| Industry 4.0: a way from mass customization to mass personalization production Y Wang, HS Ma, JH Yang, KS Wang Advances in manufacturing 5, 311-320, 2017 | 533 | 2017 |
| Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem H Zhu, Y Wang, K Wang, Y Chen Expert Systems with Applications 38 (8), 10161-10169, 2011 | 371 | 2011 |
| Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario Z Li, Y Wang, KS Wang Advances in Manufacturing 5, 377-387, 2017 | 282 | 2017 |
| Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network Z Zhang, Y Wang, K Wang Journal of Intelligent Manufacturing 24, 1213-1227, 2013 | 257 | 2013 |
| A hybrid intelligent method for modelling the EDM process K Wang, HL Gelgele, Y Wang, Q Yuan, M Fang International Journal of Machine Tools and Manufacture 43 (10), 995-999, 2003 | 248 | 2003 |
| A deep learning approach for anomaly detection based on SAE and LSTM in mechanical equipment Z Li, J Li, Y Wang, K Wang The International Journal of Advanced Manufacturing Technology 103, 499-510, 2019 | 139 | 2019 |
| The application of Industry 4.0 technologies in sustainable logistics: a systematic literature review (2012–2020) to explore future research opportunities X Sun, H Yu, WD Solvang, Y Wang, K Wang Environmental Science and Pollution Research, 1-32, 2022 | 90 | 2022 |
| Automatic detection of false positive RFID readings using machine learning algorithms H Ma, Y Wang, K Wang Expert Systems with Applications 91, 442-451, 2018 | 84 | 2018 |
| A deep learning driven method for fault classification and degradation assessment in mechanical equipment Z Li, Y Wang, K Wang Computers in industry 104, 1-10, 2019 | 73 | 2019 |
| A generic genetic algorithm for product family design J Jiao, Y Zhang, Y Wang Journal of Intelligent Manufacturing 18, 233-247, 2007 | 69 | 2007 |
| A study on adaptation lightweight architecture based deep learning models for bearing fault diagnosis under varying working conditions J Wu, T Tang, M Chen, Y Wang, K Wang Expert Systems with Applications 160, 113710, 2020 | 68 | 2020 |
| How AI affects the future predictive maintenance: a primer of deep learning K Wang, Y Wang Advanced Manufacturing and Automation VII 7, 1-9, 2018 | 65 | 2018 |
| Intelligent fault diagnosis and prognosis approach for rotating machinery integrating wavelet transform, principal component analysis, and artificial neural networks Z Zhang, Y Wang, K Wang The international journal of advanced manufacturing technology 68, 763-773, 2013 | 64 | 2013 |
| A data-driven method based on deep belief networks for backlash error prediction in machining centers Z Li, Y Wang, K Wang Journal of Intelligent Manufacturing 31, 1693-1705, 2020 | 62 | 2020 |
| A conceptual framework to develop green textiles in the aeronautic completion industry: a case study in a large manufacturing company N Moreira, LA de Santa-Eulalia, D Aït-Kadi, T Wood–Harper, Y Wang Journal of Cleaner Production 105, 371-388, 2015 | 52 | 2015 |
| The optimization for hyperbolic positioning of UHF passive RFID tags H Ma, Y Wang, K Wang, Z Ma IEEE Transactions on Automation Science and Engineering 14 (4), 1590-1600, 2017 | 51 | 2017 |
| Application of long short-term memory neural network to sales forecasting in retail—a case study Q Yu, K Wang, JO Strandhagen, Y Wang Advanced Manufacturing and Automation VII 7, 11-17, 2018 | 43 | 2018 |
| Mining data streams with skewed distribution by static classifier ensemble Y Wang, Y Zhang, Y Wang Opportunities and challenges for next-generation applied intelligence, 65-71, 2009 | 37 | 2009 |
| A novel method for the evaluation of fashion product design based on data mining BR Li, Y Wang, KS Wang Advances in Manufacturing 5, 370-376, 2017 | 30 | 2017 |
| Advanced manufacturing and automation VII K Wang, Y Wang, JO Strandhagen, T Yu Springer, 2018 | 24 | 2018 |
