請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/76953完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳政鴻(Cheng-Hung Wu) | |
| dc.contributor.author | Tzu-Hsuan Shen | en |
| dc.contributor.author | 沈子暄 | zh_TW |
| dc.date.accessioned | 2021-07-10T21:41:28Z | - |
| dc.date.available | 2021-07-10T21:41:28Z | - |
| dc.date.copyright | 2020-08-13 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-04 | |
| dc.identifier.citation | Akl, A., El-Henawy, I., Salah, A., Li, K. L. (2019). Optimizing deep neural networks hyperparameter positions and values. Journal of Intelligent Fuzzy Systems, 37(5), 6665-6681. doi:10.3233/jifs-190033 Akyol, K. (2020). Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection. Expert Systems with Applications, 148, 9. doi:10.1016/j.eswa.2020.113239 AlandiHallaj, M., Assadian, N. (2017). Soft landing on an irregular shape asteroid using Multiple-Horizon Multiple-Model Predictive Control. Acta Astronautica, 140, 225-234. doi:10.1016/j.actaastro.2017.08.019 Alfieri, A., Matta, A., Pedrielli, G. (2015). Mathematical programming models for joint simulation-optimization applied to closed queueing networks. Annals of Operations Research, 231(1), 105-127. doi:10.1007/s10479-013-1480-7 Alon, G., Kroese, D. P., Raviv, T., Rubinstein, R. Y. (2005). Application of the cross-entropy method to the buffer allocation problem in a simulation-based environment. Annals of Operations Research, 134(1), 137-151. doi:10.1007/s10479-005-5728-8 Alonso-Monsalve, S., Suarez-Cetrulo, A. L., Cervantes, A., Quintana, D. (2020). Convolution on neural networks for high-frequency trend prediction of cryptocurrency exchange rates using technical indicators. Expert Systems with Applications, 149, 15. doi:10.1016/j.eswa.2020.113250 Amaran, S., Sahinidis, N. V., Sharda, B., Bury, S. J. (2014). Simulation optimization: a review of algorithms and applications. 4or-a Quarterly Journal of Operations Research, 12(4), 301-333. doi:10.1007/s10288-014-0275-2 Amirabadi, M. A., Kahaei, M. H., Nezamalhosseini, S. A. (2020). Novel suboptimal approaches for hyperparameter tuning of deep neural network under the shelf of optical communication. Physical Communication, 41, 12. doi:10.1016/j.phycom.2020.101057 Aszemi, N. M., Dominic, P. D. D. (2019). Hyperparameter Optimization in Convolutional Neural Network using Genetic Algorithms. International Journal of Advanced Computer Science and Applications, 10(6), 269-278. Bang, H. T., Yoon, S., Jeon, H. (2020). Application of machine learning methods to predict a thermal conductivity model for compacted bentonite. Annals of Nuclear Energy, 142, 7. doi:10.1016/j.anucene.2020.107395 Bekker, J., Aldrich, C. (2011). The cross-entropy method in multi-objective optimisation: An assessment. European Journal of Operational Research, 211(1), 112-121. doi:10.1016/j.ejor.2010.10.028 Bengio, Y. (2012). Practical recommendations for gradient-based training of deep architectures. Botev, Z. I., Kroese, D. P., Rubinstein, R. Y., L’Ecuyer, P. (2013). The Cross-Entropy Method for Optimization. Botev, Z. I., Kroese, D. P., Taimre, T. (2007). Generalized cross-entropy methods with applications to rare-event simulation and optimization. Simulation-Transactions of the Society for Modeling and Simulation International, 83(11), 785-806. doi:10.1177/0037549707087067 Budi Santosa, M. A. B. a. S. E. W. (2011). A Cross Entropy-Genetic Algorithm for m-Machines No-Wait Job-ShopScheduling Problem. Journal of Intelligent Learning Systems and Applications, 171-180. Carbery, C. M., Woods, R., Marshall, A. H. (2019). A new data analytics framework emphasising preprocessing of data to generate insights into complex manufacturing systems. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 233(19-20), 6713-6726. doi:10.1177/0954406219866867 Chai, R., Tsourdos, A., Savvaris, A., Xia, Y. Q., Chai, S. C. (2020). Real-Time Reentry Trajectory Planning of Hypersonic Vehicles: A Two-Step Strategy Incorporating Fuzzy Multiobjective Transcription and Deep Neural Network. Ieee Transactions on Industrial Electronics, 67(8), 6904-6915. doi:10.1109/tie.2019.2939934 Chaitrali S. Dangare, S. S. A. (2012). Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques. International Journal of Computer Applications, 44-48. Chen, C., Bollas, G. M. (2018). Dynamic Optimization of a Subcritical Steam Power Plant Under Time-Varying Power Load. Processes, 6(8), 19. doi:10.3390/pr6080114 Chen, P. Y., Huang, J. J. (2019). A Hybrid Autoencoder Network for Unsupervised Image Clustering. Algorithms, 12(6), 11. doi:10.3390/a12060122 Choi, D. K. (2019). Data-Driven Materials Modeling with XGBoost Algorithm and Statistical Inference Analysis for Prediction of Fatigue Strength of Steels. International Journal of Precision Engineering and Manufacturing, 20(1), 129-138. doi:10.1007/s12541-019-00048-6 Cui, Q., Wu, S., Liu, Q., Zhong, W., Wang, L. (2020). MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation. Ieee Transactions on Knowledge and Data Engineering, 32(2), 317-331. doi:10.1109/tkde.2018.2881260 Dami Choi, C. J. S., Zachary Nado, Jaehoon Lee, Chris J. Maddison, George E. Dahl. (2019). On Empirical Comparisons Of Optimizers For Deep Learning. De Boer, P. T., Kroese, D. P., Mannor, S., Rubinstein, R. Y. (2005). A tutorial on the cross-entropy method. Annals of Operations Research, 134(1), 19-67. doi:10.1007/s10479-005-5724-z Diederik Kingma, J. B. (2014). Adam: A method for stochastic optimization. Djork-Arné Clevert, T. U., Sepp Hochreiter. (2015). Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs). Dong, W., Huang, Y. M., Lehane, B., Ma, G. W. (2020). XGBoost algorithm-based prediction of concrete electrical resistivity for structural health monitoring. Automation in Construction, 114, 11. doi:10.1016/j.autcon.2020.103155 Dou, J., Yunus, A. P., Merghadi, A., Shirzadi, A., Nguyen, H., Hussain, Y., . . . Yamagishi, H. (2020). Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Science of the Total Environment, 720, 16. doi:10.1016/j.scitotenv.2020.137320 Dozat, T. (2016). INCORPORATING NESTEROV MOMENTUM INTO ADAM. E. H. Glaessgen, D. S. S. (2012). The digital twin paradigm for future NASA and US air force vehicles. Farrugia, S., Ellul, J., Azzopardi, G. (2020). Detection of illicit accounts over the Ethereum blockchain. Expert Systems with Applications, 150, 11. doi:10.1016/j.eswa.2020.113318 Frank Hutter, H. H., Kevin Leyton-Brown. (2014). An Efficient Approach for Assessing Hyperparameter Importance. Gerault, R., Peton, N., Pearson, D. W., Hunt, F., Dray, G. (1998). Several methods usable in production systems prediction. Mathematical and Computer Modelling, 27(5), 99-113. doi:10.1016/s0895-7177(98)00022-3 Gertz, M., Grosse-Butenuth, K., Junge, W., Maassen-Francke, B., Renner, C., Sparenberg, H., Krieter, J. (2020). Using the XGBoost algorithm to classify neck and leg activity sensor data using on-farm health recordings for locomotor-associated diseases. Computers and Electronics in Agriculture, 173, 10. doi:10.1016/j.compag.2020.105404 Größler, A. (2010). An Exploratory System Dynamics Model of Strategic Manufacturing Capabilities. Journal of Manufacturing Technology Management, 21(6). Guha, M. (2018). Revealing cohesive zone shape and location inside blast furnace. Ironmaking Steelmaking, 45(9), 787-792. doi:10.1080/03019233.2017.1338385 Han, Z. Z., Shang, M. Y., Liu, Z. B., Vong, C. M., Liu, Y. S., Zwicker, M., . . . Chen, C. L. P. (2019). SeqViews2SeqLabels: Learning 3D Global Features via Aggregating Sequential Views by RNN With Attention. Ieee Transactions on Image Processing, 28(2), 658-672. doi:10.1109/tip.2018.2868426 Hao, Q., Guan, L. W., Wang, J. S., Wang, L. P. (2011). Dynamic Feedforward Control of a Novel 3-PSP 3-DOF Parallel Manipulator. Chinese Journal of Mechanical Engineering, 24(4), 676-684. doi:10.3901/cjme.2011.04.676 Hashem, S. (1997). Optimal linear combinations of neural networks. Neural Networks, 10(4), 599-614. doi:10.1016/s0893-6080(96)00098-6 He, X. X., Luo, J. G., Zuo, G. G., Xie, J. C. (2019). Daily Runoff Forecasting Using a Hybrid Model Based on Variational Mode Decomposition and Deep Neural Networks. Water Resources Management, 33(4), 1571-1590. doi:10.1007/s11269-019-2183-x Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., . . . Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition. Ieee Signal Processing Magazine, 29(6), 82-97. doi:10.1109/msp.2012.2205597 Hinton, G. E., Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504-507. doi:10.1126/science.1127647 Hou, K. X., Li, Y. H. (2019). Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving. Processes, 7(6), 18. doi:10.3390/pr7060334 Huang, C. H. (2014). Modified Neural Network for Dynamic Control and Operation of a Hybrid Generation Systems. Journal of Applied Research and Technology, 12(6), 1154-1164. doi:10.1016/s1665-6423(14)71674-8 Huang, J. C., Ko, K. M., Shu, M. H., Hsu, B. M. (2020). Application and comparison of several machine learning algorithms and their integration models in regression problems. Neural Computing Applications, 32(10), 5461-5469. doi:10.1007/s00521-019-04644-5 Hullen, G., Zhai, J. Y., Kim, S. H., Sinha, A., Realff, M. J., Boukouvala, F. (2020). Managing uncertainty in data-driven simulation-based optimization. Computers Chemical Engineering, 136, 16. doi:10.1016/j.compchemeng.2019.106519 Ilyas, S., Zia, S., Butt, U. M., Letchmunan, S., Nisa, Z. U. (2020). Predicting the Future Transaction from Large and Imbalanced Banking Dataset. International Journal of Advanced Computer Science and Applications, 11(1), 273-286. Jalali, H., Van Nieuwenhuyse, I. (2015). Simulation optimization in inventory replenishment: aclassification. Iie Transactions, 47(11), 1217-1235. doi:10.1080/0740817x.2015.1019162 Jerez, T., Kristjanpoller, W. (2020). Effects of the validation set on stock returns forecasting. Expert Systems with Applications, 150, 12. doi:10.1016/j.eswa.2020.113271 Ji, X. J., Tong, W. D., Liu, Z. C., Shi, T. L. (2019). Five-Feature Model for Developing the Classifier for Synergistic vs. Antagonistic Drug Combinations Built by XGBoost. Frontiers in Genetics, 10, 13. doi:10.3389/fgene.2019.00600 Jiang, S. C., Xiao, R., Wang, L., Luo, X., Huang, C., Wang, J. H., . . . Nie, X. M. (2019). Combining Deep Neural Networks and Classical Time Series Regression Models for Forecasting Patient Flows in Hong Kong. IEEE ACCESS, 7, 118965-118974. doi:10.1109/access.2019.2936550 Jin, Q. W., Fan, X. T., Liu, J., Xue, Z. X., Jian, H. D. (2019). Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific. Atmosphere, 10(6), 20. doi:10.3390/atmos10060341 Kikkawa, T. (2012). New Rolling Method Of Reversing Cold Rolling Mill. Koehn, D., Lessmann, S., Schaal, M. (2020). Predicting online shopping behaviour from clickstream data using deep learning. Expert Systems with Applications, 150, 16. doi:10.1016/j.eswa.2020.113342 Koutsoukas, A., Monaghan, K. J., Li, X. L., Huan, J. (2017). Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data. Journal of Cheminformatics, 9, 13. doi:10.1186/s13321-017-0226-y Krizhevsky, A., Sutskever, I., Hinton, G. E. (2017). ImageNet Classification with Deep Convolutional Neural Networks. Communications of the Acm, 60(6), 84-90. doi:10.1145/3065386 Kroese, D. P., Hui, K. P., Nariai, S. (2007). Network reliability optimization via the cross-entropy method. Ieee Transactions on Reliability, 56(2), 275-287. doi:10.1109/tr.2007.895303 Le, L. T., Nguyen, H., Zhou, J., Dou, J., Moayedi, H. (2019). Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost. Applied Sciences-Basel, 9(13), 22. doi:10.3390/app9132714 Lee, T., Kwak, B. J., Yu, J., Lee, J. H., Noh, Y., Moon, Y. H. (2020). Deep-learning approach to predict a severe plastic anisotropy of caliber-rolled Mg alloy. Materials Letters, 269, 3. doi:10.1016/j.matlet.2020.127652 Lei, X. Y., Pan, H. G., Huang, X. D. (2019). A Dilated CNN Model for Image Classification. IEEE ACCESS, 7, 124087-124095. doi:10.1109/access.2019.2927169 Li, W., Ding, S., Wang, H., Chen, Y., Yang, S. L. (2020). Heterogeneous ensemble learning with feature engineering for default prediction in peer-to-peer lending in China. World Wide Web-Internet and Web Information Systems, 23(1), 23-45. doi:10.1007/s11280-019-00676-y Li, Y., Liu, D., Li, H. Q., Li, L., Li, Z., Wu, F. (2019). Learning a Convolutional Neural Network for Image Compact-Resolution. Ieee Transactions on Image Processing, 28(3), 1092-1107. doi:10.1109/tip.2018.2872876 Liu, J. L., Chen, N. G., Ji, H. (2020). LEARNABLE DOUGLAS-RACHFORD ITERATION AND ITS APPLICATIONS IN DOT IMAGING. Inverse Problems and Imaging, 14(4), 683-700. doi:10.3934/ipi.2020031 Lu, H. F., Ma, X. (2020). Hybrid decision tree-based machine learning models for short-term water quality prediction. Chemosphere, 249, 12. doi:10.1016/j.chemosphere.2020.126169 Lu, L. Q., Yi, Y. H., Huang, F. L., Wang, K. L., Wang, Q. (2019). Integrating Local CNN and Global CNN for Script Identification in Natural Scene Images. IEEE ACCESS, 7, 52669-52679. doi:10.1109/access.2019.2911964 Lu, Y. Q., Liu, C., Wang, K. I. K., Huang, H. Y., Xu, X. (2020). Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues. ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING, 61, 14. doi:10.1016/j.rcim.2019.101837 Luo, X. Y., Li, X. M., Wang, Z. Y., Liang, J. (2019). Discriminant autoencoder for feature extraction in fault diagnosis. Chemometrics and Intelligent Laboratory Systems, 192, 10. doi:10.1016/j.chemolab.2019.103814 M. Dolinska, C. B. B. (1995). Dynamic control of flexible manufacturing systems. The International Journal of Advanced Manufacturing Technology, 10, 131-138. Mekonnen, B. K., Yang, W., Hsieh, T. H., Liaw, S. K., Yang, F. L. (2020). Accurate prediction of glucose concentration and identification of major contributing features from hardly distinguishable near-infrared spectroscopy. Biomedical Signal Processing and Control, 59, 15. doi:10.1016/j.bspc.2020.101923 Meng, L. H., Ding, S. F., Xue, Y. (2017). Research on denoising sparse autoencoder. International Journal of Machine Learning and Cybernetics, 8(5), 1719-1729. doi:10.1007/s13042-016-0550-y Merkuryeva, G., Bolshakov, V. (2015). SIMULATION-BASED FITNESS LANDSCAPE ANALYSIS AND OPTIMISATION OF COMPLEX PROBLEMS. Technological and Economic Development of Economy, 21(6), 899-916. doi:10.3846/20294913.2015.1107654 Mu, R. H., Zeng, X. Q. (2019). A Review of Deep Learning Research. Ksii Transactions on Internet and Information Systems, 13(4), 1738-1764. doi:10.3837/tiis.2019.04.001 Nils Reimers, I. G. (2017). Reporting Score Distributions Makes a Difference: Performance Study of LSTM-networks for Sequence Tagging. Nitish Shirish Keskar, D. M., Jorge Nocedal, Mikhail Smelyanskiy, Ping Tak Peter Tang. (2016). On large-batch training for deep learning: Generalization gap and sharp minima. Qu, Y., Lin, Z. K., Li, H. L., Zhang, X. R. (2019). Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data. IEEE ACCESS, 7, 170106-170115. doi:10.1109/access.2019.2952655 Samuel L. Smith, P.-J. K., Chris Ying, Quoc V. Le. (2017). Don't decay the learning rate, increase the batch size. Santucci, J. F., Capocchi, L. (2015). Optimization via simulation of catchment basin management using a discrete-event approach. Simulation-Transactions of the Society for Modeling and Simulation International, 91(1), 43-58. doi:10.1177/0037549714559608 Schleich, B., Anwer, N., Mathieu, L., Wartzack, S. (2017). Shaping the digital twin for design and production engineering. Cirp Annals-Manufacturing Technology, 66(1), 141-144. doi:10.1016/j.cirp.2017.04.040 Schmidhuber, J. (2014). Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117. Sergey Ioffe, C. S. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Sheridan, R. P., Wang, W. M., Liaw, A., Ma, J. S., Gifford, E. M. (2016). Extreme Gradient Boosting as a Method for Quantitative Structure-Activity Relationships. Journal of Chemical Information and Modeling, 56(12), 2353-2360. doi:10.1021/acs.jcim.6b00591 Shimodaira, H. (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2), 227-244. doi:10.1016/s0378-3758(00)00115-4 Si, M. X., Xiong, Y., Du, S., Du, K. (2020). Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods. Atmospheric Measurement Techniques, 13(4), 1693-1707. doi:10.5194/amt-13-1693-2020 Simonov, M. Y., Shaimanov, G. S., Simonov, Y. N. (2016). Formation of Zones of Plastic Strain in Quenched and Tempered Steel 09G2S During Dynamic Tests. Metal Science and Heat Treatment, 57(11-12), 746-751. doi:10.1007/s11041-016-9952-4 Smith, L. (2018). A disciplined approach to neural network hyper-parameters: part 1 – learning rate, batch size, momentum, and weight decay. Smith, L. N. (2017). Cyclical learning rates for training neural networks. IEEE. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. (2014). Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research, 15, 1929-1958. Stephen Merity, N. S. K., Richard Socher. (2017). Regularizing and Optimizing LSTM Language Models. Subramaniyan, M., Skoogh, A., Salomonsson, H., Bangalore, P., Bokrantz, J. (2018). A data-driven algorithm to predict throughput bottlenecks in a production system based on active periods of the machines. Computers Industrial Engineering, 125, 533-544. doi:10.1016/j.cie.2018.04.024 Tao, F., Zhang, M. (2017). Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing. IEEE ACCESS, 5, 20418-20427. doi:10.1109/access.2017.2756069 Teinemaa, I., Dumas, M., Leontjeva, A., Maggi, F. M. (2018). Temporal stability in predictive process monitoring. Data Mining and Knowledge Discovery, 32(5), 1306-1338. doi:10.1007/s10618-018-0575-9 Tianqi Chen, C. G. (2016). XGBoost: A Scalable Tree Boosting System. Wang, S. H., Chen, Z., Chen, S. N. (2019). Applicability of deep neural networks on production forecasting in Bakken shale reservoirs. Journal of Petroleum Science and Engineering, 179, 112-125. doi:10.1016/j.petrol.2019.04.016 Wen, S. H., Chen, J. H., Qin, G. Q., Zhu, Q. G., Wang, H. B. (2018). An improved fuzzy model predictive control algorithm based on the force/position control structure of the five-degree of freedom redundant actuation parallel robot. International Journal of Advanced Robotic Systems, 15(5), 8. doi:10.1177/1729881418804979 Wenjie Huang , Q. Z., Wei Xu, Hongjiao Fu, Mingming Wang, Xun Liang. (2015). A Novel Trigger Model for Sales Prediction with Data Mining Techniques. DATA SCIENCE JOURNAL. Xavier Glorot, Y. B. (2010). Understanding the difficulty of training deep feedforward neural networks. Journal of Machine Learning Research, 9, 249-256. Xu, C. L., Chen, H. X., Xun, W. D., Zhou, Z. X., Liu, T., Zeng, Y. K., Ahmad, T. (2019). Modal decomposition based ensemble learning for ground source heat pump systems load forecasting. Energy and Buildings, 194, 62-74. doi:10.1016/j.enbuild.2019.04.018 Xu, J., Huang, E., Hsieh, L., Lee, L. H., Jia, Q. S., Chen, C. H. (2016). Simulation optimization in the era of Industrial 4.0 and the Industrial Internet. Journal of Simulation, 10(4), 310-320. doi:10.1057/s41273-016-0037-6 Yan, L., Zhen, T., Kong, J. L., Wang, L. M., Zhou, X. L. (2020). Walking Gait Phase Detection Based on Acceleration Signals Using Voting-Weighted Integrated Neural Network. Complexity, 2020, 14. doi:10.1155/2020/4760297 Yann LeCun, Y. B. a. G. H. (2015). Deep learning. Nature, 521, 436-444. Yoo, Y. (2019). Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches. Knowledge-Based Systems, 178, 74-83. doi:10.1016/j.knosys.2019.04.019 Yu, J. B., Liu, G. L. (2020). Knowledge extraction and insertion to deep belief network for gearbox fault diagnosis. Knowledge-Based Systems, 197, 13. doi:10.1016/j.knosys.2020.105883 Yu, Y. B., Adu, K., Tashi, N., Anokye, P., Wang, X. X., Ayidzoe, M. A. (2020). RMAF: Relu-Memristor-Like Activation Function for Deep Learning. IEEE ACCESS, 8, 72727-72741. doi:10.1109/access.2020.2987829 Zeiler, M. D. (2012). ADADELTA: An Adaptive Learning Rate Method. Zhang, G. J., Liu, Y., Jin, X. N. (2020). A survey of autoencoder-based recommender systems. Frontiers of Computer Science, 14(2), 430-450. doi:10.1007/s11704-018-8052-6 Zhang, J. S., Yang, X. X., Deng, X. L., Lin, H. J. (2019). Trajectory control method of stratospheric airships based on model predictive control in wind field. Proceedings of the Institution of Mechanical Engineers Part G-Journal of Aerospace Engineering, 233(2), 418-425. doi:10.1177/0954410017735128 Zheng, M. R., Tang, W. W., Zhao, X. (2019). Hyperparameter optimization of neural network-driven spatial models accelerated using cyber-enabled high-performance computing. International Journal of Geographical Information Science, 33(2), 314-345. doi:10.1080/13658816.2018.1530355 Zhong, J. C., Sun, Y. S., Peng, W., Xie, M. Z., Yang, J. H., Tang, X. W. (2018). XGBFEMF: An XGBoost-Based Framework for Essential Protein Prediction. Ieee Transactions on Nanobioscience, 17(3), 243-250. doi:10.1109/tnb.2018.2842219 Zhou, X. Z., Xie, L., Zhang, P., Zhang, Y. N. (2017). Online object tracking based on BLSTM-RNN with contextual-sequential labeling. Journal of Ambient Intelligence and Humanized Computing, 8(6), 861-870. doi:10.1007/s12652-017-0514-4 Zhou, Y. D., Cahya, S., Combs, S. A., Nicolaou, C. A., Wang, J. B., Desai, P. V., Shen, J. (2019). Exploring Tunable Hyperparameters for Deep Neural Networks with Industrial ADME Data Sets. Journal of Chemical Information and Modeling, 59(3), 1005-1016. doi:10.1021/acs.jcim.8b00671 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/76953 | - |
| dc.description.abstract | 本研究以Digital Twin之概念出發，藉由實時的蒐集現實系統運作之數據，打造虛擬系統，建立可逆冷軋機之機器學習預測模型，並結合模擬最佳化演算進行板形優化、提高製程品質。軋機採用往復軋延使得最終鋼板達到目標板形，所謂板形即為衡量鋼板平整程度之指標，板形受先前軋延、機器參數及控制變數等影響。 現行軋機以內建之模糊控制系統進行製程控制，但時常發生最終板形與目標不符的問題。受限於問題之長時間、不穩定狀態且包含許多非線性因子等特性，無法運用微分方程處理，因此本研究以過去資料為基礎，導入人工智慧的方法來模擬且實時更新實際軋延系統之動態。 本研究以集成學習的XGBoost模型打造虛擬軋機，在給定必要參數後即可精準的模擬軋延成果，接著結合模擬最佳化的概念，運用Cross-Entropy演算法計算在不同入口板形及目標板形的情況下所對應最適之控制參數，提升整體製程品質，實現智能製造。 | zh_TW |
| dc.description.abstract | Based on the concept of digital twin, we collect and update the data of the actual system in real-time. In this research, we establish a virtual rolling system to simulate the output of the reversing cold rolling mill. By integrating the virtual rolling system and simulation optimization model to optimize the output shape and improve the process quality. The steel strip moves back and forth between the rollers to reduce the thickness incrementally and to be formed into the expected shape. Shape means the flatness and the thickness of the steel plate which is affected by previous rolling, machine parameters, and control parameters. The current control method of the reversing cold rolling mill is based on the built-in fuzzy control system, which often results in a gap between the exit shape and the target shape. Since this is a long-term and unstable nonlinear system, it cannot be processed by differential equations. Therefore, based on past data, we use artificial intelligence methods to simulate and update the dynamic of the rolling system. We use XGBoost model as the virtual rolling mill. As long as the entry shape, machine parameters, and control parameters are given, the virtual rolling system can accurately simulate the exit shape. Based on the virtual rolling mill, we implement the Cross-Entropy algorithm to compute the appropriate control parameters that make the exit shape and the target shape as similar as possible to improve the process quality. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-10T21:41:28Z (GMT). No. of bitstreams: 1 U0001-0308202023540000.pdf: 4264125 bytes, checksum: 52cf875b48d96c9d523cb77823dedc56 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | Acknowledgement I 中文摘要 II Abstract III Contents IV List of Figures VI List of Tables IX Chapter 1 Introduction 1 1.1 Background and Motivation 3 1.1.1 Definition of Digital Twin 3 1.1.2 Digital Twin in Industry 4 1.1.3 Motivation and Plan 4 1.2 Objective 5 1.3 Methods and Process 6 1.3.1 Introduction of Rolling Cold Mill 6 1.3.2 Fuzzy Control System 7 1.3.3 Limitation of ASU 8 1.3.4 Summary 8 Chapter 2 Literature Review 11 2.1 Prediction System 11 2.1.1 Ensemble Learning 13 2.1.2 Deep Learning 16 2.2 System Dynamic Control 18 2.2.1 Simulation Optimization 19 2.3 Summary 22 Chapter 3 Data Preprocessing 23 3.1 Data Combination and Processing 23 3.2 Time Delay 27 3.3 Features 29 Chapter 4 Machine Learning – XGBoost Model 32 4.1 Algorithm of XGBoost 32 4.2 XGBoost Model 34 4.3 Hyperparameter Tuning 35 Chapter 5 Deep Learning – DNN Model 38 5.1 Algorithm of Deep Neural Network 38 5.2 DNN Model 39 5.3 Hyperparameter Tuning 41 Chapter 6 Result Analysis 57 Chapter 7 Simulation Optimization 59 7.1 Algorithm of Cross-Entropy 60 7.2 Architecture and Limitations of Simulation Optimization Model 62 7.3 Results of the Optimization 63 Chapter 8 Conclusion and Future Research 72 8.1 Conclusion 72 8.2 Future Research 73 Reference 76 | |
| dc.language.iso | en | |
| dc.subject | 模擬最佳化 | zh_TW |
| dc.subject | 自動板形控制 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 板形辨識 | zh_TW |
| dc.subject | 虛擬軋機 | zh_TW |
| dc.subject | Shape recognition | en |
| dc.subject | Machine learning | en |
| dc.subject | Automatic shape control | en |
| dc.subject | Simulation Optimization | en |
| dc.subject | Virtual rolling mill | en |
| dc.title | 結合機器學習與模擬最佳化之製程控制-以可逆冷軋機為例 | zh_TW |
| dc.title | Machine Learning and Simulation Optimization in Process Control - A Case Study in Reversing Cold Rolling Mill | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 喻奉天,胡毓仁,余承叡 | |
| dc.subject.keyword | 自動板形控制,機器學習,板形辨識,虛擬軋機,模擬最佳化, | zh_TW |
| dc.subject.keyword | Automatic shape control,Machine learning,Shape recognition,Virtual rolling mill,Simulation Optimization, | en |
| dc.relation.page | 85 | |
| dc.identifier.doi | 10.6342/NTU202002332 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2020-08-04 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工業工程學研究所 | zh_TW |
| 顯示於系所單位： | 工業工程學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| U0001-0308202023540000.pdf 未授權公開取用 | 4.16 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。