiency.
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Novel leakage detection and water loss management of urban water supply network using multiscale neural networks
TL;DR: A novel leakage detection model based on density based spatial clustering of applications with noise and multiscale fully convolutional networks (MFCN) to manage the water loss and improve leakage detection efficiency and reduce water loss is proposed.