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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collaborative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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A model-based Gait recognition method based on Gait graph convolutional networks and joints relationship pyramid mapping

arXiv

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arXiv 2020年

作者： Li, Na Zhao, Xinbo Ma, Chong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China School of Automation Northwestern Polytechnical University China

Gait, as a unique biometric feature that can be recognized at a distance, which can be widely applicated in public security. In this paper, we propose a novel model-based gait recognition method, JointsGait, which extracts gait information from human body joints. Early gait recognition methods are mainly based on appearance. The appearance-based features are usually extracted from human body silhouettes, which is not invariant to changes in clothing, and can be subject to drastic variations, due to camera motion or other external factors. In contrast to previous approaches, JointsGait firstly extracted spatio-temporal features using gait graph convolutional networks constructed by 18 2-D joints, which are less interfered by external factors. Then Joints Relationship Pyramid Mapping (JRPM) are proposed to map spatio-temporal gait features into a discriminative feature space with biological advantages according to physical structure and walking habit at various scales. Finally, we research a fusion loss strategy to help the joints features be insensitive to cross-view. Our method is evaluated on large datasets CASIA B. The experimental results show that JointsGait achieves the state-of-art performance, which is less affected by the view variations. Its recognition accuracy is higher than lasted model-based method PoseGait in all walking conditions, even outperforms most of state-of-art appearance-based methods, especially when there is a clothing variation. Copyright © 2020, The Authors. All rights reserved.

关键词： Mapping

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Research on cancer rapid diagnosis technology based on adaptive channel attention mechanism and two-dimensional spectral fusion

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Microchemical Journal 2025年 213卷

作者： Liu, Wei Chang, Chenjie Li, Jiahe Lv, Xiaoyi Liu, Yang Chen, Chen Chen, Cheng College of Software Xinjiang University Urumqi 830046 China School of Computer Science and Technology Xinjiang University Urumqi 830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi 830046 China Xinjiang Cloud Computing Application Laboratory Xinjiang Cloud Computing Engineering Technology Research Center Karamay 834000 China

With the widespread use of Artificial Intelligence (AI) in biomedicine, integrating infrared and Raman spectra with biological information offers new prospects for enhanced disease diagnosis. For these two types of data, more information can be extracted using multimodal data fusion compared to single modality, but existing studies tend to only fuse one-dimensional spectral data, ignoring the richer information of the spectra when they are converted to two-dimensional images. In this study, we propose a rapid diagnostic method for cancer, which first converts one-dimensional (1D) Raman and Infrared spectra into two-dimensional (2D) spectral images using Short-Time Fourier Transform (STFT), respectively. Then the features are fused by our designed Adaptive Graph Channel Attention Spacial Cross-Attention Fusion Module (ASCFM). This fusion module innovatively uses the adaptive graph channel attention mechanism to strengthen the model's ability to capture the key information of different spectral features, and utilizes the spatial attention fusion module and the cross-attention module for spatial information extraction and fusion. The output fused features effectively avoid feature information loss through this multimodal feature extraction and fusion, which greatly improves the performance of the model. In this study, Gradient-weighted Class Activation Mapping is also used to make the decision-making process of the neural network more interpretable. The results show that the accuracy of 2D Raman spectral images fused with 2D infrared spectral images by ASCFM followed by ResidualNetwork (ResNet) for diagnosis of lung cancer and glioma datasets is 98.88 % and 96.38 %, which suggests that it is a highly promising adjunctive technique for cancer diagnosis. © 2025

关键词： Auxiliary diagnostic technology Deep learning Infrared spectroscopy Lung cancer Multimodal fusion Raman spectroscopy

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An Ultra-compact Broadband TE-pass Nanofocusing Structure

An Ultra-compact Broadband TE-pass Nanofocusing Structure

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2020 Asia Communications and Photonics Conference, ACP 2020 and International Conference on Information Photonics and Optical Communications, IPOC 2020

作者： Zhu, Kejian Pengfei, Xu Sun, Pengfei Liu, Xingpeng Li, Haiou Zhou, Zhiping Nano-optoelectronics Frontier Center of Ministry of Education Peking University Beijing100871 China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Department of Electronics Beijing100871 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China

We propose a broadband structure which integrates polarization and nanofocusing functions into a single device with 3.8x4 μm footprint. The bandwidth is over 200 nm with insert loss (IL) smaller than 1.68 dB. © ...

ISBN: (纸本)9781943580828

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A Multimodal Deep Learning Approach for Very Short-Term Solar Forecasts using Sky Images and Historical Numerical Data

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Renewable Energy 2025年

作者： Anto Leoba Jonathan Olusola Bamisile Dongsheng Cai Chukwuebuka Joseph Ejiyi Joseph Junior Nkou Nkou Kombou Victor Chiagoziem C. Ukwuoma Liu Wei Qi Huang College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan P.R. 610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan P.R. 610059 China Energy Environment and Society Division University of Dundee United Kingdom School of Computer Science and Engineering (School of Cyber Security) Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China (UESTC) Chengdu 611731 China

The increased solar energy integration into the power system introduces various issues due to its intermittent nature. As solar power penetration increases, grid management becomes increasingly complex, highlighting the importance of precise solar power forecasts. This paper addresses a critical challenge in solar power integration, highlighting the significance of very short-term solar forecasting (VSTSF) for grid operators. We propose a novel MULSKIN hybrid framework that combines a custom ResNet and a novel targeted feature attention mechanism (TFAM), which dynamically focuses on the most relevant regions in sky images, improving the model’s ability to capture essential cloud dynamics and spatiotemporal patterns. Unlike existing methods in literature that either use image (sky images) or numerical (meteorological) data, the proposed MULSKIN approach integrates both data types, allowing for a more holistic understanding of atmospheric conditions. This paper contributes to advancing the field of solar forecasting by presenting a new, effective, and scalable solution suitable for operational deployment in modern energy systems. With six years of high-resolution data, the model outperforms traditional machine learning (ML), deep learning (DL) models, and baseline reference models, achieving a forecast skill score of 32.84% and a root mean square error (RMSE) of 54.36W/m 2 . Our Results indicate that incorporating sky images alongside numerical meteorological data significantly enhances forecasting accuracy, with the MULSKIN model demonstrating its superiority in capturing dynamic cloud movements and weather patterns over short time horizons. The findings underscore the potential of combining multimodal data sources to address the challenges of VSTSF, offering promising improvements in solar irradiance prediction for real-time grid operations.

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Multi-Modal Transformer for Accelerated MR Imaging

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Shadow detection of moving objects in traffic monitoring video 9

Shadow detection of moving objects in traffic monitoring vid...

引用

9th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2020

作者： Zhang, Mingrui Zhao, Wenbing Li, Xiying Wang, Dan Beijing University of Technology College of Computer Science and Technology Beijing China Sun Yat-sen University Intelligent Transportation Research Center School of Intelligent System Engineering Guangzhou China Key Laboratory of Intelligent Transportation System of Guangdong Province Guangzhou China Key Laboratory of Video Image Intelligent Analysis and Application Technology Ministry of Public Security Guangzhou China

Moving object detection is an important application of computer vision. Commonly used foreground separation algorithms such as Gaussian mixture modeling, ViBe, frame difference method, etc., do not consider the color of shadow and recognize the shadow of a moving object as a part of the moving object. In many cases, the shadow detection effect is not good. Focus on the detection of moving object shadows in traffic surveillance videos, this paper improves the existing ViBe algorithm, considers the color characteristics of the shadows, recognizes the shadows as part of the background, gains a smaller amount of calculation and better effect of shadow detection with the advantages of ViBe. © 2020 IEEE.

关键词： Security systems

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A study on the differences of attitude learning and learning behavior sequences for university computer MOOC+ SPOC Course 15

A study on the differences of attitude learning and learning...

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15th International Conference on computer science and Education, ICCSE 2020

作者： Changhua, Zhao Chunqiao, Mi Huaihua University School of Computer Science and Engineering Huaihua418000 China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua Hunan418000 China Key Lab. of Intelligent Contr. Technol. for Wuling-Mountain Ecological Agriculture in Hunan Province Huaihua Hunan418000 China

ISBN: (纸本)9781728172675

The outbreak of COVID-19 make University computer MOOC+SPOC course face opportunities and challenges. There are some problems in MOOC+SPOC course teaching, such as learners' curiosity decreasing, lack of accomplishment and etc. In order to deal with these problems, this study explores the correlation and differences in the learning behavior sequences of learners with different attitudes to high happiness, medium happiness, and low happiness. Through the learners' comments on University computer MOOC+SPOC course, the learner's attitude information is tapped. By analyzing learning behavior sequences, this study finds out the degree of learning behavior change of learners with different attitudes. The result of this study can improve the existing University computer MOOC+SPOC instructional design and promote the learning mode to move towards intelligent personalized adaptive learning. © 2020 IEEE.

关键词： Curricula

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VentSR: A Self-Rectifying Deep Learning Method for Extubation Readiness Prediction

VentSR: A Self-Rectifying Deep Learning Method for Extubatio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lin Zeng Haoran Ma Long Xiang Shikui Tu Ying Wang Liebin Zhao Lei Xu Department of Computer Science and Engineering Center for Cognitive Machines and Computational Health (CMaCH) Shanghai Jiao Tong University Shanghai China Department of Critical Care Medicine Shanghai Children’s Medical Center School of Medicine Pediatric AI Clinical Application and Research Center Shanghai Children’s Medical Center Shanghai Jiao Tong University Shanghai China Shanghai Engineering Research Center of Intelligence Pediatrics (SERCIP) Shanghai China Department of Neonatology Second People’s Hospital of Kashgar Xinjiang Uygur Autonomous Region China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Timely recognition of extubation readiness is critical, because prolonged and premature intubation will lead to sever complications and costs. Clinical assessment is time consuming and challenging and it has attracted increasing attention of machine learning in recent years. However, the data used for extubation predictions have the following flaws: 1) Manual recording errors and missing data; 2) Unreliable ventilation labels due to inadequate judgement from clinicians. Both may possibly lead to wrong ventilation labels, but existing machine learning methods for extubation prediction largely ignored this critical issue. In this paper, we proposed a self-rectifying deep learning method for extubation readiness prediction, called VentSR. It improves the prediction performance by a self-rectifying strategy, and the rectification is achieved through model training without clinical experience. To be detailed, VentSR firstly identifies possibly wrong samples by two components: Inconsistency between K-means and Labels (IKL) and Inconsistency between Model Predictions and Labels (IPL). IKL partitions a rough subset, and IPL iteratively refines this subset through training. Additionally, we designed Adjustment Operation to enhance IPL ability for refinement. Samples identified in this subset are rectified and used to train the model. The unrectified test set is directly fed into the trained model to obtain prediction results. Experiments demonstrate that VentSR outperforms other baselines. Further comparisons on high-confidence test set indicate that VentSR achieves 79.4 AUPRC, increasing by 26.0%. Feature importance analysis and case study illustration again reveals that VentSR are of potential practical usage of informing clinicians with accurate extubation readiness.

关键词： Deep learning Training Costs Databases Manuals Predictive models Ventilation

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Research on Crop Disaster Stress Risk Mapping System Based on Agriculture Big Data

Research on Crop Disaster Stress Risk Mapping System Based o...

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Electronic Information Technology and Smart Agriculture (ICEITSA), International Conference on

作者： Chunqiao Mi Xiaoning Peng Ling Peng Changhua Zhao Xiaowu Deng School of Computer Science and Engineering Huaihua University Huaihua Hunan China Key Laboratory of Intelligent Control Technology for Wuling-Mountain Ecological Agriculture Huaihua University Huaihua Hunan China Key Laboratory of Wuling-Mountain Health Big Data Intelligent Processing and Application Hunan Province Universities Huaihua University Huaihua Hunan China

ISBN: (纸本)9781665413015

At present, the research on crop disaster risk is mostly carried out from the perspective of numerical simulation in one factor aspect, and there is a lack of information system practice that can integrate disaster spatio-temporal analysis technologies such as environment characteristic information extraction, geographic data scale transformation, spatial analysis and map visualization. Based on agriculture big data and highlighting the spatial characteristics of crop disasters, in this study a method of crop disaster risk mapping is designed and a prototype system is implemented, which function is composed of agriculture big data cleaning, scale conversion, multi-source data fusion, feature information extraction, risk probability prediction and map visualization. A specific application of the system is also demonstrated by taking the production of thematic map of wind-induced lodging risk probability distribution during maize jointing and tasseling stage as an example, which shows great help for avoiding maize lodging risk in agriculture practice. The research results have a good role in improving the technical level of agriculture disaster mapping and analysis, and improving the efficiency and effect of disaster prevention and reduction in agriculture production. It has important academic significance for promoting the application of big data in agriculture disaster management and establishing a new perspective of crop disaster mapping, and has important practice application value for optimizing the fine zoning of crop planting area and strengthening the accurate management of agriculture disaster risk.

关键词： Crops Data visualization Prototypes Production Big Data Information retrieval Agriculture

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