机器学习、分布式调度、计算器视觉、联邦学习

(1) 聚焦可解释性问题，实现最优稳态调节

提出延迟通信下完全异步分布式预测算法的收敛性条件和分析，由于生产过程中产品质量在进行检测前为预估值，若等到实际质量评估完后，与实际情形具备通信延迟特征，针对此问题提出完全异步分布式预测算法的收敛性条件和分析，透过神经网络补偿回馈延迟，降低算法的计算限制。生产过程中参数众多，参数之间具备共轭现象，在无法获得完全生产信息限制下，提出状态识别与抽象归纳算法，实现动态决策优化。为针对网络化生产系统的分布式最优稳态调节问题，针对目前生产状态进行识别，配合补偿机制，提出抗干扰分布式补偿控制算法，稳定生产状态。相关发表著作如下:

1.  Li Li, Wang Yong, Lin Kuo-Yi * (2020), “Predictive Maintenance Based on Opportunistic Production-Maintenance Synchronization” Journal of Intelligent Manufacturing. (SCI)

2.  Yu Qingyun, Yang Haolin, Lin, Kuo-Yi, Li Li* (2020), “A Predictive Dispatching Rule Assisted by Multi-Layer Perceptron for Scheduling Wafer Fabrication Lines,” ASME Journal of Computing and Information Science in Engineering. (SCI)

3.  Yu Qingyun, Yang Haolin, Lin Kuo-Yi, Li Li* (Accept), “A Self-Organized Scheduling Method for Scheduling Problems of Semiconductor Manufacturing System” Journal of Intelligent Manufacturing. (SCI)

4.  Chien, Chen-Fu and Lin, Kuo-Yi (2016), “Constructing the Framework of Semiconductor Overall Fab Performance Indices for Total Resource Management,” Journal of Management & Systems, Vol. 23, No. 4, pp. 451-474. (In Chinese)(TSSCI)

5.  Lin, Kuo-Yi, Hsu, C.-Y. and Yu, H.-C. (2014), “A Virtual Metrology Approach for Maintenance Compensation to Improve Yield in Semiconductor Manufacturing,” International Journal of Computational Intelligence Systems, Vol. 7, No. 2, pp. 66-73. (SCI)

(2) 着力抽样分布策略，优化参数计算决策

提出在有限数据及数据不平衡下的网络图谱深度学习算法，由于生产过程中产品不良品为稀缺数据，如何从有限数据中提取对故障诊断有帮助的知识，同时针对大量的良品数据来将数据特性最大化，提出了最少训练抽样策略，并将相应结果推广到评价函数。生产过程中参数众多，参数计算复杂，在有限的计算资源与时间限制下，提出参数优化机制讨论抽样与计算复杂性的折衷，协助抽样决策。针对生产诊断问题，提出了一种无需初始化的故障分类模型，缩小问题范围。相关发表著作如下:

1.  Gao Yunlong, Yang Chengyu, Lin Kuo-Yi, Pan Jinyan, Li Li∗ (2020),“Conditional semi-fuzzy c-means clustering for imbalanced dataset” IET Image Processing (SCI)

2.  Li Li, Fan Yuxi, Tse Mike, Lin Kuo-Yi * (Accepted), “A Review of Applications in Federated Learning” Computers & Industrial Engineering. (SCI)

3.  Li Li, Wang Yong, Hsu Chia Yu, Li Yibing, Lin Kuo-Yi * (Accepted), “A L-measure Evaluation Metric for Fake Information Detection Model with Binary Class Imbalance Problem” Enterprise Information Systems. (SCI)

4.  Chien Chen-Fu, Lin Kuo-Yi, Sheu J. and Wu C. (2016), “Retrospect and Prospect on Operations and Management Journals in Taiwan: From Industry 3.0 to Industry 3.5,” Journal of Management, Vol. 33, No. 1, pp. 87-103. (In Chinese) (TSSCI)

5.  Yu, H.-C., Lin, Kuo-Yi, and Chien, Chen-Fu (2014), “Hierarchical indices to detect equipment condition changes with high dimensional data for semiconductor manufacturing,” Journal of Intelligent Manufacturing, Vol. 25, pp. 933-943. (SCI)

  (3) 探索参数共轭关系，推理设备诊断状态

提出在线学习参数最优阶层丛集分析的神经编码，由于生产过程中传感器搜集的参数众多且表现具备随机性扰动，任何随机的跳动均可能造成产品、设备的问题，如何预先透过参数变化找出共轭关系降低生产不确定性为重要任务，针对此问题提出神经编码可依据实时生产状态提供参数共轭关系，并透过数据证明推导算法的全局收敛性。提出基于变分贝叶斯方法和采样梯度估计的实时学习机制，最大限度的保证了算法的可行性。提出了全周期多时间尺度的推理机制，由于生产过程中设备随着时间过程会产生设备老化与状态改变之现象，研究针对此问题发展全周期多时间尺度的推理机制，透过数据诊断设备现况，配合设备维护与保养，实现了鲁棒、实时的分析模型，推理生产状态。相关发表著作如下:

1.  Lin Kuo-Yi* (2018), “User Experience–Based Product Design for Smart Production to Empower Industry 4.0 in the Glass Recycling Circular Economy,” Computers & Industrial Engineering, Vol. 125, pp. 729-738. (SCI)

2.  Lin Kuo-Yi* (2018), “A text mining approach to capture user experience for new product development,” International Journal of Industrial Engineering-Theory Applications and Practice, Vol. 25, No. 1, pp. 108-121. (SCI)

3.  Hsieh Ying-Che, Lin Kuo-Yi, Lu Chao and Rong Ke (2017), “Governing a sustainable business ecosystem in Taiwan’s circular economy: The story of Spring Pool Glass,” Sustainability, Vol. 9, No. 6, pp. 1068. (SSCI)

4.  Lin Kuo-Yi, Chien Chen-Fu, and Kerh Rhoann, (2016), “UNISON Framework of Data-Driven Innovation for Extracting User Experience of Product Design of Wearable Devices,” Computers & Industrial Engineering, Vol. 99, pp. 487-502.  (SCI)

5. Chien Chen-Fu, Kerh Rhoann, Lin Kuo-Yi, and Annie Pei-I Yu (2016), “Data-driven innovation to capture user-experience product design: An empirical study for notebook visual aesthetics design,” Computers & Industrial Engineering, Vol. 99, pp. 162-173. (SCI)

6.  Chien, Chen-Fu, Lin, Kuo-Yi and Yu, A., (2014), “A User-experience of tablet operating system: An experimental investigation of Windows 8, iOS 6, and Android 4.2,” Computers & Industrial Engineering, Vol. 73, pp. 75-84. (SCI)

1.    Li Li, Fan Yuxi, Tse Mike, Lin Kuo-Yi * (2020), “A Review of Applications in Federated Learning” Computers & Industrial Engineering. (SCI)

2.   Li Li, Wang Yong, Hsu Chia Yu, Li Yibing, Lin Kuo-Yi * (2020), “A L-measure Evaluation Metric for Fake Information Detection Model with Binary Class Imbalance Problem” Enterprise Information Systems. (SCI)

3.   Yu Qingyun, Yang Haolin, Lin Kuo-Yi, Li Li* (2020), “A Self-Organized Scheduling Method for Scheduling Problems of Semiconductor Manufacturing System” Journal of Intelligent Manufacturing. (SCI)

4.   Li Li, Wang Yong, Lin Kuo-Yi * (2020), “Predictive Maintenance Based on Opportunistic Production-Maintenance Synchronization” Journal of Intelligent Manufacturing. (SCI)

5.    Gao Yunlong, Yang Chengyu, Lin Kuo-Yi, Pan Jinyan, Li Li∗ (2020),“Conditional semi-fuzzy c-means clustering for imbalanced dataset” IET Image Processing (SCI)

6.    Yu Qingyun, Yang Haolin, Lin, Kuo-Yi, Li Li* (2020), “A Predictive Dispatching Rule Assisted by Multi-Layer Perceptron for Scheduling Wafer Fabrication Lines,” ASME Journal of Computing and Information Science in Engineering. (SCI)

7.   Lin Kuo-Yi* (2018), “User Experience–Based Product Design for Smart Production to Empower Industry 4.0 in the Glass Recycling Circular Economy,” Computers & Industrial Engineering, Vol. 125, pp. 729-738. (SCI)

8.    Lin Kuo-Yi* (2018), “A text mining approach to capture user experience for new product development,” International Journal Of Industrial Engineering-Theory Applications And Practice, Vol. 25, No. 1, pp. 108-121. (SCI)

9.    Lin Kuo-Yi*, Yu A., Chu Pei-Chun and Chien Chen-Fu (2017), “User-Experience-Based Design of Experiments for New Product Development of Consumer Electronics and an Empirical Study,” Journal of Industrial and Production Engineering, Vol. 34, No. 7, pp. 504-519. (EI/TSSCI)

10.  Chien Chen-Fu, Hou Jiang-Liang, Wu Chien-Wei, Lin Kuo-Yi*, Hu, Yi-Fen and Chu, Pei-Chun (2017), “Investigation of Total Quality Management in Higher Education Institutional Research－An Empirical Study of National Tsing Hua University,” Journal of Management & Systems, Vol. 24, No. 4, pp. 591-614. (TSSCI)

11.  Hsieh Ying-Che, Lin Kuo-Yi, Lu Chao and Rong Ke (2017), “Governing a sustainable business ecosystem in Taiwan’s circular economy: The story of Spring Pool Glass,” Sustainability, Vol. 9, No. 6, pp. 1068. (SSCI) 

12.  Lin Kuo-Yi, Chien Chen-Fu, and Kerh Rhoann, (2016), “UNISON Framework of Data-Driven Innovation for Extracting User Experience of Product Design of Wearable Devices,” Computers & Industrial Engineering, Vol. 99, pp. 487-502.  (SCI)

13.   Chien Chen-Fu, Kerh Rhoann, Lin Kuo-Yi, and Annie Pei-I Yu (2016), “Data-driven innovation to capture user-experience product design: An empirical study for notebook visual aesthetics design,” Computers & Industrial Engineering, Vol. 99, pp. 162-173. (SCI) 

14.  Chien Chen-Fu, Lin Kuo-Yi, Sheu J. and Wu C. (2016), “Retrospect and Prospect on Operations and Management Journals in Taiwan: From Industry 3.0 to Industry 3.5,” Journal of Management, Vol. 33, No. 1, pp. 87-103. (In Chinese) (TSSCI) 

15.  Chien, Chen-Fu and Lin, Kuo-Yi (2016), “Constructing the Framework of Semiconductor Overall Fab Performance Indices for Total Resource Management,” Journal of Management & Systems, Vol. 23, No. 4, pp. 451-474. (In Chinese)(TSSCI)

16.   Chien, Chen-Fu, Lin, Kuo-Yi and Yu, A., (2014), “A User-experience of tablet operating system: An experimental investigation of Windows 8, iOS 6, and Android 4.2,” Computers & Industrial Engineering, Vol. 73, pp. 75-84. (SCI) 

17.  Yu, H.-C., Lin, Kuo-Yi, and Chien, Chen-Fu (2014), “Hierarchical indices to detect equipment condition changes with high dimensional data for semiconductor manufacturing,” Journal of Intelligent Manufacturing, Vol. 25, pp. 933-943. (SCI) 

18.   Lin, Kuo-Yi, Hsu, C.-Y. and Yu, H.-C. (2014), “A Virtual Metrology Approach for Maintenance Compensation to Improve Yield in Semiconductor Manufacturing,” International Journal of Computational Intelligence Systems, Vol. 7, No. 2, pp. 66-73. (SCI)

19.   Chien, Chen-Fu, Chen, C., and Lin, Kuo-Yi (2013), “An Investigation of Planning a Research Park in Hsinchu Science Park Area,” Journal of Management & Systems, Vol. 20, No. 2, pp. 227-255. (In Chinese)(TSSCI)

20.   Chien, Chen-Fu and Lin, Kuo-Yi (2012), “Manufacturing intelligence for Hsinchu Science Park semiconductor sales prediction,” Journal of the Chinese Institute of Industrial Engineers, Vol. 29, No. 2, pp.98-110. (EI) 

21.   Hsu, C.-Y., Chien, Chen-Fu, Lin, Kuo-Yi, and Chien, C. (2010), “Data Mining for Yield Enhancement in TFT-LCD Manufacturing and an Empirical Study,”Journal of the Chinese Institute of Industrial Engineers, Vol. 27, No. 2, pp.140-156. (EI) 

20191118. 2019年全国先进生产系统理论与应用研讨会暨制造系统领域NSFC基金诊断论坛隆重举行，

2019年全国先进生产系统理论与应用研讨会暨制造系统领域NSFC基金诊断论坛隆重举行 (dhu.edu.cn)

20191204. 同济大学林国义副教授学术报告顺利举行, 

同济大学林国义副教授学术报告顺利举行 (ustb.edu.cn)