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Research Article

ScTCN-LightGBM: a hybrid learning method via transposed dimensionality-reduction convolution for loading measurement of industrial material

ORCID Icon, , , &
Article: 2278275 | Received 13 Jul 2023, Accepted 27 Oct 2023, Published online: 09 Nov 2023

Reference

  • Bi, J., Zhang, X., Yuan, H., Zhang, J., & Zhou, M. (2022). A hybrid prediction method for realistic network traffic with temporal convolutional network and LSTM. IEEE Transactions on Automation Science and Engineering, 19(3), 1869–1879. https://doi.org/10.1109/TASE.2021.3077537
  • Chen, X., Zhang, B., & Gao, D. (2021). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32(4), 971–987. https://doi.org/10.1007/s10845-020-01600-2
  • Chen, Z., Wang, C., Li, J., Zhang, S., & Ouyang, Q. (2022). Multi-agent collaborative control parameter prediction for intelligent precision loading. Applied intelligence, 52, 15961–15979. https://doi.org/10.1007/s10489-022-03297-7
  • Choudhary, S., & Sharma, R. (2023). CNN-based Battlefield classification and camouflage texture generation for real environment. International Journal of Computational Science and Engineering, 26(3), 231–242. https://doi.org/10.1504/IJCSE.2023.131514
  • Diao, C., Zhang, D., Liang, W., Li, K., Hong, Y., & Gaudiot, J. (2023). A novel spatial-temporal multi-scale alignment graph neural network security model for vehicles prediction. IEEE Transactions on Intelligent Transportation Systems, 24(1), 904–914. https://doi.org/10.1109/TITS.2022.3140229
  • Ehteram, M., & Ghanbari-Adivi, E. (2023). Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): An advanced python code for predicting groundwater level. Environmental Science and Pollution Research, 30, 92903–92921. https://doi.org/10.1007/s11356-023-28771-8
  • Esmaeilzehi, A., Ahmad, M. O., & Swamy, M. N. S. (2021). Srnharb: A deep light-weight image super resolution network using hybrid activation residual rlocks. Signal Processing: Image Communication, 99, 116509. https://doi.org/10.1016/j.image.2021.116509
  • Fan, J., Zhang, K., Huang, Y., Zhu, Y., & Chen, B. (2023). Parallel spatio-temporal attention-based TCN for multivariate time series prediction. Neural Computing & Applications, 35(18), 13109–13118. https://doi.org/10.1007/s00521-021-05958-z
  • Hussien, M., Abdelmoaty, A., Elsaadany, M., Ahmed, M., Gagnon, G., Nguyen, K., & Cheriet, M. (2023). Carrier frequency offset estimation in 5G NR: Introducing gradient boosting machines. IEEE Access, 11, 34128–34137. https://doi.org/10.1109/ACCESS.2023.3263053
  • Im, D., Han, D., Choi, S., Kang, S., & Yoo, H. (2020). DT-CNN: An energy-efficient dilated and transposed convolutional neural network processor for region of interest based image segmentation. Ieee Transactions on Circuits and Systems I-Regular Papers, 67(10), 3471–3483. https://doi.org/10.1109/TCSI.2020.2991189
  • Jalali, S. M. J., Osorio, G. J., Ahmadian, S., Lotfi, M., Campos, V. M. A., Shafie-khah, M., Khosravi, A., & Catalao, J. P. S. (2022). New hybrid deep neural architectural search-based ensemble reinforcement learning strategy for wind power forecasting. IEEE Transactions on Industry Applications, 58(1), 15–27. https://doi.org/10.1109/TIA.2021.3126272
  • Jiang, W., Ling, L., Zhang, D., Lin, R., & Zeng, L. (2023). A time series forecasting model selection framework using CNN and data augmentation for small sample data. Neural Processing Letters, https://doi.org/10.1007/s11063-022-11113-z
  • Ju, Y., Sun, G., Chen, Q., Zhang, M., Zhu, H., & Rehman, M. U. (2019). A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting. IEEE Access, 7, 28309–28318. https://doi.org/10.1109/ACCESS.2019.2901920
  • Khan, Y. A., Shan, Q. S., Liu, Q., & Abbas, S. Z. (2021). A nonparametric copula-based decision tree for two random variables using MIC as a classification index. Soft Computing, 25(15), 9677–9692. https://doi.org/10.1007/s00500-020-05399-1
  • Li, W., Wei, Y., An, D., Jiao, Y., & Wei, Q. (2022). LSTM-TCN: Dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network. Environ Sci Pollut Res, 29, 39545–39556. https://doi.org/10.1007/s11356-022-18914-8
  • Lu, X., Yang, S., & Mao, S. (2021). Hybrid modeling for LCC circuit based on machine learning. 2021 IEEE 1st International Power Electronics and Application Symposium (PEAS), 1–4. https://doi.org/10.1109/peas53589.2021.9628776
  • Luo, S., & Chen, T. (2020). Two derivative algorithms of gradient boosting decision tree for silicon content in blast furnace system prediction. IEEE Access, 8, 196112–196122. https://doi.org/10.1109/ACCESS.2020.3034566
  • Mo, J., Wang, R., Cao, M., Yang, K., Yang, X., & Zhang, T. (2023). A hybrid temporal convolutional network and prophet model for power load forecasting. Complex & Intelligent Systems, 9, 4249–4261. https://doi.org/10.1007/s40747-022-00952-x
  • Park, S., & Shin, Y. G. (2022). Generative residual block for image generation. Applied Intelligence, 52(7), 7808–7817. https://doi.org/10.1007/s10489-021-02858-6
  • Peralta, D., & Saeys, Y. (2020). Robust unsupervised dimensionality reduction based on feature clustering for single-cell imaging data. Applied Soft Computing, 93, 106421. https://doi.org/10.1016/j.asoc.2020.106421
  • Qiu, T., Zhang, M., Liu, X., Liu, J., Chen, C., & Zhao, W. (2021). A directed edge weight prediction model using decision tree ensembles in industrial internet of things. IEEE Transactions on Industrial Informatics, 17(3), 2160–2168. https://doi.org/10.1109/TII.2020.2995766
  • Ren, L., Dong, J., Wang, X., Meng, Z., Zhao, L., & Deen, M. J. (2021). A data-driven auto-CNN-LSTM prediction model for lithium-Ion battery remaining useful life. IEEE Transactions on Industrial Informatics, 17(5), 3478–3487. https://doi.org/10.1109/TII.2020.3008223
  • Semmelmann, L., Henni, S., & Weinhardt, C. (2022). Load forecasting for energy communities: A novel LSTM-XGBoost hybrid model based on smart meter data. Energy Informatics, 5. https://doi.org/10.1186/s42162-022-00212-9
  • Sheng, Z., Wang, H., Chen, G., Zhou, B., & Sun, J. (2021). Convolutional residual network to short-term load forecasting. Applied Intelligence, 51(4), 2485–2499. https://doi.org/10.1007/s10489-020-01932-9
  • Sun, H., & Fan, Y. (2023). Fault diagnosis of rolling bearings based on CNN and LSTM networks under mixed load and noise. Multimedia Tools and Applications, https://doi.org/10.1007/s11042-023-15325-w
  • Tuna, T., Beke, A., & Kumbasar, T. (2021). Correction to: Deep learning frameworks to learn prediction and simulation focused control system models. Applied Intelligence, 52(1), 680–680. https://doi.org/10.1007/s10489-021-02538-5
  • Wan, A., Yang, J., Chen, T., Yang, J., Li, K., & Zhou, Q. (2022). Dynamic pollution emission prediction method of a combined heat and power system based on the hybrid CNN-LSTM model and attention mechanism. Environmental Science and Pollution Research, 29(46), 69918–69931. https://doi.org/10.1007/s11356-022-20718-9
  • Wang, D., Han, C., Wang, L., Cai, E., & Zhang, P. (2023a). Surface roughness prediction of large shaft grinding via attentional CNN-LSTM fusing multiple process signals. International Journal of Advanced Manufacturing Technology, 126, 4925–4936. https://doi.org/10.1007/s00170-023-11454-6
  • Wang, T., Wang, X., Jiang, Y., Sun, Z., Liang, Y., Hu, X., & Ruan, J. (2023b). Hybrid machine learning approach for evapotranspiration estimation of fruit tree in agricultural cyber-physical systems. Ieee Transactions. on Cybernetics, 53(9), 5677–5691. https://doi.org/10.1109/TCYB.2022.3164542
  • Wang, Y., Chen, J., Chen, X., Zeng, X., Kong, Y., Sun, S., Guo, Y., & Liu, Y. (2021). Short-Term load forecasting for industrial customers based on TCN-LightGBM. IEEE Transactions on Power Systems, 36(3), 1984–1997. https://doi.org/10.1109/TPWRS.2020.3028133
  • Wen, Y., Zhu, X., Li, D., Zhao, Q., Cheng, Q., & Peng, Y. (2021). Proteomics-based prognostic signature and nomogram construction of hypoxia microenvironment on deteriorating glioblastoma (GBM) pathogenesis. Scientific Reports, 11. https://doi.org/10.1038/s41598-021-95980-x
  • Xie, X., Wang, B., Wan, T., & Tang, W. (2020). Multivariate abnormal detection for industrial control systems using 1D CNN and GRU. IEEE Access, 8, 88348–88359. https://doi.org/10.1109/ACCESS.2020.2993335
  • Yan, J., Mu, L., Wang, L., Ranjan, R., & Zomaya, A. Y. (2020). Temporal convolutional networks for the advance prediction of ENSO. Scientific Reports, 10. https://doi.org/10.1038/s41598-020-65070-5
  • Yang, G., Du, S., Duan, Q., & Su, J. (2022). Short-term price forecasting method in electricity spot markets based on attention-LSTM-mTCN. Journal of Electrical Engineering & Technology, 17(2), 1009–1018. https://doi.org/10.1007/s42835-021-00973-5
  • Zhang, S., Yu, H., & Zhu, G. (2022). An emotional classification method of Chinese short comment text based on ELECTRA. Connection Science, 34(1), 254–273. https://doi.org/10.1080/09540091.2021.1985968
  • Zhang, S., Zhu, A., Zhu, G., Wei, Z., & Li, G. (2023). Building fake review detection model based on sentiment intensity and PU learning. IEEE Transactions on Neural Networks and Learning Systems, 1–14. https://doi.org/10.1109/tnnls.2023.3234427
  • Zheng, W., Zhang, S., Yang, C., & Hu, P. (2023). Lightweight multilayer interactive attention network for aspect-based sentiment analysis. Connection Science, 35. https://doi.org/10.1080/09540091.2023.2189119