Yang Chang

505 total citations
18 papers, 402 citations indexed

About

Yang Chang is a scholar working on Control and Systems Engineering, Safety, Risk, Reliability and Quality and Artificial Intelligence. According to data from OpenAlex, Yang Chang has authored 18 papers receiving a total of 402 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Control and Systems Engineering, 5 papers in Safety, Risk, Reliability and Quality and 5 papers in Artificial Intelligence. Recurrent topics in Yang Chang's work include Fault Detection and Control Systems (6 papers), Machine Fault Diagnosis Techniques (5 papers) and Reliability and Maintenance Optimization (5 papers). Yang Chang is often cited by papers focused on Fault Detection and Control Systems (6 papers), Machine Fault Diagnosis Techniques (5 papers) and Reliability and Maintenance Optimization (5 papers). Yang Chang collaborates with scholars based in China, Taiwan and Germany. Yang Chang's co-authors include Huajing Fang, Yong Zhang, Zhao‐Xu Chen, Jing Zhang, Haoran Shen, Huifang Ma, Zhixin Li, Liang Chang, Jianxiao Zou and Zheng Li and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Applied Energy and IEEE Access.

In The Last Decade

Yang Chang

17 papers receiving 385 citations

Peers

Yang Chang
Li Cai China
Xiujuan Zheng China
Yuefeng Liu China
Ruihua Jiao China
Jingdong Lin China
Liu Datong China
Enhui Liu United States
Dongzhen Lyu China
Jiyu Zhang United States
Xiaoqiong Pang China
Li Cai China
Yang Chang
Citations per year, relative to Yang Chang Yang Chang (= 1×) peers Li Cai

Countries citing papers authored by Yang Chang

Since Specialization
Citations

This map shows the geographic impact of Yang Chang's research. 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 Yang Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yang Chang more than expected).

Fields of papers citing papers by Yang Chang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yang Chang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yang Chang. The network helps show where Yang Chang may publish in the future.

Co-authorship network of co-authors of Yang Chang

This figure shows the co-authorship network connecting the top 25 collaborators of Yang Chang. A scholar is included among the top collaborators of Yang Chang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yang Chang. Yang Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Yang, Chun, et al.. (2023). A Novel Deep Parallel Time-Series Relation Network for Fault Diagnosis. IEEE Transactions on Instrumentation and Measurement. 72. 1–13. 4 indexed citations
2.
Wang, Xiaoyu, Yang Chang, Hexuan Wang, et al.. (2022). Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage. BMC Medical Informatics and Decision Making. 22(1). 278–278. 2 indexed citations
3.
Chang, Yang, Huifang Ma, Liang Chang, & Zhixin Li. (2022). Community detection with attributed random walk via seed replacement. Frontiers of Computer Science. 16(5). 6 indexed citations
4.
Zhang, Jing, et al.. (2022). Health state assessment of bearing with feature enhancement and prediction error compensation strategy. Mechanical Systems and Signal Processing. 182. 109573–109573. 46 indexed citations
5.
Wu, Di, et al.. (2022). Constrained Adaptive Projection with Pretrained Features for Anomaly Detection. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 2059–2065. 3 indexed citations
6.
Chang, Yang, et al.. (2022). Remaining useful life prediction of degraded system with the capability of uncertainty management. Mechanical Systems and Signal Processing. 177. 109166–109166. 7 indexed citations
7.
Li, Yi, Yong Zhang, Yang Chang, Liu Zhang, & Zhenxing Liu. (2021). Remaining useful life prediction of tool with BiGRU-Attention and improved Particle Filter. 46. 1–6. 3 indexed citations
8.
9.
10.
Fang, Huajing, et al.. (2019). Research on Remaining Useful Life Prediction Based on Nonlinear Filtering for Lithium-ion Battery. 26 3. 185–189. 1 indexed citations
11.
Chang, Yang & Huajing Fang. (2019). A hybrid prognostic method for system degradation based on particle filter and relevance vector machine. Reliability Engineering & System Safety. 186. 51–63. 90 indexed citations
12.
Liu, Haijiao, Huifang Ma, Yang Chang, Zhixin Li, & Wenjuan Wu. (2019). Target Community Detection With User’s Preference and Attribute Subspace. IEEE Access. 7. 46583–46594. 6 indexed citations
13.
14.
Chang, Yang, Huajing Fang, & Yong Zhang. (2017). A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery. Applied Energy. 206. 1564–1578. 189 indexed citations
15.
Chang, Yang, et al.. (2016). Simultaneous layer-aware and region-aware partitioning for 3D IC. 31. 502–505. 2 indexed citations
16.
Chen, Zhao‐Xu, Huajing Fang, & Yang Chang. (2016). Weighted Data-Driven Fault Detection and Isolation: A Subspace-Based Approach and Algorithms. IEEE Transactions on Industrial Electronics. 63(5). 3290–3298. 29 indexed citations
17.
Wu, Chao-Cheng, et al.. (2009). Real-time processing of simplex growing algorithm. 44. V–220. 2 indexed citations
18.
Xu, Wenhua, Zheng Qin, Lei Ji, & Yang Chang. (2009). A Feature Weighted Ensemble Classifier on Stream Data. 52. 1–5. 2 indexed citations

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.

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