Computer Science ›› 2026, Vol. 53 ›› Issue (1): 89-96.doi: 10.11896/jsjkx.241200190
• Database & Big Data & Data Science • Previous Articles Next Articles
WANG Cheng, JIN Cheng
CLC Number:
| [1]ZONG B,SONG Q,MIN MARTIN R Q,et al.Deep autoenco-ding gaussian mixture model for unsupervised anomaly detection[C]//International Conference on Learning Representations.2018. [2]YAIRI T,TAKEISHI N,ODA T,et al.A data-driven healthmonitoring method for satellite housekeeping data based on probabilistic clustering and dimensionality reduction[J].IEEE Transactions on Aerospace and Electronic Systems,2017,53(3):1384-1401. [3]ZHOU Q H,HE S B,LIU H Y,et al.Label-free multivariate time series anomaly detection[J].IEEE Transactions on Know-ledge and Data Engineering,2024,36(7):3166-3179. [4]FREHNER R B,WU K S,SIM A,et al.Detecting Anomalies in Time Series Using Kernel Density Approaches[J].IEEE Access,2024,12:33420-33439. [5]SHEN L F,LI Z C,KWOK J.Timeseries anomaly detectionusing temporal hierarchical one-class network[J].Advances in Neural Information Processing Systems,2020,33:13016-13026. [6]SHIN Y J,LEE S,TARIQ S,et al.Itad:integrative tensor-based anomaly detection system for reducing false positives of satellite systems[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management.2020:2733-2740. [7]DONG C,TAO J F,CHAO Q,et al.Subsequence time series clustering-based unsupervised approach for anomaly detection of axial piston pumps[J].IEEE Transactions on Instrumentation and Measurement,2023,72:1-12. [8]HUNDMAN K,CONSTANTINOU V,LAPORTE C,et al.Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.2018:387-395. [9]TARIQ S,LEE S,SHIN Y J,et al.Detecting anomaliesin space using multivariate convolutional LSTM with mixtures of probabilistic PCA[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:2123-2133. [10]PARK D,HOSHI Y,KEMP CHARLES C.A multimodal ano-maly detector for robot-assisted feeding using an lstm-based variational autoencoder[J].IEEE Robotics and Automation Letters,2018,3(3):1544-1551. [11]SU Y,ZHAO Y J,NIU C H,et al.Robust anomaly detection for multivariate time series through stochastic recurrent neural network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:2828-2837. [12]LI Z H,ZHAO Y J,HAN J Q,et al.Multivariate time seriesanomaly detection and interpretation using hierarchical inter-metric and temporal embedding[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.2021:3220-3230. [13]XU J,WU H,WANG J,et al.Anomaly Transformer:Time Series Anomaly Detection with Association Discrepancy[C]//International Conference on Learning Representations.2022. [14]SUN Y Y,CHEN Z D,FENG C,et al.UMTS-Mixer:Time Series Anomaly Detection Based on Temporal Correlation and Channel Correlation[J].Computer Systems and Applications,2024,33(1):127-133. [15]YE L,HE Z.Multiscale time series anomaly detection incorporating wavelet decomposition[J].Journal of Computer Applications,2024,44(10):3300-3306. [16]GONG D,LIU L Q,LE V,et al.Memorizing normality to detect anomaly:Memory-augmented deep autoencoder for unsupervised anomaly detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:1705-1714. [17]PARK H J,NOH J,HAM B.Learning memory-guided normality for anomaly detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:14372-14381. [18]SONG J,KIM K,OH J,et al.MEMTO:Memory-guided transformer for multivariate time series anomaly detection[J].arXiv:2312.02530,2023. [19]LIU Z M,WANG Y X,VAIDYA S,et al.KAN:Kolmogorov-arnold networks[J].arXiv:2404.19756,2024. [20]LIU Z M,MA P C,WANG Y X,et al.KAN 2.0:Kolmogorov-Arnold Networks Meet Science[J].arXiv:2408.10205,2024. [21]SIDHARTH S S.Chebyshev polynomial-based kolmogorov-arnold networks:An efficient architecture for nonlinear function approximation[J].arXiv:2405.07200,2024. [22]AGHAEI A A.fKAN:Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions[J].arXiv:2406.07456,2024. [23]BOZORGASL Z,CHEN H.Wav-KAN:Wavelet kolmogorov-arnold networks[J].arXiv:2405.12832,2024. [24]LI C X,LIU X Y,LI W Y,et al.U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation[J].arXiv:2406.02918,2024. [25]VACA-RUBIO C J,BLANCO L,PEREIRA R,et al.Kolmogorov-arnold networks(kans) for time series analysis[J].arXiv:2405.08790,2024. [26]GENET R,INZIRILLO H.TKAN:Temporal Kolmogorov-Arnold Networks[J].arXiv:2405.07344,2024. [27]SONODA S,MURATA N.Neural network with unboundedactivation functions is universal approximator[J].Applied and Computational Harmonic Analysis,2017,43(2):233--268. [28]LAI M J,SHEN Z M.The kolmogorov superposition theoremcan break the curse of dimensionality when approximating high dimensional functions[J].arXiv:2112.09963,2021. [29]ELFWING S,UCHIBE E,DOYA K.Sigmoid-weighted linearunits for neural network function approximation in reinforcement learning[J].Neural networks,2018,107:3-11. [30]SUBAKAN C,RAVANELLI M,CORNELL S,et al.Attention is all you need in speech separation[C]//2021 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).2021:21-25. [31]SARFRAZ M S,CHEN M Y,LAYER L,et al.Position:Quo Vadis,Unsupervised Time Series Anomaly Detection?[C]//Proceedings of the 41st International Conference on Machine Learning.2024:43461-43476. [32]SU Y,ZHAO Y J,NIU C H,et al.Robust anomaly detection for multivariate time series through stochastic recurrent neural network[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.2019:2828-2837. [33]HUNDMAN K,CONSTANTINOU V,LAPORTE C,et al.Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.2018:387-395. [34]LI D,CHEN D C,JIN B H,et al.MAD-GAN:Multivariateanomaly detection for time series data with generative adversarial networks[C]//International Conference on Artificial Neural Networks.2019:703-716. [35]ABDULAAL A,LIU Z H,LANCEWICKI T.Practical approach to asynchronous multivariate time series anomaly detection and localization[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.2021:2485-2494. [36]WU R,KEOGH E J.Current time series anomaly detectionbenchmarks are flawed and are creating the illusion of progress[J].IEEE Transactions on Knowledge and Data Engineering,2021,35(3):2421-2429. |
| [1] | CHEN Yuansheng, CHEN Shunjue, MO Xuan, WU Weigang, LI Jialun. Deep Learning Training Time Prediction Algorithm Integrating Multi-dimensional Operator Features [J]. Computer Science, 2026, 53(5): 129-136. |
| [2] | LI Tengjia, MA Chun’ai. Multi-scale Transformer Oil Price Prediction Framework with AEMD and Trend Cross-attention [J]. Computer Science, 2026, 53(5): 157-163. |
| [3] | YANG Hongju, ZHANG Ziyang, LI Yao. Frequency Driven Multi-scale Image Super-resolution Method [J]. Computer Science, 2026, 53(5): 218-227. |
| [4] | GUO Jingchen, YANG Kuiwu, DING Mengdi, WEI Jianghong. Survey of Adversarial Sample Attacks for Vision Transformer [J]. Computer Science, 2026, 53(5): 404-418. |
| [5] | GAO Tai, REN Yanzhang, WANG Huiqing, LI Ying, WANG Bin. KGMamba:Gene Regulatory Network Prediction Model Based on Kolmogorov-Arnold Network Optimizing Graph Convolutional Network and Mamba [J]. Computer Science, 2026, 53(4): 101-111. |
| [6] | ZHANG Xueqin, WANG Zhineng, LI Jinsheng, LU Yisong, LUO Fei. Key Node Identification in Temporal Social Networks Based on Deep Learning and Multi-feature Fusion [J]. Computer Science, 2026, 53(4): 143-154. |
| [7] | GU Bokai, LIU Dun, SUN Yang. STWD-DLFRD:Multi-granularity Fake Review Detection via Sequential Three-way Decisions and Deep Learning [J]. Computer Science, 2026, 53(4): 188-196. |
| [8] | XIN Yichen, LI Shichong, CHEN Bin, CHENG Zhangtao, LI Ye, ZHOU Fan. Enhancing Temporal Knowledge Graph Reasoning Method with Graph Information Bottleneck and Transformer [J]. Computer Science, 2026, 53(4): 393-405. |
| [9] | ZHENG Cheng, BAN Qingqing. Knowledge-assisted and Reinforced Syntax-driven for Aspect-based Sentiment Analysis [J]. Computer Science, 2026, 53(4): 406-414. |
| [10] | YIN Chuang, LIU Jianyi, ZHANG Ru. Cross-modal Fusion Few-sample Ransomware Classifier:Multimodal Encoding Based on Pre-trained Models [J]. Computer Science, 2026, 53(4): 435-444. |
| [11] | FU Yukai, LI Qingzhen, DONG Zhixue, SHI Dongli, ZHAO Peng. Pedestrian Re-identification Methods Based on Limited Target Data and Deep Learning [J]. Computer Science, 2026, 53(3): 287-294. |
| [12] | YU Ding, LI Zhangwei. Prediction Method of RNA Secondary Structure Based on Transformer Architecture [J]. Computer Science, 2026, 53(3): 375-382. |
| [13] | DU Jiantong, GUAN Zeli, XUE Zhe. Multi-task Learning-based Ophthalmic Video Feature Fusion and Multi-dimensional Profiling [J]. Computer Science, 2026, 53(3): 383-391. |
| [14] | SU Ruitao, REN Jiongjiong, CHEN Shaozhen. Deep Learning-based Neural Differential Distinguishers for GIFT-128 and ASCON [J]. Computer Science, 2026, 53(3): 453-458. |
| [15] | CHEN Han, XU Zefeng, JIANG Jiu, FAN Fan, ZHANG Junjian, HE Chu, WANG Wenwei. Large Language Model and Deep Network Based Cognitive Assessment Automatic Diagnosis [J]. Computer Science, 2026, 53(3): 41-51. |
|
||