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Partially-Supervised Graph Derivation Network with Meta-Learning for Time Series Anomaly Detection

IEEE Internet of Things Journal 2025年

作者： Zhu, Sanli Li, Yuan Xu, Kang Xu, Junjun College of Automation and Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Nanjing University of Science and Technology China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing University of Science and Technology Nanjing China

Time series anomaly detection is essential in various fields such as industrial monitoring, cybersecurity, and finance. Traditional supervised methods often face challenges due to the limited availability of labeled anomaly instances for training. Moreover, these methods struggle to deal with intricate systems that incorporate information about topological structures. In this paper, we propose a novel approach called the Partially-Supervised Graph Derivation Network with Meta Learning (PS-GDNML) for time series anomaly detection. PS-GDNML combines the power of graph-based representations, partially-supervised learning, and meta-learning to enhance the effectiveness and robustness of anomaly detection. The method represents time series data as a graph, where each data point is a node, and temporal relationships are captured through edges. By leveraging a graph attention neural network (GAT), the model effectively captures complex dependencies and relationships within the data. To address the scarcity of labeled anomaly instances, PS-GDNML adopts a partially-supervised learning framework. It utilizes both labeled and unlabeled data, enabling the model to learn from the available information and generalize to detect anomalies in unseen data. Additionally, to explore the underlying commonalities of data across different time periods and enhance the model’s adaptability, we adopted a new meta-learning method called Task Relation Meta-Learner (TRMLearner). The purpose of this project is to utilize task relationships to guide the meta-learning optimization process. We evaluated the performance of PS-GDNML on benchmark datasets and compared it with state-of-the-art anomaly detection methods. The experimental results demonstrate that, even with a restricted set of labeled instances, our method excels at accurately detecting anomalies. Furthermore, the meta-learning component enhances the model’s capacity to generalize to novel and evolving anomaly patterns. © 2014 IEEE.

关键词： Self-supervised learning

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Task Offloading and Multidimensional Resource Allocation for Semantic-Assisted High-Speed Railway with Multi-access MEC 3rd

Task Offloading and Multidimensional Resource Allocation for...

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Qu, Jiaming Guo, Qichang Yuan, Jiabin Zhang, Wei Beijing University of Posts and Telecommunications School of Electronic Engineering Beijing China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing China China Datang Corporation Science and Technology General Research Institute Ltd. Beijing China

ISBN: (纸本)9789819624676

In high-speed railway communication scenarios, most of the computing tasks generated by trains are compute-intensive and delay-sensitive. Multi-access mobile edge computing (MEC) technology is very promising in resolving the contradiction between this situation and the limited local resources. Traditionally, MEC servers are deployed at base stations along the railway. We introduce dynamic MEC servers assisted by unmanned aerial vehicles (UAVs) additionally to deal with excessive server loads and the real-time nature of trains and tasks. Moreover, to complete real-time tasks, trains usually need abundant resources to transmit a large amount of raw data to MECs. However, limited local resources cannot meet the needs. Therefore, we propose a semantic-assisted communication model, introducing semantic encoding, decoding, extraction and feature aggregation to reduce resource overhead and computing delay while ensuring the accuracy of semantic understanding. Next, we conduct research on multi-dimensional resource allocation with the goal of minimizing delays and maximizing cache resource utilization by presenting our problem formulation. We propose a scheme based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to enable MEC agents to collaborate in offloading tasks and allocate resources. Lastly, we conduct simulation experiments and compare with two schemes: centralized resource management and random offloading resource allocation, verifying that the proposed scheme can achieve higher resource utilization and QoS satisfaction rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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Latency-aware Joint Task Offloading and Energy Control for Cooperative Mobile Edge Computing

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IEEE Transactions on Services Computing 2025年

作者： Fan, Weibei Xiao, Fu Pan, Yao Chen, Xiaobai Han, Lei Yu, Shui Nanjing University of Posts and Telecommunications College of Computer Nanjing210003 China University of Technology Sydney School of Computer Science Australia

In the application of the Internet of Things (IoT), existing cloud edge collaboration technologies face the problem of poor coordination of heterogeneous resources. In this paper, we propose CFEMC, which is a novel Cloud-Fog-Edge Multi-layer Collaboration resource scheduling framework for IoT. Firstly, we design a collaborative resource scheduling framework based on semi-distributed artificial intelligence. It can achieve collaborative optimization of cloud/edge computing resource allocation under the constraints of high reliability and low latency. Secondly, we present a workflow applications scheduling strategy based on the proposed collaborative resource scheduling framework. This can solve the problem of unstable computing performance and transmission bandwidth during the scheduling process. Finally, the extensive and real data supported simulation results show that CFEMC has advantages in terms of energy consumption, delay and throughput compared with other benchmark strategies. Against CEC [36] and PSO [31], the average throughput increases by 16.37% and 24.21%, and the total queuing delay decreases by 54.23% and 58.12%, respectively. © 2025 IEEE. All rights reserved.

关键词： Mobile edge computing

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A High Destruction-Resistant Resilient Networking Platform for Air-to-Ground Cooperative Communications

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China Communications 2025年第4期22卷 27-41页

作者： Dong Ping Ren Jiaxin Guo Jiannan Zhang Yuzhen Liu Qianwen Amr Tolba Huaxin Consultation Design Research Institute Co. Ltd.Hangzhou 310000China School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China China Academy of Information and Communications Technology Beijing 100191China The Computer Science Department Community CollegeKing Saud UniversityRiyadh 11437Saudi Arabia

With the continuous advancement of communication and unmanned aerial vehicle(UAV)technologies,the collaborative operations of diverse platforms,including UAVs and ground vehicles,have been significantly ***,battlefield uncertainties,such as equipment failures and enemy attacks,can impact these collaborative operations'stability and communication *** this end,we design a highly destruction-resistant air-ground cooperative resilient networking platform that aims to enhance the robustness of network communications by integrating ground vehicle information for UAV network *** then incorporates the concept of virtual guiding force,enabling the UAV swarm to adaptively configure its network layout based on ground vehicle information,thereby improving network destruction *** results demonstrate that the UAV swarm involved in the proposed platform exhibits balanced flight energy consumption and excellent performance in network destruction resistance.

关键词： air-to-ground coordination network destruction resistance SpringBoot UAV

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Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

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Digital Communications and Networks 2025年第1期11卷 83-91页

作者： Yanbo Zhang Zheng Yang Jingjing Cui Xianfu Lei Yi Wu Jun Zhang Chao Fang Zhiguo Ding College of Photonic and Electronic Engineering Fujian Normal UniversityFuzhou 350007China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Institute of Mobile Communications Southwest Jiaotong UniversityChengdu 610031China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Faculty of Information Technology Beijing University of TechnologyBeijingChina School of Electrical and Electronics Engineering The University of ManchesterManchester M139PLUK

In this paper,the application of Non-Orthogonal Multiple Access(NOMA)is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential *** support secure transmissions from legitimate users,two NOMA Secrecy Sum Rate Transmit Beam Forming(NOMA-SSR-TBF)schemes are proposed to maximise the SSR of a Base Station(BS)with sufficient and insufficient transmit *** BS with sufficient transmit power,an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate *** addition,for BS with insufficient transmit power,a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate *** specifically,iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial *** results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works,such as the maximized artificial jamming power scheme,the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

关键词： NOMA Secure transmission Artificial jamming Beamforming design

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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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Concept-Aware Entity Alignment Network for Industrial Knowledge Graph

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IEEE Transactions on Industrial Informatics 2025年第6期21卷 4316-4323页

作者： Wu, Shuai Tong, Wei Hou, Yuhong Li, Ping Yang, Weidong Wu, Edmond Q. Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Automation and College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210023 China School of Mechanical Engineering Northwest University of Technology Xi'an710072 China Chinese Flight Test Establishment Technology and Engineering Center of Chinese Flight Test Establishment Xi'an710076 China School of Biological Science and Medical Engineering Beihang University Beijing100191 China Fudan University Shanghai200433 China

The industrial knowledge graph (IKG) can improve the cognitive intelligence of the manufacturing system and is recognized as one of the cores of the next-generation industrial management information system. Due to the multisource heterogeneous nature of industrial data, aligning entities with the same semantics (entity alignment) is the core technology for building large-scale, high-coverage IKGs. Existing approaches show that embedded learning of IKGs performs well for this task. However, most advanced methods ignore concept information when learning topological information about IKGs. Inspired by the ontology matching theory, in this article, we realize the importance of entity concepts in alignment. The conceptual semantics of entities can usually be obtained through the is-a relation. However, the IKG is usually constructed by triples (entity, relation, entity) automatically extracted from a large text corpus. This will lead to entities in the IKG having problems such as lacking conceptual information, belonging to multiple concepts, or having different concept granularities. To solve the two problems of lacking conceptual information and different concept granularity, we propose the concept-aware entity alignment network (CAEA), aggregating bidirectional relations and attributes to get the entity concept semantics by a novel concept-aware graph attention mechanism. The excellent performance of the CAEA can better support the construction of large and complete IKGs and support downstream applications such as industrial knowledge recommendation and assisted decision-making. To verify the performance of the CAEA on the IKG, we construct a new entity alignment benchmark using industrial control network security data and verify the effectiveness of the CAEA on the new benchmark and several mainstream datasets. Experimental results show that our method outperforms other state-of-the-art (SOTA) methods and promotes the development of IKGs. © 2025 IEEE. All rights rese

关键词： Knowledge graph

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Unsupervised Cross-Domain Facial Expression Recognition via Class Adaptive self-Training

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IEEE Transactions on Affective Computing 2025年

作者： Wang, Shanmin Liu, Qingshan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Nanjing University of Posts and Telecommunications PengCheng Laboratory China

Unsupervised Cross-Domain Facial Expression Recognition (CD-FER) aims to transfer the recognition ability from annotated source domains to unlabeled target domains. Despite the advancements in CD-FER techniques based on marginal distribution matching, certain inherent properties of facial expressions, such as implicit class margins and imbalanced class distributions, still leave room for improvement in existing models. In this paper, we propose a Class-Adaptive Self-Training (CAST) model for unsupervised CD-FER. In addition to domain alignment, the CAST model leverages self-training to learn pseudo labels and dually enhance aligned representations for explicit class distinction, considering implicit class margins. Furthermore, the CAST model conducts a comprehensive analysis of the negative effects of class distributions on pseudo-label learning from perspectives of class-level representation distributions and predicted probabilities, and subsequently proposes specific solutions. By jointly matching class-level representation distributions and class distributions, the CAST model successfully alleviates conditional distribution discrepancies between domains, which is particularly pertinent for facial expression properties. Experimental results, including assessments on multiple target domains and evaluations of multiple FER models, demonstrate the effectiveness, superiority, and universality of the CAST model. © 2010-2012 IEEE.

关键词： Risk assessment

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Coherence resonance, parameter estimation and self-regulation in a thermalsensitive neuron

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Cognitive Neurodynamics 2025年第1期19卷 1-13页

作者： Guo, Qun Zhou, Ping Zhang, Xiaofeng Zhu, Zhigang College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou 730050 China School of Science Chongqing University of Posts and Telecommunications Chongqing 430065 China Department of Physics Lanzhou University of Technology Lanzhou 730050 China

In this work, two capacitors connected by a thermistor are used to explore the electrical property of double-layer membrane in a neuron, which the membrane property is sensitive to changes of temperature and two capacitive variables are used to measure the potentials of inner and outer membrane. The circuit characteristics and energy definition for the neural circuit and its equivalent neuron model in oscillator form are clarified from physical aspect. Considering the shape deformation of cell membrane under external physical stimuli and energy injection, intrinsic parameters of the neuron can be controlled with adaptive growth under energy flow, an adaptive control law is proposed to regulate the firing modes accompanying with energy shift. In presence of noisy excitation, coherence resonance can be induced and confirmed by taming the noise intensity carefully. The distributions of CV (coefficient variability) and average energy value < H > vs. noise intensity provide a feasible way to predict the coherence resonance and even stochastic resonance in the neural activities. Adaptive parameter observers are designed to identify the unknown parameters in this neuron model. The research findings of this study lay a foundation for the design of temperature-adaptive biomimetic neuromorphic devices and the research on multi-functional perception neural networks with temperature sensitivity. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Double membrane neuron Neural circuit Neuron energy Parameter identification

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CMCache: An Adaptive Cross-Level Data Placement Method for Multi-Level Cache

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

作者： Zeng, Zhaoyang Tan, Yujuan Ma, Zhulin Li, Jiali Zhao, Sanle Liu, Duo Chen, Xianzhang Ren, Ao Chongqing University College of Computer Science Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400044 China Chongqing Universityof Posts and Telecommunications College of Software Engineering China Chongqing University College of Big Data and Software Engineering Chongqing China

Multi-level cache systems enhance I/O performance by optimizing data placement across various cache levels from a global perspective. However, existing methods often struggle to place data at the optimal cache level promptly due to their reliance on historical access patterns and inflexible placement strategies. These methods face two main challenges: (1) For already cached data with sufficient access history, existing approaches only optimize movement between adjacent cache levels, potentially delaying data arrival at its globally optimal cache level and leading to unnecessary bandwidth consumption and increased latency. (2) For newly entered data without access history, current methods cannot accurately predict their future hotness and simply place them at a fixed cache level (i.e. first or last level), overlooking future accesses of new data and potentially resulting in high cache miss rates or cache pollution. To address these issues, we propose CMCache, an adaptive cross-level data placement method for multi-level cache. CMCache applies distinct placement strategies for cached and new data to reach the optimal level timely, considering their different characteristics. It also logically divides cache space into two sections to manage cached and new data separately, dynamically adjusting section sizes based on access patterns. This approach significantly improves data placement efficiency, achieving up to an 89% reduction in miss rates and a 79% decrease in average response times compared to existing methods. © 2025 IEEE.

关键词： Cache memory

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