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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Linear Convergent Distributed Nash Equilibrium Seeking With Compression

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IEEE Transactions on Automatic control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

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Ptychographic characterization of spatiotemporal wavepackets

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Optica 2025年第4期12卷 498-504页

作者： Liu, Yuanyuan Xu, Yingming Zhang, Nianjia Liu, Dawei Wei, Lijin Cao, Qian Shi, Junhui Zhan, Qiwen School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Zhangjiang Laboratory 100 Haike Road Shanghai201204 China Research Center for Novel Computational Sensing and Intelligent Processing Zhejiang Lab. Hangzhou311100 China College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

Exciting advances in spatiotemporal wavepackets have shown great potential in a range of applications, including particle manipulation, time-resolved imaging, and unique light–matter interactions. In particular, spatiotemporal optical vortice (STOV) pulse beams characterized by a helical phase structure in space and time are a recently emerging type of complex structured ultrashort pulse. However, the high-resolution measurement and manipulation of ultrashort spatiotemporal wavepackets still face major challenges due to their inseparable properties in space and time. In this study, based on spectra-encoded ptychography, we implement a simple, compact, reference-free STOV characterization technique with a temporal resolution of 6 fs and a spatial resolution of 11.05 µm. This technique can be employed to analyze various intricate spatiotemporal structural beams, contributing to the growing field of spatiotemporally engineered ultrafast laser pulse beams. Additionally, as a lensless measurement technique, this characterization method may be extended to other spectral ranges, including the extreme ultraviolet and X-ray domains. © 2025 Optica Publishing Group.

关键词： Wave packets

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Optimization of BLE Broadcast Mode in Offline Finding Network

arXiv

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arXiv 2025年

作者： Zhang, Li Feng, Cheng Xia, Tian School of Computer and Control Engineering Northeast Forestry University China School of Measurement-control Technology and Communications Engineering Harbin University of Science and Technology China

In the Offline Finding Network(OFN), offline Bluetooth tags broadcast to the surrounding area, the finder devices receiving the broadcast signal and upload location information to the IoT(Internet of Things) cloud servers, thereby achieving offline finding of lost items. This process is essentially a Bluetooth low energy (BLE) neighbor discovery process(NDP). In the process, the variety of Bluetooth scan modes caused by the scan interval and scan window settings affects the discovery latency of finder devices finding the tag broadcast packets. To optimize the experience of searching for lost devices, we propose the CPBIS-mechanism, a certain proportion broadcast-intervals screening mechanism that calculates the most suitable two broadcast intervals and their proportion for offline tags. This reduces discovery latency in the BLE NDP, improves the discovery success rate, further enhances the user experience. To our knowledge, we are the first to propose a comprehensive solution for configuring the broadcast interval parameters of advertisers in BLE NDP, particularly for configurations involving two or more broadcast intervals. We evaluated the results obtained by CPBIS on the nRF52832 chip. The data shows that the CPBIS-mechanism achieves relatively low discovery latencies for multiple scan modes. © 2025, CC BY.

关键词： Bluetooth

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Fed-Autoformer: A Federated Learning-Based Battery Anomaly Detection Approach for Electric Vehicle Charging with Privacy Preservation 30th

Fed-Autoformer: A Federated Learning-Based Battery Anomaly D...

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30th International Conference on Computational and Experimental engineering and Sciences, ICCES 2024

作者： Yan, Xuhan Yang, Shunbo Guo, Yiguo Fu, Yimu Wu, Sixing Li, Jianbin School of Control and Computer Engineering North China Electric Power University Beijing China Economic Technology Research Institute State Grid Shandong Electric Power Company Shandong Jinan City China

ISBN: (纸本)9783031829062

The battery management system ensures the safe operation of electric vehicles (EVs) by detecting abnormalities in the battery energy storage system. However, the extensive and widely dispersed data in EV battery data, along with the presence of users’ private details, raise a demand for effective anomaly detection and privacy protection. To tackle those two challenges, we propose Fed-Autoformer, a distributed anomaly detection architecture based on real vehicle charging data to detect battery anomalies and protect privacy. Thus, we first propose a novel anomaly detection model for EV charging data based on the Autoformer encoder architecture. In particular, it enables an autocorrelation attention mechanism to extract and consider the intrinsic correlation of battery data features (e.g., voltage, current, state of charge (SOC), etc.) for anomaly detection. In addition, we introduce a federated learning architecture to protect the privacy of battery charging data and improve the federated learning training process based on the differences between vehicle batteries in charging stations to ensure detection accuracy while protecting the privacy of EV charging data. Finally, we comprehensively analyze the feasibility and effectiveness of our proposed architecture using a real EV charging dataset. Our proposed federated learning approach for EV battery anomaly detection not only effectively protects privacy through federated learning but also improves the accuracy by 3.2% compared to most mainstream time series anomaly detection models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Charging stations

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LLCoach: Generating Robot Soccer Plans Using Multi-role Large Language Models

LLCoach: Generating Robot Soccer Plans Using Multi-role Larg...

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27th RoboCup International Symposium, 2024

作者： Brienza, M. Musumeci, E. Suriani, V. Affinita, D. Pennisi, A. Nardi, D. Bloisi, D.D. Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy School of Engineering University of Basilicata Potenza Italy Department of International Humanities and Social Sciences International University of Rome Rome Italy

ISBN: (纸本)9783031858581

The deployment of robots into human scenarios necessitates advanced planning strategies, particularly when we ask robots to operate in dynamic, unstructured environments. RoboCup offers the chance to deploy robots in one of those scenarios, a human-shaped game represented by a soccer match. In such scenarios, robots must operate using predefined behaviors that can fail in unpredictable conditions. This paper introduces a novel application of Large Language Models (LLMs) to address the challenge of generating actionable plans in such settings, specifically within the context of the RoboCup Standard Platform League (SPL) competitions where robots are required to autonomously execute soccer strategies that emerge from the interactions of individual agents. In particular, we propose a multi-role approach leveraging the capabilities of LLMs to generate and refine plans for a robotic soccer team. The potential of the proposed method is demonstrated through an experimental evaluation, carried out simulating multiple matches where robots with AI-generated plans play against robots running human-built code. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anthropomorphic robots

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Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm

Optimizing the LSTM Model for Electricity Price Forecasting ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Ahmadvand, Fatemeh Imani, Mahmood Hosseini Andani, Hamid Taheri Andani, Mansour Taheri Politecnico di Torino Department of Control and Computer Engineering Torino Italy Torino Italy Isfahan University of Technology Department of Electronic Engineering Isfahan Iran School of Manufacturing Systems and Networks Arizona State University TempeAZ United States

ISBN: (纸本)9798331541125

Achieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price. © 2025 IEEE.

关键词： Long short-term memory

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A Dimensional Difference-Based Population Size Adjustment Framework for Gannet Optimization Algorithm 16th

A Dimensional Difference-Based Population Size Adjustment Fr...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Pan, Jeng-Shyang Han, Kunpeng Chu, Shu-Chuan Li, Zhi Zhang, Li College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Artificial Intelligence Nanjing University of Information Science and Engineering Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan School of Information and Control Engineering Qingdao University of Technology Qingdao266590 China

ISBN: (纸本)9789819615346

In this paper, an improved Gannet optimization algorithm (DDPSA-GOA) is presented. The Gannet optimization algorithm (GOA) was formed by mathematically studying the various foraging behaviors of Gannet. GOA has had better results in the past. A framework DDPSA to improve GOA is cited in this paper. DDPSA aims to adjust population size and replace stagnant individuals with different strategies by determining the stage of the population. The framework divides the population into two stages, premature integration and evolutionary stagnation, which are two population states with stagnant individual replacement mechanisms. Optimal elimination replacement or elite replacement is adopted for different stages. The GOA algorithm in the DDPSA framework was tested against the initial GOA and other comparative algorithms in the CEC 2013 test set. Experimental results show that DDPSA-GOA outperforms other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Robust Residual Compensation control for Disturbance Suppression to Enhance Performance of Grid-Forming Inverter

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International Journal of Circuit Theory and Applications 2025年第0期

作者： Zhang, Shufeng Liu, Changan Shi, Yuntao School of Control and Computer Engineering North China Electric Power University Beijing China School of information North China University of Technology Beijing China School of Electrical and Control Engineering North China University of Technology Beijing China

Grid-forming (GFM) inverter has been proposed as a practical solution for improving the stability and resilience of modern power systems. However, the GFM inverter exhibits low inertia characteristic and is susceptible to external disturbance and uncertainty. Motivated by this, this paper presents a residual compensation control strategy to improve the performance of the GFM inverter. To address the impact of measurement noise with outliers on state estimation and residual generation, the Huber function is employed as a cost function in the design of a robust Kalman filter-based residual generator. Subsequently, a robust residual compensation controller is developed to achieve effective disturbance suppression. The generalized system model of the GFM inverter is established based on the model matching theory. Two algebraic Riccati equations corresponding to the full-information system and output feedback system are then formulated based on the generalized model to facilitate the solution of the robust residual compensation controller. Simulation and experiment results demonstrate that the robust Kalman filter-based residual generator and the robust residual compensation controller, as presented, effectively suppress measurement noise and load-side disturbance, thereby improving the performance of the GFM inverter. © 2025 John Wiley & Sons Ltd.

关键词： Cost functions

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Computational imaging based on single-photon detection: a survey

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Artificial Intelligence Review 2025年第8期58卷 1-54页

作者： Pu, Yanyun Zhu, Chengyuan Yao, Gongxin Li, Chao Pan, Yu Yang, Kaixiang Yang, Qinmin State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University Hangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China

The rapid advancements in single-photon detectors with picosecond timing resolution over the past decade have significantly driven the development of time-correlated single-photon counting (TCSPC) for computational imaging applications, including bioimaging and remote sensing. In this review, we utilize the CiteSpace tool to create knowledge maps and perform a bibliometric analysis of this research area. Furthermore, we provide a comprehensive overview of the key challenges associated with computational imaging using temporal single-photon counting. We also highlight how these challenges have been addressed under extreme conditions to establish a reference model for future imaging solutions. We examine the performance evaluation parameters of single-photon detectors to enhance the understanding of detector array scaling and their application in constructing efficient computational imaging systems. Lastly, we aim to elucidate the current technical challenges in single-photon detector-based computational imaging and explore their potential future developments.

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