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Differentially Private Distributed Algorithms for Aggregative Games With Directed Communication Graphs

IEEE TRANSACTIONS ON AUTOMATIC control 2025年第4期70卷 2652-2658页

作者： Guo, Kai-Yuan Wang, Yan-Wu Luo, Yun-Feng Xiao, Jiang-Wen Liu, Xiao-Kang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Due to the transmission of information during seeking the Nash equilibrium and the possible leaking of sensitive information deduced from the transmitted information, it is urgent to propose privacy-preserving seeking algorithms for aggregative games. This article proposes two & varepsilon;-differentially private distributed Nash equilibrium seeking algorithms for aggregative games under directed communication graphs with row- and column-stochastic adjacency matrices, respectively. By utilizing the diameters of players' strategy sets, Laplacian noise free from the uniformly upper bound information of gradients is proposed to achieve & varepsilon;-differential privacy and guarantee the algorithms being fully distributed. To avoid the noise accumulating in the estimate of the aggregate strategy, a noise deduction mechanism is employed to ensure the accuracy of the algorithms. The tradeoff between accuracy and privacy level is investigated. Simulation examples and comparisons with existing result are carried out to verify the effectiveness of our algorithms and theorems.

关键词： Games Noise Privacy Aggregates Accuracy Cost function Laplace equations Vectors Nash equilibrium Costs Aggregative games differentially privacy directed communication graphs

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Subjective-uncertainty-oriented dynamic renting framework for energy storage sharing

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APPLIED ENERGY 2025年 378卷

作者： He, Yan Xiao, Jiang-Wen Wang, Yan-Wu Liu, Zhi-Wei He, Shi-Yuan Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

In recent years, shared energy storage has gained significant attention for mitigating the supply and demand imbalance caused by the intermittency of distributed renewable energy. Considering the subjective perception of prosumers when facing uncertainty, this paper proposes anew dynamic competitive on-demand renting framework for energy storage capacity (ESC) sharing to increase energy storage utilization, increase energy storage operator (ESO) profits, and reduce prosumer costs. In this framework, a demand-based dynamic capacity pricing mechanism is introduced, modeling the relationship between ESO and prosumers as a Stackelberg game while establishing a generalized Nash equilibrium (GNE) problem among prosumers. ESO determines the dynamic capacity pricing mechanism, while prosumers determine the hourly renting capacity based on demand. In capacity sharing, prospect theory is introduced for the first time to describe the subjective perceptions of prosumers when facing the uncertainty of renewable energy. Moreover, the existence of SE and the uniqueness of GNE are analyzed, followed by a summary and proposal of a method to determine the existence of equilibrium in a nested generalized non-cooperative Stackelberg game. Simulations show the effectiveness of the proposed framework on improving the ESC utilization rate, the impact of subjective perceptions on prosumers' decision-making, and the profit favorability of the correct estimation of subjective perceptions on ESO. Specifically, the framework increases ESO utilization by 24.07% and profit by 13.73%.

关键词： Energy storage sharing Prospect theory Dynamic capacity price Stackelberg game Generalized Nash equilibrium problem Distributed optimization

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Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation

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APPLIED ENERGY 2025年 190卷

作者： Chen, Yuejiang Xiao, Jiang-Wen Wang, Yan-Wu Luo, Yunfeng Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

Probabilistic forecasting plays an important role in the safety, stability and operation of power system. The traditional quantile regression method of non-parametric probability forecasting has the problem of crossingquantile. Besides, current neural network methods for wind farm cluster power forecasting often overlook the spatio-temporal correlation among related wind farms. To solve these problems, a cluster power forecasting model (CFM) considering spatio-temporal correlation is proposed in this paper. A novel spatial pattern attention (SPA) combining the advantages of convolutional neural network and attention mechanism is used to extract the spatial information. An improved multi-horizon quantile recurrent neural network (IMQ-RNN) and an improved non-crossing quantile regression (INCQR) strategy are used as the output module of CFM to produce high quality forecasting results. Numerical simulations are conducted by using public real-world data from the Global Energy Forecasting Competition 2014. The results show that the proposed model has excellent performance in both deterministic forecasting and probabilistic forecasting.

关键词： Probabilistic forecasting Spatio-temporal correlation Non-crossing quantile regression Wind power

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Evolutionary Game-Based Regulatory Strategy Optimization for Information Transmission Prices in Integrated Energy Systems

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ENERGIES 2025年第6期18卷 1452-1452页

作者： Cui, Kun Chi, Ming Zhao, Yong Liu, Zhiwei Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China

The rapid evolution of Integrated Energy Systems (IESs) demands robust management of information transmission, which is critical for real-time monitoring, coordination, and operational efficiency. However, the increasing complexity and costs associated with information exchange necessitate effective pricing mechanisms to ensure economic sustainability and optimal resource allocation. This paper presents an evolutionary game-theoretic framework to analyze regulatory strategies governing information transmission within IES. In the context of market dynamics, both market regulators and communication network operators are considered as actors with bounded rationality, emphasizing their strategic interplay within the system. The main contributions include formulating a model that treats communication network operators as independent entities, enhancing the understanding of IES member diversity and interactivity. This study introduces evolutionary game dynamics, providing new insights into optimizing regulatory policies. This paper also innovates by considering asset utilization in defining effective assets, potentially curbing excessive investment by communication network operators and preventing information transmission prices from soaring. A case study is provided to reveal the dynamics and equilibrium states of the regulatory game, offering theoretical support for refining regulatory strategies in IESs.

关键词： evolutionary game theory integrated energy system information transmission price regulatory strategies asset utilization

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PDE-Based Deployment of Heterogeneous Nonlinear Multiagents: A Single-Point control Scheme

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第1期21卷 30-39页

作者： Man, Jingtao Xiao, Qiang Sheng, Yin Zeng, Zhigang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China

This article develops a methodology employing partial differential equations (PDEs) to facilitate the exponential deployment of large-scale heterogeneous nonlinear multiagent systems (MASs). The considered MASs comprise a multitude of nonlinear first-order agents (FOAs) and second-order agents (SOAs). Two heterogeneous nonlinear PDEs are established to model the considered MASs by designing appropriate network communication protocols. Unlike previous PDE-based approaches for multiagent deployment, the topological weights between neighboring agents are defined as series-dependent. An informed agent, which is able to measure the location information of other agents and transmit its location information to neighboring agents through the communication network, is placed between the final FOA and the initial SOA. This novel network-based control scheme is referred to as single-point control, which could ensure the well-posedness and exponential stability of the error system. Accordingly, pointwise and distributed measurements are employed for delay-free and time-delayed cases, respectively. Numerical examples are provided in 3-D space to substantiate the obtained theoretical results.

关键词： Mathematical models Logic Informatics Formation control Velocity measurement Space heating Protocols Propagation Position measurement Partial differential equations Heterogeneous nonlinear multiagent systems (MASs) partial differential equations (PDEs)-based methodology pointwise and distributed measurements series-dependent topological weights single-point control

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Zero-Shot Defect Detection With Anomaly Attribute Awareness via Textual Domain Bridge

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IEEE SENSORS JOURNAL 2025年第7期25卷 11759-11771页

作者： Zhang, Zhe Chen, Shu Huang, Jian Ma, Jie Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China China Nucl Power Operat Technol Corp Ltd Wuhan 430074 Peoples R China

Visual defect detection is crucial for industrial quality control in intelligent manufacturing. Previous research requires target-specific data to train the model for each inspection task. However, due to the challenges of collecting proprietary data and model-training time costs, zero-shot defect detection (ZSDD) has become an emerging topic in the field. ZSDD, which requires models trained with auxiliary data, can detect defects on different products without target-data training. Recently, large pretrained vision-language models (VLMs), such as contrastive language-image pre-training model (CLIP), have demonstrated revolutionary generality with competitive zero-shot performance across various downstream tasks. However, VLMs have limitations in defect detection, which are designed to focus on identifying category semantics of the objects rather than sensing object attributes (defective/nondefective). The current VLMs-based ZSDD methods require manually crafted text prompts to guide the discovery of anomaly attributes. In this article, we propose a novel ZSDD method, namely attribute-aware CLIP, to adapt CLIP for anomaly attribute discovery without designing specific textual prompts. The core is designing a textual domain bridge, which transforms simple general textual prompt features into prompt embeddings better aligned with the attribute awareness. This enables the model to perceive the attributes of objects by text-image feature matching, bridging the gap between object semantic recognition and attribute discovery. Additionally, we perform component clustering on the images to break down the overall object semantics, encouraging the model to focus on attribute awareness. Extensive experiments on 16 real-world defect datasets demonstrate that our method achieves state-of-the-art (SOTA) ZSDD performance in diverse class-semantic datasets.

关键词： Defect detection Feature extraction Semantics Bridges image reconstruction Data models Visualization Training Sensors Manufacturing Attribute awareness component clustering defect detection intelligent manufacturing textual domain bridge vision-language model (VLM) zero-shot

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SDI: A sparse drift identification approach for force/torque sensor calibration in industrial robots

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NEUROCOMPUTING 2025年 620卷

作者： Qiao, Xuechun Xu, Chenxuan Wang, Yasen Ma, Guijun Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China China Telecom Res Inst Guangzhou 510630 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China

Calibrating force/torque (F/T) sensor drift is an enduring objective for robotic precise force control. This article presents a novel drift identification method to discover the dynamics of F/T sensor drifts from noisy measurement data, which is conducive to accurate sensor drift compensation. In the drift identification method, a linear dynamical model with measurement noise is formulated to characterize the evolution of sensor drift, and an expectation-maximization optimization framework which integrates Kalman smoothing with sparse Bayesian learning is put forward to identify the parameters of the linear dynamical model using F/T sensor measurement data. The effectiveness of the proposed drift identification method is validated on extensive robotic experiments including scenarios with unloaded mass, loaded mass, and contact force. Experimental results demonstrate the superiority of the proposed drift identification method compared to the conventional least square method for sensor calibration.

关键词： Force/torque sensor Drift calibration Expectation-maximization optimization Kalman smoothing Sparse Bayesian learning Industrial robots

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Tightly coupled stereo vision-inertial odometry based on point and line feature

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MEASUREMENT SCIENCE AND TECHNOLOGY 2025年第1期36卷

作者： Peng, Gang Cao, Yihao Chen, Shanliang Guo, Yubo He, Dingxin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Educ China Engn Res Ctr Autonomous Intelligent Unmanned Syst Wuhan 430074 Peoples R China

To improve the accuracy and robustness of visual simultaneous localization and mapping (SLAM) in low-texture environments, this paper proposes a robust and fast stereo vision inertial SLAM pose estimation method that combines point and line features with an inertial measurement unit (IMU). The method tightly couples visual point and line features with IMU constraints, forming a least-squares problem through the error of each constraint term for nonlinear optimization. To address the issues of over-segmentation and time consumption in traditional line segment detection (LSD) algorithms, an improved LSD algorithm is adopted to accelerate line feature extraction. This approach merges nearby line segments based on spatial geometric relationships and filters out invalid segments, improving the time efficiency of the algorithm. Finally, experiments conducted in low-texture environments demonstrate that our algorithm achieves high localization accuracy and robustness.

关键词： line feature pose measurement tightly coupled real time visual-inertial odometry

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Flocking Behaviors of a Modified Cucker-Smale Model on Riemannian Manifolds With Attractive-Repulsive Force

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第3期70卷 1809-1823页

作者： Su, Housheng Yu, Yan Zheng, Wei Xing Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Educ Minist China Wuhan Peoples R China Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW 2751 Australia

This article proposes a modified Cucker-Smale model in view of a double-integrator multiagent dynamic system on complete Riemannian manifolds with an attractive-repulsive force. Then, its flocking behaviors, such as collision avoidance, velocity alignment, and aggregation, are researched. On the basis of covariant derivative and parallel transport, the original Cucker-Smale model on complete Riemannian manifolds can reach velocity alignment. By using the logarithm map on Riemannian manifolds, an attractive-repulsive force is added, which enables all agents to achieve collision avoidance and aggregation on Riemannian manifolds. The proposed model is consistent with the related model on the Euclidean space. Moreover, five specific complete Riemannian manifolds are taken into consideration: the unit sphere, the hyperboloid, the infinite cylinder, the unit circle, and the special orthogonal group. After giving the corresponding covariant derivatives, parallel transports, and logarithm maps, the explicit forms of the proposed model on those manifolds are shown. In the meantime, simulations are provided to verify the theoretical conclusions for all the aforementioned manifolds.

关键词： Manifolds Vectors Force Collision avoidance Attitude control Aerospace electronics Tensors Aggregation collision avoidance Cucker-Smale model flocking Riemannian manifold velocity alignment

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Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment

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NATIONAL SCIENCE REVIEW 2025年第6期12卷 nwaf086页

作者： Wang, Ziwei Li, Siyang Wu, Dongrui Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Proc & Intelligent Control Minist Educ Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Hubei Key Lab Brain Inspired Intelligent Syst Wuhan 430074 Peoples R China

Epilepsy significantly impacts global health, affecting about 65 million people worldwide, along with various animal species. The diagnostic processes of epilepsy are often hindered by the transient and unpredictable nature of seizures. Here we propose a multi-space alignment approach based on cross-species and cross-modality electroencephalogram (EEG) data to enhance the detection capabilities and understanding of epileptic seizures. By employing deep learning techniques, including domain adaptation and knowledge distillation, our framework aligns cross-species and cross-modality EEG signals to enhance the detection capability beyond traditional within-species and within-modality models. Experiments on multiple surfaces and intracranial EEG datasets of humans and canines demonstrated substantial improvements in detection accuracy, achieving over 90% AUC scores for cross-species and cross-modality seizure detection with extremely limited labeled data from the target species/modality. To our knowledge, this is the first study that demonstrates the effectiveness of integrating heterogeneous data from different species and modalities to improve EEG-based seizure detection performance. This is a pilot study that provides insights into the challenges and potential of multi-species and multi-modality data integration, offering an effective solution for future work to collect huge EEG data to train large brain models. The first study that demonstrates the effectiveness of integrating heterogeneous data from different species and modalities to improve EEG-based seizure detection performance.

关键词： electroencephalogram automatic seizure detection transfer learning domain adaptation knowledge distillation

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