Chi Man VONG 黃志文

Associate Professor
Associate Head of Department of Computer and Information Science (CIS)

Academic Qualification

  • PhD in Software Engineering, FST, University of Macau (2005)
  • M.Sc. in Software Engineering, FST, University of Macau (2000)
  • B.Sc. in Software Engineering, FST, University of Macau (1996)

Working Experience

  • AUG-2017 to present: Associate Head, Dept. of Computer and Information Science, Faculty of Science and Technology,
    University of Macau
  • AUG-2013 to present: Associate Professor, Dept. of Computer and Information Science, Faculty of Science and Technology, University of Macau
  • JAN-2006 to AUG-2013: Assistant Professor, Dept. of Computer and Information Science, Faculty of Science and Technology, University of Macau
  • JAN-2000 to DEC-2005: Lecturer, Dept. of Computer and Information Science, Faculty of Science and Technology, University of Macau

Award and Honor

  • 1st prize, Academic Paper Award 2022, by Shenzhen-HK-Macau Scientific Projects Competition, April 2023.
  • 2nd prize, Contribution Award to Computing Technology 2022, by Shenzhen Association of Science and Technology, March 2023
  • Best Teaching Award Winner (2020-2022), by CCE, University of Macau (UM), January 2023.
  • FST Research Excellence Award Nominee, by University of Macau (UM), March 2023.
  • FST Reserach Excellence Award Winner, by University of Macau (UM), May 2018.
  • Best Presentation Award, International Symposium on Extreme Learning Machines (ELM 2012), Singapore, 11-13 December, 2012.
  • Long Service Award of 25 years, by University of Macau (UM), 2021.

Teaching

Postgraduate Courses

  • Advanced Topics in Computer Science (CISC8001)
  • Principles of Artificial Intelligence (CISC7013)

Undergraduate Course

  • Artificial Intelligence (CISC3012)

Supervised Postgraduate Students

Ph.D. graduates

• HUANG Jintao, Large-scale High-dimensional Multi-label Learning with Label Ambiguity, Sep 2020 – Jun 2023.

        1st Job: Research Assistant Professor, Hong Kong Baptist University, Hong Kong  (2023).

• LAI Qi, Broad Learning System and its Application in Weakly Supervision , Sep 2019 – Jun 2023.

        1st Job: Post-doc Fellow, University of Macau, Macau (2023).

• LIU Peng, Effective data-mining approaches on small data, distributed data and incremental data, Sep 2017 – Jun 2023.

        1st Job: Senior Algorithm Engineer, Tencent, China (2023).

• LUO Jiahua, Scalable and Memory-efficient Sparse Bayesian Learning for Classifications, Sep 2018 – Jun 2022.

        1st Job: Senior Algorithm Engineer, Tencent, China (2022).

• GAN Yanfen, Effective and Efficient Detection for Multimedia Information Forensics, Sep 2018 – Jun 2022.

        1st Job: Associate Professor, Guandong Polytechnic of Industry and Commerce, China (2022).

• YAN Tao (Co-sup), Sep 2018 – Dec 2021.

        1st Job: Lecturer, Hubei University of Arts and Science, China (2022).

• WONG Chi Chong, Deep Learning for Semantic Understanding: From 2D Images to 3D Point Clouds, Sep 2017 – Dec 2021.

        1st Job: Assistant Professor, Macau University of Science and Technology, Macau (2022).

• WANG Weiru, Secure Cloud Storage via Encrypted Machine Learning Algorithms, Sep 2014 – Jun 2020.

        1st Job: Lecturer, Beijing University of Technology, China (2020).

• CHEN Chuangquan, Compressive Extreme Learning Machines for Supervised Learning, Unsupervised Learning, and Semi-supervised Learning, Sep 2016 – Dec 2019.

        1st Job: Lecturer, Wuyi University, China (2019).

• DU Jie, Batch and Online Imbalance and Multi-label Classification, Sep 2015 – Mar 2019.

        1st Job: Assistant Professor, Shenzhen University, China (2018).

• GAO Xianghui (Co-sup), Development of an Initial-Training-Free Online Extreme Learning Machine with Applications to Automotive Engine Calibration and Control, Sep 2013 – JUN 2017.

        1st Job: Lecturer, Heibei University, China (2017).

MSc. graduates

• JIANG Xinyu, Fast and Robust Direct Depth-Inertial Odometry for Low Texture Environment, Sep 2018 –May 2021.

• TAI Keng-iam,  Detection of Safety Driving Based on Driver Eyes and Face, Sep 2015 – Nov 2019.

• WONG Chi-man, Extreme Learning Machine for Multi-class Classification, Sept 2015  – May 2018.

• GE Xiaowei, Peak-sensitive method for time-delay and peak/valley problems in time series forecasting, Sept 2014 – Sept 2017.

• LIU Yan, Translation Hypothesis Re-ranking for Statistical Machine Translation, Sept 2014 – Jun 2017.

• WEN Yuan, Improved Re-ranking model for Translation Hypothesis Using Extreme Learning Machine, Sept 2013 – Aug 2016.

• ZHANG Rui, Application of Support Vector Machines and Principal Components Analysis to Damage Impact Location, Sept 2007 – Aug 2014.

• FU Heming, Comparison Study of SVM and BPNN for Damage Impact Location Based on PCA, 2011 – Jul 2014.

• LUO Jiahua, Compact Modelling by Sparse Bayesian Extreme Learning Machine, Sept 2011 – Jun 2014.

• Chiu Chi-chong, Online Sequential Prediction of Minority Class of Suspended Particulate Matters by Meta-Cognitive OS-ELM,, Sept 2010 – Aug 2013.

• YANG Jingyi, An Improved Algorithm for Data Filtering Based on Variation for Short Term Air Pollution Prediction in Macau, Sept 2009 – Jul 2012.

• HUANG He, Case-based Expert System using Wavelet Packet Transform and Kernel-based Feature Manipulation for Engine Spark Ignition Diagnosis, Jan 2009 – Apr 2010.

• NG Man-chi, Support Vector Machines for Automotive Engine Ignition Signal Analysis and Inspection, Sept 2008 – Aug 2009.

• BAO Ruihe, Case-based Reasoning for Automotive Electronic Control Unit Calibration, Sept 2007 – Jun 2009.

Research

Research Interests

  • Computer Vision; Semantic Segmentation; Visual SLAM; Machine Learning Algorithms

Research Projects 

External Funding (as Principal Investigator)
  • Low-cost Dynamic High-precision Autonomous Localization and Scene Understanding for Autonomous Driving, funded by Shenzhen Municipal Technology and Innovation Committee (STIC), 2023-2025.
  • Key Technologies of Low-cost and high-precision 3D SLAM in Large Scale Scenario, funded by Science and Technology Development Fund of Macau (FDCT), 2023-2025.
  • High-precision and low-cost positioning system for warehousing mobile robots, funded by Wuyi University, Jiangmen, China, 2022-2024
  • The Key Technology of Deep Fuzzy Learning for Small Samples of Medical Images and Its Application in COVID-19 images, funded by Science and Technology Development Fund of Macau (FDCT), 2021-2023.
  • Intelligent Diagnosis of Gastric Intestinal Metaplasia by using Gastroscope images and Incremental Broad Learning Systems, funded by Science and Technology Development Fund of
    Macau (FDCT), 2020-2022.
  • Research on Human-like Analysis, Reasoning, Auxiliary Diagnosis and Treatment of Medical Big Data, funded by Science and Technology Development Fund of Macau (FDCT), 2019-2021.
  • Online Learning for Multi-label Data Streams, funded by Science and Technology Development Fund of Macau (FDCT), 2017-2019.
  • SDTAM (Semi-dense Tracking and Mapping) and Its Application to Real-time 3D Reconstruction, funded by Science and Technology Development Fund of Macau (FDCT), 2016-2017.
  • Efficient and Parsimonious Modeling by Sparse Bayesian Extreme Learning Machine, funded by Science and Technology Development Fund of Macau (FDCT), 2014-2015.
  • Modelling and Optimization of Automotive Engine Dynamic Performance under Numerical and Nominal Data, funded by Science and Technology Development Fund of Macau (FDCT), 2007-2009.

Internal Funding (as Principal Investigator)

  • Object-aware 3D Reconstruction under Abrupt Dynamic Objects based on Graph Neural Network with Robust Object Trajectory Prediction, 2024-2025.
  • The Key Technology of Deep Learning based 3D Visual Semantic Understanding, 2023-2024.
  • Research and Development on Deep Convolutional Neural Network based Smart Parking Lots Detection System, 2020-2022.
  • Secure Cloud Storage using Homomorphic Encrypted Machine Learning, 2018-2020.
  • Highly Efficient Semantic Semi-dense 3D Reconstruction using Quadcopters, 2017-2019.
  • Compact Modelling by Sparse Bayesian Extreme Learning Machine for Big Data, 2014-2016.
  • Modelling and Optimization of Bio-fuelled Automotive Engines using Advanced Machine Learning Techniques, 2013-2015.
  • Simultaneous Fault Diagnosis of Automotive Engine Based on Ignition Patterns, 2012-2013.
  • Forecasting Daily Ambient Air Pollution Using Kernel Learning Methods with Wavelet Analysis, 2011-2012.
  • CBR-based Engine Spark Ignition Diagnosis with Wavelet Packet Transform and Kernel Principal Components Analysis, 2010-2011.
  • Forecasting Daily Ambient Air Pollution based on Wavelet Least Squares Support Vector Machines, 2010-2011.
  • Signal Analysis of Automotive Engine Spark Ignition System using Case-based Reasoning (CBR) and Case-based Maintenance (CBM), 2009-2010.
  • Modelling and Optimization of Automotive Engine Dynamic Performance under Numerical and Nominal Data, 2006-2007.
  • Data Preprocessing and Modelling of Advanced Automotive Engines, 2005-2006.
  • Prediction of Vehicle Performance using Neural Networks, 2004-2005.
  • Application of Data Mining Techniques for Automotive Engine Data Analysis, 2002-2003.

Research Publications

Top-tier Conference Papers

  1. (CCF-A) C.C. Wong, C.M. Vong, Persistent Homology based Graph Convolution Network for Fine-grained 3D shape Segmentation, Proceedings of IEEE/CVF International
    Conference on Computer Vision (ICCV 2021), Oct 2021.
  2. (CCF-A) C.C. Wong, C.M. Vong, Efficient Outdoor 3D Cloud Semantic Segmentation for Critical Road Objects and Distributed Contexts, 16th European Conference on Computer Vision (ECCV 2020), Glasgow, UK, Aug 2020.
  3. (CCF-A) C.M. Wong, F. Feng, W. Zhang, C.M. Vong, H. Chen, Y. Zhang, P. He, H. Chen, K. Zhao, H.J. Chen. Improving Conversational Recommender System by Pretraining Billion-scale Knowledge Graph, 2021 IEEE 37th International Conference on Data Engineering (ICDE 2021), 2021.
  4. (CCF-A) Z. Han, X. Wang, C.M. Vong, Y.S. Liu, M. Zwicker. 3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), 2019.

Journal Papers 

  • Accepted and in press
  1. (SCI-E) Y. Sun, C.M. Vong, S. Wang. Fast AUC Maximization Learning Machine with Simultaneous Outlier Detection, IEEE Transactions on Cybernetics, In Press, 2022.
  2. (SCI-E) J. Huang, C.M. Vong, P. Chen, Y. Zhou, Accurate and Efficient Large-scale Multi-label Learning with Reduced Feature Broad Learning System using Label Correlation. IEEE Transactions on Neural Networks and Learning Systems, In Press, 2022.
  3. (SCI-E) J. Huang, W. Qian, C.M. Vong, W. Ding, W. Shu, Q. Huang, Multi-Label Feature Selection via Label Enhancement and Analytic Hierarchy Process. IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 7(4), 2023.
  4. (SCI-E) J. Du, P. Liu, C.M. Vong, C. Chen, T. Wang, C. Chen. Class Incremental Learning Method With Fast Update and High Retainability Based on Broad Learning System. IEEE Transactions on Neural Networks and Learning Systems, In Press, 2023. 
  5. (SCI-E) Q. Lai, C.M. Vong, J. Zhou, Y. Zhou, C. Chen, Fast Broad Multiview Multi-Instance Multilabel Learning (FBM3L) with Viewwise Intercorrelation. IEEE Transactions on Neural Networks and Learning Systems, In Press, 2023. 
  6. (SCI-E) J. Du, X. Zhang, P. Liu, C.M. Vong, T. Wang. An Adaptive Deep Metric Learning Loss Function for Class-Imbalance Learning via Intraclass Diversity and Interclass Distillation. IEEE Transactions on Neural Networks and Learning Systems, In Press, 2023. 
  7. (SCI-E) Q. Lai,  J. Zhou, Y. Gan, C.M. Vong, C. Chen, Single-stage Broad Multi-Instance Multi-Label Learning (BMIML) with Diverse Intercorrelations and Its Applications to Medical Image Classification. IEEE Transactions on Emerging Topics in Computational Intelligence, In Press, 2023. 
  8. (SCI-E)  H.  Li, C.M. Vong, Z. Wan, Multi-graph Embedding for Partial Label Learning. Neural Computing and Applications, In Press, 2023. 
  • 2023
  1. (SCI-E) J. Huang,  C.M. Vong, G. Wang, W. Qian, Y. Zhou, C. Chen. Joint Label Enhancement and Label Distribution Learning Via Stacked GRaph Regularization-Based Polynomial Fuzzy Broad Learning System. IEEE Transactions on Fuzzy Systems, Vol. 31(9), 2023.
  2. (SCI-E) J. Huang, C.M. Vong, W. Qian, Q. Huang,, Y. Zhou. Online Label Distribution Learning using Random Vector Functional Link Network. IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. 7(4), 2023.
  3. (SCI-E) J. Du, Y. Zhou, P. Liu, C.M. Vong, T. Wang. Parameter-free loss for class-imbalanced deep learning in image classification,  IEEE Transactions on Neural Networks and Learning Systems, Vol. 34(6), 3234-3240, 2023.
  4. (SCI-E) E. Zhou,  C.M. Vong, Y. Nojima, S. Wang, A Fully Interpretable First-order TSK Fuzzy System and Its Training with Negative Entropic and Rule-stability-based Regularization, IEEE Transactions on Fuzzy Systems, Vol. 31(7), 2023.
  5. (SCI-E)  P. Liu, J. Du, C.M. Vong,. A Novel Sequential Structure for Lightweight Multi-scale Feature Learning under Limited Available Images. Neural Networks, Vol. 164, 2023. 
  6. (SCI-E) Z. Jia, Z. Liu,  C.M. Vong, S. Wang, Y. Cai. DC-DC Buck Circuit Fault Diagnosis with Insufficient State Data based on Deep Model and Transfer Strategy. Expert Systems with Applications, Vol. 213, 2023.
  7. (SCI-E) H. Yue, Y. Huang, C.M. Vong, Y. Jin, Z. Zeng, M. Yu, C. Chen. NRSTRNet: A Novel Network for Noise-Robust Scene Text Recognition. International Journal of Computational Intelligence Systems, Vol. 16(1), 2023.
  • 2022
  1. (SCI-E) H. Huang, H. Rong, Z. Yang, C.M. Vong, Recursive Least Mean Dual p-power Solution to the generalization of Evolving Fuzzy System under Multiple Noises, Information Sciences, Vol.609, 2022.
  2. (SCI-E) Q. Lai, C.M. Vong, P.K. Wong, S. Wang, T. Yan, I.Choi, H. Yu, Multi-scale Multi-instance Joint Learning Broad Network (M3JLBN) for Gastric Intestinal Metaplasia Subtype Classification, Knowledge-base Systems, Vol.249, 2022.
  3. (SCI-E)  C.C. Wong,  C.M. Vong, X. Jiang, Y. Zhou. Feature-based Direct Tracking and Mapping for Real-time Noise-robust Outdoor 3D Reconstruction Using Quadcopters, IEEE Transactions on Intelligent Transportation Systems, Vol. 23 (11), 2022.
  4. (SCI-E) C. Chen,  C.M. Vong, S. Wang, H. Wang, M. Pang, Easy Domain Adaptation for Cross-subject Multi-view Emotion Recognition, Knowledge-based Systems, Vol. 239, 2022.
  5. (SCI-E) Z. Yang, H. Rong, P.K. Wong, P. Angelov, C.M. Vong, C. Chiu, Z. Yang, A Novel Multiple Feature-based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine,  Cognitive Computation, Vol. 14(2), 2022.
  6. (SCI-E) Y. Liu, J. Du, C.M. Vong, G. Yue, J. Yu, Y. Wang, B. Lei, T. Wang. Scale-adaptive Super-Feature based MetricUNet for Brain Tumor Segmentation, Biomedical Signal Processing and Control, Vol.73, 2022.
  7. (SCI-E) R.Xie, C.M. Vong, P. Chen, S. Wang, Dynamic Network Structure: Doubly Stacking Broad Learning Systems with Residuals and Simpler Linear Model Transmission, IEEE Transactions on Emerging Topics in Computational Intelligence Vol.6(6), 2022.
  8. (SCI-E) J. Zhong, Y. Gan,  C.M. Vong, J. Yang, J. Zhao, J. Luo. Effective and Efficient Pixel-level Detection for Diverse Video Copy-Move Forgery Types, Pattern Recognition, Vol. 122, 2022.
  9. (SCI-E) Y. Gan, J. Zhong, C.M. Vong, A Novel Copy-Move Forgery Detection Algorithm via Feature Label Matching and Hierarchical Segmentation Filtering, Information Processing & Management Vol. 59(1), 2022.
  • 2021
  1. (SCI-E) S. Gu,  C.M. Vong, P.K. Wong, S.T. Wang. Fast Training of Adversarial Deep Fuzzy Classifier by Downsizing Fuzzy Rules with Gradient Guided Learning,  IEEE Transactions on Fuzzy Systems, Vol. 30(6), 1967-1980, 2021.
  2. (SCI-E) Z. W. Xu, C.M. Vong, C.C. Wong, Q. Liu, Ground Plane Context Aggregation Network for Day-and-Night on Vehicular Pedestrian Detection, IEEE Transactions on
    Intelligent Transportation Systems    , Vol. 22(10), Oct 2021.
  3. (SCI-E) H. Huang, H.J. Rong, Z.X.Yang, C.M. Vong, Jointly evolving and compressing fuzzy system for feature reduction and classification, Information Sciences,
    Vol. 579, pp. 218-230, 2021    .
  4. (SCI-E) J.H. Luo, Y.F. GAN,  C.M. Vong, C.M. Wong, C. Q. Chen, Scalable and memory-efficient sparse learning for classification with approximate Bayesian regularization priors, Neurocomputing, Vol. 457, pp. 106-116, 2021.
  5. (SCI-E) H.Y. Li, C.M.Vong, P.K. Wong, W.F. Ip, T. Yan, I.C. Choi, H.H. Yu. A Multi-feature Fusion Method for Image Recognition of Gastrointestinal Metaplasia (GIM), Biomedical Signal Processing and Control, Vol. 69, 102909, August 2021.
  6. (SCI-E) W.S. Gan, P.K. Wong, G.K. Yu, R. C. Zhao, C.M. Vong,  Light-weight Network for Real-time Adaptive Stereo Depth Estimation, Neurocomputing,Vol. 441, pp. 118-127, 2021.
  7. (SCI-E) J.H. Luo,  C.M. Vong, Z.B. Liu, C.Q. Chen, An Inverse-Free and Scalable Sparse Bayesian Extreme Learning Machine For Classification, IEEE Access, Vol. 9, pp.87543-87551, 2021.
  8. (SCI-E) W. Huang, P.K. Wong, K.I. Wong, C.M. Vong, J. Zhao, Adaptive Neural Control of Vehicle Yaw Stability with Active Front Steering using an Improved Random Projection Neural Network, Vehicle System Dynamics, Vol.59(3), pp. 396-414, 2021.
  9. (SCI-E) J.H. Luo, C.M. Wong,  C.M. Vong. Multinomial Bayesian Extreme Learning Machine for Sparse and Accurate Classification, Neurocomputing, Vol. 423, pp. 24-33, 2021.
  • 2020
  1. (SCI-E) J. Du, C.M. Vong, C.L. Chen. Novel Efficient RNN and LSTM-like Architectures: Recurrent and Gated Broad Learning Systems and Their Applications for Text Classification, IEEE Transactions on Cybernetics, Vol. 51(3), pp. 1586-1597, 2020.
  2. (SCI-E) Z. Bian,  C.M. Vong, P.K. Wong, S.T. Wang. Fuzzy KNN Method with Adaptive Nearest Neighbors. IEEE Transactions on Cybernetics, Vol. 52(6), 2020.
  3.  (SCI-E) Z. Jia, Z.B. Liu,  Y.F. Gan, C.M. Vong, M. Pecht, A Deep Forest based Fault Diagnosis Scheme for Electronics-Rich Analog Circuit Systems, IEEE Transactions on Industrial Electronics, Vol. 68(10), 2020.
  4. (SCI-E)  T. Yan, P.K. Wong, I.C. Choi, C.M. Vong, H.H. Yu. Intelligent Diagnosis of Gastric Intestinal Metaplasia based on Convolutional Neural Network and limited number of endoscopic images, Computers in Biology and Medicine, Vol. 126, 104026, 2020.
  5. (SCI-E) H. Lin, J. Zhou,  C.M. Vong, Q. Liu, Novel Up-scale Feature Aggregation for Object Detection in Aerial Images, Neurocomputing, Vol. 441, pp. 364-374, 2020.
  6. (SCI-E) F. Wang, Z. Xu, Y.F. Gan, C.M. Vong, Q. Liu, SCNet: Scale-aware Coupling-Structure Network for Efficient Video Object Detection, Neurocomputing, Vol. 404, pp. 283-293, 2020.
  7. (SCI-E) P.K. Wong, W. Huang, C.M. Vong, Z.X.Yang, Adaptive Neural Tracking Control for Automotive Engine Idle Speed Regulation using Extreme Learning Machine. Neural Computing and Applications, Vol. 32(18), pp. 14399-14009, 2020.
  8. (SCI-E) F. Wu, C.M. Vong, Q. Liu, Tracking Objects with Partial Occlusion by Background Alignment, Neurocomputing, Vol. 402, pp. 1-13, 2020.
  9. (SCI-E)  C.M. Vong, J. Du. Accurate and Efficient Sequential Ensemble Learning for Highly Imbalanced Multi-class Data, Neural Networks, Vol. 128, pp. 268-278, 2020.
  10. (SCI-E) C.Q. Chen, C.M. Vong, P.K.Wong, K.I. Tai, Approximate Empirical Kernel Map-based Iterative Extreme Learning Machine for Clustering. Neural Computing and Applications, Vol. 32(12), pp. 8031-8046, 2020.
  11. (SCI-E) C.Q. Chen, Y.F. Gan, C.M. Vong. Extreme Semi-Supervised Learning for Multi-class Classification. Neurocomputing, Vol. 376, pp. 103-118, 2020.
  12. (SCI-E) W.R. Wang, Y.F. Gan, C.M. Vong,  C.Q. Chen, Homo-ELM: Fully Homomorphic Extreme Learning Machine. International Journal of Machine Learning and Cybernetics, Vol. 11, pp. 1531-1540, 2020.
  • 2019
  1. (SCI-E) Han, Z., Liu, Z., Han, J., Vong, C.M., Bu, S., Chen, CLP, Unsupervised Learning 3D Local Feature from Raw Voxels based on A Novel Permutation Voxelization Strategy, IEEE Transactions on Cybernetics, Vol. 49(2), pp.481-494, 2019.
  2. (SCI-E) J. Du, C.M. Vong, C.Q. Chen, P. Liu, Z.B. Liu. Supervised Extreme Learning Machine based Auto-encoder for Discriminative Feature Learning. IEEE Access, Vol. 8, pp. 11700-11709, 2019.
  3. (SCI-E) J. Zhou, C.M. Vong, Q. Liu, Z. Wang. Scale Adaptive Image Cropping for UAV Object Detection, Neurocomputing,Vol. 366, pp. 305-313, 2019.
  4. (SCI-E) C.C. Wong, Y.F. Gan, C.M. Vong. Efficient Outdoor Video Semantic Segmentation Using Feedback-based Fully Convolution Neural Network. IEEE Transactions on Industrial Informatics, Vol.16(8), pp. 5128-5136, 2019.
  5. (SCI-E) P.K. Wong, X.H. Gao, K.I. Wong, C.M. Vong, Z.X. Yang. Initial-training-free Online Sequential Extreme Learning Machine based Adaptive Engine Air-fuel Ration Control. International Journal of Machine Learning and Cybernetics, Vol. 10(9), pp. 2245-2259, 2019.
  6. (SCI-E) Z. Han, C.M. Vong. Efficient Outdoor Video Semantic Segmentation Using Feedback-based Fully Convolution Neural Network. IEEE Transactions on Industrial Informatics, Vol.16(8), pp. 5128-5136, 2019.
  7. (SCI-E) J. Du, C.M. Vong. Robust Online Multi-Label Learning under Dynamic Changes in Data Distribution with Labels, IEEE Transactions on Cybernetics, Vol. 50(1), pp. 374-385, 2019.
  8. (SCI-E) Z. Han, H. Lu, Z. Liu, C.M. Vong, Y.S. Liu, J. Han, C.L.P. Chen,, SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN with Attention, IEEE Transactions on Image Processing, Vol. 28(8), pp. 3986-3999, 2019.
  9. (SCI-E) Z. Jia, Z. Liu, C.M. Vong, M. Pecht. A Rotating Machinery Fault Diagnosis Method based on Feature Learning of Thermal Images. IEEE Access, Vol.7, pp. 12348-12359, 2019.
  • 2018
  1. (SCI-E) Vong, C.M., Du Jie, Wong, C.M., Cao, J., Postboosting using Extended G-mean for Online Sequential Multiclass Imbalance Learning, IEEE Transactions on Neural Networks and Learning Systems, Vol. 29(12), pp. 6163-6177, 2018.
  2. (SCI-E) Vong, C.M., Chen, C. Q., Wong, P.K., Empirical Kernel Map-Based Multilayer Extreme Learning Machines for Representation Learning, Neurocomputing, Vol. 310, pp. 265-276, 2018.
  3. (SCI-E) J.W Cao, T.L.Wang, L.M. Shang, X. Lai, C.M. Vong, B. Chen, An Intelligent Propagation Distance Estimation Algorithm Based on Fundamental Frequency Energy Distribution for Periodic Vibration Localization, Journal of Franklin Institute, Vol. 355(4), pp.1539-1558, 2018.
  4. (SCI-E) Z. Liu, Z. Jia, C.M. Vong, J. Han, C. Yan, M. Pecht, A Patent Analysis of Prognostics and Health Management (PHM) Innovations for Electrical Systems, IEEE Access, Vol. 6, pp. 18088-18107, 2018.
  5. (SCI-E) P. Zhu, Y. Chun, Y. Cheng, X. Huang, J.W. Cao, C.M. Vong, P.K. Wong, An improved feature extraction algorithm for automatic defect identification based on eddy current pulsed thermography, Mechanical Systems and Signal Processing, Vol. 113, pp. 5-21, December 2018.
  6.  (SCI-E) J.W Cao, T.L.Wang, L.M. Shang, J.Z. Wang, C.M. Vong, C. Yin, X.G. Huang, A Novel Distance Estimation Algorithm for Periodic Surface Vibrations based on Frequency Band Energy Percentage Feature, Mechanical Systems and Signal Processing, Vol. 113, pp.222-236, December 2018.
  7. (SCI-E) Chen, C. Q., Vong, C.M., Wong, C.M., Wang, W., Wong, P.K., Efficient Extreme Learning Machine via Very Sparse Random Projection, Soft Computing, Vol. 22(11), pp. 3563-3574, June 2018.
  8. (SCI-E) Wong, C.M, Vong, C.M., Wong, P.K., Cao, J. Kernel-based Multilayer Extreme Learning Machines for Representation Learning, IEEE Transactions on Neural Networks and Learning Systems, Vol. 29 (3), pp. 757-762, Mar, 2018.
  9. (SCI-E) Han, Z., Liu, Z., Vong, C.M., Liu, Y., Bu, S., Han, J., Chen, CLP, Deep Spatiality: Unsupervised Learning of Spatially-Enhanced Global and Local 3D Features by Deep Neural Network with Coupled Softmax, IEEE Transactions on Image Processing, Vol. 27(6), pp. 3049-3063, March 2018.
  10. (SCI-E) Wong, P.K., Gao, X. H., Wong, K.I., Vong, C.M., Efficient Point-by-point Engine Calibration using Machine Learning and Sequential Design of Experiment Strategies, Journal of the Franklin Institute, Vol. 355(4), pp. 1517-1538, March 2018.
  11. (SCI-E) Wong, P.K., Gao, X.H., Wong K.I., Vong, C.M., Online Extreme Learning Machine based Modeling and Optimization for Point-by-point Engine Calibration, Neurocomputing, Vol. 227, pp. 187-197, February, 2018.
  • 2017
  1. (SCI-E) Du, J., Vong, C.M., Pun, C.M., Wong, P.K., Ip, W.F., Post-boosting of classification boundary for imbalanced data using geometric mean, Neural Networks, Vol. 96, pp. 101-114, Dec, 2017.
  2. (SCI-E) Vong, C.M., Liu, Y., Cao, J., Yin, C. Cascaded Re-ranking Modelling of Translation Hypotheses using Extreme Learning Machines, Applied Soft Computing, Vol. 58, pp. 681-689, Sept 2017. 
  3. (SCI-E) Liu, Y., Vong, C.M., Wong, P.K. Extreme Learning Machine for Huge Hypotheses Re-ranking in Statistical Machine Translation, Cognitive Computation, Vol. 9(2), pp. 285-294, 2017.
  4. (SCI-E) Liu, Z., Jia, Z., Vong, C.M., Bu, S. Han, J., Tang, X., Capturing High Discriminative Fault Features for Electronics-rich Analog System via DeepLearning, IEEE Transactions on Industrial Informatics, Vol. 13(3), pp. 1213-1226, 2017.
  5. (SCI-E) Han, Z., Liu, Z., Han, J., Vong, C.M., Bu, S., Chen, CLP, Mesh Convolutional Restricted Boltzmann Machines for Unsupervised Learning of Features with Structure Preservation on 3D Meshes, IEEE Transactions on Neural Networks and Learning Systems, Vol. 28 (10), pp. 2268-2281, 2017.
  6. (SCI-E) Han, Z., Liu, Z., Vong, C.M., Liu, Y., Bu, S., Han, J., Chen, CLP, BoSCC: Bag of Spatial Context Correlations for Spatially Enhanced 3D Shape Representation, IEEE Transactions on Image Processing, Vol. 26(8), pp.3707-3720, 2017.
  7. (SCI-E) Wong, AHH, Li, HR, Jia, YW, Mak, PI, Martins, RPD, Liu, Y, Vong, C.M., et. al., Drug Screening of Cancer Cell Lines and Human Primary Tumors using Droplet Microfluidics, Scientific Reports, Vol. 7, DOI: 10.1038/s41598-017-08831-z, 2017.
  8. (SCI-E) Wang, W., Vong, C.M., Yang Y., Wong P.K., Encrypted Image Classification based on Multilayer Extreme Learning, Multidimensional Systems and Signal Processing, Vol. 28(3), pp. 851-865, 2017.
  9. (SCI-E) Rong, H., Yang, Z., Wong, P.K., Vong, C.M. Adaptive self-learning fuzzy autopilot design for uncertain bank-to-turn missiles, Journal of Dynamic Systems, Measurement, and Control, Vol. 139(4), DOI:10.1115/1.4035091, 2017.
  10. (SCI-E) Rong,
    H., Yang, Z., Wong, P.K., Vong, C.M., Zhao, G. A novel meta-cognitive fuzzy-neural model with backstepping strategy for adaptive control of uncertain nonlinear systems, Neurocomputing, Vol. 230, pp. 332-344, 2017.
  11. (SCI-E) Rong, H., Yang, Z., Wong, P.K., Vong, C.M., Zhao, G. Self-evolving fuzzy model-based controller with online structure and parameter learning for hypersonic vehicle, Aerospace Science and Technology, Vol. 64 (5), pp. 1-15, 2017.
  12. (SCI-E) Wong, P.K., Zhong, J.H., Yang, Z.X., Vong, C.M., A New Framework for Intelligent Simultaneous-fault Diagnosis of Rotating Machinery using Pairwise-coupled sparse Bayesian Extreme Learning Committee Machine, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, Vol. 231(6), pp. 1146-1161, 2017.
  13. (SCI-E) Lin C., Pun, C.M., Vong, C.M., Adjeroh, D., Efficient Shape Classification using Region Descriptors, Multimedia Tools and Applications (SCI-E), Vol. 76(1), pp. 83-102, 2017.
  • 2016
  1. (SCI-E) Cao, J., Hao, J., Lai, X., Vong, C.M., Luo, M., Ensemble Extreme Learning Machine and Sparse Representation Classification, Journal of the Franklin Institute (SCI-E), Vol. 353 (17), pp. 4526-4541, 2016.
  2. (SCI-E) Han, Z., Liu, Z., Han, J., Vong, C.M., Bu, S., Li, X. Unsupervised 3D local Feature Learning by Circle Convolutional Restricted Boltzmann Machine, IEEE Transactions on Image Processing, Vol. 25(11), pp. 5331-5344, 2016.
  3. (SCI-E) Wong, P.K., Wong, H.C., Vong, C.M., Wong, K. I., Online Wavelet Least-Squares Support Vector Machine Fuzzy Predictive Control for Engine Lambda Regulation, International Journal of Engine Research, Vol. 17(8), pp. 866-885, 2016.
  4. (SCI-E) Wong, P.K., Gao, X.H., Wong, K.I., Vong, C.M., An Analytic Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias, Cognitive Computation, Vol.
    8(4), pp. 746-756, 2016.
  5. (SCI-E) Gao, X., Wong, K.I., Wong, P.K., Vong, C.M., Adaptive Control of Rapidly Time-varying Discrete-time System using Initial-Training-Free Online Extreme Learning Machine, Neurocomputing, Vol. 194, pp. 117-125, 2016.
  • 2015
  1. (SCI-E) Wong, P.K., Zhong, J.H., Yang, Z.X., Vong, C.M., Sparse Bayesian Extreme Learning Committee Machine for Engine Simultaneous Fault Diagnosis. Neurocomputing, Vol. 174, pp.
    331-343, 2016
  2. (SCI-E) Wong, P. K., Vong, C. M., Wong, K. I., Ma, Z. Q. Development of a wireless inspection and notification system with minimum monitoring hardware for real-time vehicle engine health inspection. Transportation Research Part C, Vol. 58. pp. 29-45, 2015.
  3. (SCI-E) Vong, C.M., Ip, W.F., Wong, P.K., Imbalanced Learning for Air Pollution by Meta-Cognitive Online Sequential Extreme Learning Machine. Cognitive Computation, 7(3), pp.381-391, 2015.
  4. (SCI-E) Vong, C.M., Tai, K.I., Pun, C.M., Wong, P.K., Fast and Accurate Face Detection by Sparse Bayesian Extreme Learning Machine. Neural Computing and Applications, 26(5). pp. 1149-1156, 2015.
  5. (SCI-E) Wong, K.I., Wong, P.K., Cheung, C.S., Vong, C.M., Modeling and Optimization of Biodiesel Engine Performance using Kernel-based Extreme Learning Machine and Cuckoo Search. Renewable Energy, Vol. 74, pp.640-647, 2015.
  6. (SCI-E) Wong, P.K., Wong, H.C., Vong, C.M., Iong, T.M., Wong, K.I., Gao, X.H., Fault Tolerance Automotive Air-Ratio Control using Extreme Learning Machine Model Predictive Controller. Mathematical Problems in Engineering, Volume 2015, Article ID 317142, 10 pages. DOI:10.1155/2015/317142.
  7. (SCI-E) Huang, G.B., Bai, Z., Kasun, L.L.C., Vong, C.M., Local Receptive Fields Based Extreme Learning Machine. IEEE Computational Intelligence Magazine, Vol. 10(2). pp. 18-29, 2015.
  • 2014
  1. (SCI-E) Luo, J.H., Vong, C.M., Wong, P.K. Sparse Bayesian Extreme Learning Machine for Multi-classification. IEEE Transactions on Neural Networks and Learning Systems, Vol. 25(4), pp. 836 – 843, 2014.
  2. (SCI-E) Vong, C.M., Wong, P.K., Simultaneous-fault Detection based on Qualitative Symptom Descriptions for Automotive Engine Diagnosis. Applied Soft Computing, Vol. 13, pp. 4428-4441.
  3. (SCI-E) Fu, H., Vong, C.M., Wong, P.K., Yang, Z.X. Fast Detection of Impact Location using Kernel Extreme Learning Machine. Neural Computing and Applications, Vol. 27(1), pp. 121-130, 2016.
  4. (SCI-E) Wong, P.K., Wong, H.C., Vong, C.M., Xie, Z.C., Huang, S., Model Predictive Engine Air-ratio Control using Online Sequential Extreme Learning. Neural Computing and Applications, Vol. 27(1), pp. 79-92, 20167.
  5. (SCI-E) Vong, C.M., Ip, W.F., Wong, P.K., Variation-oriented Data Filtering for Improvement in Model Complexity of Air Pollutant Prediction Model. Mathematical Problems in Engineering, Volume 2014, Article ID 310478, 13 pages. DOI:10.1155/2014/310478.
  6. (SCI-E) Wong, P.K., Vong, C.M., Gao, X.H., Wong, K.I., Adaptive Control using Fully Online Sequential Extreme Learning Machine and a Case Study on Engine Air-Fuel Ratio Regulation. Mathematical Problems in Engineering, Volume 2014, Article ID 246964, 11 pages. DOI:10.1155/2014/246964.
  7. (SCI-E) Wong, K.I., Vong, C.M., Wong, P.K., Luo, J.H., Sparse Bayesian Extreme Learning Machine and its Application to Biofuel Engine Performance. Neurocomputing, Vol.149, September, pp. 397-404, 2014.
  8. (SCI-E) Vong, C. M., Ip, W. F., Wong, P. K., & Chiu, C. C. Predicting minority class for suspended particulate matters level by extreme learning machine. Neurocomputing, Vol. 128, March,
    pp. 136-144, 2014.
  9. (SCI-E) Wong, P.K., Yang, Z.X., Vong, C.M., Zhong, J.H., Real-Time Fault Diagnosis for Gas Turbine Generator Systems by using Extreme Learning Machine. Neurocomputing, Vol. 128, March, pp. 249-257, 2014.
  • 2013 and before.
  1. (SCI-E) Cambria, E., Huang, G.B., Kasun, L.L.C., Zhou, H.M., Vong, C.M., et. al., Extreme Learning Machines [Trends & Controversies]. IEEE Intelligent Systems, Vol. 28(No. 6), pp. 31-59, 2013.
  2. (SCI-E) Wong, P. K., Vong,C. M., Cheung, C. S. & Wong, K. I. Diesel engine modelling using extreme learning machine under scarce and exponential data sets. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, Vol. 21 (No. Suppl. 2), pp. 87-98, December, 2013
  3. (SCI-E) Wong, K.I., Wong, P.K., Cheung, C.S., Vong, C.M.  Modelling of Diesel Engine Performance using Advanced Machine Learning Methods under Scarce and Exponential Data Set, Applied Soft Computing, Vol.13(No.11), pp.4428-4441, 2013.
  4. (SCI-E) Yang, Z.X., Wong, P. K., Vong, C.M.  Zhong, J.H., Liang, J.J.Y. Simultaneous-fault Diagnosis of Gas Turbine Generator Systems using a Pairwise-coupled Probabilistic Classifier, Mathematical Problems in Engineering, Vol. 2013, Article ID 827128, 14 pages, 2013, http://dx.doi.org/10.1155/2013/827128
  5. (SCI-E) Vong, C.M.  Wong, P. K., Ip, W.F., Chiu, C.C. Simultaneous Fault Diagnosis of Automotive Engine Ignition Systems using Prior Domain Knowledge and Relevance Vector Machine, Mathematical Problems in Engineering, Vol. 2013, Article ID 974862, 19 pages, 2013, http://dx.doi.org/10.1155/2013/974862.
  6. (SCI-E) Wong, K.I., Wong, P. K., Cheung , C.S., Vong, C.M.  , Modelling and Optimization of Biodiesel Engine Performance using Advanced Machine Learning Methods, Energy, Vol. 55, June, pp. 519-528, 2013.
  7. (SCI-E) Vong, C. M., Wong, P. K.,  Ip, W. F. A new framework of simultaneous-fault diagnosis using pairwise probabilistic multi-label classification for time-dependent patterns. IEEE Transactions on Industrial Electronics. Vol. 60(No. 8), pp. 3372-3385, 2013.
  8. (EI) Wong, P.K., Vong, C.M., Ip, W.F., Wong, H.C. (2012) Flexibility study on telemetric vehicle emission examination. International Journal of Satellite Communications Policy and Management. Vol.1(No. 2-3), pp. 220-229.
  9. (SCI-E) Yang, Z.X., Wong, P.K., Vong, C.M., and Lo, K.M. (2012) Constraint based Adaptive Shape Deformation Technology for Customized Product Development, International Journal of Materials & Product Technology. Vol. 44(No. 1-2), pp. 1-16, 2012.
  10. (EI) Wong, H.C., Wong, P.K. and Vong, C.M. (2012) Model Predictive Engine Air-Ratio Control Using Online Sequential Relevance Vector Machine, Journal of Control Science and Engineering, Article ID 731825, 15 pages, 2012. DOI: 10.1155/2012/731825.
  11. (EI) Vong, C.M., Ip, W.F., Wong, P.K., Yang J.Y. (2012) Air Quality Modelling using Support Vector Machines, Journal of Control Science and Engineering, Article ID 518032, 11 pages, 2012. DOI: 10.1155/2012/518032.
  12. (SCI-E) Wong, P.K., Wong, H.C., Vong, C.M. (2012) Online time-sequence incremental and decremental least squares support vector Machines for Engine Air-ratio Prediction, International Journal of Engine Research, Vol. 13(No. 1), pp. 28-40.
  13. (SCI-E) Wong, P. K., Xu, Q. S., Vong, C. M., & Wong, H. C. (2012) Rate-Dependent Hysteresis Modeling and Control of a Piezostage Using Online Support Vector Machine and Relevance Vector Machine. IEEE Transactions on Industrial Electronics, Vol. 59(No. 4), pp. 1988-2001. 
  14. (SCI-E) Vong, C.M., Wong, P.K., Ip, W. I. (2011). Case-based Expert System using Wavelet Packet Transform and Kernel-based Feature Manipulation for Engine Ignition System Diagnosis. Engineering Applications of Artificial Intelligence, Vol. 24 (No. 7), pp. 1281-1294.
  15. (SCI-E) Vong, C.M., Wong, P.K. (2011). Engine Ignition Diagnosis with Wavelet Packet Transform and Multi-class Least Squares Support Vector Machines. Expert Systems with Applications, Vol. 38 (No.7), pp.8563-8670.
  16. (SCI-E) Vong, C. M., Wong, P. K., Tam, L. M., & Zhang, Z.Y. (2011) Ignition Pattern Analysis for Automotive Engine Trouble Diagnosis using Wavelet Packet Transform and Support Vector Machines. Chinese Journal of Mechanical Engineering, Vol. 24 (No.5), pp. 870-878. 
  17. (SCI-E) Vong, C. M., Wong, P. K., & Ip, W. F. (2010). Support Vector Classification using Domain Knowledge and Extracted Pattern Features for Diagnosis of Engine Ignition Systems. Journal of the Chinese Society of Mechanical Engineers, Vol. 31(No. 5). pp. 363-374.
  18. (SCI-E) Vong, C.M., Wong, P.K., Ip, W. F. (2010). Engine Ignition System Diagnosis using Case-based Reasoning Enhanced with Wavelet Packet Transform and Clustering. Information, Vol. 13(No. 6), pp.2081-2092, NOV, 2010.
  19. (SCI-E) Vong, C.M., Wong, P.K. (2010). Case-based Adaptation for Automotive Engine Electronic Control Unit Calibration. Expert Systems with Applications, Vol. 37 (No.4), pp.3184-3194. 
  20. (SCI-E) Wong, P. K.,Tam, L. M., Li, K, & Vong, C.M. (2010). Engine Idle-Speed System Modelling and Control Optimization Using Artificial Intelligence. Proceedings of the Institution of Mechanical Engineers, Part D, Journal of Automobile Engineering, Vol. 224 (No.1), pp.55-72. 
  21. (EI) Wong, P. K., Vong, C.M., Tam, L. M., & Li, K. (2008). Data preprocessing and modelling of electronically-controlled automotive engine power performance using kernel principal components analysis and least squares support vector machines. International Journal of Vehicle Systems Modelling and Testing, Vol. 3(No. 4), pp. 312-330. 
  22. (SCI-E) Vong, C.M., Wong ,P.K., & Li, Y.P. (2006). Prediction of Automotive Engine Power and Torque Using Least Squares Support Vector Machines and Bayesian Inference. Engineering Application of Artificial Intelligence (EAAI), Vol. 19(No.3), pp. 227-297.
  23. (EI) Vong, C.M., Wong, P.K., Li, Y.P. & Ho, C.M. (2005). Modelling of Modern Automotive Petrol Engine  (SCI-E) Performance Using Support Vector Machines. Journal of Zhejiang University SCIENCE A (JZUS A) – An International Journal, Vol. 6A (no.1), pp. 1-8.
  24. (SCI-E) Vong, C.M., Leung T.P., & Wong, P.K. (2002). Case-based Reasoning and Adaptation in Hydraulic Production Machine Design. Engineering Application of Artificial Intelligence (EAAI), Vol.15(No.1), pp. 567-585.
  25. Vong, C.M., Wong, P.K., Li, Y.P. (2003). Impact of Internet Cafes on Macao Youth. Journal of Youth Studies, Vol.6 (No.2), pp. 103-107.
  26. Vong, C.M., Li, Y.P., Wong, P.K., Mak, P.U. & Vai, M.I. (2002). The application of Case-based Reasoning in Macau. Journal of Macau Research Studies, Vol. 15, pp. 231- 248.

Conference Papers 

  1. M. Fu, C.M. Wong, H. Zhu, Y.J. Huang, Y.P. Li, X. Zheng, J. Wu, C.M. Vong, J. Yang, DAliM: Machine Learning Based Intelligent Lucky Money Determination for Large-Scale E-Commerce
    Businesses, Proceedings of the 16th International Conference on Service-Oriented Computing (ICSOC 2018), pp. 740-755, 2018.
  2. P.K. Wong, W. Huang, K.I. Wong, C.M. Vong, Adaptative Control of Vehicle Yaw Rate with Active Steering System and Extreme Learning Machine – A Pilot Study, Proceedings of The 8th International Conference on Extreme Learning Machines, Yantai, China, 2017 (In press).
  3. J. Du, C.M. Vong, Y. Chang, Y. Jiao, Online Sequential Extreme Learning Machine with Under-Sampling and Over-Sampling for Imbalanced Big Data Classification, Proceedings of the International Conference on Extreme Learning Machine 2016 (ELM2016), pp. 229-239, Singapore, December, 2016.
  4. Vong, C.M., Wong, P.K., Ma, Z.Q., Wong, K.I. (2014). Application of RFID Technology and the Maximum Spanning Tree Algorithm for Solving Vehicle Emissions in Cities on Internet of Things. In Proceedings of IEEE World Forum on Internet of Things (WF-IoT) 2014, pp. 347-352, Seoul, Korea, March, 2014.
  5. Vong, C.M., Wong, P.K., Wong, K.I., Ma, Z.Q. (2013). Inspection and Control of Vehicle Emissions through Internet of Things and Traffic Lights. In Proceedings of 2013 International Conference on Connected Vehicles & Expo, pp. 863-868, Las Vegas, Nevada, USA, December, 2013.
  6. Wong, H.C., Wong, P.K., Vong, C.M., Xie, Z.C., Huang, S.J. (2013). Model Predictive Fuzzy Control of Air-ratio for Automotive Engines. Proceedings of the International Conference on Aerospace, Mechanical, Automotive and Materials Engineering (ICMAME 2013), pp.164-169, Venice, Italy, April, 2013.
  7. Wong, P.K., Wong, H.C., Vong, C.M. (2012). Modelling and Prediction of Automotive Engine Air-ratio Using Relevance Vector Machine. Proceedings of the 12th Conference on Control, Automation, Robotics and Vision (ICARCV 2012), pp. 1710-1715, Guangzhou, China, December, 2012.
  8. Wong, P.K., Vong, C.M., Ip, W.F., Wong H.C. (2012). Preliminary Study on Telemetric Vehicle Emission Examination, Lecture Notes in Electrical Engineering 113 LNEE, pp. 443-451, 2012.
  9. Wong, P.K., Vong, C.M., Zhang, Z.Y., Xu, Q.S. (2011). Fault diagnosis of automotive engines using fuzzy relevance vector machine, Communications in Computer and Information Science 164 CCIS, pp. 213-220, 2011.
  10. Vong, C.M., Wong, P.K., Ip, W.F., Yang, Z.X. (2011), Case-based design for hydraulic power circuit, Communications in Computer and Information Science 163 CCIS, pp. 269-275.
  11. Vong, C.M., Wong, P.K., Ip, W.F. (2011), Simultaneous Faults Diagnosis for Automotive Ignition Patterns, Proceedings of the 2011 International Conference on Machine Learning and Cybernetics, Vol. 3, IEEE press, pp.1324-1330, Guilin, China, July, 2011.
  12. Yang, J.Y., Ip, W.F., Vong, C.M. and Wong, P.K., (25/06/2011) Effect of Choice of Kernel in Support Vector Machines on Ambient Air Pollution Forecasting, Proceedings of 2011 International Conference on System Science and Engineering (ICSSE 2011), Published by IEEE, pp. 552-557, Macau, June 2011.
  13. Vong, C.M., Wong, P.K., Ip, W.F., (25/06/2011) Framework of Vehicle Emission Inspection and Control Through RFID and Traffic Lights, Proceedings of 2011 International Conference on System Science and Engineering (ICSSE 2011), Published by IEEE, pp. 597-600, Macau, June 2011.
  14. Wong, P.K., Vong, C.M., Ip, W.F. (21/05/2010). Modelling of Petrol Engine Power using Incremental Least-Square Support Vector Machines for ECU calibration. 2010 International Conference on Optoelectronics and Image Processing (ICOIP), pp. 12 – 15, Hainan, China, 2010.
  15. Vong, C.M., Wong, P. K., & Ip W. F. (18/08/2010). Case-based Classification System with Clustering for Automotive Engine Spark Ignition Diagnosis. IEEE/ACIS 9th International Conference on Computer and Information Science (ICIS), pp. 17 – 22, Yamgata, Japan, 2010.
  16. Ip, W.F., Vong, C.M., Wong, P.K. (18/08/2010). Least Squares Support Vector Machines for Daily Ambient Air Pollution Forecasting. IEEE/ACIS 9th International Conference on Computer and Information Science (ICIS), pp. 23 – 28, Yamagata, Japan, 2010.
  17. Wong, P.K., Wong, H.C., Vong, C.M., Ip, W.F. (18/08/2010). Predictive Air-Ratio Controller for Automotive Engines Based on Online Neural Network: Design and Experimental Evaluation. IEEE/ACIS 9th International Conference on Computer and Information Science (ICIS), pp. 275 – 279, Yamagata, Japan, 2010.
  18. Vong, C.M., Huang, H., & Wong, P. K. (20/06/2010). Engine Spark Ignition Diagnosis with Wavelet Packet Transform and Case-based Reasoning. IEEE International Conference on Information and Automation (ICIA), pp. 565 – 570, Heilongjiang, China, 2010.
  19. Ip, W.F., Vong, C. M., Yang, J. Y. (20/06/2010). Forecasting Daily Ambient Air Pollution Based on Least Squares Support Vector Machines. IEEE International Conference on Information and Automation (ICIA), pp. 571 – 575, Heilongjiang, China, 2010.
  20. Wong, P.K., Vong, C.M., Wong, H.C., Li, K. (2/12/2009) Modelling and Prediction of Spark-ignition Engine Power Performance using Incremental Least Squares Support Vector Machines. Proceedings of the 2nd International Symposium on Computational Mechanics and the 12th International Conference on Enhancement and Promotion of Computational Methods in Engineering and Science, pp.179 – 184, Hong Kong & Macao, 2009.
  21. Vong, C.M., Huang, H., Wong, P.K. (2/12/2009). Case-based Reasoning for Automotive Engine Performance Tune-up. Proceedings of the 2nd International Symposium on Computational Mechanics and the 12th International Conference on Enhancement and Promotion of Computational Methods in Engineering and Science, pp. 185 –190, Hong Kong & Macao, 2009.
  22. Huang, H., Vong, C.M. and Wong, P.K. (2/12/2009). Signal Analysis of Automotive Engine Spark Ignition System using Case-based reasoning (CBR) and Case-based Maintenance (CBM). Proceedings of the 2nd International Symposium on Computational Mechanics and the 12th International Conference on Enhancement and Promotion of Computational Methods in Engineering and Science, pp. 459 – 464, Hong Kong & Macao, 2009.
  23. Vong, C.M., Wong, P. K., & Huang, H. (22/06/2009). Case-based Reasoning for Automotive Engine Electronic Control Unit Calibration.  IEEE International Conference on Information and Automation (ICIA), pp. 1380 – 1385, Zhuhai, China, 2009.
  24. Vong, C.M., Wong, P. K., & Zhang R. (22/06/2009). Incremental Modelling of Automotive Engine Performance using LS-SVM. IEEE International Conference on Information and Automation (ICIA), pp. 1537 – 1540, Zhuhai, China, 2009.
  25. Wong, P. K., Mok, K.W., & Vong, C.M. (10/2008). Design and Control of an Electromechanical Variable Rotary Valve System for Four-stroke Engines. Proceedings of AVEC ’08, pp. 887 – 892, Kobe, Japan.
  26. Vong, C.M., Wong, P. K., Li, K., & Zhang, R. (10/2008). A Study on Online LS-SVM for Modelling of Electronically-controlled Automotive Engine Performance. Proceedings of AVEC ’08, pp. 905 – 910, Kobe, Japan.
  27. Wong, P. K., Tam, L.M., Vong, C.M., & Li, K. (12/2006). Modelling and Prediction of Automotive Engine Specific Fuel Consumption Using Support Vector Machines. Symposium on City Energy Technology. Macao.
  28. Vong, C.M., Wong, P.K., Tam L.M. & Li, K. (2006). Preprocessing of Automotive Engine ECU Data. Proceedings of the 4th Regional Inter-University Postgraduate Electrical and Electronics Engineering Conference (RIUPEEEC 2006), Macao.
  29. Wong, P. K., Tam, L.M., Vong, C.M., & Li, K. (12/2006). Modelling and Prediction of Automotive Engine Specific Fuel Consumption Using Support Vector Machines. Symposium on City Energy Technology. Macao.
  30. Wong, P.K., Vong, C.M., Ho, C.M., & Li, Y.P. (2004). Prediction of Automotive Engine Power and Torque Using Support Vector Machines. Proceedings of Symposium on Applied Science and Technology in Macau 2004, Macao.
  31. Wong, P.K., Vong, C.M., Ho, C.M., & Li, Y.P. (2004). Prediction of Automotive Engine Power and Torque Using Support Vector Machines. Proceedings of Symposium on Applied Science and Technology in Macau 2004, Macao.
  32. Vong, C.M., Chan I.W., Chang, C.P., & Leong, W.K. (2004). Data Pre-processing for Automotive Engine Tune-up. Proceedings of Symposium on Applied Science and Technology in Macau 2004, Macao.
  33. Vong, C.M., Chan I.W., Chang, C.P., & Leong, W.K. (2004). Data Post-processing for Automotive Engine Tune-up. Proceedings of Symposium on Applied Science and Technology in Macau 2004, Macao.
  34. Vong, C.M., Wong, P.K., & Li, Y.P. (2003). Neural Networks in Automotive Engine Tuning. Proceedings of EPMESC IX – Computational Methods in Engineering and Science, Macao.
  35. Vong, C.M., Wong, P.K., & Li, Y.P. (2003). Case-based Reasoning in Airpot Terminal Planning. Proceedings of EPMESC IX – Computational Methods in Engineering and Science, Macao.
  36. Vong, C.M., Li, Y.P., & Wong, P.K. (2003). Knowledge Acquisition Through Case-based Adaptation for Hydraulic Power Machine Design. Proceedings of 5th International Conference on Enterprise Information Systems (ICEIS), Angers, France.
  37. Vong, C.M., Wong, P.K., & Li, Y.P. (2002). Automotive Engine Tuning with Data Mining Techniques. Proceedings of Symposium on Technological Innovation in Macau 2002, Macao.

Book/Chapter Contributions 

  1. J.W. Cao, C.M. Vong, Y. Miche, A. Lendasse (Eds.) Proceedings of ELM-2019, Published by Springer.
  2. J.W. Cao, C.M. Vong, Y. Miche, A. Lendasse (Eds.) Proceedings of ELM-2018, Published by Springer.
  3. J.W. Cao, C.M. Vong, Y. Miche, A. Lendasse (Eds.) Proceedings of ELM-2017, Published by Springer.
  4. J.W. Cao, E. Cambria, A. Lendasse, Y. Miche, C.M. Vong (Eds.) Proceedings of ELM-2016, Published by Springer.
  5. Lendasse, A., Vong, C.M., Toh, K.A., Huang, G.B.(Eds.) Advances in Extreme Learning Machines (ELM2015), SI of Neurocomputing, Elsevier, 2017.

Professional Affiliations

  • IEEE Senior Member (2013 – Now)
  • Treasurer, Macau Association of Promotion of Science and Technology (澳門科學技術協進會), (2006 – present)
  • Secretary-general, Macau Association of Promotion of Science and Technology  (澳門科學技術協進會), (2000 – 2006)
  • Member (Macau Representative), Association of Youths of Hainan Province, China (中國海南省青年聯合會, 澳門區委員) (2005-2010)

Professional and University Services

  • Associate Editor of Neurocomputing (SCI-E, JCR 2022 IF: 5.719) (2017 – Now).
  • Subject Editor of IET Electronics Letters (SCI-E, JCR 2022 IF: 1.54) (2018 – Now).
  • Program Committee (co-)chair of International Conference on Extreme Learning Machines 2013, 2016, 2021.
  • Competition co-chair of International Conference on Extreme Learning Machines 2014.
  • Guest editor of Neurocomputing (SCI-E) on Special Issue of ELM2014, ELM2015, ELM2016, ELM2017.
  • Guest editor of International Journal of Advanced Robotic Systems (SCI-E) 2014.
  • Associate Head of Department of Computer and Information Science (2016 – Now).
  • Panel Chair of student admission (BSc, MSc, PhD) to CIS program (2014 to now).
  • PI of UM FST Summer Camp on 3D Reconstruction using Quadcopters (2014-2019).

Software Download

The following software were developed under the support of the University of Macau and are for academic use only.

a) SBELM

This toolbox includes MATLAB code for

  1. Sparse Bayesian Extreme Learning Machine (SBELM) for multi-class classification
  2. Bayesian Extreme Learning Machine (BELM) for multi-class classification

For any bugs of the toolbox, please send to cmvong@um.edu.mo.

Disclaimer: Any personal or academic use of this toolbox should cite the following paper. For any commercial use of this toolbox, please contact us at cmvong@um.edu.mo for permission.

Jiahua Luo, Chi Man Vong, and Pak Kin Wong (2014), “Sparse Bayesian Extreme Learning Machine for Multi-classification”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 25(4), pp. 836-843, 2014.

The toolbox can be downloaded here.

b) ML-KELM

This toolbox includes MATLAB code for

  1. Multi-layer Kernel Extreme Learning Machine for Auto-Encoder

For any bugs of the toolbox, please send to cmvong@um.edu.mo.

Disclaimer: Any personal or academic use of this toolbox should cite the following paper. For any commercial use of this toolbox, please contact us at cmvong@um.edu.mo for permission.

Chi Man Wong, Chi Man Vong, Pak Kin Wong, Jiuwen Cao (2017), “Kernel-based Multi-layer Extreme Learning Machine for Representation Learning”, IEEE Transactions on Neural Networks and Learning Systems, accepted manuscript for publication in 2017.

The toolbox can be downloaded here.

Contact Details

Faculty of Science and Technology
University of Macau, E11
Avenida da Universidade, Taipa,
Macau, China

Room: E11-4013
Telephone: (853) 8822-4357
Fax: (853) 8822-2426
Email: cmvong6J