Publications

Google Scholar

Researchgate

”*” student under my supervision

Journal Articles

  • Li, Y.*, Zhao, C., and Liu, C., 2022, “Model-Informed Generative Adversarial Network (MI-GAN) for Optimal Power Flow (OPF) Problem,” IISE Transactions. (In Press) Link

  • Shi, Z.*, Li, Y.*, and Liu, C., 2024, “Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System,” Journal of Intelligent Manufacturing. (Accpeted for Publication) ArXiv Link

  • Islam, M., Liu, C., Cai, C., Shah, J., and Fend, Y., 2024, “A User-Centered Smart Inhaler Algorithm for Targeted Drug Delivery in Juvenile Onset Recurrent Respiratory Papillomatosis Treatment Integrating Computational Fluid Particle Dynamics and Machine Learning,” Physics of Fluids. Vol 36 (2), pp. 021912. Link
    • This work was selected as the ”Editor’s Pick” and featured in the journal’s science highlight
    • The link of the Journal’s Science Highlight (scilight)

  • Li, Y.*, Shi, Z.*, and Liu, C., 2023, “Transformer-enabled Generative Adversarial Imputation Network with Selective Generation (SGT-GAIN) for Missing Region Imputation,” IISE Transactions. (In Press) Link

  • Shi, Z.*, Oskolkov, B.*, Tian, W., Kan, C., and Liu, C., 2024, “Sensor Data Protection through Integration of Blockchain and Camouflaged Encryption in Cyber-physical Manufacturing Systems,” ASME Journal of Computing and Information Science in Engineering. Vol. 24(7), pp. 071004. Link

  • Ha, C., Yao, D., Xu, Z., Liu, C., Liu, H., Elkins, D., Kiles, M., Deshpande, V., Kong, Z., Bauchy, B., and Zheng, X., 2023, “Rapid Inverse Design of Metamaterials based on Prescribed Mechanical Behavior through Machine Learning,” Nature Communications. Vol. 14 (1), pp. 5765. Link

  • Dogan, A.*, Li, Y.*, Odo, C., Sonawane, K., Lin, Y., and Liu, C., 2023, “A Utility-Based Machine Learning-Driven Personalized Lifestyle Recommendation for Cardiovascular Disease Prevention,” Journal of Biomedical Informatics. Vol. 141, p. 104342 Link
    • This work received the 2022 IISE Graduate Research Award (MS student Ayse Dogan)

  • Kosolwattana, T., Liu, C., Hu, R., Han, S., Chen, H., and Lin, Y., 2024, “A Self-inspected Adaptive SMOTE Algorithm (SASMOTE) for Highly Imbalanced Data Classification,” BioData Mining. Vol. 16 (15) Link

  • Ashar, H., Singh, A., Kishore, D., Neel, T., More S., Liu C., Dugat, D., and Ranjan, A., 2023, “Enabling Chemo-Immunotherapy with HIFU in Canine Cancer Patients,” Annals of Biomedical Engineering. Link.

  • Liu, C., Wang, R., Ho, I., Kong, Z., Williams, C., Babu, S., and Joslin, C., 2023, “Toward Online Surface Morphology Measurement in Additive Manufacturing Using a Deep Learning-based Approach,” Journal of Intelligent Manufacturing. Vol. 34 (6), pp. 2673-2689. Link

  • Shi, Z.*, Mamun, A., Kan, C., Tian, W., and Liu, C., 2023, “An LSTM-Autoencoder Based Online Side Channel Monitoring Approach for Cyber-physical Attack Detection in Additive Manufacturing,” Journal of Intelligent Manufacturing. 34, pp. 1815–1831 Link

  • Xiao, P., Shi, Z.*, Liu, C., and Darren, D., 2022, “Characteristics of circulating small non-coding RNAs in plasma and serum during human aging,” Aging Medicine. Vol. 6(1), pp. 35-48 Link

  • Li, Y.*, Shi, Z.*, Liu, C., Tian, W., Kong, Z., and Williams, C., 2022, “Augmented Time Regularized Generative Adversarial Network (ATR-GAN) for Data Augmentation in Online Process Anomaly Detection,” IEEE Transactions on Automation Science and Engineering. Vol. 19 (4), pp. 3338-3355 Link
    • This work is selected as the finalist of the Data Challenge Award, QSR Section, INFORMS, 2019

  • Bappy, M., Liu, C., Bian, L., and Tian, W., 2022, “Morphological Dynamics-based Anomaly Detection towards In-situ Layer-wise Certification for Directed Energy Deposition Processes,” ASME Journal of Manufacturing Science and Engineering. Vol. 144 (11), pp. 111007 Link

  • Shi, Z.*, Mandal, S., Harimkar, S., and Liu, C., 2022, “Hybrid Data-Driven Feature Extraction-Enabled Surface Modeling for Metal Additive Manufacturing,” International Journal of Advanced Manufacturing Technology. Vol. 121 (7), pp. 4643-4662. Link

  • Liu, C., Tian, W., and Kan, C., 2022, “When AI Meets Additive Manufacturing: Challenges and Emerging Opportunities for Human-Centered Products Development,” Journal of Manufacturing Systems. Vol. 64 pp. 648-656. Link

  • Mamun, A., Liu, C., Kan, C., and Tian, W., 2022, “Securing Cyber-Physical Additive Manufacturing Systems by In-situ Process Authentication using Streamline Video Analysis,” Journal of Manufacturing Systems. Vol. 62 pp. 429-440. Link

  • Li, Y.*, VanOsdol, J., Ranjan, A., and Liu, C., 2022, “A Multilayer Network-Enabled Ultrasonic Video Analysis Approach for Online Cancer Drug Delivery Monitoring,” Computer Methods and Programs in Biomedicine. Vol, 213, p. 106505. Link

  • Chen, Y., Abu-Heiba, A., Kassaee, S., Liu, C., Liu, G., Starke, M., Smith, B., and Momen, A., 2022, “Coupled Heat-Power Operation of Smart Buildings via Modular Pumped Hydro Storage,” ASME Journal of Energy Resources Technology. Vol. 144 (7), pp. 070912. Link

  • Ye, Z., Liu, C., Tian, W., and Kan, C., 2021, “In-situ Point Cloud Fusion for Layer-wise Monitoring of Additive Manufacturing,” Journal of Manufacturing Systems. Vol. 61 pp. 210-222. Link

  • Shi, Z.*, Kan, C., Tian, W., and Liu, C., 2021, “A Blockchain-based G-code Protection Approach for Cyber-Physical Security in Additive Manufacturing,” ASME Journal of Computing and Information Science in Engineering. Vol.21(4), pp.041007. Link
  • Liu, C., Kong, Z., Babu, S., Joslin, C., and Ferguson, J., 2021, “An Integrated Manifold Learning Approach for High Dimensional Data Feature Extractions and its Applications to Online Process Monitoring of Additive Manufacturing,” IISE Transactions, Vol.53(11), pp.1215-1230. Link
    • This work is featured in the Industrial and Systems Engineer magazine (ISE) Magazine, October 2021
    • This work received the Best Paper Award, QSR Section, INFORMS, 2017
  • Liu, C., Law, A., Roberson, D. and Kong, Z.,2019, “Image Analysis-based Closed Loop Quality Controlfor Additive Manufacturing with Fused Filament Fabrication,” Journal of Manufacturing Systems. Vol.51, pp.75-86. Link
    • This work received the Best Paper Award, QCRE Track, IISE, 2017
  • Liu, J., Liu, C., Bai, Y., Rao, P., Kong, Z. and Williams, C., 2019, “Layer-wise Spatial Modeling of Porosityin Additive Manufacturing,” IISE Transactions, Vol.51(2), pp.109-123. Link
    • This work is featured in the Industrial and Systems Engineer magazine (ISE) Magazine, January 2019

  • Liu, C., Kapoor, A., VanOsdol, J., Ektate, K., Kong, Z., and Ranjan, A., 2018, “A Spectral Fiedler Field-based Contrast Platform for Imaging of Nanoparticles in Colon Tumor,” Scientific Reports, 8(1), 11390. Link

  • Tootooni, M., Liu, C., Roberson, D., Rao, P., and Kong, Z., 2016, “Online Non-contact Surface Finish Measurement in Machining using Graph-based Image Analysis,” Journal of Manufacturing Systems, Vol.41, pp.266-2. Link

Pre-print articles

  • Shi, Z., Xie, T., Liu, C., and Li, Y., “Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Additive Manufacturing,” arXiv. Link

  • Li, Y.*, and Liu, C., 2023, “Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System,” arXiv. Link

  • Li, Y.*, Lin, Y., and Liu, C., 2022, “A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare,” arXiv. Link

    • This work received the Best Poster Award in the 2020 IJCAI BOOM Workshop

Conference Papers

  • Yangue, E.*, Ye, Z., Kan, C., and Liu, C., 2023, “Integrated Deep Learning-based Online Layer-wise Surface Prediction of Additive Manufacturing,” Manufacturing Letters. Vol. 35, pp. 760-769. Link

  • Ye, Z., Liu, C., and Kan, C., 2023, “Stereo Vision enabled Flexible In-situ Process Authentication of Additive Manufacturing,” Manufacturing Letters. Vol. 35, pp. 1155-1162. Link

  • Ray, B., Oskolkov, B.*, Liu, C., Leblanc, Z., and Tian, W., 2023, “FFF-based Metal and Ceramic Additive Manufacturing: Production Scaleup from a Stream of Variation Analysis Perspective,” Manufacturing Letters. Vol. 35, pp. 811-821. Link

  • O’Hara, J.*, Li, Y.*, Zhang, Z.∗, and Liu, C., 2023, “Data-driven Diabetic Retinopathy (DR) Prediction with the Assistance of a Score-based Diffusion Model,” Proceedings of 2023 IISE Annual Conference. Accepted.
    • This work has been selected to present in the 2024 Research Day at the Capitol, which highlights the exceptional undergraduate research going on across the State of Oklahoma (Student: Jacob O’Hara)
  • Slater, K.*, Li, Y.*, Wang, Y., Shan, Y., and Liu, C., 2023, “A Generative Adversarial Network (GAN)-Assisted Data Quality Monitoring Approach for Out-of-Distribution Detection of High Dimensional Data,” Proceedings of 2023 IISE Annual Conference. Accepted.
    • This work won the 2nd place award of Undergraduate Student Technical Paper Competition, at the 2023 IISE South Central Regional Conference (Student: Kent Slater)

  • Chen, Y., Chen, J., Liu, C., Liu, G., Ferrari, M., and Sundararajan, A., 2023, “Integrated Modeling and Optimal Operation of Multi-Energy System for Coastal Community,” Proceedings of the 2023 IEEE International Conference on Electro Information Technology (eIT). Link

  • Yu, Z., and Liu, C., 2023, “Implementing Application-Driven Data Analytics in Engineering and Engineering Technology Courses With Student Feedback Assessment, ” Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Accepted for Publication.

  • Shi, Z.*, Li, Y.*, and Liu, C., 2022, “Knowledge Distillation-enabled Multi-stage Incremental Learning for Online Process Monitoring in Advanced Manufacturing,” Proceedings of 2022 IEEE International Conference on Data Mining (ICDM) Workshops, pp. 860-867. Link

  • Li, Y.*, Zhao, C., and Liu, C., 2022, “Solving Non-linear Optimization Problem in Engineering by Model-Informed Generative Adversarial Network (MI-GAN),” Proceedings of 2022 IEEE International Conference on Data Mining (ICDM) Workshops, pp. 198-205. Link

  • Li, Y.*, Dogan, A.*, and Liu, C., 2022, “Ensemble Generative Adversarial Imputation Network with Selective Multi-Generator (ESM-GAIN) for Missing Data Imputation,” Proceedings of 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), pp. 807-812. Link

  • Zhang, Z.*, Li, Y.*, and Liu, C., 2022, “Collaborative Discrimination-Enabled Generative Adversarial Network (CoD-GAN) for the Data Augmentation in Imbalanced Classification,” Proceedings of 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), pp. 1510-1515. Link

  • Wang, Z., Liu, C., and Yao, B., 2022, “Multi-Branching Neural Network for Myocardial Infarction Prediction,” Proceedings of 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), pp. 2118-2123. Link

  • Zhou, H., Liu, C., Tian, W., and Kan, C., 2021, “Echo State Network Learning for the Detection of Cyber Attacks in Additive Manufacturing,” Proceedings of 2021 17th IEEE International Conference on Automation Science and Engineering (CASE), pp. 177-182. Link

  • Shi, Z.*, Liu, C., Kan, C., Tian, W., and Chen, Y., 2021, “A Blockchain-Enabled Approach for Online Stream Sensor Data Protection in Cyber-Physical Manufacturing Systems,” Proceedings of the ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Virtual, Online. August 17-19, 2021. Link

  • Chen, Y., Abu-Heiba, A., Kassaee, S., Liu, C., Liu, G., Starke, M., Smith, B., and Momen, A., 2021, “Heat Based Power Augmentation for Modular Pumped Hydro Storage in Smart Buildings Operation”, Proceedings of the ASME 2021 15th International Conference on Energy Sustainability, Virtual, Online. June 16–18, 2021. Link

  • Shi, Z.*, Mandal, S., Harimkar, S., and Liu, C., 2021, “Surface Morphology Analysis Using Convolutional Autoencoder in Additive Manufacturing with Laser Engineered Net Shaping,” Procedia Manufacturing, Vol. 53, pp. 16-23. Link

  • Mamun, A., Liu, C., Kan, C., and Tian, W., 2021, “Real-time Process Authentication for Additive Manufacturing Processes based on In-situ Video Analysis,” Procedia Manufacturing, Vol. 53, pp. 697-704. Link

  • Chen, Y., Kou, X., Olama, M., Zandi, H., Liu, C., Kassaee, S., Smith, B., Abu-Heiba, A., and Momen, A., 2020, “Bi-Level Optimization for Electricity Transaction in Smart Community with Modular Pump Hydro Storage,” Proceedings of the ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Virtual, August 17-19, 2020. Link

  • Liu, C., Kan, C., and Tian, W., 2020, “An Online Side Channel Monitoring Approach for Cyber-Physical Attack Detection of Additive Manufacturing,” Proceedings of the ASME2020 15th International Manufacturing Science and Engineering Conference, Virtual, September 3, 2020. Link

  • Ye, Z., Liu, C., Tian, W., and Kan, C.,2020, “A Deep Learning Approach for the Identification of Small Process Shifts in Additive Manufacturing using 3D Point Clouds,” Procedia Manufacturing, Vol.48, pp.770-775. Link

  • Liu, C., Wang, R., Kong, Z., Babu, S., Joslin, C., and Ferguson, J., 2019, “Real-time 3D Surface Measurement in Additive Manufacturing Using Deep Learning,” The Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium - An Additive Manufacturing Conference, Austin, TX, August 12-14, 2019. Link

  • Liu, C., Roberson, D. and Kong, Z., “Textural Analysis-based Online Closed-Loop Quality Control for Additive Manufacturing Process,” IISE Annual Conference, Pittsburgh, PA, May 20-23, 2017. Link

Book Chapters

  • Dou, C., Elkins, D., Kong, Z., and Liu, C., 2023, “Online Monitoring and Control of Polymer Additive Manufacturing Processes,” ASM Handbook Vol. 24A. Link