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Unsupervised Meteorological Downscaling Based on Dual Learning and Subgrid-scale Auxiliary Information

Advances in Atmospheric Sciences 2025年第1期42卷 53-66页

作者： Jing HU Jialing MU Xiaomeng HUANG Xi WU Department of Computer Science Chengdu University of Information TechnologyChengdu 610225China Department of Earth System Science Ministry of Education Key Laboratory for Earth System ModelingInstitute for Global Change StudiesTsinghua UniversityBeijing 100084China

Climate downscaling is used to transform large-scale meteorological data into small-scale data with enhanced detail,which finds wide applications in climate modeling,numerical weather forecasting,and renewable *** deeplearning-based downscaling methods effectively capture the complex nonlinear mapping between meteorological data of varying scales,the supervised deep-learning-based downscaling methods suffer from insufficient high-resolution data in practice,and unsupervised methods struggle with accurately inferring small-scale specifics from limited large-scale inputs due to small-scale *** article presents DualDS,a dual-learning framework utilizing a Generative Adversarial Network–based neural network and subgrid-scale auxiliary information for climate *** a learning method is unified in a two-stream framework through up-and downsamplers,where the downsampler is used to simulate the information loss process during the upscaling,and the upsampler is used to reconstruct lost details and correct errors incurred during the *** dual learning strategy can eliminate the dependence on high-resolution ground truth data in the training process and refine the downscaling results by constraining the mapping *** findings demonstrate that DualDS is comparable to several state-of-the-art deep learning downscaling approaches,both qualitatively and ***,for a single surface-temperature data downscaling task,our method is comparable with other unsupervised algorithms with the same dataset,and we can achieve a 0.469 dB higher peak signal-to-noise ratio,0.017 higher structural similarity,0.08 lower RMSE,and the best correlation *** summary,this paper presents a novel approach to addressing small-scale uncertainty issues in unsupervised downscaling processes.

关键词： downscaling unsupervised deep learning dual learning auxiliary information

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Edge-Cloud Cooperation-Driven Intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

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IEEE Internet of Things Journal 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi’an710121 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xidian University Advanced Manufacturing Technology Innovation Center Guangzhou Institute of Technology Guangzhou510555 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou510006 China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy. © 2014 IEEE.

关键词： Circular economy

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Bilateral Pricing for Dynamic Association in Federated Edge Learning

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IEEE Transactions on Mobile Computing 2025年第6期24卷 4684-4697页

作者： Pan, Bangqi Lu, Jianfeng Cao, Shuqin Liu, Jing Tian, Wenlong Li, Minglu Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Dalian University of Technology Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence China University of South China School of Computer Science and Technology Hengyang421001 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Listen with Seeing: Cross-modal Contrastive Learning for Audio-Visual Event Localization

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IEEE Transactions on Multimedia 2025年 27卷 2650-2665页

作者： Sun, Chao Chen, Min Zhu, Chuanbo Zhang, Sheng Lu, Ping Chen, Jincai Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China School of Computer Science and Technology Wuhan430074 China South China University of Technology School of Computer Science and Engineering Guangzhou510640 China Pazhou Laboratory Guangzhou510640 China

In real-world physiological and psychological scenarios, there often exists a robust complementary correlation between audio and visual signals. Audio-Visual Event Localization (AVEL) aims to identify segments with Audio-Visual Events (AVEs) that contain both audio and visual tracks in unconstrained videos. Prior studies have predominantly focused on audio-visual cross-modal fusion methods, overlooking the fine-grained exploration of the cross-modal information fusion mechanism. Moreover, due to the inherent heterogeneity of multi-modal data, inevitable new noise is introduced during the audio-visual fusion process. To address these challenges, we propose a novel Cross-modal Contrastive Learning Network (CCLN) for AVEL, comprising a backbone network and a branch network. In the backbone network, drawing inspiration from physiological theories of sensory integration, we elucidate the process of audio-visual information fusion, interaction, and integration from an information-flow perspective. Notably, the Self-constrained Bi-modal Interaction (SBI) module is a bi-modal attention structure integrated with audio-visual fusion information, and through gated processing of the audio-visual correlation matrix, it effectively captures inter-modal correlation. The Foreground Event Enhancement (FEE) module emphasizes the significance of event-level boundaries by elongating the distance between scene events during training through adaptive weights. Furthermore, we introduce weak video-level labels to constrain the cross-modal semantic alignment of audio-visual events and design a weakly supervised cross-modal contrastive learning loss (WCCL Loss) function, which enhances the quality of fusion representation in the dual-branch contrastive learning framework. Extensive experiments conducted on the AVE dataset for both fully supervised and weakly supervised event localization, as well as Cross-Modal Localization (CML) tasks, demonstrate the superior performance of our model compa

关键词： Information fusion

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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Subthreshold Depression Recognition and Correlation Study from Pulse Condition via Stacking Ensemble Algorithm 1st

Subthreshold Depression Recognition and Correlation Study f...

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1st International Conference on Extended Reality, ICXR 2024

作者： Jiang, Han Li, Ming Gao, Yang Li, Peiru State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9789819636785

Subthreshold depression, a transition state to depression, seriously hinders the early diagnosis of depression. Current studies mostly use heterogeneous definitions of subthreshold depression, making the results of such meta-analyses questionable. Therefore, it is of vital significance to develop an objective method for the diagnosis of subthreshold depression based on objective criteria. In traditional Chinese medicine (TCM), symptoms similar to subthreshold depression have been extensively explored. However, diagnostic methods in TCM still depend heavily on the experience of doctors and lack integration with modern diagnostic techniques, which makes it challenging to explain the pathogenesis of subthreshold depression. Consequently, we propose an explainable framework, based on a stacking ensemble algorithm, for subthreshold depression recognition from biomarkers in the pulse waveform and concepts of pulse in TCM. In this method, Naive Bayes, Random Forest, Extremely Randomized Trees, Categorical Boosting and Logistic Regression are chosen as basic learners, and XGBoost is selected as the meta-classifier. Based on the five-fold cross-validation method, grid search method and repetition of training, the stacking ensemble model shows superiority on most performance evaluation metrics including AUC, F1 scores, MCC, precision and sensitivity. Besides, by analyzing the Adjusted Odds Ratio of features in the pulse waveform, we obtained four features that have a high correlation with the occurrence of subthreshold depression and derived physiological changes in patients with subthreshold depression based on their physiological significance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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Effects of visual rotation axis and clockwise/counterclockwise directions on circular vection in virtual reality 24

Effects of visual rotation axis and clockwise/counterclockwi...

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2024 International Conference on virtual Reality, Image and Signal Processing, ICVISP 2024

作者： Xu, Xiaotian Song, Zhe Zhang, Kexin Xu, Yan Zhang, Yuankun Fan, Xiaoya Pu, Fang Key Laboratory of Human Motion Analysis and Rehabilitation Technology of the Ministry of Civil Affairs Beihang University Beijing China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Liaoning Dalian China State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing China

ISBN: (纸本)9798400710926

Vection, an illusion of self-motion induced by visual stimulus frequently experienced in virtual reality, significantly impacts user experiences in gaming, driving simulation, and rehabilitation training. User often experience circular vection, an illusion of self-rotation. However, the effects of visual rotation axes and rotation directions on circular vection remain elusive. We employ a 3 (rotation axis: sagittal, coronal, vertical) × 2 (rotation direction: clockwise, counterclockwise) repeated-measures design. Twenty participants were recruited. They were exposed to a virtual visual rotating drum at 60 °/s to induce circular vection. The real-time estimation of perceived self-motion rotation velocity (PSRV) was obtained. Results indicated that during the 30-second constant rotation, the group-level PSRV showed an initial rapid increase followed by a slower rise. A significant interaction effect between rotation axis and direction was revealed. The rotation direction showed no significant effect on PSRV under sagittal and vertical conditions. However, under coronal condition, the PSRV was significantly higher when rotating counterclockwise, compared to rotation clockwise. In addition, when rotating counterclockwise, the highest PSRV appeared under sagittal condition, with the lowest under the coronal condition and the middle under the vertical condition. These findings provide valuable insights for designing VR scenarios with better user experience by vection manipulation. © 2024 Copyright held by the owner/author(s).

关键词： virtual reality

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3DGCformer: 3-Dimensional Graph Convolutional transformer for multi-step origin–destination matrix forecasting

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Applied Intelligence 2025年第6期55卷 1-18页

作者： Huang, Yiou Deng, Hao Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Origin–Destination (OD) matrices for a relatively long period in the future. However, multi-step Origin–Destination Matrix Forecasting (ODMF) is a non-trivial problem. First, previous ODMF models only forecast the OD matrix for the next time-step, and they cannot perform well on long-term multi-step forecasts due to error accumulation. Second, many ODMF methods capture spatial and temporal dependencies with separate modules, which is insufficient to model spatio-temporal correlations in the time-varying OD matrix sequence. To address the challenges in multi-step ODMF, we propose 3-Dimensional Graph Convolutional Transformer (3DGCformer). As an enhancement of the original 3DGCN, we propose a novel Origin–Destination Feature Propagation (ODFP) rule between 3DGCN layers and integrate 2 3DGCNs with different spatio-temporal graphs and corresponding feature propagation rules to model the formation of OD flows in a more comprehensive way. For multi-step forecasts, 3DGCformer uses Transformer to capture long-term global temporal dependency, and adapt its decoder using labeled tokens to avoid error accumulation and improve time efficiency. To avoid information loss as the number of regions increases, we propose a patch embedding approach to convert data from 3DGCNs to the Transformer module. We perform extensive experiments on 4 real-world human mobility datasets, and the results show that our proposed model outperforms the state-of-the-art methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolution

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Handwritten W-Net for High-Frequency Guided Single-Image Super-Resolution

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IEEE Access 2025年 13卷 73124-73134页

作者： Yang, Xiang Fang, Chen Xie, Xiaolan Dong, Minggang Guilin University of Technology College of Computer Science and Engineering Guangxi Guilin China Guilin University of Technology Guangxi Key Laboratory of Embedded Technology and Intelligent System Guangxi Guilin China Guilin University of Technology College of Physics and Electronic Information Engineering Guangxi Guilin China

Super-resolution (SR) imaging involves converting low-resolution images into corresponding high-resolution data in specific scenarios. Recent advances in intelligent image reconstruction algorithms have been accompanied by impressive visual outcomes in SR generative adversarial networks (GANs). However, existing research has methodological limitations owing to the constraints posed by the model architecture and loss guidance used for optimization. Consequently, unnatural artifacts often appear in the complex backgrounds of images, leading to unappealing visual results. To address these problems, we propose an enhanced U-Net, known as the handwritten W-Net (HW-Net) architecture, as well as a loss function that uses high-frequency components for guidance. The former improves the best approximate representation of image regions, whereas the latter guides the generation of SR images with more detailed features. We experimentally implemented a biased strategy called parameter transfer, which is suitable for hybrid parameter models. This approach facilitates the blending of weight parameters between similar but different models, significantly improving the peak signal-to-noise ratio and structural similarity index measure metrics without sacrificing the learned perceptual image patch similarity. Across nine SR benchmark datasets, the proposed combinations, with and without parameter transfer, demonstrated excellent performance across multiple evaluation metrics, surpassing the state-of-the-art SRGAN. The code is available at https://***/DaHuaSen/H2SR. © 2013 IEEE.

关键词： Image enhancement

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Multi-level Multi-task representation learning with adaptive fusion for multimodal sentiment analysis

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Neural Computing and Applications 2025年第3期37卷 1491-1508页

作者： Zhu, Chuanbo Chen, Min Li, Haomin Zhang, Sheng Liang, Han Sun, Chao Liu, Yifan Chen, Jincai Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Hubei Wuhan430074 China School of Computer Science and Engineering South China University of Technology Guangdong Guangzhou510641 China Pazhou Laboratory Guangdong Guangzhou510640 China School of Computer Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China Hubei Wuhan430074 China

Multimodal sentiment analysis is an active task in multimodal intelligence, which aims to compute the user’s sentiment tendency from multimedia data. Generally, each modality is a specific and necessary perspective to express human sentiment, providing complementary and consensus information unavailable in a single modality. Nevertheless, the heterogeneous multimedia data often contain inconsistent and conflicting sentiment semantics that limits the model performance. In this work, we propose a Multi-level Multi-task Representation Learning with Adaptive Fusion (MuReLAF) network to bridge the semantic gap among different modalities. Specifically, we design a modality adaptive fusion block to adjust modality contributions dynamically. Besides, we build a multi-level multimodal representations framework to obtain modality-specific and modality-shared semantics by the multi-task learning strategy, where modality-specific semantics contain complementary information and modality-shared semantics include consensus information. Extensive experiments are conducted on four publicly available datasets: MOSI, MOSEI, SIMS, and SIMSV2(s), demonstrating that our model exhibits superior or comparable performance to state-of-the-art models. The achieved accuracies are 86.28%, 86.07%, 84.46%, and 82.78%, respectively, showcasing improvements of 0.82%, 0.84%, 1.75%, and 1.83%. Further analyses also indicate the effectiveness of our model in sentiment analysis. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Multi-task learning

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