Ieee-Caa Journal of Automatica Sinica最新文献_第4页

Latent Factorization of Tensors in Hyperbolic Space for Spatiotemporal Traffic Data Imputation 基于双曲空间张量的时空交通数据隐式分解

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-28 DOI: 10.1109/JAS.2024.124911

Hao Wu;Lei Yang;Zhetao Zhang

{"title":"Latent Factorization of Tensors in Hyperbolic Space for Spatiotemporal Traffic Data Imputation","authors":"Hao Wu;Lei Yang;Zhetao Zhang","doi":"10.1109/JAS.2024.124911","DOIUrl":"https://doi.org/10.1109/JAS.2024.124911","url":null,"abstract":"Dear Editor, This letter presents a novel Hyperbolic Latent Factorization of Tensors (HLFT) model for spatiotemporal traffic data imputation. High-quality spatiotemporal traffic data is vital in various intelligent traffic applications. Owing to the malfunctions of sensors or unstable data transmission in complex traffic scenarios, traffic data commonly suffer from severe incompleteness or corruption, making their reliable imputation greatly important for facilitating various subsequent tasks like smart routing. A latent factorization of tensors (LFT) model is frequently utilized for missing traffic data imputation. However, an existing LFT model is commonly built in a Euclidean space. Recent research demonstrates that a hyperbolic space is roomier than a Euclidean space, where a hyperbolic vector can represent richer information than a Euclidean vector does. Motivated by this discovery, this letter innovatively presents the HLFT model with two-fold ideas: 1) defining the model learning objective in a hyperbolic space for high imputation accuracy owing to the high representation ability of the hyperbolic vector, and 2) building a parameter learning scheme according to the hyperbolic space characteristics for efficient model training. Extensive experimental results on four real traffic datasets reveal that the proposed HLFT model obtains significantly computational efficiency and prediction accuracy gains when tasked with the missing spatiotemporal traffic data imputation in comparison with state-of-the-art models.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 8","pages":"1991-1993"},"PeriodicalIF":18.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11669847","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Instructing the Learning of Language Model with the Token Interpretation to Improve Language Understanding 用符号解释指导语言模型学习提高语言理解能力

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-28 DOI: 10.1109/JAS.2026.125900

Tianzhu Chen;Yashen Wang;Huan Chang;Guanghong Liu;Dongfang Li;Law Rob;Xin Xu;Edmond Q. Wu

{"title":"Instructing the Learning of Language Model with the Token Interpretation to Improve Language Understanding","authors":"Tianzhu Chen;Yashen Wang;Huan Chang;Guanghong Liu;Dongfang Li;Law Rob;Xin Xu;Edmond Q. Wu","doi":"10.1109/JAS.2026.125900","DOIUrl":"https://doi.org/10.1109/JAS.2026.125900","url":null,"abstract":"Pretrained language models (PLMs) have established state-of-the-art performance across diverse natural language understanding (NLU) tasks. This study reveals that seman-tic-rich explanations of lexical units can effectively guide PLM learning processes. We propose a novel language understanding enhancement method with token interpretation (LUETI) that addresses two critical limitations in conventional PLMs: Incomplete token semantics caused by isolated contextual learning and insufficient semantic encoding in embedding matrices. LUETI operates through dual mechanisms, augmenting token represen-tations by integrating hidden states with corresponding token interpretations and refining embedding spaces using interpretation-derived semantic vectors for token prediction. LUETI, which is implemented as a plug-in module for standard architectures, demonstrates significant improvements on BERT and GLM, achieving average performance gains of 3.36% and 4.87% respectively on the SuperGLUE benchmark with equivalent parameters and training data. Note that LUETI-equipped models attain comparable performance to baseline PLMs using only 60% of pretraining data. Findings establish token interpretation as a computationally efficient but semantically powerful enhancement strategy for language model pretraining.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 8","pages":"1915-1925"},"PeriodicalIF":18.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation, and Future Trends 涡扇发动机剩余使用寿命预测的深度域自适应：方法，评估和未来趋势

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-28 DOI: 10.1109/JAS.2025.125843

Yucheng Wang;Mohamed Ragab;Yubo Hou;Min Wu;Xiaoli Li;Zhenghua Chen

{"title":"Deep Domain Adaptation for Turbofan Engine Remaining Useful Life Prediction: Methodologies, Evaluation, and Future Trends","authors":"Yucheng Wang;Mohamed Ragab;Yubo Hou;Min Wu;Xiaoli Li;Zhenghua Chen","doi":"10.1109/JAS.2025.125843","DOIUrl":"https://doi.org/10.1109/JAS.2025.125843","url":null,"abstract":"Remaining Useful Life (RUL) prediction for turbofan engines plays a vital role in predictive maintenance, ensuring operational safety and efficiency in aviation. Although data-driven approaches using machine learning and deep learning have shown potential, they face challenges such as limited data and distribution shifts caused by varying operating conditions. Domain Adaptation (DA) has emerged as a promising solution, enabling knowledge transfer from source domains with abundant data to target domains with scarce data while mitigating distributional shifts. Given the unique properties of turbofan engines-such as complex operating conditions, high-dimensional sensor data, and slower-changing signals-it is essential to conduct a focused review of DA techniques specifically tailored to turbofan engines. To address this need, this paper provides a comprehensive review of DA solutions for turbofan engine RUL prediction, analyzing key methodologies, challenges, and recent advancements. A novel taxonomy tailored to turbofan engines is introduced, organizing approaches into methodology-based (how DA is applied), alignment-based (where distributional shifts occur due to operational variations), and problem-based (why certain adaptations are needed to address specific challenges). This taxonomy offers a multidimensional view that goes beyond traditional classifications by accounting for the distinctive characteristics of turbofan engine data and the standard process of applying DA techniques to this area. Additionally, we evaluate selected DA techniques on turbofan engine datasets, providing practical insights for practitioners and identifying key challenges. Future research directions are identified to guide the development of more effective DA techniques, advancing the state of RUL prediction for turbofan engines.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 8","pages":"1783-1803"},"PeriodicalIF":18.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148837048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Distributed Pre-Defined-Time Solution to Time-Varying Constrained Optimization Problems Over Multiplex Networks 多路网络时变约束优化问题的分布式预定义时间解

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-28 DOI: 10.1109/JAS.2026.125816

Siyu Chen;Kaili Xiang;Yongduan Song

引用次数: 0

Data-Driven Reinforcement Learning and Approximate Optimal Control for Linear Time-Varying Systems 线性时变系统的数据驱动强化学习与近似最优控制

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-28 DOI: 10.1109/JAS.2025.125639

Jin-Gang Zhao;Deyi Wang;Jun Zhao;Guoliang Chen

引用次数: 0

Uncertainty Explicit Learning: An Interval Generalized Neural Network with Adaptivity for Anomaly Identification Under Data Uncertainty 不确定性显式学习：数据不确定性下具有自适应的区间广义神经网络异常识别

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-27 DOI: 10.1109/JAS.2025.125939

Sijia Wang;Kai Wang;Shumei Zhang;Chunhua Yang

{"title":"Uncertainty Explicit Learning: An Interval Generalized Neural Network with Adaptivity for Anomaly Identification Under Data Uncertainty","authors":"Sijia Wang;Kai Wang;Shumei Zhang;Chunhua Yang","doi":"10.1109/JAS.2025.125939","DOIUrl":"https://doi.org/10.1109/JAS.2025.125939","url":null,"abstract":"In real industrial processes, the collected process data often suffer from measurement uncertainty due to noise interference, sensor drift, and harsh operating environment. The existence of uncertainty may undermine the correlation structure of original data, resulting in the failure of traditional machine learning methods in anomaly identification. In this study, an interval generalized radial basis function neural network (IGRBFNN) based on key fault feature extraction is proposed for abnormal condition identification under data uncertainty. Specifically, kernel density estimation (KDE) is first introduced to explicitly characterize the uncertainty-contaminated data in interval form. For interval process data, an interval linear discriminant analysis (ILDA) method is developed to maximize the projection distance between different fault categories and minimize the projection distance within the same fault category, thus achieving key fault feature extraction under the mask of uncertainty. Subsequently, by embedding a generalized Gaussian function with adaptive characteristics and combining the mathematical theory of interval analysis, an IGRBFNN model is further constructed to enhance the generalization ability of traditional neural networks. Finally, motivated by autonomous clustering, an interval generalized self-organizing map (IGSOM) network is developed to intelligently learn the model parameters of IGRBFNN. Extensive experiments on two cases demonstrate the flexibility and applicability of the constructed anomaly identification model.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 8","pages":"1865-1882"},"PeriodicalIF":18.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148822948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Finite-Time Parameter Self-Learning Disturbance Rejection Control of MSV: Methodology and Validation MSV的有限时间参数自学习抗扰控制：方法与验证

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2025-01-31 DOI: 10.1109/JAS.2024.124917

Bo Peng;Nan Gu;Dan Wang;Zhouhua Peng

引用次数: 0

A Knowledge-Imparting Generative Modelling Framework for Heterogeneous Federated Learning 面向异构联邦学习的知识传授生成建模框架

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-28 DOI: 10.1109/JAS.2026.125840

Hongyao Chen;Tianyang Xu;Xiaojun Wu;Josef Kittler

{"title":"A Knowledge-Imparting Generative Modelling Framework for Heterogeneous Federated Learning","authors":"Hongyao Chen;Tianyang Xu;Xiaojun Wu;Josef Kittler","doi":"10.1109/JAS.2026.125840","DOIUrl":"https://doi.org/10.1109/JAS.2026.125840","url":null,"abstract":"Federated learning aims to provide security for client data privacy in practical machine learning applications. In principle, a global server aggregates the models produced by local clients to obtain a global model. However, the server is challenged when collaborating with local clients handling non-identically distributed data without authorisation to access it. Therefore, advanced solutions advocate the use of generative modules to deliver surrogate data to local clients during a server-agent interaction, without revealing private particulars. We argue that such a unidirectional transfer of surrogate patterns cannot fully represent and harmonise knowledge during the server-client interactions. To this end, we propose a knowledge-imparting generative modelling framework (FedKIG) based on adversarial feature learning and bidirectional knowledge distillation, to explore the potential of interactive generative modelling. In particular, Fed-KIG trains a feature discriminator for each local client to identify the surrogate patterns extracted by the global model. Under the supervision of the local feature discriminators, the server learns a global generator to generate pseudo samples that convey its global perspective. In this manner, local models are enabled to absorb global knowledge, thereby mitigating the training data divergence caused by data heterogeneity. In addition, we develop a bidirectional knowledge distillation strategy to support the entire learning process. This strategy breaks the rigidity of federated distillation by updating knowledge transfer between the server and the clients iteratively, thus overcoming the learning-forgetting issue. The proposed privacy-protected server-client interaction solution supports explicit knowledge generation for exploitation in federated learning. Extensive experimental results indicate that FedKIG significantly improves the generalisation performance and the stability of the model in heterogeneous federated learning scenarios.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 8","pages":"1938-1951"},"PeriodicalIF":18.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain-Inspired GNSS Spoofing Attack Cognition Using Spiking Neural Network for Train Positioning 基于脉冲神经网络的列车定位脑启发GNSS欺骗攻击认知

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-28 DOI: 10.1109/JAS.2025.125903

Si-Qi Wang;Jiang Liu;Bai-Gen Cai;Jian Wang;De-Biao Lu;Wei Jiang

{"title":"Brain-Inspired GNSS Spoofing Attack Cognition Using Spiking Neural Network for Train Positioning","authors":"Si-Qi Wang;Jiang Liu;Bai-Gen Cai;Jian Wang;De-Biao Lu;Wei Jiang","doi":"10.1109/JAS.2025.125903","DOIUrl":"https://doi.org/10.1109/JAS.2025.125903","url":null,"abstract":"Global Navigation Satellite System (GNSS) has been an exciting growing research field in railway transportation due to its accuracy, low cost, and global availability. However, GNSS-based train positioning is susceptible to spoofing attacks in complex railway operation environments due to GNSS vulnerabilities. Aiming at resilience assurance of train positioning using GNSS, this paper proposes a spoofing attack cognition solution based on brain-inspired intelligence. Specifically, a multi-domain joint Spoofing Detector (SD) is proposed based on the Spiking Neural Network (SNN) to perceive the cybersecurity threat from spoofing attacks. Furthermore, the active spoofing protection solution is designed using a brain-inspired cognition approach considering different spoofing attack situation phases. An Attack Situation Recognizer (ASR) network combining signal decomposition and identification is proposed, which features SNN-optimized residual blocks and the cross-attention fusion modules, leading to enhanced recognition capability at a low computational cost level. Experiments based on field data and a GNSS spoofing injection testbench platform demonstrate the performance of this solution in spoofing attack identification and situation cognition over different referencing methods. The results illustrate that the proposed solution aligns with the railway Positioning, Navigation & Timing (PNT) resilience requirement by leveraging the advantages of brain-like intelligence in enhancing the trustworthiness of GNSS positioning in railway transportation systems.","PeriodicalId":54230,"journal":{"name":"Ieee-Caa Journal of Automatica Sinica","volume":"13 8","pages":"1883-1903"},"PeriodicalIF":18.3,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148836775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Supervised Spatio-Temporal Contrastive Learning for FDIA Detection in the Smart Grid 基于自监督时空对比学习的智能电网FDIA检测

IF 18.3 1区计算机科学

Ieee-Caa Journal of Automatica Sinica Pub Date : 2026-08-01 Epub Date: 2026-08-28 DOI: 10.1109/JAS.2026.125741

Xiaohan Huang;Zhenyong Zhang

引用次数: 0