Professor Ying Liu

BEng & MEng (Chongqing Univ, China), MSc (SMA, NTU), PhD (SMA, NUS)

2025

• Alasmari, L. et al. 2025. Assessing the circular transformation of warehouse operations through simulation. Applied Sciences 15 (20) 10910. (10.3390/app152010910)
• Alasmari, L. et al. 2025. Assessing the circular transformation of warehouse operations through simulation. Presented at: The International Conference on Industry 4.0 and Smart Manufacturing (ISM) Prague, Czech Republic 20-22 November 2024. Vol. 253., pp.1124-1133. (10.1016/j.procs.2025.01.174)
• Chen, C. et al., 2025. A multi-scale graph pyramid attention network with knowledge distillation towards edge computing robotic fault diagnosis. Expert Systems with Applications 260 125469. (10.1016/j.eswa.2024.125469)
• Chen, Q. , Chen, Y. and Liu, Y. 2025. A systems engineering-integrated approach for functional architecture design of cyber-physical products. Journal of Engineering Design (10.1080/09544828.2025.2569021)
• Chen, Y. et al., 2025. A multi-dimensional data integration approach for machining system multi-level energy consumption modelling and prediction. International Journal of Computer Integrated Manufacturing (10.1080/0951192X.2025.2478011)
• Ji, P. et al., 2025. Resilience enhancement of complex manufacturing systems based on critical theory. Journal of Engineering Design (10.1080/09544828.2025.2537466)
• Jin, J. et al., 2025. Empowering design innovation using AI-generated content. Journal of Engineering Design 36 (1), pp.1-18. (10.1080/09544828.2024.2401751)
• Jin, Z. et al. 2025. Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP. Autonomous Intelligent Systems 5 (1) 2. (10.1007/s43684-024-00088-4)
• Lambay, A. J. et al. 2025. Generating synthetic datasets with deep learning models for human physical fatigue analysis. Machines 13 (3) 235. (10.3390/machines13030235)
• Li, S. et al., 2025. Agile conceptual design and validation based on multi-source product data and large language models: a review, framework, and outlook. Journal of Engineering Design 36 (4), pp.473-503. (10.1080/09544828.2025.2476879)
• Liao, X. et al., 2025. Large language model assisted fine-grained knowledge graph construction for robotic fault diagnosis. Advanced Engineering Informatics 65 (Part A) 103134. (10.1016/j.aei.2025.103134)
• Tang, J. et al., 2025. A generic process mining framework for uncovering hierarchical process model. Presented at: 31st International Conference on Engineering, Technology, and Innovation- ICE IEEE/ITMC Valencia, Spain 16-19 June 2025. Proceedings of the International Conference on Engineering, Technology, and Innovation. IEEE. , pp.1-10. (10.1109/ice/itmc65658.2025.11106526)
• Wan, Y. et al. 2025. Empowering LLMs by hybrid retrieval-augmented generation for domain-centric Q&A in smart manufacturing. Advanced Engineering Informatics 65 103212. (10.1016/j.aei.2025.103212)
• Wan, Y. et al., 2025. Prompting large language models based on semantic schema for text-to-Cypher transformation towards domain Q&A. Decision Support Systems 199 114553. (10.1016/j.dss.2025.114553)
• Wan, Y. et al. 2025. Facilitating design for additive manufacturing with KG-based retrieval-augmented generation. Presented at: 31st International Conference on Engineering, Technology, and Innovation- ICE IEEE/ITMC Valencia, Spain 16-19 June 2025. Proceedings of the International Conference on Engineering, Technology, and Innovation. IEEE. (10.1109/ICE/ITMC65658.2025.11106597)
• Wang, Z. et al., 2025. Advancements in knowledge graphs (KGs) for engineering design. Journal of Engineering Design (10.1080/09544828.2025.2500880)
• Wu, S. et al., 2025. Quantitative prediction model for lithium-ion battery life uncertainty based on DAE-CNN-BiGRU quantile regression. Journal of Energy Storage 123 116771. (10.1016/j.est.2025.116771)
• Wu, S. et al., 2025. Research on the contact dynamic characteristics of gears coupled with tooth surface micromorphology and spalling faults. Journal of vibration engineering & technologies. 13 (5) 314. (10.1007/s42417-025-01885-8)
• Wu, S. et al., 2025. TVMS calculation model for gear tooth tip chipping failure with EHL. Engineering Failure Analysis 167 (PartA) 108971. (10.1016/j.engfailanal.2024.108971)
• You, Y. et al. 2025. A human digital twin approach for fatigue-aware task planning in human-robot collaborative assembly. Computers & Industrial Engineering 200 110774. (10.1016/j.cie.2024.110774)
• Yu, T. et al., 2025. Large language models for PHM: a review of optimization techniques and applications. Autonomous Intelligent Systems 5 (1) 18. (10.1007/s43684-025-00100-5)
• Zhang, S. , Guo, X. and Liu, Y. 2025. Enhancing machine learning models for vertical farm energy forecasting: impact of data smoothing and feature selection. Presented at: 10th International Conference on Cloud Computing and Big Data Analytics Chengdu, China 24-26 April 2025. Proceedings of the 10th International Conference on Cloud Computing and Big Data Analytics. IEEE. , pp.151-160. (10.1109/ICCCBDA64898.2025.11030388)
• Zhao, K. et al. 2025. Digital twin-supported BESS decision support of RUL-based maintenance. Presented at: 2025 IEEE International Conference on Engineering, Technology, and Innovation (ICE/ITMC) Valencia, Spain 16-19 June 2025. IEEE(10.1109/ice/itmc65658.2025.11106621)
• Zhao, K. et al. 2025. A Hierarchical and Self-Evolving Digital Twin (HSE-DT) method for multi-faceted battery situation awareness realisation. Machines 13 (3) 175. (10.3390/machines13030175)
• Zhao, K. et al. 2025. Digital twin-supported battery state estimation based on TCN-LSTM neural networks and transfer learning. CSEE Journal of Power and Energy Systems 11 (2), pp.567-579. (10.17775/CSEEJPES.2024.00900)
• Zhou, T. et al. 2025. Enabling interactive AI in industry 5.0 with RAG-enhanced GenAI Chatbots. Presented at: 31st International Conference on Engineering, Technology, and Innovation- ICE IEEE/ITMC Valencia, Spain 16-19 June 2025. Proceedings of the International Conference on Engineering, Technology, and Innovation. IEEE. , pp.1-10. (10.1109/ICE/ITMC65658.2025.11106634)
• Zhu, Y. et al., 2025. Service-oriented multi-stakeholder collaborative network optimization for EoL batteries recycling. Flexible Services and Manufacturing Journal (10.1007/s10696-025-09609-6)

2024

• Bao, X. et al., 2024. Abnormal pattern recognition for online inspection in manufacturing process based on multi-scale time series classification. Journal of Manufacturing Systems 76 , pp.457-477. (10.1016/j.jmsy.2024.08.005)
• Chen, C. et al., 2024. Compact convolutional transformers- generative adversarial network for compound fault diagnosis of industrial robot. Engineering Applications of Artificial Intelligence 138 (PartA) 109315. (10.1016/j.engappai.2024.109315)
• Chen, Z. et al. 2024. A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling. Information Fusion 101 101985. (10.1016/j.inffus.2023.101985)
• Guo, Y. et al., 2024. Integrated modeling for retired mechanical product genes in remanufacturing: A knowledge graph-based approach. Advanced Engineering Informatics 59 102254. (10.1016/j.aei.2023.102254)
• Hejazi, S. Z. , Packianather, M. and Liu, Y. 2024. A novel customised load adaptive framework for induction motor fault classification utilising MFPT bearing dataset. Machines 12 (1) 44. (10.3390/machines12010044)
• Jin, Z. et al. 2024. Compact convolutional transformer for bearing remaining useful life prediction. Presented at: IWAR 2023- VII International Workshop on Autonomous Remanufacturing Caserta, Italy 18-19 October 2023. Published in: Fera, M. et al., Advances in Remanufacturing. IWAR 2023. Lecture Notes in Mechanical Engineering Cham, Switzerland: Springer. , pp.227-238. (10.1007/978-3-031-52649-7_18)
• Lambay, A. et al. 2024. Machine learning assisted human fatigue detection, monitoring, and recovery: a review. Digital Engineering 1 100004. (10.1016/j.dte.2024.100004)
• Lambay, A. J. et al. 2024. Model training through synthetic data generation:Investigating the impact on human physical fatigue. Presented at: International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences Nice, France 24 - 27 July 2024. Published in: Nunes, I. L. ed. AHFE International: Human Factors and Systems Interaction. Vol. 154.New York: AHFE Open Access. , pp.22-31. (10.54941/ahfe1005349)
• Li, Y. et al. 2024. Towards an energy consumption optimisation framework in selective laser sintering system: Leveraging deep learning and FPGA technologies. Presented at: 4th ICPR AEM Poznań 2024 Poznan, Poland 28 June- 3 July 2024.
• Liu, Q. et al., 2024. An investigation of mixed-model assembly line balancing problem with uncertain assembly time in remanufacturing. Computers & Industrial Engineering 198 110676. (10.1016/j.cie.2024.110676)
• Liu, Y. et al. 2024. High-fidelity digital twin modelling for predictive maintenance state-of-the-art. Presented at: The 51st International Conference on Computers and Industrial Engineering (CIE51) Sydney, Australia 9-11 December 2024.
• Wan, Y. et al. 2024. Making knowledge graphs work for smart manufacturing: Research topics, applications and prospects. Journal of Manufacturing Systems 76 , pp.103-132. (10.1016/j.jmsy.2024.07.009)
• Wang, T. et al., 2024. Data privacy preserving for centralized robotic fault diagnosis with modified dataset distillation. Journal of Computing and Information Science in Engineering 24 (10) 101005. (10.1115/1.4066096)
• Xiao, A. et al., 2024. Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction. Autonomous Intelligent Systems 4 (1) 10. (10.1007/s43684-024-00072-y)
• Yan, W. et al., 2024. A stochastic programming approach for EOL electric vehicle batteries recovery network design under uncertain conditions. Scientific Reports 14 (1) 876. (10.1038/s41598-024-51169-6)
• Yang, J. , Liu, Y. and Morgan, P. 2024. Sensor and data: key elements of human-machine interaction for human-centric smart manufacturing. Presented at: 5th International Conference on Industry 4.0 and Smart Manufacturing Lisbon, Portugal 22-24 November 2023. Published in: Longo, F. , Shen, W. and Padovano, A. eds. Elsevier. , pp.191-200. (10.1016/j.procs.2024.01.019)
• Yang, J. , Liu, Y. and Morgan, P. L. 2024. Human-machine interaction towards Industry 5.0: Human-centric smart manufacturing. Digital Engineering 2 100013. (10.1016/j.dte.2024.100013)
• You, Y. , Liu, Y. and Ji, Z. 2024. Human digital twin for real-time physical fatigue estimation in human-robot collaboration. Presented at: The 25th IEEE International Conference on Industrial Technology Bristol, UK 25-27 March 2024. Proceedings of the 25th IEEE International Conference on Industrial Technology. IEEE. , pp.1-6. (10.1109/ICIT58233.2024.10541029)
• Zhang, X. et al., 2024. An intelligent surface roughness prediction method based on automatic feature extraction and adaptive data fusion. Autonomous Intelligent Systems 4 (1) 27. (10.1007/s43684-024-00083-9)
• Zhou, T. , Liu, Y. and Kumar, M. 2024. AI-powered chatbots for improving interactive user experience: state-of-the-art. Presented at: The 51st International Conference on Computers and Industrial Engineering (CIE51) Sydney, Australia 9-11 December 2024.

2023

• Chen, C. et al. 2023. The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems 71 , pp.581-594. (10.1016/j.jmsy.2023.10.010)
• Chen, C. et al., 2023. Reinforcement learning-based distant supervision relation extraction for fault diagnosis knowledge graph construction under industry 4.0. Advanced Engineering Informatics 55 101900. (10.1016/j.aei.2023.101900)
• Feng, Z. et al., 2023. An energy consumption estimation method for the tool setting process in CNC milling based on the modular arrangement of predetermined time standards. Energies 16 (20) 7064. (10.3390/en16207064)
• Guo, X. et al., 2023. Harnessing multi-domain knowledge for user-centric product conceptual design. Journal of Computing and Information Science in Engineering 23 (6) 060807. (10.1115/1.4062456)
• Guo, X. et al., 2023. Design for product resilience: concept, characteristics and generalization. Journal of Engineering Design 34 (5-6), pp.368-382. (10.1080/09544828.2023.2225844)
• Hejazi, S. , Packianather, M. and Liu, Y. 2023. A Novel approach using WGAN-GP and conditional WGAN-GP for generating artificial thermal images of induction motor faults. Presented at: 27th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2023) 06-08 September 2023. Vol. 225.Elsevier. , pp.3681-3691. (10.1016/j.procs.2023.10.363)
• Hu, F. et al. 2023. Task-driven data fusion for additive manufacturing: framework, approaches, and case studies. Journal of Industrial Information Integration 34 100484. (10.1016/j.jii.2023.100484)
• Huang, Z. et al., 2023. A smart conflict resolution model using multi-layer knowledge graph for conceptual design. Advanced Engineering Informatics 55 101887. (10.1016/j.aei.2023.101887)
• Jiang, Y. et al., 2023. Knowledge-based design: a function-knowledge reasoning model for product conceptual design. In: Kyratsis, P. , Manavis, A. and Davim, J. P. eds. Computational Design and Digital Manufacturing. Management and Industrial Engineering , pp.119-139. (10.1007/978-3-031-21167-6_6)
• Li, C. et al. 2023. Two-port measurement of packaging parasitic inductance in SiC half-bridge power modules considering mutual inductance*. Presented at: 3rd International Conference on Industrial Electronics for Sustainable Energy Systems Shanghai, China 26-28 July 2023. Proceedings of 3rd International Conference on Industrial Electronics for Sustainable Energy Systems. IEEE. , pp.1-6. (10.1109/IESES53571.2023.10253690)
• Li, X. et al., 2023. Industrial knowledge graph-enabled cognitive intelligence-driven mass personalization. Advanced Engineering Informatics 57 102022. (10.1016/j.aei.2023.102022)
• Li, Y. et al. 2023. A hybrid model compression approach via knowledge distillation for predicting energy consumption in additive manufacturing. International Journal of Production Research 61 (13), pp.4525-4547. (10.1080/00207543.2022.2160501)
• Lian, X. et al., 2023. Combined forecasting approach for product quality based on support vector regression and gray forecasting model. Advanced Engineering Informatics 57 102070. (10.1016/j.aei.2023.102070)
• Tang, J. et al., 2023. Process bottlenecks identification and its root cause analysis using fusion-based clustering and knowledge graph. Advanced Engineering Informatics 55 101862. (10.1016/j.aei.2022.101862)
• Wan, Y. et al. 2023. Constructing industrial knowledge graph through ontology and link prediction. Presented at: IEEE International Conference on Automation Science and Engineering (CASE) Auckland, New Zealand 26-29 August 2023. 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE). IEEE. , pp.1-6. (10.1109/CASE56687.2023.10260566)
• Wang, S. et al., 2023. Improving automotive garage operations by categorical forecasts using a large number of variables. European Journal of Operational Research 306 (2), pp.893-908. (10.1016/j.ejor.2022.06.062)
• Yang, B. , Liu, Y. and Chen, W. 2023. A twin data-driven approach for user-experience based design innovation. International Journal of Information Management 68 102595. (10.1016/j.ijinfomgt.2022.102595)

2022

• Chen, C. et al., 2022. Spatial-attention-based convolutional transformer for bearing remaining useful life prediction. Measurement Science and Technology 33 (11) 114001. (10.1088/1361-6501/ac7c5b)
• Chen, C. , Wu, D. and Liu, Y. 2022. Recent advances of AI for engineering service and maintenance. Autonomous Intelligent Systems 2 (1) 19. (10.1007/s43684-022-00038-y)
• Chen, W. , Yang, B. and Liu, Y. 2022. An integrated QFD and FMEA approach to identify risky components of products. Advanced Engineering Informatics 54 101808. (10.1016/j.aei.2022.101808)
• Chen, Y. et al., 2022. A data-driven approach design for carbon emission prediction of machining. Presented at: International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC-CIE 2022) St. Louis, MO, USA 14-17 August 2022. 42nd Computers and Information in Engineering Conference (CIE). Vol. 2.(10.1115/DETC2022-90465)
• Cheng, M. et al., 2022. A convolutional neural network with equal-resolution enhancement and gradual attention of features for tiny target detection. Presented at: IEEE 18th International Conference on Automation Science and Engineering (CASE 2022) Mexico City, Mexico 20-24 August 2022. 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE(10.1109/CASE49997.2022.9926425)
• Fu, H. and Liu, Y. 2022. A deep learning-based approach for electrical equipment remaining useful life prediction. Autonomous Intelligent Systems 2 16. (10.1007/s43684-022-00034-2)
• Guo, X. , Jiang, Y. and Liu, Y. 2022. Conceptual design for innovation: a knowledge-based approach. In: Kyratsis, P. , Efkolidis, N. and Davim, J. P. eds. Advances in Product Design Engineering. Springer. , pp.179-198. (10.1007/978-3-030-98124-2_8)
• Hejazi, S. , Packianather, M. and Liu, Y. 2022. Novel preprocessing of multimodal condition monitoring data for classifying induction motor faults using deep learning methods. Presented at: IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC) Gunupur, Odisha, India 15-17 December 2022. Proceedings of 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security. IEEE. , pp.1-6. (10.1109/iSSSC56467.2022.10051321)
• Hu, H. et al., 2022. A conceptual design specification based on user aesthetic information analysis and product functional reasoning. Machines 10 (10) 868. (10.3390/machines10100868)
• Hu, H. et al., 2022. A knowledge-based approach towards representation and archiving of aesthetic information for product conceptual design. Journal of Computing and Information Science in Engineering 22 (4) 041011. (10.1115/1.4053674)
• Hu, H. et al., 2022. A quantitative aesthetic measurement method for product appearance design. Advanced Engineering Informatics 53 101644. (10.1016/j.aei.2022.101644)
• Hu, H. , Liu, Y. and Lu, W. F. 2022. Establishing product appearance specifications with the identification of user aesthetic needs in product conceptual design. In: Kyratsis, P. , Efkolidis, N. and Davim, J. P. eds. Advances in Product Design Engineering. Springer. , pp.199-217. (10.1007/978-3-030-98124-2_9)
• Lambay, A. et al. 2022. Effects of demographic factors for fatigue detection in manufacturing. Presented at: 14th IFAC Workshop on Intelligent Manufacturing Systems (IMS 2022) Tel-Aviv, Israel 28-30 March 2022. IFAC-PapersOnLine. Vol. 55(2).Elsevier. , pp.528-533. (10.1016/j.ifacol.2022.04.248)
• Li, Y. et al. 2022. Knowledge distillation for energy consumption prediction in additive manufacturing. Presented at: 14th IFAC Workshop on Intelligent Manufacturing Systems (IMS 2022) Tel-Aviv, Israel 28-30 March 2022. IFAC-PapersOnLine. Vol. 55(2).Elsevier. , pp.390-395. (10.1016/j.ifacol.2022.04.225)
• Li, Z. et al., 2022. A material removal state prediction method based on multi-scale attention mechanism. Presented at: 42nd Computers and Information in Engineering Conference (CIE) St. Louis, USA 14-17 August, 2022. 42nd Computers and Information in Engineering Conference (CIE). Vol. 2.The American Society of Mechanical Engineers. (10.1115/DETC2022-90919)
• Lian, X. et al., 2022. Identifying risky components of display products for redesign considering user attention and failure causality. Soft Computing (10.1007/s00500-022-07660-1)
• Ma, S. et al., 2022. Prediction of mechanical properties of 3d printed lattice structures through machine learning. Journal of Computing and Information Science in Engineering 22 (3) 031008. (10.1115/1.4053077)
• Qin, J. et al. 2022. Research and application of machine learning for additive manufacturing. Additive Manufacturing 52 102691. (10.1016/j.addma.2022.102691)
• Tan, X. et al., 2022. Design and research of intelligent QA system for flight crew operating manual. Presented at: International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC-CIE 2022) St. Louis, MO, USA 14-17 August 2022. 42nd Computers and Information in Engineering Conference (CIE). Vol. 2.The American Society of Mechanical Engineers. (10.1115/DETC2022-90768)
• Tang, M. et al., 2022. Regional-based strategies for municipality carbon mitigation: a case study of Chongqing in China. Energy Reports 8 , pp.4672-4694. (10.1016/j.egyr.2022.03.135)
• Tong, Y. et al. 2022. Understanding context of use from online customer reviews using BERT. Presented at: IEEE 18th International Conference on Automation Science and Engineering (CASE 2022) Mexico City, Mexico 20-24 August 2022. 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE(10.1109/CASE49997.2022.9926649)
• Tong, Y. et al. 2022. A data-driven approach for integrating hedonic quality and pragmatic quality in user experience modelling. Journal of Computing and Information Science in Engineering 22 (6) 061002. (10.1115/1.4054155)
• Wan, Y. et al. 2022. Exploiting knowledge graph for multi-faceted conceptual modelling using GCN. Presented at: 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2021) Linz, Austria 17-19 November 2021. Vol. 200.Procedia Computer Science, Vol 200. , pp.1174-1183. (10.1016/j.procs.2022.01.317)
• Wang, S. et al., 2022. Adaptive genetic algorithm based on fuzzy reasoning for the multilevel capacitated lot-sizing problem with energy consumption in synchronizer production. Sustainability 14 (9) 5072. (10.3390/su14095072)
• Wu, B. et al., 2022. Conceptual design of intelligent manufacturing equipment based on a multi-source heterogeneous requirement mapping method. Presented at: 14th IFAC Workshop on Intelligent Manufacturing Systems (IMS 2022) Tel-Aviv, Israel 28-30 March 2022. IFAC-PapersOnLine. Vol. 55(2).Elsevier. , pp.475-480. (10.1016/j.ifacol.2022.04.239)
• Yan, W. et al., 2022. An energy data-driven approach for operating status recognition of machine tools based on deep learning. Sensors 22 (17) 6628. (10.3390/s22176628)
• Yan, Z. et al., 2022. A new method of predicting the energy consumption of additive manufacturing considering the component working state. Sustainability 14 (7) 3757. (10.3390/su14073757)
• Yang, J. et al. 2022. Review of human-machine interaction towards industry 5.0: human-centric smart manufacturing. Presented at: International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC-CIE 2022) St. Louis, MO, USA 14-17 August 2022. 42nd Computers and Information in Engineering Conference (CIE). Vol. 2.The American Society of Mechanical Engineers. (10.1115/DETC2022-89711)
• You, Y. et al. 2022. Advances of digital twins for predictive maintenance. Presented at: 3rd International Conference on Industry 4.0 and Smart Manufacturing (ISM 2021) Linz, Austria 17-19 November 2021. Vol. 200.Procedia Computer Science, Vol 200: , pp.1471-1480. (10.1016/j.procs.2022.01.348)
• You, Y. et al. 2022. From human-human collaboration to human-robot collaboration: automated generation of assembly task knowledge model. Presented at: 27th IEEE International Conference on Automation and Computing (ICAC2022) Bristol, UK 1-3 Sept 2022. 2022 27th International Conference on Automation and Computing (ICAC). IEEE. (10.1109/ICAC55051.2022.9911131)
• Yu, Z. et al., 2022. Multi-indicators decision for product design solutions: a TOPSIS-MOGA integrated model. Processes 10 (2) 303. (10.3390/pr10020303)
• Zhao, K. et al. 2022. Digital twin-driven estimation of state of charge for Li-ion battery. Presented at: IEEE International Energy Conference (ENERGYCON 2022) Riga, Latvia 09-12 May 2022. Proceedings of 2022 IEEE 7th International Energy Conference (ENERGYCON). IEEE(10.1109/ENERGYCON53164.2022.9830324)
• Zhao, Y. et al., 2022. Communication-efficient federated learning for digital twin systems of industrial internet of things. Presented at: 14th IFAC Workshop on Intelligent Manufacturing Systems (IMS 2022) Tel-Aviv, Israel 28-30 March 2022. IFAC-PapersOnLine. Vol. 55(2).Elsevier. , pp.433-438. (10.1016/j.ifacol.2022.04.232)
• Zheng, Y. et al., 2022. Prediction of remaining useful life using fused deep learning models - a case study of turbofan engines. Journal of Computing and Information Science in Engineering 22 (5) 054501. (10.1115/1.4054090)

2021

• Chen, C. et al. 2021. An integrated deep learning-based approach for automobile maintenance prediction with GIS data. Reliability Engineering and System Safety 216 107919. (10.1016/j.ress.2021.107919)
• Chen, Z. et al. 2021. Multi-sourced modelling for strip breakage using knowledge graph embeddings. Presented at: 54th CIRP Conference on Manufacturing Systems (CMS 2021) Virtual 22-24 September 2021. 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0. Vol. 104.Elsevier. , pp.1884-1889. (10.1016/j.procir.2021.11.318)
• Chen, Z. et al. 2021. Multi-faceted modelling for strip breakage in cold rolling using machine learning. International Journal of Production Research 59 (21), pp.6347-6360. (10.1080/00207543.2020.1812753)
• Guo, X. et al., 2021. Supporting resilient conceptual design using functional decomposition and conflict resolution. Advanced Engineering Informatics 48 101262. (10.1016/j.aei.2021.101262)
• Guo, X. et al., 2021. A smart knowledge deployment method for the conceptual design of low-carbon products. Journal of Cleaner Production 321 128994. (10.1016/j.jclepro.2021.128994)
• Horváth, I. et al., 2021. Smart designing of smart systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35 (2), pp.129-131. (10.1017/S0890060421000093)
• Hu, F. et al. 2021. Deep fusion for energy consumption prediction in additive manufacturing. Presented at: 54th CIRP Conference on Manufacturing Systems (CMS 2021) Virtual 22-24 September 2021. 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0. Vol. 104.Procedia CIRP Elsevier. , pp.1878-1883. (10.1016/j.procir.2021.11.317)
• Kumar, M. et al., 2021. Creating highly reliable health care organisations through reverse exchanges. Supply Chain Management 26 (3), pp.371-384. (10.1108/SCM-03-2020-0123)
• Lambay, A. et al. 2021. A data-driven fatigue prediction using recurrent neural networks. Presented at: 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA2021) Ankara, Turkey 11-13 June 2021. 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE. , pp.1-6. (10.1109/HORA52670.2021.9461377)
• Li, Y. et al. 2021. A hybrid machine learning approach for energy consumption prediction in additive manufacturing. Presented at: 25th International Conference on Pattern Recognition (ICPR 2020) Virtual 15 January 2021. Pattern Recognition. ICPR International Workshops and Challenges Virtual Event, January 10–15, 2021, Proceedings, Part IV. Lecture Notes in Computer Science/Image Processing, Computer Vision, Pattern Recognition, and Graphics Vol. 12664. Springer. , pp.622-636. (10.1007/978-3-030-68799-1_45)
• Ma, S. et al. 2021. Predicting mechanical properties of 3D printed lattice structures. Presented at: ASME IDETC/CIE 2021 Virtual 17-20 August 2021. ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASME. , pp.V002T02A053. (10.1115/DETC2021-70249)
• Qin, J. et al. 2021. Industrial Internet of Learning (IIoL): IIoT based Pervasive Knowledge Network for LPWAN – concept, framework and case studies. CCF Transactions on Pervasive Computing and Interaction 3 , pp.25-39. (10.1007/s42486-020-00050-2)
• Tang, J. et al., 2021. Automatic identification of bottleneck tasks for business process management using fusion-based text clustering. Presented at: 17th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2021) Budapest, Hungary 7-9 June 2021. IFAC-PapersOnLine. Elsevier, 54(1). , pp.1200-1205. (10.1016/j.ifacol.2021.08.142)
• Tong, Y. et al. 2021. Integrating hedonic quality for user experience modelling. Presented at: ASME IDETC/CIE 2021 Virtual 17-20 August 2021. 41st Computers and Information in Engineering Conference (CIE) Proceedings. Vol. 2.ASME. (10.1115/DETC2021-69781)
• Wu, S. et al., 2021. Research on dynamic characteristics and identification method of local defect on the roll surface. Engineering Failure Analysis 121 105063. (10.1016/j.engfailanal.2020.105063)
• Xiao, Y. et al., 2021. Research on blank optimization design based on low-carbon and low-cost blank process route optimization model. Sustainability 13 (4) 1929. (10.3390/su13041929)
• Yang, J. et al. 2021. Sliding window filter based strip breakage modelling for failure prediction. Presented at: IEEE 17th International Conference on Automation Science and Engineering (CASE 2021) Lyon, France 23-27 August 2021. 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE). IEEE. , pp.1461-1466. (10.1109/CASE49439.2021.9551660)
• Zhang, H. et al., 2021. Analysis the drivers of environmental responsibility of Chinese auto manufacturing industry based on triple bottom line. Processes 9 (5) 751. (10.3390/pr9050751)

2020

• Chen, C. et al. 2020. Automobile maintenance modelling using gcForest. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Virtual 20-24 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE. , pp.600-605. (10.1109/CASE48305.2020.9216745)
• Chen, C. et al. 2020. Predictive maintenance using cox proportional hazard deep learning. Advanced Engineering Informatics 44 101054. (10.1016/j.aei.2020.101054)
• Chen, Z. et al. 2020. A multi-source feature-level fusion approach for predicting strip breakage in cold rolling. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Virtual 20-24 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE. , pp.482-487. (10.1109/CASE48305.2020.9216854)
• Dai, C. et al., 2020. Designing smart energy network ecosystem for integrated energy services in urban areas. Presented at: IEEE 16th International Conference on Automation Science and Engineering (CASE 2020) Virtual 20-21 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE. , pp.305-310. (10.1109/CASE48305.2020.9216903)
• Ding, Z. et al., 2020. An integrated decision-making method for selecting machine tool guideways considering remanufacturability. International Journal of Computer Integrated Manufacturing 33 (7), pp.686-700. (10.1080/0951192X.2018.1550680)
• Hu, F. et al. 2020. Feature-level data fusion for energy consumption analytics in additive manufacturing. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Virtual 20-24 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE. , pp.612-617. (10.1109/CASE48305.2020.9216947)
• Jiang, Z. et al., 2020. A data-driven based decomposition?integration method for remanufacturing cost prediction of end-of-life products. Robotics and Computer-Integrated Manufacturing 61 , pp.101838. (10.1016/j.rcim.2019.101838)
• Liu, Q. et al. 2020. Data-driven concept network for inspiring designers' idea generation. Journal of Computing and Information Science in Engineering 20 (3) 031004. (10.1115/1.4046207)
• Liu, Y. et al. 2020. Cloud-based big data analytics for customer insight-driven design innovation in SMEs. International Journal of Information Management 51 102034. (10.1016/j.ijinfomgt.2019.11.002)
• Ma, S. et al., 2020. An investigation of the porosity effects on the mechanical properties and the failure modes of Ti–6Al–4V Schwarz primitive structures. Presented at: SDM-2020: 7th International Conference on Sustainable Design and Manufacturing 9-11 September 2020. Published in: Scholz, S. G. , Howlett, R. J. and Setchi, R. eds. Sustainable Design and Manufacturing 2020: Proceedings of the 7th International Conference on Sustainable Design and Manufacturing (KES-SDM 2020). Vol. 200.Smart Innovation, Systems and Technologies Springer. , pp.173-181. (10.1007/978-981-15-8131-1_16)
• Ma, S. et al. 2020. Manufacturability, mechanical properties, mass-transport properties and biocompatibility of Triply Periodic Minimal Surface (TPMS) scaffolds fabricated by selective laser melting. Materials and Design 195 109034. (10.1016/j.matdes.2020.109034)
• Oliff, H. et al. 2020. Improving human-robot-interaction utilizing learning and intelligence: a human factors-based approach. IEEE Transactions on Automation Science and Engineering 17 (3), pp.1597-1610. (10.1109/TASE.2020.2967093)
• Oliff, H. et al. 2020. Reinforcement learning for facilitating human-robot-interaction in manufacturing. Journal of Manufacturing Systems 56 , pp.326-340. (10.1016/j.jmsy.2020.06.018)
• Oliff, H. et al. 2020. The ethical use of human data for smart manufacturing: an analysis and discussion. Procedia CIRP 93 , pp.1364-1369. (10.1016/j.procir.2020.06.001)
• Shao, Y. et al., 2020. A simulation data-driven design approach for rapid product optimization. Journal of Computing and Information Science in Engineering 20 (2) 021008. (10.1115/1.4045527)
• Zhang, Z. et al., 2020. Competitive voting-based multi-class prediction for ore selection. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Virtual 20-24 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE. , pp.514-519. (10.1109/CASE48305.2020.9217017)

2019

• Chen, C. et al. 2019. Energy consumption modelling using deep learning embedded semi-supervised learning. Computers and Industrial Engineering 135 , pp.757-765. (10.1016/j.cie.2019.06.052)
• Chen, C. et al. 2019. Automobile maintenance prediction using deep learning with GIS data. Procedia CIRP 81 , pp.-. (10.1016/j.procir.2019.03.077)
• Chen, Z. et al. 2019. Characterizing strip snap in cold rolling process using advanced data analytics. Procedia CIRP 81 , pp.453-458. (10.1016/j.procir.2019.03.078)
• Chen, Z. et al. 2019. Strip snap analytics in cold rolling process using machine learning. Presented at: 2019 IEEE 15th International Conference on Automation Science and Engineering Vancouver, BC, Canada 22-26 August 2019. Proceedings 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE). IEEE. , pp.368-373. (10.1109/coase.2019.8842967)
• Goltsos, T. E. et al. 2019. The boomerang returns? Accounting for the impact of uncertainties on the dynamics of remanufacturing systems. International Journal of Production Research 57 (23), pp.7361-7394. (10.1080/00207543.2018.1510191)
• Jiang, Z. et al., 2019. Data-driven ecological performance evaluation for remanufacturing process. Energy Conversion and Management 198 111844. (10.1016/j.enconman.2019.111844)
• Qin, J. et al. 2019. Deep learning-driven particle swarm optimisation for additive manufacturing energy optimisation. Journal of Cleaner Production , pp.118702. (10.1016/j.jclepro.2019.118702)
• Song, J. et al., 2019. Effect of heat treatment on microstructure and mechanical behaviours of 18Ni-300 maraging steel manufactured by selective laser melting. Optics and Laser Technology 120 105725. (10.1016/j.optlastec.2019.105725)
• Wang, K. et al., 2019. Data and knowledge jointly driving model for design concept generation. Presented at: International Design Engineering Technical and Computers and Information in Engineering Conference Anaheim, California, USA 18-21 August 2019.
• Yang, B. et al., 2019. Exploiting user experience from online customer reviews for product design. International Journal of Information Management 46 , pp.173-186. (10.1016/j.ijinfomgt.2018.12.006)

2018

• Chen, C. et al. 2018. Energy consumption modelling using deep learning technique — a case study of EAF. Procedia CIRP 72 , pp.1063-1068. (10.1016/j.procir.2018.03.095)
• Chen, C. et al. 2018. Reliability analysis using deep learning. Presented at: ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Quebec City, Canada 26-29 August 2018. ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Volume 1B: 38th Computers and Information in Engineering Conference Quebec City, Quebec, Canada, August 26–29, 2018. ASME. , pp.V01BT02A040. (10.1115/DETC2018-86172)
• Ding, Z. et al., 2018. A big data based cost prediction method for remanufacturing end-of-life products. Procedia CIRP 72 , pp.1362-1367. (10.1016/j.procir.2018.03.129)
• Feng, Q. et al., 2018. An investigation of the mechanical properties of metallic lattice structures fabricated using selective laser melting. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 232 (10), pp.1719-1730. (10.1177/0954405416668924)
• Feng, Q. et al. 2018. Quasi-static analysis of mechanical properties of Ti6Al4V lattice structures manufactured using selective laser melting. International Journal of Advanced Manufacturing Technology 94 (5-8), pp.2301-2313. (10.1007/s00170-017-0932-7)
• Han, L. et al., 2018. An automated cloud-based big data analytics platform for customer insights. Presented at: The Second International Symposium on Big Data and Smart Sustainable Society (Bigdata-2017) Exeter, UK 21-23 June 2017. Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2017 IEEE International Conference on. IEEE. , pp.287-292. (10.1109/iThings-GreenCom-CPSCom-SmartData.2017.48)
• Jin, J. et al., 2018. Review on recent advances in information mining from big consumer opinion data for product design. Journal of Computing and Information Science in Engineering 19 (1) 010801. (10.1115/1.4041087)
• Lan, L. , Liu, Y. and Lu, W. F. 2018. Learning from the past: uncovering design process models using an enriched process mining. Journal of Mechanical Design 140 (4) 041403. (10.1115/1.4039200)
• Liang, Y. et al., 2018. Extracting topic-sensitive content from textual documents - A hybrid topic model approach. Engineering Applications of Artificial Intelligence 70 , pp.81-91. (10.1016/j.engappai.2017.12.010)
• Oliff, H. et al. 2018. A framework of integrating knowledge of human factors to facilitate HMI and collaboration in intelligent manufacturing. Procedia CIRP 72 , pp.135-140. (10.1016/j.procir.2018.03.047)
• Oliff, H. et al. 2018. Integrating intelligence and knowledge of human factors to facilitate collaboration in manufacturing. Presented at: IDETC/CIE 2018: International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Quebec City, QC, Canada 26-29 August 2018.
• Qin, F. et al., 2018. Voice of the customer oriented new product synthesis over knowledge graphs. Presented at: ASME 2018 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018) Quebec City, QC, Canada 26-29 August 2018. ASME 2018 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: Volume 1A: 38th Computers and Information in Engineering Conference Quebec City, Quebec, Canada, August 26–29, 2018. American Society of Mechanical Engineers. , pp.V01AT02A032. (10.1115/DETC2018-85909)
• Qin, J. , Liu, Y. and Grosvenor, R. 2018. Multi-source data analytics for AM energy consumption prediction. Advanced Engineering Informatics 38 , pp.840-850. (10.1016/j.aei.2018.10.008)
• Tang, M. et al., 2018. Integrating topic, sentiment and syntax for modeling online reviews: a topic model approach. Journal of Computing and Information Science in Engineering 19 (1) 011001. (10.1115/1.4041475)
• Tang, M. et al., 2018. Identifying service gaps from public patient opinions through text mining. Presented at: ICSEE 2018: International Conference on Intelligent Computing for Sustainable Energy and Environment and IMIOT 2018: International Conference on Intelligent Manufacturing and Internet of Things Chongqing, China 21-23 September 2018. Published in: Li, K. et al., Intelligent Computing and Internet of Things: First International Conference on Intelligent Manufacturing and Internet of Things and 5th International Conference on Computing for Sustainable Energy and Environment, IMIOT and ICSEE 2018, Chongqing, China,. Vol. 924.Communications in Computer and Information Science Singapore: Springer. , pp.99-108. (10.1007/978-981-13-2384-3_10)
• Wang, H. et al., 2018. A demands-matching multi-criteria decision-making method for reverse logistics. Procedia CIRP 72 , pp.1398-1403. (10.1016/j.procir.2018.03.135)
• Wang, S. et al. 2018. Reliability analysis for automobile engines: conditional inference trees. Procedia CIRP 72 , pp.1392-1397. (10.1016/j.procir.2018.03.065)
• Wang, Y. et al., 2018. Sentiment analysis by capsules. Presented at: The Web Conference 2018 Lyon, France 23-27 April 2018. Proceedings of the 2018 World Wide Web Conference. , pp.1165-1174. (10.1145/3178876.3186015)

2017

• Chen, R. et al., 2017. An automated generation method of system architecture with component's multi-criterion evaluation. Presented at: The 21st International Conference on Engineering Design Vancouver, Canada 21-25 Aug 2017.
• Kim, H. H. M. et al., 2017. Special Issue: Data-Driven Design (D3). Journal of Mechanical Design 139 (11) 110301. (10.1115/1.4037943)
• Lan, L. , Liu, Y. and Lu, W. F. 2017. Automatic discovery of design task structure using deep belief nets. Journal of Computing and Information Science in Engineering 17 (4) 041001. (10.1115/1.4036198)
• Oliff, H. and Liu, Y. 2017. Towards industry 4.0 utilizing data-mining techniques: a case study on quality improvement. Procedia CIRP 63 , pp.167. (10.1016/j.procir.2017.03.311)
• Qin, J. , Liu, Y. and Grosvenor, R. 2017. A framework of energy consumption modelling for additive manufacturing using Internet of Things. Procedia CIRP Conference on Manufacturing System 63 , pp.307-312. (10.1016/j.procir.2017.02.036)
• Qin, J. , Liu, Y. and Grosvenor, R. 2017. Data analytics for energy consumption of digital manufacturing systems using Internet of Things method. Presented at: IEEE International Conference on Automation Science and Engineering Xi'an, China 20-23 August 2017.
• Soroka, A. et al. 2017. Big data driven customer insights for SMEs in redistributed manufacturing. Procedia CIRP 63 , pp.692-697. (10.1016/j.procir.2017.03.319)
• Wang, X. et al., 2017. Organizational capability: skills related to organizational knowledge. Journal of Integrated Design and Process Science 21 (1), pp.1-3. (10.3233/jid-2017-0010)

2016

• Arastehfar, S. , Liu, Y. and Feng Lu, W. 2016. An evaluation methodology for design concept communication using digital prototypes. Journal of Mechanical Design 138 (3) 031103. (10.1115/1.4032396)
• Chen, Y. et al., 2016. A formal functional representation methodology for conceptual design of material-flow processing devices. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30 (04), pp.353-366. (10.1017/S0890060416000342)
• Dixon, A. , Liu, Y. and Setchi, R. 2016. Computer-aided ethnography in engineering design. Presented at: 28th International Conference on Design Theory and Methodology Charlotte, North Carolina, USA 21-24 August 2016. ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference: Volume 7: 28th International Conference on Design Theory and Methodology Charlotte, North Carolina, USA, August 21–24, 2016. Vol. 7.ASME Proceedings New York, NY: ASME. , pp.V007T06A007. (10.1115/DETC2016-59832)
• Feng, Q. et al., 2016. An Investigation into the quasi-static response of Ti6Al4V lattice structures manufactured using selective laser melting. In: Setchi, R. et al., Sustainable Design and Manufacturing 2016. Vol. 52, Smart Innovation, Systems and Technologies Springer. , pp.399-409. (10.1007/978-3-319-32098-4_34)
• Jin, J. et al., 2016. Understanding big consumer opinion data for market-driven product design. International Journal of Production Research 54 (10), pp.3019-3041. (10.1080/00207543.2016.1154208)
• Lan, L. , Liu, Y. and Lu, W. F. 2016. Discovering a hierarchical design process model using text mining. Presented at: 36th Computers and Information in Engineering Conference Charlotte, NC 21-24 August 2016. ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 1B.ASME Proceedings New York, NY: ASME(10.1115/DETC2016-59829)
• Li, L. et al., 2016. Enhanced SPARQL-based design rationale retrieval. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30 (4), pp.406-423. (10.1017/S089006041600038X)
• McMahon, C. , Liu, Y. and McAdams, D. 2016. Engineering design informatics. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30 (04), pp.335-336. (10.1017/S0890060416000329)
• Qin, J. , Liu, Y. and Grosvenor, R. 2016. A categorical framework of manufacturing for industry 4.0 and beyond. Procedia CIRP 52 , pp.173-178. (10.1016/j.procir.2016.08.005)

2015

• Chan, L. W. C. et al., 2015. PubMed-supported clinical term weighting approach for improving inter-patient similarity measure in diagnosis prediction. BMC Medical Informatics and Decision Making 15 43. (10.1186/s12911-015-0166-2)
• Jiang, H. et al., 2015. A methodology of integrating affective design with defining engineering specifications for product design. International Journal of Production Research 53 (8), pp.2472-2488. (10.1080/00207543.2014.975372)
• Jiang, H. et al., 2015. Rough set and PSO-based ANFIS approaches to modeling customer satisfaction for affective product design. Advanced Engineering Informatics 29 (3), pp.727-738. (10.1016/j.aei.2015.07.005)
• Jin, J. et al., 2015. Translating online customer opinions into engineering characteristics in QFD: A probabilistic language analysis approach. Engineering Applications of Artificial Intelligence 41 , pp.115-127. (10.1016/j.engappai.2015.02.006)
• Lan, L. , Liu, Y. and Lu, W. F. 2015. Discovering hidden tasks and process structure through email logs for DSM. Journal of Modern Project Management 3 , pp.24-31.
• Lan, L. et al., 2015. Automatic discovery of design task structure using deep belief nets. Presented at: 27th International Conference on Design Theory and Methodology Boston, MA 2 - 5 August 2015. Print Proceedings of the ASME 2015 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (DETC2015). Vol. 7.United States of America: American Society of Mechanical Engineers(10.1115/DETC2015-47369)
• Lan, L. , Wu, X. and Liu, Y. 2015. Designing a fast adaptive clustering approach for traffic wave simulation. Presented at: 35th Computers and Information in Engineering Conference Boston, MA 2 - 5 August 2015. ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 1B., pp.V01BT02A004. (10.1115/DETC2015-47873)
• Lim, S. C. J. , Liu, Y. and Chen, Y. 2015. Ontology in design engineering: status and challenges. Presented at: International Conference on Engineering Design 2015 (ICED 2015) Milan, Italy 27-30 July 2015. Published in: Weber, C. et al., DS 80-2 Proceedings of the 20th International Conference on Engineering Design (ICED 15). Vol. 2.ICED The Design Society. , pp.267-276.
• Setchi, R. , Liu, Y. and Li, W. 2015. Editorial for the special issue of decision support for sustainable design and manufacturing. Journal of Industrial and Production Engineering 32 (1), pp.1-2. (10.1080/21681015.2014.1000404)

2014

• Arastehfar, S. , Liu, Y. and Lu, W. F. 2014. A framework for concept validation in product design using digital prototyping. Journal of Industrial and Production Engineering 31 (5), pp.286-302. (10.1080/21681015.2014.951407)
• Jin, J. , Ji, P. and Liu, Y. 2014. Prioritising engineering characteristics based on customer online reviews for quality function deployment. Journal of Engineering Design 25 (7-9), pp.303-324. (10.1080/09544828.2014.984665)
• Li, L. et al., 2014. An approach for design rationale retrieval using ontology-aided indexing. Journal of Engineering Design 25 (7-9), pp.259-279. (10.1080/09544828.2014.969690)
• Lim, S. C. J. , Wang, S. and Liu, Y. 2014. Discovering contextual tags from product review using semantic relatedness. Journal of Industrial and Production Engineering 31 (2), pp.108-118. (10.1080/21681015.2014.895966)

2013

• Liang, Y. and Liu, Y. 2013. Rationale-based patent analysis for corporate product design. Presented at: ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Portland, OR 4 - 7 August 2013. ASME Proceedings 33rd Computers and Information in Engineering Conference. Vol. 2B.ASME. , pp.V02BT02A013. (10.1115/DETC2013-12792)
• Liu, Y. et al. 2013. Identifying helpful online reviews: A product designer's perspective. Computer-Aided Design 45 (2), pp.180-194. (10.1016/j.cad.2012.07.008)
• Liu, Y. , Lim, S. C. J. and Lee, W. B. 2013. Product family design through ontology-based faceted component analysis, selection, and optimization. Journal of Mechanical Design 135 (8) 081007. (10.1115/1.4023632)
• Montecchi, T. , Russo, D. and Liu, Y. 2013. Searching in cooperative patent classification: comparison between keyword and concept-based search. Advanced Engineering Informatics 27 (3), pp.335-345. (10.1016/j.aei.2013.02.002)

2012

• Arastehfar, S. , Liu, Y. and Lu, W. F. 2012. A new discrete event system model for supervising and controlling robotic arm path tacking tasks based on adaptive masking. Presented at: ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Chicago, IL 12–15 August 2012. ASME Proceedings 36th Mechanisms and Robotics Conference. Vol. 4.ASME. , pp.1217-1226. (10.1115/DETC2012-70588)
• Jung, J. J. et al., 2012. Advances in intelligent grid and cloud computing. Information Systems Frontiers 14 (4), pp.823-825. (10.1007/s10796-012-9349-x)
• Liang, Y. et al., 2012. Learning the "Whys": Discovering design rationale using text mining - An algorithm perspective. Computer-Aided Design 44 (10), pp.916-930. (10.1016/j.cad.2011.08.002)
• Lim, S. C. J. , Liu, Y. and Loh, H. T. 2012. An exploratory study of ontology-based platform analysis under user preference uncertainty. Presented at: ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Chicago, IL 12–15 August 2012. ASME Proceedings 32nd Computers and Information in Engineering Conference. Vol. 2.ASME. , pp.519-528. (10.1115/DETC2012-70756)
• Liu, Y. et al. 2012. Editorial for the special issue of service computing and service oriented enterprise systems in design, manufacturing and supply chain. Journal of Intelligent Manufacturing 23 (4), pp.1327-1329. (10.1007/s10845-012-0638-5)
• Liu, Y. et al. 2012. Workflow simulation for operational decision support using event graph through process mining. Decision Support Systems 52 (3), pp.685-697. (10.1016/j.dss.2011.11.003)
• Russo, D. , Montecchi, T. and Liu, Y. 2012. Functional-based search for patent technology transfer. Presented at: ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Chicago, IL 12–15 August 2012. ASME Proceedings 32nd Computers and Information in Engineering Conference. Vol. 2.ASME. , pp.529-539. (10.1115/DETC2012-70833)
• Russo, D. , Montecchi, T. and Liu, Y. 2012. Knowledge based approach for identifying TRIZ contradictions. Presented at: 12th Design Engineering Workshop (DEWS) Seoul, Korea 25 - 27 June 2012. Proceedings of 2012 Design Engineering Workshop (DEWS 2012). DEWS. , pp.134-140.

2011

• Chan, L. W. C. et al., 2011. A SNOMED supported ontological vector model for subclinical disorder detection using EHR similarity. Engineering Applications of Artificial Intelligence 24 (8), pp.1398-1409. (10.1016/j.engappai.2011.05.013)
• Jin, J. et al., 2011. Design preference centered review recommendation: A similarity learning approach. Presented at: ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Washington, DC 28–31 August 2011. ASME Proceedings 31st Computers and Information in Engineering Conference. Vol. 2.ASME. , pp.1143-1152. (10.1115/DETC2011-48181)
• Johnson Lim, S. C. , Liu, Y. and Lee, W. B. 2011. A platform selection approach based on product family ontology modeling. Presented at: ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Washington, DC 28–31 August 2011. ASME Proceedings 37th Design Automation Conference. Vol. 5.Washington, DC. , pp.1067-1076. (10.1115/DETC2011-48194)
• Lim, S. C. J. , Liu, Y. and Lee, W. B. 2011. A methodology for building a semantically annotated multi-faceted ontology for product family modelling. Advanced Engineering Informatics 25 (2), pp.147-161. (10.1016/j.aei.2010.07.005)
• Liu, Y. et al. 2011. Editorial for the special issue of information mining and retrieval in design. Advanced Engineering Informatics 25 (2), pp.117-118. (10.1016/j.aei.2011.03.003)
• Sun, A. , Liu, Y. and Lim, E. 2011. Web classification of conceptual entities using co-training. Expert Systems with Applications 38 (12), pp.14367-14375. (10.1016/j.eswa.2011.03.010)
• Zhan, J. , Liu, Y. and Loh, H. T. 2011. Multidocument summarization of engineering papers based on macro- and microstructure. Journal of Computing and Information Science in Engineering 11 (1) 011008. (10.1115/1.3563048)

2010

• Johnson Lim, S. C. , Liu, Y. and Lee, W. B. 2010. Multi-facet product information search and retrieval using semantically annotated product family ontology. Information Processing & Management 46 (4), pp.479-493. (10.1016/j.ipm.2009.09.001)
• Liang, Y. et al., 2010. Interactive interface design for design rationale search and retrieval. Presented at: ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference Montreal, Quebec, Canada 15 - 18 August 2010. ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 5.ASME. , pp.73-83. (10.1115/DETC2010-28392)
• Lim, S. C. J. , Loh, H. T. and Liu, Y. 2010. User interface design for interactive product family analysis and variants derivation. Presented at: 36th Design Automation Conference Montreal, Quebec, Canada 15–18 August 2010. Proceedings of the ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Vol. 1.The American Society of Mechanical Engineers. , pp.869-878. (10.1115/DETC2010-28390)
• Liu, Y. et al. 2010. A new design rationale representation model for rationale mining. Journal of Computing and Information Science in Engineering 10 (3) 031009. (10.1115/1.3470018)
• Liu, Y. , Liang, Y. and Lim, S. C. J. 2010. Designing graphical interfaces for design rationale search & retrieval. Presented at: CHI 2010 Atlanta, GA 14 - 15 April 2010. Extended Abstracts on Human Factors in Computing Systems. , pp.4087-4092. (10.1145/1753846.1754107)

2009

• Goh, Y. M. et al., 2009. From faceted classification to knowledge discovery of semi-structured text records. In: Abraham, A. et al., Foundations of Computational Intelligence. Vol. 6, Studies in Computational Intelligence Vol. 206.Berlin: Springer. , pp.151-169. (10.1007/978-3-642-01091-0_7)
• Liang, Y. et al., 2009. False positive reduction in urinary particle recognition. Expert Systems with Applications 36 (9), pp.11429-11438. (10.1016/j.eswa.2009.03.049)
• Lim, S. C. J. , Liu, Y. and Lee, W. B. 2009. Product analysis and variants derivation based on a semantically annotated product family ontology. Presented at: ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference San Diego, CA 30 August - 2 September 2009. ASME Proceedings 29th Computers and Information in Engineering Conference. Vol. 2.ASME: , pp.817-826. (10.1115/DETC2009-87160)
• Liu, Y. 2009. On document representation and term weights in text classification. In: Song, M. and Wu, Y. B. eds. Handbook of Research on Text and Web Mining Technologies. Hershey, PA, USA: Information Science Reference. , pp.1-22. (10.4018/978-1-59904-990-8.ch001)
• Liu, Y. et al. 2009. Editorial for the special issue of knowledge discovery and management in biomedical information systems. Information Systems Frontiers 11 (4), pp.345-347. (10.1007/s10796-009-9153-4)
• Liu, Y. and Harding, J. 2009. Editorial for the special issue of knowledge discovery and management in engineering design and manufacturing. Journal of Intelligent Manufacturing 20 (5), pp.499-500. (10.1007/s10845-008-0209-y)
• Liu, Y. , Loh, H. T. and Sun, A. 2009. Imbalanced text classification: A term weighting approach. Expert Systems with Applications 36 (1), pp.690-701. (10.1016/j.eswa.2007.10.042)
• Sun, A. , Lim, E. and Liu, Y. 2009. On strategies for imbalanced text classification using SVM: A comparative study. Decision Support Systems 48 (1), pp.191-201. (10.1016/j.dss.2009.07.011)
• Wong, A. K. S. , Chan, L. W. C. and Liu, Y. 2009. Automating object-oriented integration and visualization of multidisciplinary biomedical data in radiology workflow: Compartmental PACS model. Information Systems Frontiers 11 (4), pp.369-379. (10.1007/s10796-009-9150-7)
• Wu, Z. et al., 2009. An enhanced adaptive CUSUM control chart. IIE Transactions 41 (7), pp.642-653. (10.1080/07408170802712582)
• Zhan, J. , Loh, H. T. and Liu, Y. 2009. Gather customer concerns from online product reviews - A text summarization approach. Expert Systems with Applications 36 (2), pp.2107-2115. (10.1016/j.eswa.2007.12.039)
• Zhan, J. , Loh, H. T. and Liu, Y. 2009. On macro- and micro-level information in multiple documents and its influence on summarization. International Journal of Information Management 29 (1), pp.57-66. (10.1016/j.ijinfomgt.2008.04.011)

2008

• Chan, L. W. et al., 2008. DICOM-based multidisciplinary platform for clinical decision support: Needs and direction. In: Liu, Y. et al., Advances of Computational Intelligence in Industrial Systems. Studies in Computational Intelligence Vol. 116.Berlin: Springer. , pp.191-212. (10.1007/978-3-540-78297-1_9)
• Liu, Y. , Loh, H. T. and Lu, W. F. 2008. Deriving taxonomy from documents at sentence level. In: Prado, H. A. d. and Ferneda, E. eds. Emerging Technologies of Text Mining: Techniques and Applications. Hershey, PA, USA: Information Science Reference. , pp.99-119. (10.4018/978-1-59904-373-9.ch005)
• Liu, Y. et al. 2008. A hierarchical text classification system for manufacturing knowledge management and retrieval. International Journal of Knowledge Management Studies 2 (4), pp.406-425. (10.1504/IJKMS.2008.019749)
• Wu, Z. , Jiao, J. and Liu, Y. 2008. A binomial CUSUM chart for detecting large shifts in fraction nonconforming. Journal of Applied Statistics 35 (11), pp.1267-1276. (10.1080/02664760802320533)
• Zhan, J. , Loh, H. T. and Liu, Y. 2008. Summarizing online customer reviews automatically based on topical structure. Presented at: Web Information Systems and Technologies 3rd International Conference (WEBIST 2007) Barcelona, Spain 3-6 March 2007. Published in: Filipe, J. and Cordeiro, J. eds. Web Information Systems and Technologies: Third International Conference, WEBIST 2007, Barcelona, Spain, March 3-6, 2007, Revised Selected Papers. Lecture Notes in Business Information Processing Vol. 8. Berlin: Springer. , pp.245-256. (10.1007/978-3-540-68262-2_18)

2007

• Liu, Y. and Loh, H. T. 2007. A simple probability based term weighting scheme for automated text classification. Presented at: 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2007) Kyoto, Japan 26-29 June 2007. Published in: Okuno, H. G. and Ali, M. eds. New Trends in Applied Artificial Intelligence: 20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2007, Kyoto, Japan, June 26-29, 2007. Proceedings. Lecture Notes in Computer Science Vol. 4570. Berlin Heidelberg: Springer. , pp.33-43. (10.1007/978-3-540-73325-6_4)
• Liu, Y. and Loh, H. T. 2007. Corpus building for corporate knowledge discovery and management: A case study of manufacturing. In: Knowledge-Based Intelligent Information and Engineering Systems. Vol. 4692, Springer. , pp.542-550. (10.1007/978-3-540-74819-9_67)
• Liu, Y. et al. 2007. Handling of imbalanced data in text classification: category-based term weights. In: Kao, A. and Poteet, S. R. eds. Natural Language Processing and Text Mining. London: Springer. , pp.171-192. (10.1007/978-1-84628-754-1_10)
• Zhan, J. et al., 2007. Automatic text summarization in engineering information management. Presented at: 10th International Conference on Asian Digital Libraries (ICADL 2007) Hanoi, Vietnam 10-13 December 2007. Asian Digital Libraries. Looking Back 10 Years and Forging New Frontiers: 10th International Conference on Asian Digital Libraries, ICADL 2007, Hanoi, Vietnam, December 10-13, 2007. Proceedings. Lecture Notes in Computer Science Vol. 4822. Berlin Heidelberg: Springer. , pp.347-350. (10.1007/978-3-540-77094-7_44)

2006

• Liu, Y. , Lu, W. F. and Loh, H. T. 2006. A framework of information and knowledge management for product design and development - A text mini. Presented at: 12th IFAC Symposium on Information Control Problems in Manufacturing, 2006 Saint Etienne, France 17-19 May 2006. Published in: Morel, G. , Dolgui, A. and Pereira, C. eds. 12th IFAC Symposium on Information Control Problems in Manufacturing, 2006. Information Control Problems in Manufacturing Vol. 12. International Federation of Automatic Control. , pp.667-672. (10.3182/20060517-3-FR-2903.00339)
• Yap, I. et al., 2006. Topic detection using MFSs. Presented at: 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2006) Annecy, France 27-30 June 2006. Published in: Ali, M. and Dapoigny, R. eds. Advances in Applied Artificial Intelligence: 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Annecy, France, June 27-30, 2006. Proceedings. Lecture Notes in Computer Science Vol. 4031. Berlin Heidelberg: Springer. , pp.342-352. (10.1007/11779568_38)

2005

• Liu, Y. , Loh, H. T. and Tor, S. B. 2005. Comparison of extreme learning machine with support vector machine for text classification. Presented at: 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE 2005) Bari, Italy 22-24 June 2005. Innovations in Applied Artificial Intelligence: 18th International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2005, Bari, Italy, June 22-24, 2005. Proceedings. Lecture Notes in Computer Science Vol. 3533. Berlin: Springer. , pp.390-399. (10.1007/11504894_55)