Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
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doi.org/10.1109/tii.2019.2938890 →Countries where authors are citing Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
This map shows the geographic impact of Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy more than expected).
Fields of papers citing Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
This network shows the impact of Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy.
About Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy
This paper, published in 2019, received 244 indexed citations . Written by Xiaofeng Yuan, Jiao Zhou, Biao Huang, Yalin Wang, Chunhua Yang and Weihua Gui covering the research area of Control and Systems Engineering, Mechanical Engineering and Industrial and Manufacturing Engineering. It is primarily cited by scholars working on Control and Systems Engineering (194 citations), Artificial Intelligence (83 citations), Mechanical Engineering (78 citations), Industrial and Manufacturing Engineering (35 citations) and Analytical Chemistry (23 citations). Published in IEEE Transactions on Industrial Informatics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/tii.2019.2938890.