[1] Maurya C K, Toshniwal D. Anomaly detection in nuclear power plant data using support vector data description[C]//Proceedings of the 2014 IEEE Students'Technology Symposium. Piscataway:IEEE Press, 2014:82-86.
[2] Calivá F, De Ribeiro F S, Mylonakis A, et al. A deep learning approach to anomaly detection in nuclear reac-tors[C]//2018 International Joint Conference on Neural Networks (IJCNN). Piscataway:IEEE Press, 2018:1-8.
[3] You D D, Shen X C, Liu G J, et al. Signal anomaly identi-fication strategy based on Bayesian inference for nuclear power machinery[J]. Mechanical Systems and Signal Pro-cessing, 2021, 161:107967.
[4] Cheng M, Li Q, Lv J M, et al. Multi-scale LSTM model for BGP anomaly classification[J]. IEEE Transactions on Services Computing, 2021, 14(3):765-778.
[5] Sabokrou M, Fayyaz M, Fathy M, et al. Deep-anomaly:fully convolutional neural network for fast anomaly detec-tion in crowded scenes[J]. Computer Vision and Image Understanding, 2018, 172:88-97.
[6] 陈磊,秦凯,郝矿荣.基于集成LSTM-AE的时间序列异常检测方法[J].华中科技大学学报(自然科学版), 2021, 49(11):35-40.
[7] Szczotka A B, Shakir D I, Clarkson M J, et al. Zero-shot super-resolution with a physically-motivated downsam-pling kernel for endomicroscopy[J]. IEEE Transactions on Medical Imaging, 2021, 40(7):1863-1874.
[8] Shelhamer E, Long J, Darrell T. Fully convolutional net-works for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):640-651.
[9] Jian J, Xia W, Zhang R, et al. Multiple instance convolu-tional neural network with modality-based attention and contextual multi-instance learning pooling layer for effec-tive differentiation between borderline and malignant epi-thelial ovarian tumors[J]. Artificial Intelligence in Medi-cine, 2021, 121:102194.
[10] Labonne M, Olivereau A, Polve B, et al. Unsupervised protocol-based intrusion detection for real-world net-works[C]//2020 International Conference on Computing, Networking and Communications (ICNC). Piscataway:IEEE Press, 2020:299-303
[11] Mao W D, Lin J, Wang Z F. F-DNA:Fast convolution architecture for deconvolutional network acceleration[J]. IEEE Transactions on Very Large Scale Integration (VL-SI) Systems, 2020, 28(8):1867-1880.
[12] Ergen T, Kozat S S. Unsupervised anomaly detection with LSTM neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(8):3127-3141.
[13] Malhotra P, Ramakrishnan A, Anand G, et al. LSTMbased encoder-decoder for multi-sensor anomaly detec-tion[DB/OL]. arXiv perprint:1607.00148, 2016.
[14] Tejasri M, Lakshmi K S, Narayan K G R, et al. Unsuper-vised distance-based anomaly disclosure in RNN[J]. In-ternational Journal of Computer Sciences and Engineer-ing, 2018, 6(3):439-441.
[15] 田园,马文.基于Attention-BiLSTM的电网设备故障文本分类[J].计算机应用, 2020, 40(增刊2):24-29.
[16] 侯修群,蒋庆磊,包彬彬,等.基于相关系数的核电主泵振动异常定位方法研究[J].核科学与工程, 2021, 41(5):920-928.
[17] 周勇,朱鹏树,陈星,等.核电厂应急柴油发电机组监测数据异常检测综述[J].仪器仪表用户, 2020, 27(10):52-55, 78.
[18] 向玲,朱浩伟,丁显,等.基于CAE与BiLSTM结合的风电机组齿轮箱故障预警方法研究[J].动力工程学报, 2022, 42(06):514-521.
[19] Galante L, Banisch R. A comparative evaluation of anomaly detection techniques on multivariate time series data[J]. International Journal of Computational Science and Engineering, 2019, 18:17-29.
[20] 夏英,韩星雨.融合统计方法和双向卷积LSTM的多维时序数据异常检测[J].计算机应用研究, 2022, 39(5):1362-1367, 1409.
[21] 于旭,何亚东,杜军威,等.一种结合显式特征和隐式特征的开发者混合推荐算法[J].软件学报, 2022, 33(5):1635-1651.
[22] Hou J, Ni K, Hawari A. An artificial neural network based anomaly detection algorithm for nuclear power plants[J]. Transactions, 2019, 120(1):219-222.
[23] Bhattacharya G, Ghosh K, Chowdhury A S. Granger cau-sality driven AHP for feature weighted kNN[J]. Pattern Recognition, 2017, 66:425-436.
[24] Abdulaal A, Liu Z H, Lancewicki T. Practical approach to asynchronous multivariate time series anomaly detec-tion and localization[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery&Data Mining. New York:ACM, 2021:2485-2494.
[25] Zhang C X, Song D J, Chen Y C, et al. A deep neural network for unsupervised anomaly detection and diagno-sis in multivariate time series data[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1):1409-1416.
[26] Jin M, Liu Y, Zheng Y, et al. Anemone:Graph anoma-ly detection with multi-scale contrastive learning[C]//Proceedings of the 30th ACM International Conference on Information&Knowledge Management. New York:ACM, 2021:3122-3126.
[27] Cai R C, Xu B Y, Yang X Y, et al. An encoder-decoder framework translating natural language to database que-ries[DB/OL]. arXiv perprint:1711.06061, 2017.
[28] Chen Y R, Zhang H, Wang Y N, et al. MAMA net:multi-scale attention memory autoencoder network for anomaly detection[J]. IEEE Transactions on Medical Im-aging, 2021, 40(3):1032-1041.
[29] Shalyga D, Filonov P, Lavrentyev A. Anomaly detection for water treatment system based on neural network with automatic architecture optimization[DB/OL]. arXiv pre-print arXiv:1807.07282, 2018.
[30] Kumar A, Narapareddy V T, Aditya Srikanth V, et al. Sarcasm detection using multi-head attention based bidi-rectional LSTM[J]. IEEE Access, 2020, 8:6388-6397.
[31] Kadri F, Harrou F, Chaabane S, et al. Seasonal ARMAbased SPC charts for anomaly detection:Application to emergency department systems[J]. Neurocomputing, 2016, 173:2102-2114.
[32] Chen X, Feng F, Wu J, et al. Anomaly detection for drinking water quality via deep biLSTM ensemble[C]//Proceedings of the Genetic And Evolutionary Computa-tion Conference Companion. New York:ACM, 2018:3-4.
[33] Ye H, Ma X P, Pan Q F, et al. An adaptive approach for anomaly detector selection and fine-tuning in time series[DB/OL]. arXiv perprint:1907.07843, 2019.
[34] Xiao Q F, Shao S K, Wang J. Memory-augmented adver-sarial autoencoders for multivariate time-series anomaly detection with deep reconstruction and prediction[DB/OL]. arXiv perprint:2110.08306, 2021.
[35] Liu Y X, Lin Y F, Xiao Q F, et al. Self-adversarial vari-ational autoencoder with spectral residual for time series anomaly detection[J]. Neurocomputing, 2021, 458:349-363.
[36] Han C, Rundo L, Murao K, et al. MADGAN:Unsuper-vised medical anomaly detection GAN using multiple ad-jacent brain MRI slice reconstruction[J]. BMC Bioinfor-matics, 2021, 22(2):1-20.
[37] Kao I F, Zhou Y L, Chang L C, et al. Exploring a Long Short-Term Memory based Encoder-Decoder framework for multi-step-ahead flood forecasting[J]. Journal of Hy-drology, 2020, 583:124631.