2024
Fu, F., Wang, Z., Zhou, W., Wang, Y., Fan, J., Huang, C., . . . Li, W. (n.d.). REGLO: Provable Neural Network Repair for Global Robustness Properties. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 12061-12071). Association for the Advancement of Artificial Intelligence (AAAI). doi:10.1609/aaai.v38i11.29094DOI: 10.1609/aaai.v38i11.29094
2023
Wang, Y., Zhou, W., Fan, J., Wang, Z., Li, J., Chen, X., . . . Zhu, Q. (2023). POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 1. doi:10.1109/tcad.2023.3331215DOI: 10.1109/tcad.2023.3331215
Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems (Conference Paper)
Zhu, Q., Li, W., Huang, C., Chen, X., Zhou, W., Wang, Y., . . . Fu, F. (2023). Verification and Design of Robust and Safe Neural Network-enabled Autonomous Systems. In 2023 59th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE. doi:10.1109/allerton58177.2023.10313451DOI: 10.1109/allerton58177.2023.10313451
Hsieh, Y. -T., Chang, T. -T., Tsai, C. -J., Wu, S. -L., Bai, C. -Y., Chang, K. -C., . . . Zhu, Q. (2023). System Verification and Runtime Monitoring with Multiple Weakly-Hard Constraints. ACM TRANSACTIONS ON CYBER-PHYSICAL SYSTEMS, 7(3). doi:10.1145/3603380DOI: 10.1145/3603380
Chang, K. K. -C., Liu, X., Lin, C. -W., Huang, C., & Zhu, Q. (2023). A Safety-Guaranteed Framework for Neural-Network-Based Planners in Connected Vehicles under Communication Disturbance. In 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE. doi:10.23919/date56975.2023.10137184DOI: 10.23919/date56975.2023.10137184
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning (Conference Paper)
Wang, Y., Zhan, S., Wang, Z., Huang, C., Wang, Z., Yang, Z., & Zhu, Q. (2023). Joint Differentiable Optimization and Verification for Certified Reinforcement Learning. In Proceedings of the ACM/IEEE 14th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023). ACM. doi:10.1145/3576841.3585919DOI: 10.1145/3576841.3585919
Chen, P. -C., Liu, X., Lin, C. -W., Huang, C., & Zhu, Q. (2023). Mixed-Traffic Intersection Management Utilizing Connected and Autonomous Vehicles as Traffic Regulators. In 2023 28TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE, ASP-DAC (pp. 52-57). doi:10.1145/3566097.3567849DOI: 10.1145/3566097.3567849
Wang, Y., Zhan, S. S., Jiao, R., Wang, Z., Jin, W., Yang, Z., . . . Zhu, Q. (2023). Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments. In Proceedings of Machine Learning Research Vol. 202 (pp. 36593-36604).
2022
Manzanas Lopez, D., Althoff, M., Benet, L., Chen, X., Fan, J., Forets, M., . . . Zhu, Q. (2022). ARCH-COMP22 Category Report: Artificial Intelligence and Neural Network Control Systems (AINNCS) for Continuous and Hybrid Systems Plants. In EPiC Series in Computing. EasyChair. doi:10.29007/wfgrDOI: 10.29007/wfgr
Huang, C., Fan, J., Chen, X., Li, W., & Zhu, Q. (2022). POLAR: A Polynomial Arithmetic Framework for Verifying Neural-Network Controlled Systems. In Unknown Conference (pp. 414-430). Springer International Publishing. doi:10.1007/978-3-031-19992-9_27DOI: 10.1007/978-3-031-19992-9_27
Wang, Y., Huang, C., Wang, Z., Wang, Z., & Zhu, Q. (2022). Design-while-Verify: Correct-by-Construction Control Learning with Verification in the Loop. In PROCEEDINGS OF THE 59TH ACM/IEEE DESIGN AUTOMATION CONFERENCE, DAC 2022 (pp. 925-930). doi:10.1145/3489517.3530556DOI: 10.1145/3489517.3530556
Wang, Z., Huang, C., & Zhu, Q. (2022). Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding (Extended Abstract). In IJCAI International Joint Conference on Artificial Intelligence Vol. 2023-August (pp. 6498-6503).
Wang, Z., Huang, C., & Zhu, Q. (2022). Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding. Retrieved from http://arxiv.org/abs/2203.14141v1
Wang, Z., Huang, C., & Zhu, Q. (2022). Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding. In PROCEEDINGS OF THE 2022 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2022) (pp. 1087-1092). Retrieved from https://www.webofscience.com/
Liu, X., Huang, C., Wang, Y., Zheng, B., & Zhu, Q. (2022). Physics-Aware Safety-Assured Design of Hierarchical Neural Network based Planner. In 2022 13TH ACM/IEEE INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS (ICCPS 2022) (pp. 137-146). doi:10.1109/ICCPS54341.2022.00019DOI: 10.1109/ICCPS54341.2022.00019
2021
Wang, Y., Huang, C., Wang, Z., Xu, S., Wang, Z., & Zhu, Q. (2021). Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation. In 2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE. doi:10.1109/dac18074.2021.9586148DOI: 10.1109/dac18074.2021.9586148
Wang, Z., Huang, C., Kim, H., Li, W., & Zhu, Q. (2021). Cross-Layer Adaptation with Safety-Assured Proactive Task Job Skipping. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 20(5). doi:10.1145/3477031DOI: 10.1145/3477031
Wang, Z., Liang, H., Huang, C., & Zhu, Q. (2021). Cross-Layer Design of Automotive Systems. IEEE DESIGN & TEST, 38(5), 8-16. doi:10.1109/MDAT.2020.3037561DOI: 10.1109/MDAT.2020.3037561
Bounding Perception Neural Network Uncertainty for Safe Control of Autonomous Systems (Conference Paper)
Wang, Z., Huang, C., Wang, Y., Hobbs, C., Chakraborty, S., & Zhu, Q. (2021). Bounding Perception Neural Network Uncertainty for Safe Control of Autonomous Systems. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE. doi:10.23919/date51398.2021.9474204DOI: 10.23919/date51398.2021.9474204
Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems (Conference Paper)
Zhu, Q., Huang, C., Jiao, R., Lan, S., Liang, H., Liu, X., . . . Xu, S. (2021). Safety-Assured Design and Adaptation of Learning-Enabled Autonomous Systems. In Proceedings of the 26th Asia and South Pacific Design Automation Conference. ACM. doi:10.1145/3394885.3431623DOI: 10.1145/3394885.3431623
2020
Know the unknowns (Conference Paper)
Zhu, Q., Li, W., Kim, H., Xiang, Y., Wardega, K., Wang, Z., . . . Choi, H. (2020). Know the unknowns. In Proceedings of the 39th International Conference on Computer-Aided Design. ACM. doi:10.1145/3400302.3415768DOI: 10.1145/3400302.3415768
Huang, C., Fan, J., Chen, X., Li, W., & Zhu, Q. (2020). Divide and Slide: Layer-Wise Refinement for Output Range Analysis of Deep Neural Networks. IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 39(11), 3323-3335. doi:10.1109/TCAD.2020.3013071DOI: 10.1109/TCAD.2020.3013071
Wang, Y., Huang, C., & Zhu, Q. (2020). Energy-Efficient Control Adaptation with Safety Guarantees for Learning-Enabled Cyber-Physical Systems. Retrieved from http://dx.doi.org/10.1145/3400302.3415676
Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems (Conference Paper)
Huang, C., Xu, S., Wang, Z., Lan, S., Li, W., & Zhu, Q. (2020). Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems. In 2020 57th ACM/IEEE Design Automation Conference (DAC). IEEE. doi:10.1109/dac18072.2020.9218742DOI: 10.1109/dac18072.2020.9218742
SAW: A Tool for Safety Analysis of Weakly-Hard Systems (Chapter)
Huang, C., Chang, K. -C., Lin, C. -W., & Zhu, Q. (2020). SAW: A Tool for Safety Analysis of Weakly-Hard Systems. In Unknown Book (Vol. 12224, pp. 543-555). doi:10.1007/978-3-030-53288-8_26DOI: 10.1007/978-3-030-53288-8_26
Huang, C., Xu, S., Wang, Z., Lan, S., Li, W., & Zhu, Q. (2020). Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems. Retrieved from http://arxiv.org/abs/2005.03726v1
Navigating Discrete Difference Equation Governed WMR by Virtual Linear Leader Guided HMPC (Conference Paper)
Huang, C., Chen, X., Tang, E., He, M., Bu, L., Qin, S., & Zeng, Y. (2020). Navigating Discrete Difference Equation Governed WMR by Virtual Linear Leader Guided HMPC. In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE. doi:10.1109/icra40945.2020.9197375DOI: 10.1109/icra40945.2020.9197375
Efficient System Verification with Multiple Weakly-Hard Constraints for Runtime Monitoring (Chapter)
Wu, S. -L., Bai, C. -Y., Chang, K. -C., Hsieh, Y. -T., Huang, C., Lin, C. -W., . . . Zhu, Q. (2020). Efficient System Verification with Multiple Weakly-Hard Constraints for Runtime Monitoring. In Unknown Book (Vol. 12399, pp. 497-516). doi:10.1007/978-3-030-60508-7_28DOI: 10.1007/978-3-030-60508-7_28
ReachNN*: A Tool for Reachability Analysis of Neural-Network Controlled Systems (Conference Paper)
Fan, J., Huang, C., Chen, X., Li, W., & Zhu, Q. (2020). ReachNN*: A Tool for Reachability Analysis of Neural-Network Controlled Systems. In Unknown Conference (pp. 537-542). Springer International Publishing. doi:10.1007/978-3-030-59152-6_30DOI: 10.1007/978-3-030-59152-6_30
2019
Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems: Invited Paper (Conference Paper)
Fan, J., Huang, C., Li, W., Chen, X., & Zhu, Q. (2019). Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems: Invited Paper. In 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE. doi:10.1109/iccad45719.2019.8942059DOI: 10.1109/iccad45719.2019.8942059
Huang, C., Fan, J., Li, W., Chen, X., & Zhu, Q. (2019). ReachNN: Reachability Analysis of Neural-Network Controlled Systems. Retrieved from http://arxiv.org/abs/1906.10654v1DOI: 10.1145/3358228
Formal verification of weakly-hard systems (Conference Paper)
Huang, C., Li, W., & Zhu, Q. (2019). Formal verification of weakly-hard systems. In Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control. ACM. doi:10.1145/3302504.3311811DOI: 10.1145/3302504.3311811
Exploring weakly-hard paradigm for networked systems (Conference Paper)
Huang, C., Wardega, K., Li, W., & Zhu, Q. (2019). Exploring weakly-hard paradigm for networked systems. In Proceedings of the Workshop on Design Automation for CPS and IoT. ACM. doi:10.1145/3313151.3313165DOI: 10.1145/3313151.3313165
2018
Design Automation for Intelligent Automotive Systems (Conference Paper)
Lan, S., Huang, C., Wang, Z., Liang, H., Su, W., & Zhu, Q. (2018). Design Automation for Intelligent Automotive Systems. In 2018 IEEE International Test Conference (ITC). IEEE. doi:10.1109/test.2018.8624723DOI: 10.1109/test.2018.8624723
2017
Probabilistic Safety Verification of Stochastic Hybrid Systems Using Barrier Certificates (Conference Paper)
Huang, C., Chen, X., Lin, W., Yang, Z., & Li, X. (2017). Probabilistic Safety Verification of Stochastic Hybrid Systems Using Barrier Certificates. In ACM Transactions on Embedded Computing Systems Vol. 16 (pp. 1-19). Association for Computing Machinery (ACM). doi:10.1145/3126508DOI: 10.1145/3126508
Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control (Conference Paper)
Huang, C., Chen, X., Zhang, Y., Qin, S., Zeng, Y., & Li, X. (2017). Switched Linear Multi-Robot Navigation Using Hierarchical Model Predictive Control. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization. doi:10.24963/ijcai.2017/605DOI: 10.24963/ijcai.2017/605
2016
Family patronage, institutional patronage, and work family conflict: women’s employment status and subjective well-being in urban China (Journal article)
Wu, Y., Wang, P., & Huang, C. (2016). Family patronage, institutional patronage, and work family conflict: women’s employment status and subjective well-being in urban China. The Journal of Chinese Sociology, 3(1). doi:10.1186/s40711-016-0041-2DOI: 10.1186/s40711-016-0041-2
Tool for analyzing interference problems in aspect-oriented designs (Journal article)
Chen, X., Huang, C., Zhang, Y. F., & Mei, Y. M. (2016). Tool for analyzing interference problems in aspect-oriented designs. Ruan Jian Xue Bao/Journal of Software, 27(3), 633-644. doi:10.13328/j.cnki.jos.004985DOI: 10.13328/j.cnki.jos.004985
A Linear Programming Relaxation Based Approach for Generating Barrier Certificates of Hybrid Systems (Conference Paper)
Yang, Z., Huang, C., Chen, X., Lin, W., & Liu, Z. (2016). A Linear Programming Relaxation Based Approach for Generating Barrier Certificates of Hybrid Systems. In Unknown Conference (pp. 721-738). Springer International Publishing. doi:10.1007/978-3-319-48989-6_44DOI: 10.1007/978-3-319-48989-6_44
2015
Method of automatic test case generation for safety-critical scenarios in train control systems (Journal article)
Chen, X., Jiang, P., Zhang, Y. F., Huang, C., & Zhou, Y. (2015). Method of automatic test case generation for safety-critical scenarios in train control systems. Ruan Jian Xue Bao/Journal of Software, 26(2), 269-278. doi:10.13328/j.cnki.jos.004780DOI: 10.13328/j.cnki.jos.004780
Research on reliability and correctness assurance methods and techniques for device drivers (Journal article)
Zhang, Y. F., Huang, C., Ou, J. S., Tang, E. Y., & Chen, X. (2015). Research on reliability and correctness assurance methods and techniques for device drivers. Ruan Jian Xue Bao/Journal of Software, 26(2), 239-253. doi:10.13328/j.cnki.jos.004778DOI: 10.13328/j.cnki.jos.004778
