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arXiv 2025年

作者： Wang, Binglu Tian, Yao Wang, Shunzhou Yang, Le College of Information and Control Engineering Xi'an University of Architecture and Technology Xi'An710055 China The School of Astronautics Northwestern Polytechnical University Xi'An710072 China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen518055 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China The School of Information Science and Technology University of Science and Technology of China Hefei230022 China

The task of long-term action anticipation demands solutions that can effectively model temporal dynamics over extended periods while deeply understanding the inherent semantics of actions. Traditional approaches, which primarily rely on recurrent units or Transformer layers to capture long-term dependencies, often fall short in addressing these challenges. Large Language Models (LLMs), with their robust sequential modeling capabilities and extensive commonsense knowledge, present new opportunities for long-term action anticipation. In this work, we introduce the ActionLLM framework, a novel approach that treats video sequences as successive tokens, leveraging LLMs to anticipate future actions. Our baseline model simplifies the LLM architecture by setting future tokens, incorporating an action tuning module, and reducing the textual decoder layer to a linear layer, enabling straightforward action prediction without the need for complex instructions or redundant descriptions. To further harness the commonsense reasoning of LLMs, we predict action categories for observed frames and use sequential textual clues to guide semantic understanding. In addition, we introduce a Cross-Modality Interaction Block, designed to explore the specificity within each modality and capture interactions between vision and textual modalities, thereby enhancing multimodal tuning. Extensive experiments on benchmark datasets demonstrate the superiority of the proposed ActionLLM framework, encouraging a promising direction to explore LLMs in the context of action anticipation. Code is available at https://***/2tianyao1/***. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Machine learning algorithms to predict stroke in China based on causal inference of time series analysis

arXiv

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arXiv 2025年

作者： Zheng, Qizhi Zhao, Ayang Wang, Xinzhu Bai, Yanhong Wang, Zikun Wang, Xiuying Zeng, Xianzhang Dong, Guanghui College of Computer and Control Engineering Northeast Forestry University No.26 Hexing Road Xiangfang District Harbin150040 China Department of Anesthesiology Chongqing University Cancer Hospital No.181 Hanyu Road Shapingba District Chongqing400030 China Harbin Institute of Technology Harbin150001 China

Importance: Identifying and managing high-risk populations for stroke in a targeted manner is a key area of preventive healthcare. Objective: To assess machine learning (ML) models and causal inference of time series analysis for predicting stroke clinically meaningful model. Design: This is a retrospective cohort study and data is from China Health and Retirement Longitudinal Study (CHARLS) assessed 11,789 adults in China from 2011 to 2018. Data analysis was performed from June 1 to December 1, 2024. Setting: CHARLS adopts a multi-stage probability sampling method, covering samples from 28 provinces, and collects data every two years through computer-aided personal interviews (CAPI). Participants: This study employed a combination of Vector Autoregression (VAR) model and Graph Neural Networks (GNN) to systematically construct dynamic causal inference. Multiple classic classification algorithms were compared, including Random Forest, Logistic Regression, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Gradient Boosting, and Multi Layer Perceptron (MLP). The SMOTE algorithm was used to undersample a small number of samples and employed Stratified K-fold Cross Validation. Main Outcome(s) and Measure(s): AUC (Area Under the Curve), Accuracy, Precision, Recall, F1 Score, and Matthews Correlation Coefficient (MCC). Results: This study included a total of 11,789 participants, including 6,334 females (53.73%) and 5,455 males (46.27%), with an average age of 65 years. Introduction of dynamic causal inference features has significantly improved the performance of almost all models. The area under the ROC curve of each model ranged from 0.78 to 0.83, indicating significant difference (P © 2025, CC BY.

关键词： Graph neural networks

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MULSAM: Multidimensional Attention with Hardware Acceleration for Efficient Intrusion Detection on Vehicular CAN Bus

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Xu, He Shi, Xiaokang Liu, Hansheng Wang, Yanwen Lu, Jiwu Zeng, Haibo Li, Renfa Wu, Di Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology Hunan Changsha410082 China Hunan University College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China Virginia Tech Department of Electrical and Computer Engineering BlacksburgVA24061 United States Hunan University College of Computer Science and Electronic Engineering Hunan Changsha410082 China

controller Area Network (CAN) protocol is an efficient standard enabling communication among Electronic control Units (ECUs). However, the CAN bus is vulnerable to malicious attacks because of a lack of defense features. In this article, a novel vehicle Intrusion Detection System (IDS) is developed. The challenge is that existing techniques of Intrusion Detection Systems (IDS) rarely consider attacks with small-batch, which are characterized by their small attack scale and concealed attack patterns, posing a significant threat to driving safety. To solve this problem, we developed an algorithm model that merges Multi-dimensional Long Short-Term Memory (MD-LSTM) and Self-Attention Mechanism (SAM), shortly named MULSAM. The MULSAM model was compared with other baseline models, including Stacked LSTM, MD-LSTM, etc. Experiments show that our approach has the best detection accuracy (98.98%) and training stability. Further, to speed up the inference of MULSAM on edge, the hardware accelerator is implemented on FPGA devices using technologies such as parallelization, modular, pipeline, and fixed-point quantization. Experiments show that our FPGA-based acceleration scheme has a better energy efficiency than the CPU platform. Even with a certain degree of quantification, the acceleration model for MULSAM still displays a high detection accuracy of 98.81% and a low latency of 1.88 ms. © 1982-2012 IEEE.

关键词： Intrusion detection

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Optimized fractional-order PID controller for temperature control of CSTH: a comparative study with PID cascade controller

Optimized fractional-order PID controller for temperature co...

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International Conference on Energy, Power, Environment, control and Computing (ICEPECC 2025)

作者： Abdulaziz F. Alaswadi Nezar M. Alyazidi Fatima N. Al-Aswadi Amjed Abbas Ahmed Department of Control and Instrumentation Engineering King Fahd University of Petroleum and Minerals Dhahran 31261 Saudi Arabia Institute of Computer Science & Digital Innovation (ICSDI) UCSI University Kuala Lumpur 56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia

ISBN: (数字)9781837243150

controlling the temperature in chemical reactors, heating furnaces, distillation columns, and reboilers for circulating heatin g fluids is crucial across all industries. This project is a simulation-based study where both PID-cascade and fractional order PID (FO-PID) controllers were used to control the temperature of water in the continuously stirred tank heater system. The simulation was designed by using SIMULINK. Initially, the work reproduces results from a previous study that evaluated the performance of PID cascade controller applied for controlling the temperature of continuous stirred tank heater. Building on this foundat ion, a novel fractional-order PID (FO-PID) controller was designed and its parameters were optimized using the Whale Optimizer Algorithm (WOA). Results showed that the FO-PID outperforms the PID-cascade controller in terms of settling time and percentage overshoot. This study demonstrates significant advancements in process innovation and optimization, supporting sustainable industrial systems. The FO-PID controller in comparison to the Cascade PID controller reduced the percentage overshoot by approximately 97%, reduced settling time by 87% and maintained comparable steady-state error.

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An efficient schedulability analysis based on worst-case interference time for real-time systems

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Science China(Information Sciences) 2024年第9期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of Computer Science and Technology Xidian University China Academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

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LDNet-RUL: Lightweight Deformable Neural Network for Remaining Useful Life Prognostics of Lithium-ion Batteries

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IEEE Transactions on Power Electronics 2025年

作者： Wu, Xiankui Li, Penghua Deng, Zhongwei Liu, Zhitao Kurboniyon, Mekhrdod S. Xiang, Sheng Yin, Gang Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications School of Automation Chongqing400065 China Chongqing University of Science and Technology School of Computer Science and Engineering Chongqing401331 China University of Electronic Science and Technology School of Mechanical and Electrical Engineering Chengdu611731 China Zhejiang University Institute of Cyber-Systems and Control Zhejiang 310058 China National Academy of Sciences of Tajikistan Center of Innovative Development of Science and New Technologies Dushanbe734025 Tajikistan Chongqing University College of Resource and Safety Engineering Chongqing400044 China

Deep neural networks (DNNs)-based battery remaining useful life (RUL) prognostics models suffer from high computational costs, long inference times, and a tendency to overfit, posing significant challenges for deployment on memory-constrained edge devices. To address this, LDNet-RUL, a lightweight yet high-performance model, is proposed for lithiumion battery lifetime prognostics. Specifically, a deformable depthwise convolution unit is designed that efficiently adapts to variations in input features with relatively low computational cost, which leverages fewer parameters and an adaptive receptive field to capture battery degradation dynamics. A multi-scale feature extraction method is introduced to extract degradation features from both short-term and long-term data during the battery charging process, which makes the method applicable to various battery charging protocols. To address overfitting and accelerate model inference, an adaptive channel selector, an adaptive residual connector, and a progressive dimensionalitygrouped bottleneck filter are incorporated to refine feature selection and reduce the model's complexity, boosting the model's overall performance. Experiments on vehicle lithium-ion battery datasets demonstrate that LDNet-RUL achieves state-of-the-art (SOTA) performance, with average RMSE of 0.23 and Score of 0.49. The model is lightweight, with only 7.92K parameters, achieves inference times of 3.94ms on GPU and CPU, and 5.39ms on the NVIDIA Jetson TX2. © 1986-2012 IEEE.

关键词： Convolution

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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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A Note on the Local Observability of Uniform Hypergraphs

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PAMM 2025年第1期25卷

作者： Daniel Gerbet Klaus Röbenack Institute of Control Theory Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany

Hypergraphs generalize graphs in such a way that edges may connect any number of nodes. If all edges are adjacent to the same number of nodes, the hypergraph is called uniform. Thus, a graph is a 2-uniform hypergraph. Each uniform hypergraph can be identified with an autonomous dynamical state-space system, whose vector field is composed of homogenous polynomials. We consider the observability of dynamical systems, which possess this special structure. In control theory, the observability of a dynamical system asks for the possibility to reconstruct the systems state from an output trajectory, where the output is a projective measurement of the systems state. In addition, one can ask for the observable subspace of the state. Nonlinear systems, although observable at almost all points in the state space, may possess not-locally observable ones. The existence and location of such states are important for observability analysis and observer design. Recently, the observability of these hypergraph systems has been studied for the general case. We apply an extended deterministic observability test to uniform hypergraphs and also compute the not-observable states for different linear output maps. We are able to confirm the results of the recent study, at least for graphs with a small number of nodes, due to the computational complexity of our method. In addition, the not-locally observable states are computed for different combinations of measured nodes.

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Unleashing the Potentials of IoT with Focus on Energy and Path Loss for Internet of Medical Things

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Journal of Circuits, Systems and computers 2025年

作者： Hussain, Altaf Li, Shuaiyong Attar, Razaz Waheeb Al Dabel, Maryam M. Bashir, Ali Kashif Alhomoud, Ahmed Hussain, Tariq School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China Management Department College of Business Administration Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Saudi Arabia Department of Computing and Mathematics Manchester Metropolitan University United Kingdom Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science College of Science Northern Border University Arar91431 Saudi Arabia School of Computer Science and Technology Zhejiang Gongshang University Hangzhou310018 China

The Internet of Things (IoT) encompasses a broad platform of sensor networks incorporating independent wireless networks. With advancements in sensor technology and IoT-enabled networks, their applications in the medical field have led to the development of the Internet of Medical Things (IoMT). In IoMT, sensor nodes monitor and evaluate patient conditions such as heartbeat, blood sugar levels, blood pressure and temperature, and can also remotely track patient activities through remote analysis. These IoMT systems utilize tiny sensors with limited communication ranges to gather essential patient information. Wireless devices are equipped with a short range and need a direct communication path. However, transmitting data from the source node to the destination node ultimately results in energy consumption and path loss. Path loss models and energy consumption models are essential to address these issues. In this paper, we propose a novel routing protocol named Energy Efficient and Path Loss Preserving (EEPLP) for IoMT. The EEPLP protocol focuses on energy efficiency and path loss preservation based on the relay approach. Two models are being proposed, one for path loss and the other for energy consumption. Finally, both models are merged since the major contribution is to avoid path loss and enhance the protocol's energy efficiency. The EEPLP evaluates the state-of-the-art existing approaches of IoMT. The protocol is evaluated by simulating conditions and compared with other similar routing protocols already deployed in the IoMT;it has been observed that the EEPLP scheme has the potential to be maneuvered in IoMT structures with core targets of energy efficiency as well as path loss preservation techniques. © 2025 World Scientific Publishing Company.

关键词： Sensor nodes

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Maximal η-clique maintenance over uncertain graph streams

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Information Sciences 2025年 717卷

作者： Ma, Ziyi Wang, Liqing Yang, Jianye Zhou, Xu Li, Kenli Gao, Cuiyun School of Artificial Intelligence Hebei University of Technology Tianjin 300401 China Chinacoal Beijing Coal Mining Machinery Co. Ltd. Beijing 102400 China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou 543002 China Department of New Networks PengCheng Laboratory Shenzhen 518055 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou 510006 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen 518055 China

Maximal clique enumeration is a critical task for analyzing graph data and has a wide range of applications, such as community detection, protein complex identification, and group recommendation. Although many efficient algorithms have been proposed, most of them are designed for static or deterministic graphs. However, in many real-life applications, graphs are often highly dynamic and nondeterministic. In this paper, we study the problem of maximal η-clique maintenance (shorted as MCM) over uncertain graph streams. Given an uncertain graph G and a probability threshold η, MCM reports all incremental η-cliques in G for each update edge of G. To the best of our knowledge, we are the first to systematically study this problem. We first develop a local pivot-based enumeration technique that utilizes the pivot priority of candidates to achieve effective pruning during the maximal η-clique enumeration procedure. Based on the technique, we propose a competitive baseline approach LPMCM, which finds newly generated maximal η-cliques and internal maximal η-cliques. Although LPMCM avoids repeated enumeration of the same maximal η-cliques, it still requires expensive costs in terms of η-clique maximality determination. To further enhance maintenance efficiency, we introduce an approach HLPMCM using the hash-based maximality determination technique and an improved internal maximal η-clique enumeration technique. Extensive experiments show that HLPMCM can achieve 1.1x-19.0x speedup over the competitors and consumes slightly more memory. © 2025 Elsevier Inc.

关键词： Incremental maintenance Maximal clique Pivot enumeration Uncertain graph streams

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