陳榮輝 教授

授課科目:
程序控制,高等程序控制,先進機械學習與數據分析於工業的應用,製程模擬與最適化設計
辦公室:
工509
實驗室:
程序系統工程實驗室
E-mail:
jason@wavenet.cycu.edu.tw
電 話:
03-2654107

現職

    中原大學化學工程學系 教授

學歷

    美國田納西大學化學工程學系 博士
    國立台灣大學 化學工程學系 碩士
    私立中原大學 化學工程學系 學士

研究專長

    製程控制效能評估與診斷、人工智能(機器學習)於廠級大數據、程序設計與控制的整合最適化(含節能系統設計、及學習式控制

研究領域

    (Research Interests: Development of Data-Driven Techniques)  
    Process Monitoring and Diagnosis System
    Iterative Learning Design
    Process Optimization Design
    Performance Assessment of Control Loops

著作

    • J. Liu, J. Hou and J. ChenDual-layer Feature Extraction Based Soft Sensor Methods and Applications to Industrial Polyethylene ProcessesComput. Chem. Eng., 154, 107469, 2021.

    • X. Wu, J. Chen, L. Xie, Y.-S. Lee, C.-I Chen and H. Su, Application of Convolutional Neural Networks for Multi-Stage Semiconductor ProcessesJ. Chem. Eng. Japan, 54, 449-455, 2021.

    • S. K. Ooi, D. Tanny, J. Chenand K. WangDeveloping Semi-supervised Variational Autoencoder-Generative Adversarial Network Models to Enhance Quality Prediction PerformanceChemometer Intell. Lab., 217, 104385, 2021.

    • Y. Lyu, J. Chen, and Z. Song, Synthesizing Labeled Data to Enhance Soft Sensor Performance in Data-scarce Regions, Control Eng. Pract,  115, 104903, 2021. 

    • L.-X. You and J. Chen, A Variable Relevant Multi-Local PCA Modeling Scheme to Monitor a Nonlinear Chemical ProcessChem. Eng. Sci., 246, 116851, 2021.

    • L. Zhu, Z. Li and J. Chen, Evaluating and Predicting Energy Efficiency Using Slow Feature Partial Least Squares Method for Large-scale Chemical PlantsEnergy, 230, 120582, 2021. 

    • Z. Li, Y.-S. Lee, J. Chen and Y. Qian, Variable Moving Window PLS Models for Long-term NOx Emission Prediction of Coal-fired Power PlantsFuel, 296, 120441, 2021.

    • M. Ren, J. Chen, P. Shi and G. Yan, Statistical Information Based Two-Layer Model Predictive Control with Dynamic Economy and Control Performance for Non-Gaussian Stochastic ProcessJ. Franklin Inst., 358, 2279-2300, 2021.

    • Mu, T. Liu, C. Xue and J. Chen, Semi-Supervised Learning based Calibration Model Building of NIR spectroscopy for In-Situ Measurement of Biochemical Processes under Insufficiently and Inaccurately Labeled Sample, IEEE Trans. Instrum. Meas. 70, 2509912, 2021.

    • J. Liu, J. Chenand D. WangGlobal-local Based Wavelet Functional Principal Component Analysis for Fault Detection and Diagnosis in Batch ProcessesChemometer Intell. Lab. 212, 104279, 2021.

    • J. Liu, J. Chenand D. WangLinear and Exponential Fault-assistant Feature Extraction Methods for Process Monitoring, Control Eng. Pract, 109, 104732, 2021.

    • K. Wang, X. Yuan, J. Chen and Y. Wang Supervised and Semi-supervised Probabilistic Learning with Deep Neural Networks for Concurrent Process-Quality MonitoringNeural Networks, 136, 54-62, 2021.

    • Y.-S. Lee and J. Chen, Enhancing Monitoring Performance of Data Sparse Nonlinear Processes through Information Sharing among Different Grades Using Gaussian Mixture Prior Variational Autoencoders, Chemometer Intell. Lab., 208, 104219, 2021.

研討會論文

    • Y.-S. Lee, O. S. Kit, D. Tanny and J. Chen, Maintaining Soft-Sensor Models Using Latent Dynamic Variational Autoencoders, 11th International Symposium on Advanced Control of Chemical Processes (ADCHEM), June 13-16, 2021, Venice, Italy. (Virtual)

    • D. Tanny and J. Chen, Developing Dynamic Soft Sensor Based Variational Autoencoders, 9th The International Symposium on Design, Operation & Control of Chemical Processes (PSE Asia 2020), Nov. 4-6, 2020, Taipei. (Virtual)

    • Y.-S. Lee and J. Chen, Boosting Monitoring Performance for Nonlinear Processes with Limited Samples Using Gaussian Mixture Latent Distribution in Variational Autoencoders, 9th The International Symposium on Design, Operation & Control of Chemical Processes (PSE Asia 2020), Nov. 4-6, 2020, Taipei. (Virtual)

    • Y.-C. Zhang, L. L. T. Chan and J. Chen, Application of Convolutional Neural Network Based Variational Auto-encoder Model to the Image Monitoring of Combustion Processes, 59th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Sept. 23-26, 2020, Chiang Mai, Thailand. (Virtual)

    • D. Tanny, J. Chen and K. Wang, Developing Variational Autoencoders with Differential Entropy Soft Sensor Models for Nonlinear Processes, 21st IFAC World Congress, July 12-17, 2020, Berlin, Germany. (Virtual)

    • K. Wang, J. Chen and Y. Wang, Developing a deep learning estimator to learn nonlinear dynamic systems, 21st IFAC World Congress, July 12-17, 2020, Berlin, Germany. (Virtual)

    • L. Zhu, Z. Li and J. Chen, An Industrial Process Monitoring Scheme with Moving Window Slow Feature Analysis, 21st IFAC World Congress, July 12-17, 2020, Berlin, Germany. (Virtual)

    • Q. Chen, J. Chen, X. Lang, L. Xie, C. Jiang and H. Su, Detecting and Characterizing Nonlinearity-induced Oscillations in Process Control Loops Based on Adaptive Chirp Mode Decomposition, 2020 American Control Conference, July 1-3, 2020, Denver, CO, USA. (Virtual)

    • Z. Li, J. Chen and C.-I. Chen, Prognostics of Tool Failing Behavior Based on Auto-associative Gaussian Process Regression for Semiconductor Manufacturing, 2020 IEEE International Conference on Industrial Technology (ICIT) Feb. 26-28, 2020, Buenos Aires, Argentina. (Virtual)