Research outputs
Selected research outputs
- BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations (Conference Paper - 2021)
- Interval Change-Point Detection for Runtime Probabilistic Model Checking (Conference Paper - 2020)
- Assessing safety-critical systems from operational testing: A study on autonomous vehicles (Journal article - 2020)
- Probabilistic Model Checking of Robots Deployed in Extreme Environments (Conference Paper - 2019)
- A Safety Framework for Critical Systems Utilising Deep Neural Networks (Conference Paper - 2020)
2026
Trustworthy text-to-image diffusion models: A timely and focused survey
Zhang, Y., Chen, Z., Cheng, C. -H., Ruan, W., Huang, X., Zhao, D., . . . Zhao, X. (2026). Trustworthy text-to-image diffusion models: A timely and focused survey. INFORMATION FUSION, 133. doi:10.1016/j.inffus.2026.104264
Uncertainty-Aware Measurement of Scenario Suite Representativeness for Autonomous Systems
A Hierarchical Imprecise Probability Approach to Reliability Assessment of Large Language Models
Quantifying Fidelity: A Decisive Feature Approach to Comparing Synthetic and Real Imagery
A Reinforcement Learning Method for UAV Delivery Scheduling Under Dynamic Pricing
Hu, Z., Cao, Y., Jiang, K., Jo, M., Xiao, L., & Zhao, X. (2026). A Reinforcement Learning Method for UAV Delivery Scheduling Under Dynamic Pricing. IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 12, 4105-4119. doi:10.1109/TCCN.2025.3630060
Interpreting Safety: A LLM and STPA Approach
Qi, Y., Chen, S., Yin, X., Ruan, W., Khastgir, S., Ruan, J., . . . Huang, X. (2026). Interpreting Safety: A LLM and STPA Approach. In Lecture Notes in Computer Science (pp. 3-18). Springer Nature Singapore. doi:10.1007/978-981-95-7081-2_1
Probabilistic Robustness in Deep Learning: A Concise Yet Comprehensive Guide
Zhao, X. (2026). Probabilistic Robustness in Deep Learning: A Concise Yet Comprehensive Guide. In Adversarial Example Detection and Mitigation Using Machine Learning (pp. 209-222). Springer Nature Switzerland. doi:10.1007/978-3-031-99447-0_15
Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs
Cheng, C. -H., Wu, C., Zhao, X., Bensalem, S., & Ruess, H. (2026). Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs. In Lecture Notes in Computer Science (pp. 73-91). Springer Nature Switzerland. doi:10.1007/978-3-032-05435-7_5
2025
Probabilistic Robustness for Free? Revisiting Training via a Benchmark
Non-Parametric Probabilistic Robustness: A Conservative Metric with Optimized Perturbation Distributions
A Scalable Framework for Safety Assurance of Self-Driving Vehicles based on Assurance 2.0
Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs
SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model
SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model
Huang, Z., Hu, J., Li, X., He, Y., Zhao, X., Peng, B., . . . Cheng, G. (2025). SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model. In 2025 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR) (pp. 28831-28841). doi:10.1109/CVPR52734.2025.02685
Uncertainty-oriented dynamic topology optimization for cross-scale concurrent design considering improved size-controlling strategy
Zhao, X., Wang, L., & Liu, Y. (2025). Uncertainty-oriented dynamic topology optimization for cross-scale concurrent design considering improved size-controlling strategy. RELIABILITY ENGINEERING & SYSTEM SAFETY, 257. doi:10.1016/j.ress.2025.110819
Risk Controlled Image Retrieval
Cai, K., Lu, C. X., Zhao, X., Huang, W., & Huang, X. (2025). Risk Controlled Image Retrieval. In THIRTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, AAAI-25, VOL 39 NO 26 (pp. 27224-27232). Retrieved from https://www.webofscience.com/
Hyper-CycleGAN: A New Adversarial Neural Network Architecture for Cross-Domain Hyperspectral Data Generation
He, Y., Seng, K. P., Ang, L. M., Peng, B., & Zhao, X. (2025). Hyper-CycleGAN: A New Adversarial Neural Network Architecture for Cross-Domain Hyperspectral Data Generation. APPLIED SCIENCES-BASEL, 15(8). doi:10.3390/app15084188
TAIJI: Textual Anchoring for Immunizing Jailbreak Images in Vision Language Models
Safety analysis in the era of large language models: A case study of STPA using ChatGPT
Qi, Y., Zhao, X., Khastgir, S., & Huang, X. (2025). Safety analysis in the era of large language models: A case study of STPA using ChatGPT. MACHINE LEARNING WITH APPLICATIONS, 19. doi:10.1016/j.mlwa.2025.100622
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory
Wu, S., Zhao, X., & Huang, X. (2025). Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory. In Lecture Notes in Computer Science (pp. 182-196). Springer Nature Singapore. doi:10.1007/978-981-96-6585-3_13
ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models Against Stochastic Perturbation
Zhang, Y., Tang, Y., Ruan, W., Huang, X., Khastgir, S., Jennings, P., & Zhao, X. (2025). ProTIP: Probabilistic Robustness Verification on Text-to-Image Diffusion Models Against Stochastic Perturbation. In Unknown Book (Vol. 15090, pp. 455-472). doi:10.1007/978-3-031-73411-3_26
The Impact of Live Polling Quizzes on Student Engagement and Performance in Computer Science Lectures: A Post-COVID19 Study
Zhao, X. (2025). The Impact of Live Polling Quizzes on Student Engagement and Performance in Computer Science Lectures: A Post-COVID19 Study. In Proceedings of the 17th International Conference on Computer Supported Education (pp. 291-298). SCITEPRESS - Science and Technology Publications. doi:10.5220/0013218700003932
2024
Is Difficulty Calibration AllWe Need? Towards More Practical Membership Inference Attacks
He, Y., Li, B., Wang, Y., Yang, M., Wang, J., Hu, H., & Zhao, X. (2024). Is Difficulty Calibration AllWe Need? Towards More Practical Membership Inference Attacks. In PROCEEDINGS OF THE 2024 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, CCS 2024 (pp. 1226-1240). doi:10.1145/3658644.3690316
Bayesian learning for the robust verification of autonomous robots
Zhao, X., Gerasimou, S., Calinescu, R., Imrie, C., Robu, V., & Flynn, D. (2024). Bayesian learning for the robust verification of autonomous robots. COMMUNICATIONS ENGINEERING, 3(1). doi:10.1038/s44172-024-00162-y
Robust RL with LLM-Driven Data Synthesis and Policy Adaptation for Autonomous Driving
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory
Is Difficulty Calibration All We Need? Towards More Practical Membership Inference Attacks
A survey of safety and trustworthiness of large language models through the lens of verification and validation
Huang, X., Ruan, W., Huang, W., Jin, G., Dong, Y., Wu, C., . . . Mustafa, M. A. (2024). A survey of safety and trustworthiness of large language models through the lens of verification and validation. Artificial Intelligence Review, 57(7). doi:10.1007/s10462-024-10824-0
Demonstrating software reliability using possibly correlated tests: Insights from a conservative Bayesian approach
Salako, K., & Zhao, X. (2024). Demonstrating software reliability using possibly correlated tests: Insights from a conservative Bayesian approach. QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL, 40(3), 1197-1220. doi:10.1002/qre.3460
Representation-Based Robustness in Goal-Conditioned Reinforcement Learning
Yin, X., Wu, S., Liu, J., Fang, M., Zhao, X., Huang, X., & Ruan, W. (2024). Representation-Based Robustness in Goal-Conditioned Reinforcement Learning. In THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 19 (pp. 21761-21769). Retrieved from https://www.webofscience.com/
Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems
Dong, Y., Zhao, X., Wang, S., & Huang, X. (2024). Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems. IEEE Robotics and Automation Letters, 1-8. doi:10.1109/lra.2024.3364471
Bridging formal methods and machine learning with model checking and global optimisation
Bensalem, S., Huang, X., Ruan, W., Tang, Q., Wu, C., & Zhao, X. (2024). Bridging formal methods and machine learning with model checking and global optimisation. Journal of Logical and Algebraic Methods in Programming, 137, 100941. doi:10.1016/j.jlamp.2023.100941
Augmenting Scenario Description Languages for Intelligence Testing of Automated Driving Systems
Tang, Y., da Costa, A. A. B., Irvine, P., Dodoiu, T., Zhang, Y., Zhao, X., . . . Jennings, P. (2024). Augmenting Scenario Description Languages for Intelligence Testing of Automated Driving Systems. In 2024 35TH IEEE INTELLIGENT VEHICLES SYMPOSIUM, IEEE IV 2024 (pp. 1112-1118). doi:10.1109/IV55156.2024.10588707
Hyper-Cyclegan: A New Adversarial Neural Network Architecture for Cross-Domain Hyperspectral Data Generation
Instance-Level Safety-Aware Fidelity of Synthetic Data and its Calibration
Cheng, C. -H., Stoeckel, P., & Zhao, X. (2024). Instance-Level Safety-Aware Fidelity of Synthetic Data and its Calibration. In 2024 IEEE 27TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC (pp. 2354-2361). doi:10.1109/ITSC58415.2024.10920032
ODD-based Query-time Scenario Mutation Framework for Autonomous Driving Scenario databases
Tang, Y., Raj, D., Zhao, X., Zhang, X., da Costa, A. A. B., Khastgir, S., & Jennings, P. (2024). ODD-based Query-time Scenario Mutation Framework for Autonomous Driving Scenario databases. In 2024 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ICRA 2024 (pp. 2197-2203). doi:10.1109/ICRA57147.2024.10610412
Position: Building Guardrails for Large Language Models Requires Systematic Design
Dong, Y., Mu, R., Jin, G., Qi, Y., Hu, J., Zhao, X., . . . Huang, X. (2024). Position: Building Guardrails for Large Language Models Requires Systematic Design. In Proceedings of Machine Learning Research Vol. 235 (pp. 11375-11394).
TARP-VP: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models
Chen, Z., Zhang, Y., Wang, F., Zhao, X., Huang, X., & Ruan, W. (2024). TARP-VP: Towards Evaluation of Transferred Adversarial Robustness and Privacy on Label Mapping Visual Prompting Models. In ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 37 (NEURIPS 2024). Retrieved from https://www.webofscience.com/
What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety-Critical Systems
Bensalem, S., Cheng, C. -H., Huang, W., Huang, X., Wu, C., & Zhao, X. (2024). What, Indeed, is an Achievable Provable Guarantee for Learning-Enabled Safety-Critical Systems. In Unknown Conference (pp. 55-76). Springer Nature Switzerland. doi:10.1007/978-3-031-46002-9_4
2023
Hierarchical Distribution-aware Testing of Deep Learning
Huang, W., Zhao, X., Banks, A., Cox, V., & Huang, X. (2024). Hierarchical Distribution-aware Testing of Deep Learning. ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 33(2). doi:10.1145/3625290
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
Dong, Y., Huang, W., Bharti, V., Cox, V., Banks, A., Wang, S., . . . Huang, X. (2023). Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance. ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS, 22(3). doi:10.1145/3570918
Short-term Load Forecasting with Distributed Long Short-Term Memory
Dong, Y., Chen, Y., Zhao, X., & Huang, X. (2023). Short-term Load Forecasting with Distributed Long Short-Term Memory. In 2023 IEEE POWER & ENERGY SOCIETY INNOVATIVE SMART GRID TECHNOLOGIES CONFERENCE, ISGT. doi:10.1109/ISGT51731.2023.10066368
The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments
Salako, K., & Zhao, X. (2023). The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments. IEEE Transactions on Software Engineering, 1-9. doi:10.1109/tse.2022.3233802
Cycle-Consistent Generative Adversarial Network Architectures for Audio Visual Speech Recognition
He, Y., Seng, K. P., Ang, L. -M., & Zhao, X. (2023). Cycle-Consistent Generative Adversarial Network Architectures for Audio Visual Speech Recognition. In 2023 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC) (pp. 1-6). IEEE. doi:10.1109/icspcc59353.2023.10400358
Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation
Dong, Y., Li, Z., Zhao, X., Ding, Z., & Huang, X. (2023). Decentralised and Cooperative Control of Multi-Robot Systems through Distributed Optimisation. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 2023-May (pp. 1421-1429).
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Huang, W., Zhao, X., Jin, G., & Huang, X. (2023). SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability. In 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV (pp. 1988-1998). doi:10.1109/ICCV51070.2023.00190
STPA for Learning-Enabled Systems: A Survey and A New Practice
Qi, Y., Dong, Y., Khastgir, S., Jennings, P., Zhao, X., & Huang, X. (2023). STPA for Learning-Enabled Systems: A Survey and A New Practice. In 2023 IEEE 26TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS, ITSC (pp. 1381-1388). doi:10.1109/ITSC57777.2023.10422520
2022
The Unnecessity of Assuming Statistically Independent Tests in Bayesian Software Reliability Assessments
Short-term Load Forecasting with Distributed Long Short-Term Memory
Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance
Dependable learning-enabled multiagent systems
Huang, X., Peng, B., & Zhao, X. (2022). Dependable learning-enabled multiagent systems. AI COMMUNICATIONS, 35(4), 407-420. doi:10.3233/AIC-220128
Coverage-Guided Testing for Recurrent Neural Networks
Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J., & Huang, X. (2022). Coverage-Guided Testing for Recurrent Neural Networks. IEEE TRANSACTIONS ON RELIABILITY, 71(3), 1191-1206. doi:10.1109/TR.2021.3080664
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Dong, Y., Zhao, X., & Huang, X. (2021). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. Retrieved from http://dx.doi.org/10.1109/IROS47612.2022.9981794
A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems
Embedding and Extraction of Knowledge in Tree Ensemble Classifiers
Huang, W., Zhao, X., & Huang, X. (n.d.). Embedding and Extraction of Knowledge in Tree Ensemble Classifiers. Machine Learning. Retrieved from http://arxiv.org/abs/2010.08281v2
A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems
Qi, Y., Conmy, P. R., Huang, W., Zhao, X., & Huang, X. (2022). A Hierarchical HAZOP-Like Safety Analysis for Learning-Enabled Systems. In Ceur Workshop Proceedings Vol. 3215.
Bridging Formal Methods and Machine Learning with Global Optimisation
Huang, X., Ruan, W., Tang, Q., & Zhao, X. (2022). Bridging Formal Methods and Machine Learning with Global Optimisation. In Unknown Book (Vol. 13478, pp. 1-19). doi:10.1007/978-3-031-17244-1_1
Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking
Dong, Y., Zhao, X., & Huang, X. (2022). Dependability Analysis of Deep Reinforcement Learning based Robotics and Autonomous Systems through Probabilistic Model Checking. In 2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS) (pp. 5171-5178). doi:10.1109/IROS47612.2022.9981794
2021
Embedding and Extraction of Knowledge in Tree Ensemble Classifiers
Conservative Confidence Bounds in Safety, from Generalised Claims of Improvement Statistical Evidence
Salako, K., Strigini, L., & Zhao, X. (2021). Conservative Confidence Bounds in Safety, from Generalised Claims of Improvement & Statistical Evidence. In 51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN 2021) (pp. 451-462). doi:10.1109/DSN48987.2021.00055
Detecting Operational Adversarial Examples for Reliable Deep Learning
Zhao, X., Huang, W., Schewe, S., Dong, Y., & Huang, X. (2021). Detecting Operational Adversarial Examples for Reliable Deep Learning. In 51ST ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS - SUPPLEMENTAL VOL (DSN 2021) (pp. 5-6). doi:10.1109/DSN-S52858.2021.00013
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
Coverage Guided Testing for Recurrent Neural Networks
Coverage Guided Testing for Recurrent Neural Networks
Huang, W., Sun, Y., Zhao, X., Sharp, J., Ruan, W., Meng, J., & Huang, X. (n.d.). Coverage Guided Testing for Recurrent Neural Networks. IEEE Transactions on Reliability.
Detecting Operational Adversarial Examples for Reliable Deep Learning
Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles
Zhao, X., Huang, W., Banks, A., Cox, V., Flynn, D., Schewe, S., & Huang, X. (2021). Assessing the Reliability of Deep Learning Classifiers Through Robustness Evaluation and Operational Profiles. Retrieved from http://arxiv.org/abs/2106.01258v1
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2021). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. In UNCERTAINTY IN ARTIFICIAL INTELLIGENCE, VOL 161 Vol. 161 (pp. 887-896). Retrieved from https://www.webofscience.com/
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2020). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. Retrieved from http://arxiv.org/abs/2012.03058v5
BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations
Zhao, X., Huang, W., Huang, X., Robu, V., & Flynn, D. (2021). BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations. In Proceedings of Machine Learning Research Vol. 161 (pp. 408-418).
2020
Assessing safety-critical systems from operational testing: A study on autonomous vehicles
Zhao, X., Salako, K., Strigini, L., Robu, V., & Flynn, D. (2020). Assessing safety-critical systems from operational testing: A study on autonomous vehicles. INFORMATION AND SOFTWARE TECHNOLOGY, 128. doi:10.1016/j.infsof.2020.106393
Interval Change-Point Detection for Runtime Probabilistic Model Checking
Zhao, X., Calinescu, R., Gerasimou, S., Robu, V., & Flynn, D. (2020). Interval Change-Point Detection for Runtime Probabilistic Model Checking. In 2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020) (pp. 163-174). doi:10.1145/3324884.3416565
Assessing Safety-Critical Systems from Operational Testing: A Study on Autonomous Vehicles
A Safety Framework for Critical Systems Utilising Deep Neural Networks
On reliability assessment when a software-based system is replaced by a thought-to-be-better one
Littlewood, B., Salako, K., Strigini, L., & Zhao, X. (2020). On reliability assessment when a software-based system is replaced by a thought-to-be-better one. RELIABILITY ENGINEERING & SYSTEM SAFETY, 197. doi:10.1016/j.ress.2019.106752
A Safety Framework for Critical Systems Utilising Deep Neural Networks
Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M., & Huang, X. (2020). A Safety Framework for Critical Systems Utilising Deep Neural Networks. In COMPUTER SAFETY, RELIABILITY, AND SECURITY, SAFECOMP 2020 Vol. 12234 (pp. 244-259). doi:10.1007/978-3-030-54549-9_16
A Safety Framework for Critical Systems Utilising Deep Neural Networks
Zhao, X., Banks, A., Sharp, J., Robu, V., Flynn, D., Fisher, M., & Huang, X. (2020). A Safety Framework for Critical Systems Utilising Deep Neural Networks. In Computer Safety, Reliability, and Security (Vol. 12234, pp. 244-259). Springer Nature. doi:10.1007/978-3-030-54549-9_16
2019
UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS
Osborne, M., Lantair, J., Shafiq, Z., Zhao, X., Robu, V., Flynn, D., & Perry, J. (2019). UAS Operators Safety and Reliability Survey: Emerging Technologies towards the Certification of Autonomous UAS. In 2019 4TH INTERNATIONAL CONFERENCE ON SYSTEM RELIABILITY AND SAFETY (ICSRS 2019) (pp. 203-212). doi:10.1109/icsrs48664.2019.8987692
Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing
Zhao, X., Robu, V., Flynn, D., Salako, K., & Strigini, L. (2019). Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing. In 2019 IEEE 30TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE) (pp. 13-23). doi:10.1109/ISSRE.2019.00012
The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation
Tang, W., Flynn, D., Brown, K., Valentin, R., & Zhao, X. (2019). The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation. In OCEANS 2019 MTS/IEEE SEATTLE. Retrieved from https://www.webofscience.com/
The Design of a Fusion Prognostic Model and Health Management System for Subsea Power Cables
Tang, W., Flynn, D., Brown, K., Valentin, R., & Zhao, X. (2019). The Design of a Fusion Prognostic Model and Health Management System for Subsea Power Cables. In OCEANS 2019 MTS/IEEE SEATTLE. doi:10.23919/oceans40490.2019.8962816
Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management
Assessing the Safety and Reliability of Autonomous Vehicles from Road Testing
Machine learning methods for wind turbine condition monitoring: A review
Stetco, A., Dinmohammadi, F., Zhao, X., Robu, V., Flynn, D., Barnes, M., . . . Nenadic, G. (2019). Machine learning methods for wind turbine condition monitoring: A review. RENEWABLE ENERGY, 133, 620-635. doi:10.1016/j.renene.2018.10.047
Probabilistic Model Checking of Robots Deployed in Extreme Environments
Probabilistic Model Checking of Robots Deployed in Extreme Environments
Zhao, X., Robu, V., Flynn, D., Dinmohammadi, F., Fisher, M., & Webster, M. (2019). Probabilistic Model Checking of Robots Deployed in Extreme Environments. In THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE (pp. 8066-8074). Retrieved from https://www.webofscience.com/
Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management
Zhao, X., Osborne, M., Lantair, J., Robu, V., Flynn, D., Huang, X., . . . Ferrando, A. (2019). Towards Integrating Formal Verification of Autonomous Robots with Battery Prognostics and Health Management. In SOFTWARE ENGINEERING AND FORMAL METHODS (SEFM 2019) Vol. 11724 (pp. 105-124). doi:10.1007/978-3-030-30446-1_6
2018
Verifiable Self-Certifying Autonomous Systems
Fisher, M., Collins, E., Dennis, L., Luckcuck, M., Webster, M., Jump, M., . . . Zhao, X. (2018). Verifiable Self-Certifying Autonomous Systems. In 2018 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) (pp. 341-348). IEEE. doi:10.1109/issrew.2018.00028
Conservative claims for the probability of perfection of a software-based system using operational experience of previous similar systems
Zhao, X., Littlewood, B., Povyakalo, A., Strigini, L., & Wright, D. (2018). Conservative claims for the probability of perfection of a software-based system using operational experience of previous similar systems. RELIABILITY ENGINEERING & SYSTEM SAFETY, 175, 265-282. doi:10.1016/j.ress.2018.03.032
2017
Modeling the probability of failure on demand (<i>pfd</i>) of a 1-out-of-2 system in which one channel is "quasi-perfect"
Zhao, X., Littlewood, B., Povyakalo, A., Strigini, L., & Wright, D. (2017). Modeling the probability of failure on demand (<i>pfd</i>) of a 1-out-of-2 system in which one channel is "quasi-perfect". RELIABILITY ENGINEERING & SYSTEM SAFETY, 158, 230-245. doi:10.1016/j.ress.2016.09.002
2016
Conservative Claims about the Probability of Perfection of Software-based Systems
Zhao, X., Littlewood, B., Povyakalo, A., & Wright, D. (2015). Conservative Claims about the Probability of Perfection of Software-based Systems. In 2015 IEEE 26TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING (ISSRE) (pp. 130-140). Retrieved from https://www.webofscience.com/
2014
A Dependability Case Construction Approach Based on Dependability Deviation Analysis
Zhang, D. J., Lu, M. Y., & Zhao, X. Y. (2014). A Dependability Case Construction Approach Based on Dependability Deviation Analysis. Applied Mechanics and Materials, 543-547, 3682-3687. doi:10.4028/www.scientific.net/amm.543-547.3682
2012
A New Approach to Assessment of Confidence in Assurance Cases
Zhao, X., Zhang, D., Lu, M., & Zeng, F. (2012). A New Approach to Assessment of Confidence in Assurance Cases. In Unknown Conference (pp. 79-91). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33675-1_7
2011
Variance Analysis Based Software Fault Localization
Variance Analysis Based Software Fault Localization (2011). In Unknown Conference (pp. 333-339). ASME Press. doi:10.1115/1.859858.paper46