IEEE/CAA Journal of Automatica Sinica
Citation:  T. Sun, C. Wang, H. L. Dong, Y. N. Zhou, and C. Guan, “A novel parameteroptimized recurrent attention network for pipeline leakage detection,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 4, pp. 1064–1076, Apr. 2023. doi: 10.1109/JAS.2023.123180 
[1] 
X. H. Li, G. M. Chen, and H. W. Zhu, “Quantitative risk analysis on leakage failure of submarine oil and gas pipelines using Bayesian network,” Process Safety and Environmental Protection, vol. 103, pp. 163–173, 2016. doi: 10.1016/j.psep.2016.06.006

[2] 
R. Xiao, Q. F. Hu, and J. Li, “Leak detection of gas pipelines using acoustic signals based on wavelet transform and support vector machine,” Measurement, vol. 146, pp. 479–489, 2019. doi: 10.1016/j.measurement.2019.06.050

[3] 
F. L. Ning, Z. H. Cheng, D. Meng, S. Duan, and J. Wei, “Enhanced spectrum convolutional neural architecture: An intelligent leak detection method for gas pipeline,” Process Safety and Environmental Protection, vol. 146, pp. 726–735, 2021.

[4] 
C. Wang, F. Han, Y. Zhang, and J. Y. Lu, “An SAEbased resampling SVM ensemble learning paradigm for pipeline leakage detection,” Neurocomputing, vol. 403, pp. 237–246, 2020. doi: 10.1016/j.neucom.2020.04.105

[5] 
S. B. Zhu, Z. L. Li, S. M. Zhang, and H. F. Zhang, “Deep belief networkbased internal valve leakage rate prediction approach,” Measurement, vol. 133, pp. 182–192, 2019. doi: 10.1016/j.measurement.2018.10.020

[6] 
C. Lee and D. Yoo, “Development of leakage detection model and its application for water distribution networks using RNNLSTM,” Sustainability, vol. 13, no. 16, p. 9262, 2021.

[7] 
B. Wang, Y. B. Guo, D. Wang, Y. Zhang, R. He, and J. Chen, “Prediction model of natural gas pipeline crack evolution based on optimized DCNNLSTM,” Mechanical Systems and Signal Processing, vol. 181, p. 109557, 2022.

[8] 
P. Xu, R. Du, and Z. B. Zhang, “Predicting pipeline leakage in petrochemical system through GAN and LSTM,” KnowledgeBased Systems, vol. 175, pp. 50–61, 2019. doi: 10.1016/j.knosys.2019.03.013

[9] 
W. B. Liu, Z. D. Wang, L. Hu, and X. H. Liu, “A deep learning approach for classifying patient attendance disposal from emergency departments,” in Proc. 15th IEEE Int. Conf. Control and Autom., Edinburgh, UK, 2019, pp. 278–283.

[10] 
W. B. Liu, Z. D. Wang, Y. Yuan, N. Y. Zeng, K. Hone, and X. H. Liu, “A novel sigmoidfunctionbased adaptive weighted particle swarm optimizer,” IEEE Trans. Cybernetics, vol. 51, no. 2, pp. 1085–1093, 2019.

[11] 
C. Wang, Z. D. Wang, Q.L. Han, F. Han, and H. L. Dong, “Novel leaderfollowerbased particle swarm optimizer inspired by multiagent systems: Algorithm, experiments, and applications,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 53, no. 3, pp. 1322–1334, 2023. doi: 10.1109/TSMC.2022.3196853

[12] 
C. Wang, Y. Zhang, J. B. Song, Q. Q. Liu, and H. L. Dong, “A novel optimized SVM algorithm based on PSO with saturation and mixed timedelays for classification of oil pipeline leak detection,” Systems Science and Control Engineering, vol. 7, no. 1, pp. 75–88, 2019.

[13] 
D. M. Wang, L. J. Zhu, J. K. Yue, J. Y. Lu, D. W. Li, and G. F. Li, “Application of variational mode decomposition based on particle swarm optimization in pipeline leak detection,” Engineering Research Express, vol. 2, no. 4, p. 045036, 2020.

[14] 
H. Zhang and X. J. Yu, “Research on oil and gas pipeline defect recognition based on IPSO for RBF neural network,” Sustainable Computing: Informatics and Systems, vol. 20, pp. 203–209, 2018. doi: 10.1016/j.suscom.2017.08.002

[15] 
X. Li, W. Zhang, and Q. Ding, “Understanding and improving deep learningbased rolling bearing fault diagnosis with attention mechanism,” Signal Processing, vol. 161, pp. 136–154, 2019. doi: 10.1016/j.sigpro.2019.03.019

[16] 
Z. Long, X. F. Zhang, L. Zhang, G. J. Qin, S. D. Huang, D. Y. Song, H. D. Shao, and G. P. Wu, “Motor fault diagnosis using attention mechanism and improved adaboost driven by multisensor information,” Measurement, vol. 170, p. 108718, 2021.

[17] 
L. Xiang, P. H. Wang, X. Yang, A. J. Hu, and H. Su, “Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism,” Measurement, vol. 175, p. 109094, 2021.

[18] 
A. H. Khan, S. Li, and X. Luo, “Obstacle avoidance and tracking control of redundant robotic manipulator: An RNNbased metaheuristic approach,” IEEE Trans. Industrial Informatics, vol. 16, no. 7, pp. 4670–4680, 2019.

[19] 
S. Hochreiter and J. Schmidhuber, “Long shortterm memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997. doi: 10.1162/neco.1997.9.8.1735

[20] 
J. Kennedy and R. C. Eberhart, “Particle swarm optimization,” in Proc. ICNN Int. Conf. Neural Networks, Perth, Australia, 1995, pp. 1942–1948.

[21] 
C. Wang, Z. D. Wang, F. Han, H. L. Dong, and H. J. Liu, “A novel PIDlike particle swarm optimizer: On terminal convergence analysis,” Complex and Intelligent Systems, vol. 8, no. 2, pp. 1217–1228, 2022.

[22] 
N. Y. Zeng, Z. D. Wang, W. B. Liu, H. Zhang, K. Hone, and X. H. Liu, “A dynamic neighborhoodbased switching particle swarm optimization algorithm,” IEEE Trans. Cybernetics, vol. 52, no. 9, pp. 9290–9301, 2022. doi: 10.1109/TCYB.2020.3029748

[23] 
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems, pp. 5998–6008, 2017.

[24] 
W. B. Liu, Z. D. Wang, N. Y. Zeng, F. E. Alsaadi, and X. H. Liu, “A PSObased deep learning approach to classifying patients from emergency departments,” Int. J. Machine Learning and Cybernetics, vol. 12, no. 7, pp. 1939–1948, 2021. doi: 10.1007/s1304202101285w

[25] 
J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,” Neural Computation, vol. 1, no. 2, pp. 270–280, 1989. doi: 10.1162/neco.1989.1.2.270

[26] 
K. Cho, B. Van, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using RNN encoderdecoder for statistical machine translation,” arXiv preprint arXiv: 1406.1078, 2014.

[27] 
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural Computation, vol. 1, no. 4, pp. 541–551, 1989. doi: 10.1162/neco.1989.1.4.541

[28] 
X. J. Shi, Z. R, Chen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo, “Convolutional LSTM network: A machine learning approach for precipitation nowcasting,” in Advances in Neural Information Processing Systems, 2015, pp. 802–810.

[29] 
Z. H. Huang, W. Xu, and K. Yu, “Bidirectional LSTMCRF models for sequence tagging,” arXiv preprint arXiv: 1508.01991, 2015.

[30] 
A. Trischler, Z. Ye, X. D. Yuan, and K. Suleman, “Natural language comprehension with the epireader,” arXiv preprint arXiv: 1606.02270, 2016.

[31] 
H. W. Chen, Z. D. Wang, J. L. Liang, and M. Z. Li, “State estimation for stochastic timevarying Boolean networks,” IEEE Trans. Autom. Control, vol. 65, no. 12, pp. 5480–5487, 2020. doi: 10.1109/TAC.2020.2973817

[32] 
H. W. Chen, Z. D. Wang, B. Shen, and J. L. Liang, “Distributed recursive filtering over sensor networks with nonlogarithmic sensor resolution,” IEEE Trans. Autom. Control, vol. 67, no. 10, pp. 5408–5415, 2022. doi: 10.1109/TAC.2021.3115473

[33] 
J. Hu, C. Q. Jia, H. Yu, and H. J. Liu, “Dynamic eventtriggered state estimation for nonlinear coupled output complex networks subject to innovation constraints,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 5, pp. 941–944, 2022. doi: 10.1109/JAS.2022.105581

[34] 
Z. R. Hu, P. Shi, and L. G. Wu, “Preserving state and control privacies in networked systems with tokenized polytopic transforms,” IEEE Trans. Circuits and Systems II: Express Briefs, vol. 69, no. 1, pp. 104–108, 2022. doi: 10.1109/TCSII.2021.3075471

[35] 
C. Q. Jia, J. Hu, D. Y. Chen, Z. P. Cao, J. P. Huang, and H. L. Tan, “Adaptive eventtriggered state estimation for a class of stochastic complex networks subject to codingdecoding schemes and missing measurements,” Neurocomputing, vol. 494, pp. 297–307, 2022. doi: 10.1016/j.neucom.2022.04.096

[36] 
Y. M. Ju, X. Tian, H. J. Liu, and L. F. Ma, “Fault detection of networked dynamical systems: A survey of trends and techniques,” Int. J. Systems Science, vol. 52, no. 16, pp. 3390–3409, 2021. doi: 10.1080/00207721.2021.1998722

[37] 
N. Li, Q. Li, and J. H. Suo, “Dynamic eventtriggered H_{∞} state estimation for delayed complex networks with randomly occurring nonlinearities,” Neurocomputing, vol. 421, pp. 97–104, 2021. doi: 10.1016/j.neucom.2020.08.048

[38] 
W. B. Liu, Z. D. Wang, L. L. Tian, S. Lauria, and X. H. Liu, “Melt pool segmentation for additive manufacturing: A generative adversarial network approach,” Computers and Electrical Engineering, vol. 92, p. 107183, 2021.

[39] 
Z. T. Liu, W. Y. Lin, X. H. Yu, J. J. RodríguezAndina, and H. J. Gao, “Approximationfree robust synchronization control for duallinearmotorsdriven systems with uncertainties and disturbances,” IEEE Trans. Industrial Electronics, vol. 69, no. 10, pp. 10500–10509, 2022. doi: 10.1109/TIE.2021.3137619

[40] 
X. Luo, Z. G. Liu, S. Li, M. S. Shang, and Z. D. Wang, “A fast nonnegative latent factor model based on generalized momentum method,” IEEE Trans. Systems,Man,and Cybernetics: Systems, vol. 51, no. 1, pp. 610–620, 2018.

[41] 
X. Luo, H. Wu, H. Q. Yuan, and M. C. Zhou, “Temporal patternaware QoS prediction via biased nonnegative latent factorization of tensors,” IEEE Trans. Cybernetics, vol. 50, no. 5, pp. 1798–1809, 2019.

[42] 
X. Luo, M. C. Zhou, S. Li, Y. N. Xia, Z. H. You, Q. S. Zhu, and H. Leung, “Incorporation of efficient secondorder solvers into latent factor models for accurate prediction of missing QoS data,” IEEE Trans. Cybernetics, vol. 48, no. 4, pp. 1216–1228, 2017.

[43] 
L. F. Ma, Z. D. Wang, Y. Chen, and X. J. Yi, “Probabilityguaranteed distributed filtering for nonlinear systems with innovation constraints over sensor networks,” IEEE Trans. Control of Network Systems, vol. 8, no. 2, pp. 951–963, 2021. doi: 10.1109/TCNS.2021.3049361

[44] 
J. H. Suo, N. Li, and Q. Li, “Eventtriggered H_{∞} state estimation for discretetime delayed switched stochastic neural networks with persistent dwelltime switching regularities and sensor saturations,” Neurocomputing, vol. 455, pp. 297–307, 2021. doi: 10.1016/j.neucom.2021.01.131

[45] 
P. Wen, X. Li, N. Hou, and S. Mu, “Distributed recursive fault estimation with binary encoding schemes over sensor networks,” Systems Science and Control Engineering, vol. 10, no. 1, pp. 417–427, 2022.

[46] 
J. J. Yang, L. F. Ma, Y. G. Chen, and X. J. Yi, “l_{2}−l_{∞} state estimation for continuous stochastic delayed neural networks via memory eventtriggering strategy,” Int. J. Systems Science, vol. 53, no. 13, pp. 2742–2757, 2022. doi: 10.1080/00207721.2022.2055192

[47] 
N. Y. Zeng, P. S. Wu, Z. D. Wang, H. Li, W. B. Liu, and X. H. Liu, “A smallsized object detection oriented multiscale feature fusion approach with application to defect detection,” IEEE Trans. Instrumentation and Measurement, vol. 71, p. 3507014, 2022.

[48] 
J. Zhang, J. Song, J. Li, F. Han, and H. Zhang, “Observerbased nonfragile H_{∞}consensus control for multiagent systems under deception attacks,” Int. J. Systems Science, vol. 52, no. 6, pp. 1223–1236, 2021. doi: 10.1080/00207721.2021.1884917

[49] 
P. F. Zhao, H. J. Liu, G. He, and D. R. Ding, “Outlierresistant l_{2}–l_{∞} state estimation for discretetime memristive neural networks with timedelays,” Systems Science and Control Engineering, vol. 9, no. 1, pp. 88–97, 2021.

[50] 
Z. Z. Zhao, W. Qian, and X. Z. Xu, “Stability analysis for delayed neural networks based on a generalized freeweighting matrix integral inequality,” Systems Science and Control Engineering, vol. 9, no. s1, pp. 6–13, 2021.

[51] 
L. Zou, Z. D. Wang, H. Geng, and X. H. Liu, “Setmembership filtering subject to impulsive measurement outliers: A recursive algorithm,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 377–388, 2021. doi: 10.1109/JAS.2021.1003826

[52] 
H. W. Chen, Z. D. Wang, B. Shen, and J. L. Liang, “Model evaluation of the stochastic boolean control networks,” IEEE Trans. Autom. Control, vol. 67, no. 8, pp. 4146–4153, 2022. doi: 10.1109/TAC.2021.3106896

[53] 
X. L. Wang, D. R. Ding, X. H. Ge, and Q.L. Han, “Supplementary control for quantized discretetime nonlinear systems under goal representation heuristic dynamic programming, IEEE Trans. Neural Networks and Learning Systems,” 2022. DOI: 10.1109/TNNLS.2022.3201521.
