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Distributed Nash Equilibrium Seeking in Coalition Games for Uncertain Euler-Lagrange systems With Application to USV Swarm Confrontation

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arXiv 2025年

作者： Yuwen, Cheng Wen, Guanghui Zhou, Jialing Luan, Meng Huang, Tingwen School of Automation Southeast University Nanjing210096 China State Key Laboratory of CNS/ATM Beijing Institute of Technology Beijing519088 China Department of System Science School of Mathematics Southeast University Nanjing211189 China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen218055 China

In this paper, a coalition game with local and coupling constraints is studied for uncertain Euler-Lagrange (EL) systems subject to disturbances with unknown bounds. In the coalition game, each agent collaborates with other agents within the same coalition to optimize its coalition’s cost function while simultaneously competing against agents in other coalitions. Under a distributed framework where each agent has access only to its own action, cost function, and constraint parameters, a distributed strategy is proposed to seek the Nash equilibrium (NE). By combining adaptive methods and sign functions, model uncertainties and disturbances with unknown bounds in the EL system are compensated and suppressed, respectively. Furthermore, an integration of adaptive methods and consensus protocols is employed to update the Lagrange multipliers of both local and coupling constraints. A dynamic average consensus is employed to estimate the gradient of coalition function, while the leader-following protocol is adopted to estimate the actions of other agents. By leveraging Lyapunov theory, the NE is proven to be asymptotically stable. Moreover, an unmanned surface vehicle swarm confrontation is meticulously modeled and analyzed in the coalition game framework. A diverse array of tasks, including formation, encirclement, and interception, are systematically formulated. A numerical example demonstrates the effectiveness of the proposed algorithm. Copyright © 2025, The Authors. All rights reserved.

关键词： Lagrange multipliers

A two-stage Deep Convolutional Generative Adversarial Network-based data augmentation scheme for agriculture image regression tasks

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A two-stage Deep Convolutional Generative Adversarial Networ...

Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Hajar Hammouch Mounim El-Yacoubi Huafeng Qin Aissam Berrahou Hassan Berbia Mohamed Chikhaoui SAMOVAR Institut Polytechnique de Paris Palaiseau France SSLab National School For Computer Science Mohammed V University in Rabat Rabat Maroc SAMOVAR CNRS Institut Polytechnique de Paris Palaiseau France Chongqing Engineering Laboratory of detection control and integrated system School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China National School For Computer Science Mohammed V University in Rabat Rabat Maroc Soil and Water Management Laboratory Institute of Agronomy & Veterinary Medicine Rabat Maroc

ISBN: (纸本)9781665426220

Recently, Deep Learning models, such as Deep Convolutional Neural Networks (CNNs), have shown remarkable performance on various computer Vision tasks. Unfortunately, many application domains, such as agriculture image analysis, do not have access to large datasets. In this study, we are interested in the prediction of soil moisture dissipation rates from aerial images using a CNN model. This entails a regression task where the CNN, given an aerial input image, has to predict a vector of regression values associated with the dissipation rates at different locations of the agricultural field. CNNs, however, require large datasets to successfully train the deep network. To this end, we present a Deep Convolutional Generative Adversarial Network (DCGAN) to generate fake agriculture images, that are realistic enough with the objective to augment the original dataset, for better generalization capabilities and more accurate predictions by the trained CNN. Unlike existing GAN-based augmentation approaches used for segmentation or classification, where only images have to be generated to augment the training set, the regression task requires both an image and its associated regression vector. In our approach, we use a two-stage scheme where the baseline CNN trained on the original dataset is harnessed to predict, for each image generated by the DCGAN, the regression vector that will serve as its ground truth in the augmented dataset. The results show that the CNN trained on the augmented dataset in this way allows a relative reduction of the Mean Absolute Error by a percentage of 20.8 w.r.t to the CNN trained on the original dataset. We believe that our approach can be generalized to any image augmentation task involving a regression task.

关键词： Training Deep learning Computational modeling Soil moisture Predictive models Agriculture Social intelligence

Energy-Efficient Cell-Free Massive MIMO Through Sparse Large-Scale Fading Processing

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arXiv 2022年

作者： Chen, Shuaifei Zhang, Jiayi Björnson, Emil Demir, Özlem Tuğfe Ai, Bo The School of Electronic and Information Engineering The Frontiers Science Center for Smart Highspeed Railway System Beijing Jiaotong University Beijing100044 China Purple Mountain Laboratories Nanjing211111 China The Department of Computer Science KTH Royal Institute of Technology Kista164 40 Sweden The Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Cell-free massive multiple-input multiple-output (CF mMIMO) systems serve the user equipments (UEs) by geographically distributed access points (APs) by means of joint transmission and reception. To limit the power consumption due to fronthaul signaling and processing, each UE should only be served by a subset of the APs, but it is hard to identify that subset. Previous works have tackled this combinatorial problem heuristically. In this paper, we propose a sparse distributed processing design for CF mMIMO, where the AP-UE association and long-term signal processing coefficients are jointly optimized. We formulate two sparsity-inducing mean-squared error (MSE) minimization problems and solve them by using efficient proximal approaches with block-coordinate descent. For the downlink, more specifically, we develop a virtually optimized large-scale fading precoding (V-LSFP) scheme using uplink-downlink duality. The numerical results show that the proposed sparse processing schemes work well in both uplink and downlink. In particular, they achieve almost the same spectral efficiency as if all APs would serve all UEs, while the energy efficiency is 2-4 times higher thanks to the reduced processing and signaling. Copyright © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

SI-YOLO: A Deep Learning Algorithm Suitable for Surgical Instrument Detection

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SSRN

SSRN 2022年

作者： Liu, Zhenzhong Zhou, Yifan Sun, Qiyuan Wang, Yan Polat, Kemal Nour, Majid Alenezi, Fayadh Alhudhaif, Adi Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin University of Technology Tianjin300384 China Tianjin300384 China Department of Electrical and Electronics Engineering Bolu Abant Izzet Baysal University Bolu14280 Turkey Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Department of Electrical Engineering College of Engineering Jouf University 72238 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj 11942 Saudi Arabia

Surgical instrument detection and tracking is one of the core technologies of surgical robots, which can assist clinicians in completing operations. However, existing methods need further improvement in terms of real-time performance and accuracy. We propose a deep learning algorithm, SI-YOLO, for detection of surgical instruments. SimAM attention mechanism is used to improve the feature extraction ability of YOLOv5. Replacing PANet with BiFPN in the neck network improves the feature fusion strategy. The α-IoU loss function is used to improve the regression accuracy of detection boxes. The mAP and FPS of the SI-YOLO reach 99.2% and 120 frames/sec, respectively. Aiming at the problem of ID transformation in the disappearance and occlusion of surgical instruments, the Inception-ResNet-v2 structure is added on the basis of the appearance feature extraction network Cosine of the DeepSort tracking algorithm. Combining SI-YOLO with the improved DeepSort implements the Tracking by Detection (TBD) algorithm. In the case of device disappearance or occlusion, the frequency of ID transitions is reduced by approximately 30%. Finally, we built a simulated surgery experimental platform, established our own data set, and realized the preliminary application of the proposed algorithm. © 2022, The Authors. All rights reserved.

关键词： Object detection

A Learning Convolutional Neural Network Approach for Network Robustness Prediction

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arXiv 2022年

作者： Lou, Yang Wu, Ruizi Li, Junli Wang, Lin Li, Xiang Chen, Guanrong The Department of Computing and Decision Sciences Lingnan University Hong Kong The Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China The College of Computer Science Sichuan Normal University Chengdu610066 China The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

Network robustness is critical for various societal and industrial networks again malicious attacks. In particular, connectivity robustness and controllability robustness reflect how well a networked system can maintain its connectedness and controllability against destructive attacks, which can be quantified by a sequence of values that record the remaining connectivity and controllability of the network after a sequence of node- or edge-removal attacks. Traditionally, robustness is determined by attack simulations, which are computationally very time-consuming or even practically infeasible. In this paper, an improved method for network robustness prediction is developed based on learning feature representation using convolutional neural network (LFR-CNN). In this scheme, higher-dimensional network data are compressed to lower-dimensional representations, and then passed to a CNN to perform robustness prediction. Extensive experimental studies on both synthetic and real-world networks, both directed and undirected, demonstrate that 1) the proposed LFR-CNN performs better than other two state-of-the-art prediction methods, with significantly lower prediction errors;2) LFR-CNN is insensitive to the variation of the network size, which significantly extends its applicability;3) although LFR-CNN needs more time to perform feature learning, it can achieve accurate prediction faster than attack simulations;4) LFR-CNN not only can accurately predict network robustness, but also provides a good indicator for connectivity robustness, better than the classical spectral measures. © 2022, CC BY.

关键词： Complex networks

Research on Fault Early Warning of Wind Turbine Pitch system based on Long Short-Term Memory Neural Network

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Research on Fault Early Warning of Wind Turbine Pitch System...

Chinese Automation Congress (CAC)

作者： Xiongwei Li Guanghua Lv Runze Zhu Gengda Li Zhongwei Lin Wenbin Wang Qingru Cui China Energy New Energy Technology Reserch Institute Co. Ltd Beijing P. R. China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Control Computer and Engineering North China Electric Power University Beijing P. R. China

ISBN: (纸本)9781665426480

To reduce the downtime of wind turbine caused by the fault of pitch system that usually has a high failure rate, a fault early warning method based on long short-term memory (LSTM) neural network is proposed. According to the operation characteristics of the pitch system, monitoring parameters recorded by SCADA, such as wind speed, generator speed, rotor speed, wind direction, pitch angle and output power, are utilized to establish the fault early warning model. Compared with the model based on BP neural network, the prediction accuracy of the LSTM model is effectively improved. The weights and thresholds of the LSTM neural network are optimized, and a reasonable threshold is set to determine the fault early warning criterion for the pitch system. To verify the effectiveness of the proposed method, the LSTM model is applied to the fault early warning for a 1.5MW wind turbine pitch system. The results show that the LSTM model can give early warning about two and a half days before the fault occurs, verifying the feasibility of the proposed method.

关键词： Automation Wind speed Neural networks Rotors Predictive models Wind power generation Generators

Dynamic Event-Triggered Adaptive control for Robust Output Regulation of Nonlinear systems with Unknown Exosystems ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2808-2813页

作者： Pin Liu Feng Xiao Bo Wei State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing 102206 China School of Control and Computer Engineering North China Electric Power University Beijing 102206 China

In this paper, robust output regulation of nonlinear systems with unknown neutral exosystems is discussed. Based on the internal model and adaptive control theory, we design the output feedback controller with a dynamic event-triggering mechanism. Under the dynamic event-triggering mechanism, the controller can be implemented in the digital platform. The effectiveness of the proposed event-triggering mechanism is illustrated through an example.

关键词： Robust output regulation nonlinear systems adaptive control internal model dynamic event-triggering mechanism

Antitumor Effects of Cds-Ppix Adducts for Nuclear-Targeted Sonodynamic Therapy

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SSRN

SSRN 2023年

作者： Xue, Juanjuan Jia, Muyue Sang, Shengbo Li, Huaqian Wang, Xiaojuan Guo, Li Duan, Qianqian Xi, Yanfeng Shanxi Key Laboratory of Micro Nano Sensors & Artificial Intelligence Perception College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Lab of Advanced Transducers and Intelligent Control System Ministry of Education Taiyuan University of Technology Taiyuan030024 China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan030024 China Department of Pathology Shanxi Cancer Hospital Shanxi Hospital Affiliated to CancerHospital Chinese Academy of Medical Sciences Cancer Hospital Affiliated to Shanxi Medical University Shanxi Taiyuan030013 China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of Orthopaedics Second Hospital of Shanxi Medical University Taiyuan030001 China Department of Pathology Shanxi Tumor Hospital Taiyuan030013 China

To achieve high-efficiency treatment to diseases, accurate delivery of drugs with a carrier into the specific organelles is of great importance. The nucleus is the ultimate target for a large number of drugs and direct delivery of drugs into nucleus can greatly improve the therapeutic treatment. Whereas, in sonodynamic therapy (SDT), the fact that most drug delivery systems can only deliver drugs to the cytoplasm of the cells and cannot act directly on the nucleus. In this paper, nucleolus-targeted carbondots (CDs) are prepared. Also, the conjugate of CDs and PpIX is synthesized by amide cross-linking and then release singlet oxygen to kill cancer cells under ultrasound radiation. The resulting CDs-PpIX nanoparticles showed significant nuclear targeting properties, and greatly improved the efficiency of acoustic dynamics treatment, as the nucleus exhibited poor tolerance to reactive oxygen species (ROS) generated during acoustic dynamics treatment. In addition, ROS as an extracellular signal can activate the p38MAPK protein pathway, which is the main mechanism of ultrasound killing of cancer cells. Our results demonstrate the great potential of CDs-PpIX in targeting cell nuclei for acoustic dynamics treatment. © 2023, The Authors. All rights reserved.

关键词： Cancer cells

A novel TSK fuzzy system incorporating multiview collaborative transfer learning for personalized epileptic EEG detection

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arXiv 2021年

作者： Li, Andong Deng, Zhaohong Lou, Qiongdan Choi, Kup-Sze Shen, Hongbin Wang, Shitong School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Key Laboratory of Digital Design and Software Technology Wuxi214122 China Centre for Smart Health Hong Kong Polytechnic University Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

—In clinical practice, electroencephalography (EEG) plays an important role in the diagnosis of epilepsy. EEG-based computer-aided diagnosis of epilepsy can greatly improve the accuracy of epilepsy detection while reducing the workload of physicians. However, there are many challenges in practical applications for personalized epileptic EEG detection (i.e., training of detection model for a specific person), including the difficulty in extracting effective features from one single view, the undesirable but common scenario of lacking sufficient training data in practice, and the no guarantee of identically distributed training and test data. To solve these problems, we propose a TSK fuzzy system-based epilepsy detection algorithm that integrates multi-view collaborative transfer learning. To address the challenge due to the limitation of single-view features, multi-view learning ensures the diversity of features by extracting them from different views. The lack of training data for building a personalized detection model is tackled by leveraging the knowledge from the source domain (reference scene) to enhance the performance of the target domain (current scene of interest), where mismatch of data distributions between the two domains is resolved with adaption technique based on maximum mean discrepancy. Notably, the transfer learning and multi-view feature extraction are performed at the same time. Furthermore, the fuzzy rules of the TSK fuzzy system equip the model with strong fuzzy logic inference capability. Hence, the proposed method has the potential to detect epileptic EEG signals effectively, which is demonstrated with the positive results from a large number of experiments on the CHB-MIT dataset. © 2021, CC BY-NC-ND.

关键词： Electrophysiology