Open Access
Issue
JNWPU
Volume 43, Number 5, October 2025
Page(s) 1014 - 1021
DOI https://doi.org/10.1051/jnwpu/20254351014
Published online 05 December 2025
  1. SHANG Dajing, TANG Rui, LI Qi, et al. Measurement of radiated sound power from a complex underwater sound source in a non-anechoic pool based on spatial averaging[J]. Journal of Sound & Vibration, 2020, 468: 115071 [Google Scholar]
  2. ZHANG Yiming, TANG Rui, LI Qi, et al. The low-frequency sound power measuring technique for an underwater source in a non-anechoic tank[J]. Measurement Science & Technology, 2018, 29(3): 035101 [Google Scholar]
  3. YU Xinyue. Research on measurement method of characteristics of transient underwater sound sources in non-free field[D]. Harbin: Harbin Engineering University, 2020 (in Chinese) [Google Scholar]
  4. XU Hongzhe, LI Qi, TANG Rui, et al. Method for measuring the low-frequency sound power from a complex sound source based on sound-field correction in a non-anechoic tank[J]. Chinese Physics B, 2023, 32(5): 504–519 [Google Scholar]
  5. ROBINSON S P, HARRIS P M, FORD B, et al. CCAUV W-K2 final report-key comparison CCAUV W-K2: calibration of hydrophones in the frequency range from 250 Hz to 500 kHz[J]. Metrologia, 2022, 59(1): 09003 [Google Scholar]
  6. LI Yitong, WU Kai, LIU Jing. Self-paced ARIMA for robust time series prediction[J]. Knowledge-Based Systems, 2023, 269: 110489. [Article] [Google Scholar]
  7. ZHANG Xianqi, WU Xilong, ZHU Guoyu, et al. A seasonal ARIMA model based on the gravitational search algorithm(GSA) for runoff prediction[J]. Water Supply, 2022, 22(8): 6959–6977. [Article] [Google Scholar]
  8. ALEMU A B, PARAKASH RAJU U J, SEID A M, et al. Comparative study of seasonal autoregressive integrated moving average and Holt-Winters modeling for forecasting monthly ground-level ozone[J]. AIP Advances, 2023, 13(3): 035303. [Article] [Google Scholar]
  9. MOHAN M, KISHORE RAJA P C, VELMURUGAN P, et al. Holt-Winters algorithm to predict the stock value using recurrent neural network[J]. Intelligent Automation & Soft Computing, 2022, 35(1): 1151–1163 [Google Scholar]
  10. OUMA Y O, MOALAFHI D B, ANDERSON G, et al. Dam water level prediction using vector autoregression, random forest regression and MLP-ANN models based on land-use and climate factors[J]. Sustainability, 2022, 14(22): 14934. [Article] [Google Scholar]
  11. IYER S, MAHAJAN A. Predicting total electron content in ionosphere using vector autoregression model during geomagnetic storm[J]. Journal of Applied Geodesy, 2021, 15(4): 279–291. [Article] [Google Scholar]
  12. VO T. An integrated dual attention with convolutional LSTM for short-term temperature forecasting[J]. Cybernetics & Systems, 2024, 55(2): 511–533 [Google Scholar]
  13. HUANG Xiaohui, TANG Jie, YANG Xiaofei, et al. A time-dependent attention convolutional LSTM method for traffic flow prediction[J]. Applied Intelligence, 2022, 52(15): 17371–17386. [Article] [Google Scholar]
  14. LUO Jiahang, ZHANG Xu. Convolutional neural network based on attention mechanism and Bi-LSTM for bearing remaining life prediction[J]. Applied Intelligence, 2022, 52(1): 1076–1091. [Article] [Google Scholar]
  15. QI Yongsheng, WANG Xinhua, YANG Xuyun, et al. Research on acoustic methods for buried PE pipeline detection based on LSTM neural networks[J]. Measurement Science and Technology, 2024, 35(9): 096001. [Article] [Google Scholar]
  16. DU Jinbo, ZENG Jie, WANG Han, et al. Using acoustic emission technique for structural health monitoring of laminate composite: a novel CNN-LSTM framework[J]. Engineering Fracture Mechanics, 2024, 309: 110447. [Article] [Google Scholar]
  17. XIONG Shanwei, ZHOU Li, DAI Yiyang, et al. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis[J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 1–14 [Google Scholar]
  18. XU Chengfeng, FENG Jian, ZHAO Pengpeng, et al. Long- and short-term self-attention network for sequential recommendation[J]. Neurocomputing, 2021, 423: 580–589. [Article] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.