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International Joint Conference on Neural Networks (IJCNN)

作者： Zixin Zhong Liyan Song Fengzhen Tang Bo Yuan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Faculty of Computing Harbin Institute of Technology Harbin China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In non-stationary data streams, the challenges of concept drift are further compounded by the issue of Intermediate Verification Latency (IVL), which can impede timely model adaptation. IVL refers to the finite delay between the arrival of data features and their corresponding labels. This delay could pose a significant challenge in adapting models to new concepts, ultimately hindering predictive performance. However, existing IVL approaches exhibit certain limitations. Some approaches passively wait for delayed labels, thereby overlooking temporarily unlabeled data. Other approaches employ pseudo-labeling for immediate model updates, but may risk losing valuable information when reverting model states to rectify previous pseudo-labeling mistakes. To overcome these limitations, we propose a novel approach called Micro-cluster based Immediate Pseudo-Labeling with Oriented Synthetic Correction (MIPLOSC). MIPLOSC leverages micro-cluster systems to effectively capture data distributions, thus facilitating its two core components: immediate pseudo-labeling and oriented synthetic correction. The immediate pseudo-labeling mechanism facilitates immediate utilization of temporarily unlabeled data, and the oriented synthetic correction mechanism enables finergrained rectification from previous erroneous pseudo-labels and concept drift, minimizing the loss of learned information. Experimental studies validated the effectiveness of MIPLOSC in addressing IVL, demonstrating its superiority over competing methods in both space consumption and predictive performance across varying degrees of label delay.

关键词： Adaptation models Costs Concept drift Noise Neural networks Predictive models Robustness

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Typical curve with G1 constraints for curve completion

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Visual Computing for Industry,Biomedicine,and Art 2021年第1期4卷 283-294页

作者： Chuan He Gang Zhao Aizeng Wang Fei Hou Zhanchuan Cai Shaolin Li School of Mechanical Engineering and Automation Beihang UniversityBeijing 100191China State Key Laboratory of Lunar and Planetary Sciences Macao University of Science and TechnologyMacao 999078China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of SciencesBeijing 100049China Faculty of Information Technology Macao University of Science and TechnologyMacao 999078China

This paper presents a novel algorithm for planar G1 interpolation using typical curves with monotonic *** G1 interpolation problem is converted into a system of nonlinear equations and sufficient conditions are provided to check whether there is a *** proposed algorithm was applied to a curve completion *** main advantages of the proposed method are its simple construction,compatibility with NURBS,and monotonic curvature.

关键词： Typical curves Monotonic curvature G1 interpolation Curve completion Euler spiral

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DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing

arXiv

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arXiv 2024年

作者： Sun, Li Huang, Zhenhao Wu, Hua Ye, Junda Peng, Hao Yu, Zhengtao Yu, Philip S. School of Control and Computer Engineering North China Electric Power University Beijing102206 China School of Computer Science Beijing University of Posts and Telecommunications Beijing100876 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Department of Computer Science University of Illinois ChicagoIL United States

Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph. In the literature, most GSL solutions either primarily focus on structure refinement with task-specific supervision (i.e., node classification), or overlook the inherent weakness of GNNs themselves (e.g., over-squashing), resulting in suboptimal performance despite sophisticated designs. In light of these limitations, we propose to study self-supervised graph structure-feature co-refinement for effectively alleviating the issue of over-squashing in typical GNNs. In this paper, we take a fundamentally different perspective of the Ricci curvature in Riemannian geometry, in which we encounter the challenges of modeling, utilizing and computing Ricci curvature. To tackle these challenges, we present a self-supervised Riemannian model, DeepRicci. Specifically, we introduce a latent Riemannian space of heterogeneous curvatures to model various Ricci curvatures, and propose a gyrovector feature mapping to utilize Ricci curvature for typical GNNs. Thereafter, we refine node features by geometric contrastive learning among different geometric views, and simultaneously refine graph structure by backward Ricci flow based on a novel formulation of differentiable Ricci curvature. Finally, extensive experiments on public datasets show the superiority of DeepRicci, and the connection between backward Ricci flow and over-squashing. Codes of our work are given in https://***/RiemanGraph/. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Privacy Preservation for Cloud-Edge-Collaborative Energy Management System Using Post-Quantum Homomorphic Encryption

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IEEE Transactions on Smart Grid 2025年

作者： Jiang, Cheng Li, Xue Du, Dajun Qian, Hong Wen, Mi Wu, Lei Findeisen, Rolf Shanghai University of Electric Power Shanghai Key Laboratory of Power Station Automation Technology College of Automation Engineering Shanghai200090 China Shanghai University Shanghai Key Laboratory of Power Station Automation Technology School of Mechatronical Engineering and Automation Shanghai200072 China Shanghai University of Electric Power College of Computer Science and Technology Shanghai201306 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610225 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States TU Darmstadt Control and Cyber-Physical Systems Laboratory Darmstadt64227 Germany

Cloud-based energy management systems (EMS) in smart grids face privacy challenges, as existing methods based on traditional homomorphic encryption support limited operations and are vulnerable to quantum attacks. We propose a privacy preservation method for smart grid energy management system, leveraging edge-assisted computing and ring learning with errors (RLWE) homomorphic encryption. Our approach enables both addition and multiplication operations on encrypted smart grid data while defending against quantum threats. We demonstrate the methods security under the RLWE hardness assumption and validate its effectiveness through an experimental platform simulating smart grid environments. This work addresses computational limitations in current privacy-preserving techniques, enhancing data security for evolving smart grid technologies © 2010-2012 IEEE.

关键词： Power management (telecommunication)

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Multi-Task Learning for Fatigue Detection and Face Recognition of Drivers via Tree-Style Space-Channel Attention Fusion Network

arXiv

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arXiv 2024年

作者： Qu, Shulei Gao, Zhenguo Chen, Xiaowei Li, Na Wang, Yakai Wu, Xiaoxiao Department of Computer Science and Technology Huaqiao University Fujian Xiamen361021 China Key Laboratory of Computer Vision Machine Learning of Fujian Provincial Universities Fujian Xiamen361021 China Department of Mechanical Engineering and Automation Huaqiao University Fujian Xiamen361021 China

In driving scenarios, automobile active safety systems are increasingly incorporating deep learning technology. These systems typically need to handle multiple tasks simultaneously, such as detecting fatigue driving and recognizing the driver’s identity. However, the traditional parallel-style approach of combining multiple single-task models tends to waste resources when dealing with similar tasks. Therefore, we propose a novel tree-style multi-task modeling approach for multi-task learning, which rooted at a shared backbone, more dedicated separate module branches are appended as the model pipeline goes deeper. Following the tree-style approach, we propose a multi-task learning model for simultaneously performing driver fatigue detection and face recognition for identifying a driver. This model shares a common feature extraction backbone module, with further separated feature extraction and classification module branches. The dedicated branches exploit and combine spatial and channel attention mechanisms to generate space-channel fused-attention enhanced features, leading to improved detection performance. As only single-task datasets are available, we introduce techniques including alternating updation and gradient accumulation for training our multi-task model using only the single-task datasets. The effectiveness of our tree-style multi-task learning model is verified through extensive validations. © 2024, CC BY.

关键词： Face recognition

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China affiliated with the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Automation Guangdong University of Technology Guangzhou510641 China Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

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Toward parallel intelligence: An interdisciplinary solution for complex systems

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The Innovation 2023年第6期4卷 152-164页

作者： Yong Zhao Zhengqiu Zhu Bin Chen Sihang Qiu Jincai Huang Xin Lu Weiyi Yang Chuan Ai Kuihua Huang Cheng He Yucheng Jin Zhong Liu Fei-Yue Wang College of Systems Engineering National University of Defense TechnologyChangsha 410073China Hunan Institute of Advanced Technology Changsha 410073China Shanghai Institute of Infectious Disease and Biosecurity Shanghai 200032China Institute of Epidemiology Helmholtz Zentrum Minchen-German Research Center for Environmental Health(GmbH)NeuherbergGermany Department of Computer Science Hong Kong Baptist UniversityHong KongChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling,analysis,management,and *** meet these demands,the parallel systems method rooted in the artificial systems,computational experiments,and parallel execution(ACP)approach has been *** method cultivates a cycle termed parallel intelligence,which iteratively creates data,acquires knowledge,and refines the actual *** the past two decades,the parallel systems method has continuously woven advanced knowledge and technologies from various disciplines,offering vers atile interdisciplinary solutions for complex systems across diverse *** review explores the origins and fundamental concepts of the parallel systems method,showcasing its accomplishments as a diverse array of parallel technologies and applica-tions while also prognosticating potential *** posit that this method will considerably augment sustainable development while enhancing interdisciplinary communication and cooperation.

关键词： execution artificial offering

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

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computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Approximate state estimation with large-scale sensor networks under event-based sensor scheduling strategy

Approximate state estimation with large-scale sensor network...

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IEEE Conference on Decision and Control

作者： Xinhui Liu Meiqi Cheng Dawei Shi Ling Shi MIIT Key Laboratory of Servo Motion System Drive and Control School of Automation Beijing Institute of Technology Beijing China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Kowloon Hong Kong China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we investigate the issues of real-time sensor scheduling and state estimator design within large-scale sensor network systems. Specifically, data redundancy sometimes occurs in large-scale sensor arrays due to the excessive proximity of sensing units in the spatial domain, leading to high similarity in their measurement data. Currently, it is difficult to account for such redundancy in sensor scheduling algorithms found in existing literature, where the optimal subset of sensors is generally selected by optimizing objective functions formulated from certain performance criteria. To tackle this problem, we introduce an event-based sensor scheduling strategy, the triggering condition of which is designed founded on the similarity of sensor data, so as to identify the most informative subset of sensors for state estimation. To evaluate the impact of the sensor scheduling protocol on system observability, we propose a new notion of $\mathscr{E}(\varepsilon)$-observability, based on which an observability criterion is derived. In addition, we have designed a set-valued state estimation algorithm, which takes into account the intricate measurement information structure inherent within the sensor selection mechanism. The performance enhancement of the proposed estimator is also investigated. Finally, numerical experiments are conducted to validate the effectiveness of the proposed estimation algorithm and to verify the performance improvement.

关键词： Protocols Event detection Scheduling algorithms Redundancy Approximation algorithms Real-time systems Sensors State estimation Observability Sensor arrays

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Wireless/wired integrated transmission for industrial cyber-physical systems: risk-sensitive co-design of 5G and TSN protocols

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science China(Information sciences) 2022年第1期65卷 56-71页

作者： Yajing ZHANG Qimin XU Xinping GUAN Cailian CHEN Mingyan LI Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management College of Computer Science Chongqing University

The growing popularity of intelligent manufacturing is driven by deterministic transmission demands of applications in industrial cyber-physical systems(ICPS). However, the ossified shortages of industrial wireless communication such as diverse quality of service(Qo S) and complex signaling processes incur a severe long tail of transmission delay distribution. As a solution, the 5 th generation(5 G) wireless communication technology provides ultra-reliable and low-latency communication(URLLC) for industry scenarios. Moreover, the newly proposed time-sensitive networking(TSN) standards guarantee the transmission determinacy by gate mechanism. In this paper, we propose a heterogeneous time-sensitive network(HTSN) co-designed by 5 G and TSN. We first develop a predictive multi-priority wireless scheduling mechanism based on semi-persistent scheduling(SPS) to reduce signaling delay by reserving resources in *** we propose an adaptive data injection mechanism for TSN based on per-stream filtering and policing(PSFP), which dynamically adjusts the priority of data for queue injection in TSN. To further reduce the long tail of delay, we employ a risk-sensitive learning method to improve the worst-case delay. Simulations on a hot rolling production scenario demonstrate that the proposed mechanisms under HTSN achieve great performance in terms of integrated delay and resource utilization.

关键词： industrial cyber-physical systems (ICPS) 5G-TSN co-design wireless/wired integrated network risk-sensitive reinforcement learning deterministic communication

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