陳建智(Chien-Chih Chen)

一、最高學歷

國立成功大學　工業與資訊管理研究所　博士

二、經歷

A主要經歷

B其他實務經驗

三、研究領域及專長

1. 大數據分析
2. 小樣本學習
3. 人工智慧(AI)演算法
4. 機器學習演算法
5. 啟發式最佳化演算法

四、研究成果：

A期刊論文

1. Chung, H.-W., Chang, C.-K., Huang, T.-H., Chen, L.-C., Chen, H.-L., Yang, S.-T., Chen, C.-C.*, Wang, K. (2023). Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study. Children, 10, 1239. https://doi.org/10.3390/children10071239【SCI; IF= 2.835】
2. Chen, Y.-Y., Wang, KC, Chung, H.-W., Chen, C.-C., Huang B.-H., Lu, I-W. (2023). Multi-Person Pose Estimation using an Ordinal Depth-Guided Convolutional Neural Network, Journal of Information Science and Engineering. 39, 1403-1420. https://doi.org/10.6688/JISE.202311_39(6).0010【SCI; IF= 1.142】
3. Chang, C.-J., Dai, W.-L., Li, D.-C., Chen, C.-C., & Li, G. (2023). An Extrapolation Non-Equigap Grey Model for Operation Management. Journal of Grey System, 35(1), 101-112. 【SCI; IF= 1.3】
4. Chang, C.-J., Chen, C.-C., Dai, W.-L., & Li, G. (2021). A new grey prediction model considering the data gap compensation. Grey Systems: Theory and Application, 11(4), 650-663. doi:10.1108/GS-07-2020-0087【SCI; IF= 3.321】
5. Chen, C.-C., Chang, C.-J., Zhuang, Z.-Y., & Li, D.-C. (2020). An envelopment learning procedure for improving prediction accuracies of grey models. Computers and Industrial Engineering, 139, 106185. doi:https://doi.org/10.1016/j.cie.2019.106185【SCI; IF= 3.518】
6. Hsu, H.-C., Chang, S., Chen, C.-C., & Wu, I. C. (2020). Knowledge-based system for resolving design clashes in building information models. Automation in Construction, 110, 103001. doi:https://doi.org/10.1016/j.autcon.2019.103001【SCI; IF= 4.313】
7. Chang, C. J., Li, D-C., Chen, C. C., & Chen, W. C. (2019). Extrapolation-based grey model for small-data-set forecasting. Economic Computation and Economic Cybernetics Studies and Research, 53(1), 171-182. https://doi.org/10.24818/18423264/53.1.19.11【SCI, SSCI, IF 0.664】
8. Li, D.-C., Lin, L.-S., Chen, C. C.., & Yu, W.-H. (2019). Using virtual samples to improve learning performance for small datasets with multimodal distributions. Soft Computing, 23(22), 11883-11900. doi:10.1007/s00500-018-03744-z【SCI; IF= 2.367】
9. Chang, C. J., Dai, W. L., Li, D-C., & Chen, C. C. (2018). Latent-Function-Based Residual Discrete Grey Model for Short-Term Demand Forecasting. Cybernetics and Systems, 49(3), 170-180. https://doi.org/10.1080/01969722.2018.1448228.【SCI; IF= 1.197】
10. Li, D-C., Yeh, C. W., Chen, C. C., & Wang, Y. T. (2018). A new grey prediction model for the return material authorization process in the TFT-LCD industry. International Journal of Advanced Manufacturing Technology, 96(5-8), 2149-2160. https://doi.org/10.1007/s00170-018-1754-y.【SCI; IF= 2.209】
11. Li, D-C., Lin, W. K., Chen, C. C., Chen, H. Y., & Lin, L. S. (2018). Rebuilding sample distributions for small dataset learning. Decision Support Systems, 105, 66-76. https://doi.org/10.1016/j.dss.2017.10.013.【SCI, IF 3.222】
12. Li, D-C., Lin, W. K., Lin, L. S., Chen, C. C., & Huang, W. T. (2016). The attribute-trend-similarity method to improve learning performance for small datasets. International Journal of Production Research, 55(7), 1898-1913. https://doi.org/10.1080/00207543.2016.1213447.【SCI, IF 2.325】
13. Li, D-C., Yeh, C. W., Chen, C. C., & Shih, H. T. (2016). Using a diffusion wavelet neural network for short-term time series learning in the wafer level chip scale package process. Journal of Intelligent Manufacturing, 27(6), 1261-1272. https://doi.org/10.1007/s10845-014-0949-9.【SCI, IF 3.035】
14. Chang, C. J., Dai, W. L., Chen, C. C. (2015). A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting. Journal of the Operational Research Society. 66(11), 1887-1894. https://www.tandfonline.com/doi/full/10.1057/jors.2015.17.【SCI, IF 1.225】
15. Li, D-C., Chen, W. C., Chang, C. J., Chen, C. C., & Wen, I. H. (2015). Practical information diffusion techniques to accelerate new product pilot runs. International Journal of Production Research, 53(17), 5310-5319. https://doi.org/10.1080/00207543.2015.1032437.【SCI, IF 2.325】
16. Chang, C. J., Li, D-C., Huang, Y. H., & Chen, C. C. (2015). A novel gray forecasting model based on the box plot for small manufacturing data sets. Applied Mathematics and Computation, 265, 400-408. https://doi.org/10.1016/j.amc.2015.05.006.【SCI, IF 1.738】
17. Li, D-C., Huang, W. T., Chen, C. C., & Chang, C. J. (2014). Employing box plots to build high-dimensional manufacturing models for new products in TFT-LCD plants. Neurocomputing, 142, 73-85. https://doi.org/10.1016/j.neucom.2014.03.043.【SCI, IF 3.317】
18. Chang, C. J., Li, D-C., Dai, W. L., & Chen, C. C. (2014). A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting. Neurocomputing, 129, 343-349. https://doi.org/10.1016/j.neucom.2013.09.024.【SCI, IF 3.317】
19. Chang, C. J., Li, D-C., Chen, C. C., & Chen, C. S. (2014). A forecasting model for small non-equigap data sets considering data weights and occurrence possibilities. Computers and Industrial Engineering, 67(1), 139-145. https://doi.org/10.1016/j.cie.2013.11.002.【SCI, IF 2.623】
20. Chang, C. J., Li, D-C., Chen, C. C., & Dai, W. L. (2013). A Grey-based rolling procedure for short-term forecasting using limited time series data. Economic Computation and Economic Cybernetics Studies and Research, 47(3), 75-90.【SCI, SSCI, IF 0.420】
21. Chang, C. J., Li, D-C., Dai, W. L., & Chen, C. C. (2013). Utilizing an adaptive grey model for short-term time series forecasting: A case study of wafer-level packaging. Mathematical Problems in Engineering, 2013, [526806]. https://doi.org/10.1155/2013/526806. 【SCI, IF 1.383】
22. Li, D-C., Huang, W. T., Chen, C. C., & Chang, C. J. (2013). Employing virtual samples to build early high-dimensional manufacturing models. International Journal of Production Research, 51(11), 3206-3224. https://doi.org/10.1080/00207543.2012.746795.【SCI, IF 2.325】
23. Li, D-C., Chang, C. J., Chen, C. C., & Chen, W. C. (2012). A grey-based fitting coefficient to build a hybrid forecasting model for small data sets. Applied Mathematical Modelling, 36(10), 5101-5108. https://doi.org/10.1016/j.apm.2011.12.050【SCI, IF 2.350】
24. Li, D-C., Chen, C. C., Chen, W. C., & Chang, C. J. (2012). Employing dependent virtual samples to obtain more manufacturing information in pilot runs. International Journal of Production Research, 50(23), 6886-6903. https://doi.org/10.1080/00207543.2011.631603.【SCI, IF 2.325】
25. Li, D-C., Chen, C. C., Chang, C. J., & Chen, W. C. (2012). Employing box-and-whisker plots for learning more knowledge in TFT-LCD pilot runs. International Journal of Production Research, 50(6), 1539-1553. https://doi.org/10.1080/00207543.2011.555430.【SCI, IF 2.325】
26. Li, D-C., Chen, C. C., Chang, C. J., & Lin, W. K. (2012). A tree-based-trend-diffusion prediction procedure for small sample sets in the early stages of manufacturing systems. Expert Systems With Applications, 39(1), 1575-1581. https://doi.org/10.1016/j.eswa.2011.08.071.【SCI, IF 3.928】
27. Li, D-C., Chang, C. J., Chen, C. C., & Chen, W. C. (2012). Forecasting short-term electricity consumption using the adaptive grey-based approach-An Asian case. Omega, 40(6), 767-773. https://doi.org/10.1016/j.omega.2011.07.007.【SCI, SSCI, IF 4.029】
28. Li, D-C., Chen, W. C., Liu, C. W., Chang, C. J., & Chen, C. C. (2012). Determining manufacturing parameters to suppress system variance using linear and non-linear models. Expert Systems With Applications, 39(4), 4020-4025. https://doi.org/10.1016/j.eswa.2011.09.067【SCI, IF 3.928】
29. Li, D-C., Chang, C. J., Chen, W. C., & Chen, C. C. (2011). An extended grey forecasting model for omnidirectional forecasting considering data gap difference. Applied Mathematical Modelling, 35(10), 5051-5058. https://doi.org/10.1016/j.apm.2011.04.006【SCI, IF 2.326】
30. Li, D-C., Chang, C. J., Chen, C. C., & Chen, C. S. (2010). Using non-equigap grey model for small data set forecasting - A color filter manufacturing example. Journal of Grey System, 22(4), 375-382.【SCI, IF 0.370】
31. Li, D-C., Liu, C. W., Fang, Y. H., & Chen, C. C. (2010). A yield forecast model for pilot products using support vector regression and manufacturing experience-the case of large-size polariser. International Journal of Production Research, 48(18), 5481-5496. https://doi.org/10.1080/00207540903100051.【SCI; IF= 1.033】。

B研討會論文

1. Lin, Y. S., Li, D. C., Chen, C. C., and Chen, H. Y. (2019). Using MTD-based Non-Equigap Grey Models for Defective Recognition in Hi-tech Industry. Paper presented at the 2019 International Congress on Grey Systems and Uncertainty Analysis (GSUA 2019), Bangkok.
2. Lin, Y. S., Cheng, W. N., Chen, C. C., Li, D. C., & Chen, H. Y. (2019, 7-11 July 2019). Generating Synthetic Samples to Improve Small Sample Learning with Mixed Numerical and Categorical Attributes. Paper presented at the 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI).

C技術報告及專書

無。

D專利或新品種

E技術移轉

五、研究計畫及產學實績

六、獲獎及其他優良事蹟

A獲獎

1. The 2019 International Congress on Grey Systems and Uncertainty Analysis (GSUA 2019) 最佳論文獎

B其他優良事蹟