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High-Resolution Lunar Brightness Temperature Model Based on Chang'e-2 MRM Data and Spatially Weighted Neural Network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Zhu, Mingwen Cai, Zhanchuan Wu, Sensen Zhang, Yuhan Li, Jiayang Macau Univ Sci & Technol Fac Innovat Engn Sch Comp Sci & Engn Macau 999078 Peoples R China Zhuhai MUST Sci & Technol Res Inst Zhuhai 519099 Peoples R China Zhejiang Univ Sch Earth Sci Zhejiang Prov Key Lab Geog Informat Syst GIS Hangzhou 310027 Peoples R China

Brightness temperature (TB) derived from micro- wave radiometers (MRMs) onboard China's Chang'e (CE) satellites has provided significant insights into the Moon's subsurface thermal conditions and evolution. However, conventional TB mapping techniques emphasize spatial correlations among observational data points while largely neglecting the influence of inherent lunar surface factors. In this study, we propose a novel TB estimation approach utilizing geographically neural network weighted regression (GNNWR) combined with multisource lunar remote sensing data to generate TB maps at a higher spatial resolution of 0.0625(degrees )x 0.0625(degrees). This method integrates crucial lunar surface parameters in heat conduction and radiation transfer models in a new framework, thereby reducing the risk of overestimation associated with high-resolution targets in sparsely distributed samples. In addition, by replacing the traditional geographically weighted regression (GWR) kernel with a spatially weighted neural network (SWNN), the model effectively addresses spatial nonstationarity and heterogeneity present in TB data and microwave radiative transfer. Comparative analyses demonstrate that the GNNWR approach achieves superior performance, as evidenced by the highest R-2 and the lowest mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). Furthermore, the generated TB maps demonstrate strong alignment with observed spatial trends. These maps also reveal fine-scale thermal features typically obscured by conventional interpolation methods, enhancing their utility for microwave thermal emission analysis and geological studies.

关键词： Moon Mathematical models Orbits Electromagnetic heating Spatial resolution Interpolation Neural networks Extraterrestrial measurements Data models computational modeling Brightness temperature (TB) Chang'e (CE)-2 geographically neural network weighted regression (GNNWR) microwave radiometer (MRM) spatial nonstationarity

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Participation-Dependent Privacy Preservation in Cross-Silo Federated Learning

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IEEE TRANSACTIONS ON SERVICES COMPUTING 2025年第1期18卷 342-356页

作者： Qin, Yanling Zheng, Xiangping Ma, Qian Liao, Guocheng Chen, Xu Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou 510275 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Sun Yat Sen Univ Sch Software Engn Guangzhou 510275 Peoples R China

In cross-silo federated learning (FL), clients of common interest cooperatively train a global model without sharing local sensitive data, but they still face potential privacy leakage due to privacy threats from malicious attackers. Although some articles have proposed effective privacy-preserving mechanisms for FL (such as differential privacy (DP)), clients in cross-silo FL are usually different companies or organizations who may behave selfishly to optimize their own benefits. In this article, we study DP-based cross-silo FL where clients selfishly decide their participation levels (i.e., data sizes for model trainings) and privacy leakage tolerance levels to trade off between model accuracy loss and privacy loss, and we model clients' interactions as a participation-dependent privacy preservation game. It is challenging to analyze the game since the comprehensive impact of participation levels and privacy leakage tolerance levels on model accuracy is unclear and the behaviors of heterogeneous clients are coupled in a highly complex manner. To capture the impact of participation and privacy preservation behaviors, we first characterize the optimality gap of DP-based cross-silo FL for both convex and non-convex models, where the privacy leakage tolerance levels and the participation levels are coupled nonlinearly. We model clients' costs based on the optimality gap, and prove that clients' selfish participation-dependent privacy preservation game is a potential game. To analyze the optimal strategies of heterogeneous clients in a stable state, we derive the closed-form expression for the unique Nash equilibrium (NE), where clients may choose full participation or partial participation, and the equilibrium privacy preservation strategy depends on clients' accuracy-privacy preference ratios. We analyze the social efficiency of the NE by calculating the price of anarchy (PoA) and show that the PoA increases with the number of clients and the heterogeneity of clients

关键词： Privacy Accuracy computational modeling Training Data models Noise Costs Games Data privacy Servers Cross-silo federated learning differential privacy game analysis incentive mechanism

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SwiftNet: A Cost-Efficient Deep Learning Framework With Diverse Applications

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1038-1047页

作者： Wen, Zhongyi Li, Qiang Wang, Yatong Xu, Luyan Shao, Huaizong Sun, Guomin Wang, Shafei Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Lab Electromagnet Space Cognit & Intelligent Cont Beijing 100089 Peoples R China Beijing Inst Technol Beijing 100081 Peoples R China

Driven by the pursuit of enhanced performance, deep learning has recently seen rapid developments in the scaling of network architectures and parameters. However, this advancement has led to extremely high computational costs, undesirable in real-time and resource-limited scenarios. To address these challenges, we propose SwiftNet, a cost-efficient deep learning framework. Our novelty lies in SwiftNet's innovative multidimensional early-exit strategy that integrates seamlessly with existing neural network architectures. The framework includes additional branch classifiers concatenated to the backbone network, allowing high-confidence samples to exit early, thereby, reducing computational load. Unlike traditional methods, SwiftNet dynamically assesses confidence levels, ensuring only low-confidence samples proceed to subsequent classifiers or the final layer, optimizing resource usage without compromising accuracy. We have validated SwiftNet on multiple neural network models and datasets, demonstrating its ability to significantly reduce the computational cost of models while maintaining neural network performance.

关键词： Training computational modeling Entropy Deep learning Artificial neural networks Optimization Accuracy Computer architecture Tuning Data models Branch classifier computational cost cost-efficient deep learning

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Happiness Prediction With Domain Knowledge Integration and Explanation Consistency

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IEEE TRANSACTIONS ON computational SOCIAL SYSTEMS 2025年

作者： Wu, Xiaohua Li, Lin Tao, Xiaohui Xing, Frank Yuan, Jingling Wuhan Univ Technol Wuhan 430070 Peoples R China Queensland Univ Technol Brisbane 4000 Australia Univ Southern Queensland Toowoomba 4350 Australia Natl Univ Singapore Singapore 119077 Singapore

Happiness prediction based on large-scale online data and machine learning models is an emerging research topic that underpins a range of issues, from personal growth to social stability. Many advanced machine learning (ML) models with explanations are used for happiness online assessment while maintaining high accuracy of results. However, expert feedback and sociological theory may be absent from these models, which limits the association between prediction results and the right reasons for why they occurred. Sociological studies have shown that primary and secondary relations are inherent in happiness factors, which can be used as domain knowledge to guide model training. Inspired by such insights, this article attempts to provide new insights into the explanation consistency from an empirical study perspective. Then this article studies how to represent and introduce domain knowledge constraints to make ML models more trustworthy. We achieve this by 1) proving that multiple prediction models with additive factor attributions will have the desirable property of primary and secondary relations consistency;and 2) showing that factor relations with quantity can be represented as an importance distribution for encoding domain knowledge. Factor explanation difference is penalized by the Wasserstein distance among prediction models. Experimental results using two online datasets show that domain knowledge of stable factor relations exists. Using this knowledge not only improves happiness prediction accuracy but also reveals more significant happiness factors for assisting decisions.

关键词： Predictive models Biological system modeling Training computational modeling Artificial neural networks Analytical models Accuracy Decision making Data models Surveys Domain knowledge explanation consistency happiness prediction primary and secondary relations

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Wind Farm Meteorological Prediction Model Based on Frequency Domain Feature Extraction Fusion Mechanism

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IEEE ACCESS 2025年 13卷 57426-57441页

作者： Liao, Yichen Gao, Ziqi Li, Xufeng Wuhan Univ Technol Sch Econ Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Int Educ Wuhan 430070 Peoples R China

Given the increasing significance of wind energy in the worldwide energy composition, the effective administration of wind farms has emerged as a crucial element in guaranteeing the reliability of the energy provision. Precise forecasting of meteorological conditions is essential for optimizing wind farm operations in this scenario. However, the current prediction model has certain limitations, namely the problem of inaccurate prediction for a long period and the problem of insufficient ability to extract frequency domain information. Among them, we found that frequency domain features are the key to affecting the above problems. Therefore, this study aims to enhance the ability of existing models to capture frequency domain feature information by introducing an innovative frequency domain feature extraction fusion mechanism, thereby significantly improving the accuracy of meteorological condition forecasting. Specifically, this study addresses the issue of long-term dependency in the model by incorporating the Frequency Enhanced Attention module. This integration improves the model's ability to reliably forecast wind farm meteorological conditions over extended periods. Furthermore, this study utilizes the LoRA (Low-Rank Adaptation) fine-tuning strategy to improve the model's generalization capacity across various wind power circumstances. In the experimental part, we verify the model's high-precision forecasting ability on coarse-grained seasonal changes. At the same time, we have extensively verified the model in four different regions, where our model outperformed existing mainstream models (Autoformer, Informer, and Transformer) by an average of 28.9%, 22.7%, and 27.0% respectively, demonstrating the broad applicability of our model. The model prediction results are better than the current mainstream models in critical indicators, proving the model's effectiveness. These experiments validate our model's efficacy and offer novel technical resources for forecasti

关键词： Predictive models Wind farms Frequency-domain analysis Data models Transformers computational modeling Weather forecasting Energy management Meteorological forecasting for wind farms energy management frequency domain modeling Transformer

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Enhancing Skeleton-Based Action Recognition With Language Descriptions From Pre-Trained Large Multimodal Models

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2025年第3期35卷 2118-2132页

作者： He, Tian Chen, Yang Gao, Xu Wang, Ling Hu, Ting Cheng, Hong Univ Elect Sci & Technol China Sch Automat Engn Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China 1 Orthoped Hosp Chengdu Chengdu 610000 Peoples R China

Skeleton data has become popular in human action recognition because of its efficacy in capturing human motion patterns while mitigating the influence of environmental noise. However, overlooking critical action-related environmental descriptors presents challenges in distinguishing actions characterized by similar body movements. To address this limitation, we propose a novel framework that integrates skeleton data with language descriptions to easily capture essential environmental information for fine-grained action recognition while maintaining the robustness of skeleton-based methods. We first develop a Language Environment Description Generation (LEDG) module that utilizes the open-world understanding ability of Large Multimodal Models to generate instance-level action-related language environment descriptions without the need to train additional modules. Then, we introduce a Skeleton-supported Environment Feature Extraction (SEFE) module that leverages the temporal dependency inherent in skeleton data to extract key semantic environmental features. Additionally, we propose an Entropy-based Feature Fusion (EFF) module to dynamically amalgamate complementary features from both skeleton and language domains. Experimental results demonstrate the superiority of our framework, which can improve the accuracy of existing skeleton-based action recognition methods and achieve state-of-the-art performance on four well-established skeleton-based action recognition benchmarks.

关键词： Feature extraction Skeleton Visualization Data mining Data models Grounding computational modeling Accuracy Noise Image segmentation Skeleton-based action recognition graph convolutional networks large multimodal models modality fusion

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Economic-Emission Coordinated Operation of Transportation-Microgrid Coupled System Based on Dynamic User Equilibrium

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第2期26卷 2381-2396页

作者： Li, Bin Liu, Zhitao Su, Hongye Zhejiang Univ Inst Cyber Syst & Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Shenzhen Polytech Univ Inst Intelligence Sci & Engn Shenzhen 518055 Peoples R China

Booming in electric vehicles and the progress of dynamic wireless charging are deepening the interdependence of transportation networks (TNs) and microgrids (MGs). An imperative exists for a framework to coordinate dynamic traffic assignment (DTA) of the TN and optimal scheduling of MGs. Considering the simultaneous route-and-departure-time choice dynamic user equilibrium (DUE), this paper presents a noncooperative framework targeting the economic-emission coordinated operation of the transportation-microgrid coupled system. This framework is a Nash-Stackelberg-Nash game, where the leader problem is an economic-emission scheduling model of MGs and the follower problem is a DUE model of DTA for the TN. The best response algorithm is adopted to solve this framework. The DUE model is converted into an equivalent fixed-point problem, while the economic-emission scheduling model of each MG is reformulated as a multi-objective mixed-integer nonlinear program (MOMINLP). Moreover, we develop an alternating operator evolutionary algorithm (AOEA) to handle this MOMINLP. Case studies demonstrate that the proposed framework enables the coupled system to operate synergistically and AOEA has a competitive performance.

关键词： Costs Power system dynamics Vehicle dynamics Power systems Carbon dioxide Junctions computational modeling Vehicles Dynamic scheduling Optimal scheduling Transportation network dynamic traffic assignment dynamic user equilibrium microgrids electric vehicles

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Reducing Uncertainty of Weak Supervised Signals via Mutual Information Techniques

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IEEE ACCESS 2025年 13卷 39306-39317页

作者： Liu, Yichen Feng, Hanlin Zhang, Xin Univ Sheffield Informat Sch Sheffield S10 2TN England Northeast Petr Univ Sch Comp & Informat Technol Daqing 163318 Peoples R China Chinese Peoples Liberat Army Gen Hosp Dept Epidemiol & Stat Grad Sch Med Sch Beijing 100853 Peoples R China

Weakly supervised learning (WSL) refers to training models using imperfect or noisy labels, which can significantly reduce the costs associated with manual labeling. However, the model performance may be affected by the uncertainty of weakly supervised signals (WSS). To address this problem, we propose a novel Mutual Information-based Network (MINet), which reduces the reliance on weak labels by maximizing the mutual information (MI) and minimizing the conditional mutual information (CMI) between representations and labels. MINet differs from existing methods by using a dual-branch model structure that follows a mutual information-based objective function. The objective of one branch is to maximize MI, which helps to extract valuable information from weak labels. The second branch reduces the dependence of data representations on their corresponding weak labels, which lessens the negative effects of WSS. By applying variational approximation and reparameterization techniques, we optimize the proposed model and achieve significant improvements in robustness and generalization on four image classification datasets. Moreover, the experiments show that the model can effectively utilize the effective information from WSS and reduce their uncertainty.

关键词： Data models Uncertainty Feature extraction Training Vectors Noise measurement Linear programming Data mining computational modeling Predictive models Weakly supervised learning mutual information image classification variational approximation random projection

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EM-Trans: Edge-Aware Multimodal Transformer for RGB-D Salient Object Detection

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第2期36卷 3175-3188页

作者： Chen, Geng Wang, Qingyue Dong, Bo Ma, Ruitao Liu, Nian Fu, Huazhu Xia, Yong Northwestern Polytech Univ Sch Comp Sci & Engn Natl Engn Lab Integrated Aero Space Ground Ocean Xian 710072 Peoples R China Zhejiang Univ Ctr Brain Imaging Sci & Technol Hangzhou 310027 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Comp Vis Dept Abu Dhabi U Arab Emirates ASTAR Inst High Performance Comp IHPC Singapore 138632 Singapore

RGB-D salient object detection (SOD) has gained tremendous attention in recent years. In particular, transformer has been employed and shown great potential. However, existing transformer models usually overlook the vital edge information, which is a major issue restricting the further improvement of SOD accuracy. To this end, we propose a novel edge-aware RGB-D SOD transformer, called EM-Trans, which explicitly models the edge information in a dual-band decomposition framework. Specifically, we employ two parallel decoder networks to learn the high-frequency edge and low-frequency body features from the low- and high-level features extracted from a two-steam multimodal backbone network, respectively. Next, we propose a cross-attention complementarity exploration module to enrich the edge/body features by exploiting the multimodal complementarity information. The refined features are then fed into our proposed color-hint guided fusion module for enhancing the depth feature and fusing the multimodal features. Finally, the resulting features are fused using our deeply supervised progressive fusion module, which progressively integrates edge and body features for predicting saliency maps. Our model explicitly considers the edge information for accurate RGB-D SOD, overcoming the limitations of existing methods and effectively improving the performance. Extensive experiments on benchmark datasets demonstrate that EM-Trans is an effective RGB-D SOD framework that outperforms the current state-of-the-art models, both quantitatively and qualitatively. A further extension to RGB-T SOD demonstrates the promising potential of our model in various kinds of multimodal SOD tasks.

关键词： Image edge detection Feature extraction Transformers Object detection computational modeling Task analysis Decoding Edge-aware model multimodal learning salient object detection (SOD) transformer

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Virtual Cities: From Digital Twins to Autonomous AI Societies

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IEEE ACCESS 2025年 13卷 13866-13903页

作者： Nechesov, Andrey Dorokhov, Ivan Ruponen, Janne Novosibirsk State Univ Artificial Intelligence Res Ctr Novosibirsk 630090 Russia

Virtual Cities (VCs) transcend simple digital replicas of real-world systems, emerging as complex socio-technical ecosystems where autonomous AI entities function as citizens. Agentic AI systems are on track to engage in cultural, economic, and political activities, effectively forming societal structure within VC. This paper proposes an integrated simulation framework that combines physical, structural, behavioral, cognitive, and data fidelity layers, allowing multi-scale simulation from microscopic interactions to macro-urban dynamics. A composite fidelity metric ( F-0 ) provides systematic approach to evaluate accuracy variations across applications in VCs. We also discuss autonomy of AI entities and classify them according to their capacity to modify goals-ranging from "tools" with fixed objectives to "entities" capable of redefining their very purpose. We also outline the requirements to define a coefficient to evaluate the degree of autonomy for AI beings. Our results demonstrate that such virtual environments can support the emergence of AI-driven societies, where governance mechanisms like Decentralized Autonomous Organizations (DAOs) and an Artificial Collective Consciousness (ACC) provide ethical and regulatory oversight. By blending horizon scanning with systems engineering method for defining novel AI governance models, this study reveals how VCs can catalyze breakthroughs in urban innovation while driving socially beneficial AI development - consequently opening a new frontier for exploring human-AI coexistence.

关键词： Artificial intelligence Virtual environments Urban areas Robots Predictive models Solid modeling Real-time systems computational modeling Rendering (computer graphics) Digital twins Virtual cities urban metaverse AI autonomy virtual twins digital twins virtual economies predictive modeling AI governance blockchain artificial collective consciousness

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