A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 11
Issue 3
IEEE/CAA Journal of Automatica Sinica
| Citation: | W. Zhang, W. Zheng, Q. Li, and F.-Y. Wang, “Parallel vision ⊇ image synthesis/augmentation,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 3, pp. 782–784, Mar. 2024. doi: 10.1109/JAS.2023.124038
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K. Hsieh, A. Phanishayee, O. Mutlu, and P. Gibbons, “The non-iid data quagmire of decentralized machine learning,” in Int. Conf. Machine Learning, 2020, pp. 4387–4398.
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K. Wang, C. Gou, N. Zheng, and J. Rehg, “Parallel vision for perception and understanding of complex scenes: Methods, framework, and perspectives,” Artificial Intelligence Review, vol. 48, pp. 299–329, 2017. doi: 10.1007/s10462-017-9569-z
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F.-Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 3, pp. 630–638, 2010. doi: 10.1109/TITS.2010.2060218
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W. Zhang, K. Wang, Y. Liu, Y. Lu, and F.-Y. Wang, “A parallel vision approach to scene-specific pedestrian detection,” Neurocomputing, vol. 394, pp. 114–126, 2020. doi: 10.1016/j.neucom.2019.03.095
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X. Li, K. Wang, Y. Tian, L. Yan, F. Deng, and F.-Y. Wang, “The paralleleye dataset: A large collection of virtual images for traffic vision research,” IEEE Trans. Intelligent Transportation Systems, vol. 20, no. 6, pp. 2072–2084, 2018.
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X. Li, Y. Wang, L. Yan, K. Wang, F. Deng, and F.-Y. Wang, “Paralleleyecs: A new dataset of synthetic images for testing the visual intelligence of intelligent vehicles,” IEEE Trans. Vehicular Technology, vol. 68, no. 10, pp. 9619–9631, 2019. doi: 10.1109/TVT.2019.2936227
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Y. Tian, X. Li, K. Wang, and F.-Y. Wang, “Training and testing object detectors with virtual images,” IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 539–546, 2018. doi: 10.1109/JAS.2017.7510841
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X. Li, K. Wang, X. Gu, F. Deng, and F.-Y. Wang, “Paralleleye pipeline: An effective method to synthesize images for improving the visual intelligence of intelligent vehicles,” IEEE Trans. Systems,Man,and Cyber.: Systems, vol. 53, no. 9, pp. 5545–5556, Sept. 2023. doi: 10.1109/TSMC.2023.3273896
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W. Zhang, J. Wang, Y. Wang, and F.-Y. Wang, “Parauda: Invariant feature learning with auxiliary synthetic samples for unsupervised domain adaptation,” IEEE Trans. Intelligent Transportation Systems, vol. 23, p. 11, 2022. doi: 10.1109/TITS.2020.3008612
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A. Gaidon, Q. Wang, Y. Cabon, and E. Vig, “Virtual worlds as proxy for multi-object tracking analysis,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2016, pp. 4340–4349.
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W. Zheng, L. Yan, W. Zhang, L. Ouyang, and D. Wen, “D→k→i: Data-knowledge-driven group intelligence framework for smart service in education metaverse,” IEEE Trans. Systems,Man,and Cyber.: Systems, vol. 53, no. 4, pp. 2056–2061, 2023. doi: 10.1109/TSMC.2022.3228849
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F.-Y. Wang, “The emergence of intelligent enterprises: From CPS to CPSS,” IEEE Intelligent Systems, vol. 25, no. 4, pp. 85–88, 2010. doi: 10.1109/MIS.2010.104
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R. Bommasani, D. A. Hudson, E. Adeli, et al., “On the opportunities and risks of foundation models,” arXiv preprint arXiv: 2108.07258, 2021.
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X. Li, P. Ye, J. Li, Z. Liu, L. Cao, and F.-Y. Wang, “From features engineering to scenarios engineering for trustworthy AI: I&i, c&c, and v&v,” IEEE Intelligent Systems, vol. 37, no. 4, pp. 18–26, 2022. doi: 10.1109/MIS.2022.3197950
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X. Li and F.-Y. Wang, “Scenarios engineering: Enabling trustworthy and effective AI for autonomous vehicles,” IEEE Trans. Intelligent Vehicles, vol. 8, no. 5, pp. 3205–3210, 2023. doi: 10.1109/TIV.2023.3269421
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W. Zheng, L. Yan, C. Gou, and F.-Y. Wang, “Computational knowledge vision: Paradigmatic knowledge based prescriptive learning and reasoning for perception and vision,” Artificial Intelligence Review, vol. 55, no. 8, pp. 5917–5952, 2022. doi: 10.1007/s10462-022-10166-9
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F.-Y. Wang, J. Guo, G. Bu, and J. Zhang, “Mutually trustworthy human-machine knowledge automation and hybrid augmented intelligence: Mechanisms and applications of cognition, management, and control for complex systems,” Frontiers of Infor. Technology &Electronic Engineering, vol. 23, no. 8, pp. 1142–1157, 2022.
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F.-Y. Wang, “The DAO to metacontrol for metasystems in metaverses: The system of parallel control systems for knowledge automation and control intelligence in CPSS,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 11, pp. 1899–1908, 2022. doi: 10.1109/JAS.2022.106022
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