Qixin Sha

843 total citations · 1 hit paper
27 papers, 599 citations indexed

About

Qixin Sha is a scholar working on Ocean Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Qixin Sha has authored 27 papers receiving a total of 599 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Ocean Engineering, 14 papers in Artificial Intelligence and 8 papers in Computer Vision and Pattern Recognition. Recurrent topics in Qixin Sha's work include Underwater Vehicles and Communication Systems (21 papers), Machine Learning and ELM (8 papers) and Adaptive Control of Nonlinear Systems (7 papers). Qixin Sha is often cited by papers focused on Underwater Vehicles and Communication Systems (21 papers), Machine Learning and ELM (8 papers) and Adaptive Control of Nonlinear Systems (7 papers). Qixin Sha collaborates with scholars based in China, United States and Finland. Qixin Sha's co-authors include Guangliang Li, Bo He, Bo He, Yue Shen, Tianhong Yan, Dianrui Wang, Jia Guo, Junhe Wan, Yan Song and Rui Nian and has published in prestigious journals such as IEEE Access, Neurocomputing and Ocean Engineering.

In The Last Decade

Qixin Sha

26 papers receiving 585 citations

Hit Papers

Path planning and obstacle avoidance for AUV: A review 2021 2026 2022 2024 2021 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Path planning and obstacle avoidance for AUV: A review
    2021 228 citations Qixin Sha, Bo He et al. Ocean Engineering profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Qixin Sha China 11 347 273 170 137 137 27 599
Yushan Sun China 13 310 0.9× 198 0.7× 175 1.0× 116 0.8× 124 0.9× 41 515
Yue Shen China 17 340 1.0× 260 1.0× 227 1.3× 148 1.1× 176 1.3× 63 732
Somaiyeh MahmoudZadeh Australia 15 324 0.9× 298 1.1× 115 0.7× 61 0.4× 132 1.0× 28 558
Yulei Liao China 17 471 1.4× 159 0.6× 422 2.5× 42 0.3× 163 1.2× 50 803
Andrew Lammas Australia 12 602 1.7× 494 1.8× 131 0.8× 37 0.3× 207 1.5× 22 736
Pedro Patrón United Kingdom 11 309 0.9× 286 1.0× 69 0.4× 173 1.3× 168 1.2× 27 565
Qinghua Luo China 13 152 0.4× 172 0.6× 57 0.3× 97 0.7× 164 1.2× 66 529
Bruno Jouvencel France 16 736 2.1× 275 1.0× 602 3.5× 126 0.9× 272 2.0× 56 1.1k
Jialing Tang China 12 174 0.5× 169 0.6× 121 0.7× 156 1.1× 62 0.5× 23 459
Abdelkrim Nemra Algeria 11 90 0.3× 248 0.9× 114 0.7× 186 1.4× 457 3.3× 58 622

Countries citing papers authored by Qixin Sha

Since Specialization
Citations

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

Fields of papers citing papers by Qixin Sha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Qixin Sha. 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 Qixin Sha. The network helps show where Qixin Sha may publish in the future.

Co-authorship network of co-authors of Qixin Sha

This figure shows the co-authorship network connecting the top 25 collaborators of Qixin Sha. A scholar is included among the top collaborators of Qixin Sha 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 Qixin Sha. Qixin Sha is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Zhang, Xiaofang, et al.. (2023). Design and Implementation of Autonomous Underwater Vehicle Simulation System Based on MOOS and Unreal Engine. Electronics. 12(14). 3107–3107. 7 indexed citations
2.
Zhang, Dengsheng, et al.. (2023). Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search. Electronics. 12(22). 4575–4575. 5 indexed citations
3.
Huang, Jie, et al.. (2022). Generative adversarial interactive imitation learning for path following of autonomous underwater vehicle. Ocean Engineering. 260. 111971–111971. 14 indexed citations
4.
Huang, Jie, et al.. (2022). Autonomous underwater vehicle formation control and obstacle avoidance using multi-agent generative adversarial imitation learning. Ocean Engineering. 262. 112182–112182. 30 indexed citations
5.
Wang, Dianrui, Yue Shen, Junhe Wan, et al.. (2021). Sliding mode heading control for AUV based on continuous hybrid model-free and model-based reinforcement learning. Applied Ocean Research. 118. 102960–102960. 28 indexed citations
6.
Huang, Jie, Randy Gómez, Keisuke Nakamura, et al.. (2021). Shaping Progressive Net of Reinforcement Learning for Policy Transfer with Human Evaluative Feedback. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 1281–1288. 6 indexed citations
7.
Lv, Pengfei, Bo He, Jia Guo, et al.. (2020). Underwater navigation methodology based on intelligent velocity model for standard AUV. Ocean Engineering. 202. 107073–107073. 27 indexed citations
8.
Sha, Qixin, et al.. (2020). Deep Interactive Reinforcement Learning for Path Following of Autonomous Underwater Vehicle. IEEE Access. 8. 24258–24268. 86 indexed citations
9.
10.
Shen, Yue, Bo He, Dianrui Wang, et al.. (2019). Drift angle compensation-based adaptive line-of-sight path following for autonomous underwater vehicle. Applied Ocean Research. 93. 101943–101943. 37 indexed citations
11.
Wang, Dianrui, Yue Shen, Qixin Sha, et al.. (2017). Controller design of an autonomous underwater vehicle using ELM-based sliding mode control.
12.
Guo, Jia, Yan Song, Qixin Sha, et al.. (2017). The application of AUV navigation based on cubature Kalman filter. 1–4. 8 indexed citations
13.
Sha, Qixin, Yan Song, Jia Guo, et al.. (2017). Classification and mosaicking of side scan sonar image. OCEANS 2017 - Aberdeen. 1–4. 2 indexed citations
14.
Mu, Xiaokai, Jia Guo, Yan Song, et al.. (2017). Application of modified EKF algorithm in AUV navigation system. OCEANS 2017 - Aberdeen. 1–4. 7 indexed citations
15.
Wang, Dianrui, Yue Shen, Qixin Sha, et al.. (2017). Heading control for an Autonomous Underwater Vehicle using ELM-based Q-learning. 3. 1–5. 1 indexed citations
16.
Song, Yan, Shujing Zhang, Bo He, et al.. (2017). Gaussian derivative models and ensemble extreme learning machine for texture image classification. Neurocomputing. 277. 53–64. 38 indexed citations
17.
Wang, Yue, Yue Shen, Kaihong Wang, et al.. (2016). Fuzzy controller used smoothing function for depth control of autonomous underwater vehicle. OCEANS 2016 - Shanghai. 40. 1–5. 6 indexed citations
18.
Yang, Xiao, Yue Shen, Kaihong Wang, et al.. (2016). Path following for an autonomous underwater vehicle using GP-LOS. OCEANS 2016 - Shanghai. 30. 1–5. 1 indexed citations
19.
20.
He, Bo, et al.. (2010). A distributed motion control system for the CRANGER Autonomous Underwater Vehicle. 322–325. 1 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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