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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Shang, Wenze Huang, Ruisen Lin, Yongji Liang, Fengyan Yin, Ming Lu, Xiao-Ping Wu, XinYu Gao, Fei Chinese Academy of Science Shenzhen Institute of Advanced Technology Guangdong Shenzhen China Hainan University Key Laboratory of Biomedical Engineering of Hainan Province School of Biomedical Engineering Haikou China Macau University of Science and Technology School of Computer Science and Engineering Taipa China

ISBN: (纸本)9798350371499

In powered lower limb prostheses and exoskeleton control, sEMG based methods have been widely explored and utilized due to non-invasive nature and ability to directly reflect user intentions. However, in practical use, neuromuscular fatigue (NMF) caused by long-term use can cause significant changes in sEMG signals. These issues are constraining sEMG's active prosthetics from the laboratory to real life. In this study, the changes in sEMG signals with muscle fatigue was investigated. sEMG data from one healthy subject and a below-knee amputee were logged and analyzed when they walked on a treadmill for a long time. They will experience from no NMF to NMF and then adapt to no NMF. A variational mode decomposition-based soft interval threshold (VMD-SIT) method was used to denoise the sEMG signals. Feature extraction methods were used to extract the features of the sEMG signal, and the distribution changes of these features during walking were analyzed. The results indicated that the NMF of the amputee was quite different from and more variable than that of the healthy subject, and the ratios of features' mean value of amputee and healthy are much higher in residual-limb side (12.35-26.18) than normal side (3.52-5.91). © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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A new biodegradable modified PVA/AZP and PCL composite scaffold for bone repair 3

A new biodegradable modified PVA/AZP and PCL composite scaff...

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2023 3rd International Conference on Industrial Manufacturing and Structural Materials, IMSM 2023

作者： Chen, Jinqing Huangfu, Shuxian Huang, Caihao Yu, Xiaoming School of Materials Science and Engineering Shenyang Ligong University Liaoning Shenyang110159 China School of Materials Science and Engineering University of Science and Technology of China Anhui Hefei230026 China Material Science Technology Dalian University of Technology Liaoning Dalian116620 China

The regeneration and repair of bone defects require implanting artificial bone materials with excellent mechanical and biological properties similar to human bones. However, the compressive strength and bioactivities of 3D printing biodegradable materials like PVA are usually not enough for weight-bearing bone defect repair. In this study, we added AZP (amorphous zinc phosphate) nanoparticles to increase the biological properties of PVA 3D printing scaffolds and filled PCL into PVA scaffolds to improve the mechanical properties. Our results revealed that the size of AZP nanoparticles was about 63.5 ± 29.3 nm, and PVA/AZP scaffold was printed with a pore size of 600 μm. The cell experiments showed that PVA scaffolds compounded with AZP nanoparticles can effectively promote cell proliferation. Therefore, the AZP nanoparticles have good biocompatibility, and the scaffold with AZP nanoparticles has the prospect of application in bone tissue engineering. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Nanoparticles

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Towards Efficient Edge Learning for Large Models in Heterogeneous Resource-limited Environments 9

Towards Efficient Edge Learning for Large Models in Heteroge...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Liu, Defang Wang, Zhibo Pang, Xiaoyi Sun, Yunan Hu, Jiahui Sun, Peng Hu, Yuke Wuhan University School of Cyber Science and Engineering China Zhejiang University School of Cyber Science and Technology China Hunan University College of Computer Science and Electronic Engineering China

ISBN: (纸本)9798350331240

Large models like transformer models cannot be directly applied and trained at the network edge since edge devices are heterogeneous and often equipped with limited resources. To collaboratively train a large model at the network edge, existing works assign sub-models of the large model with proper sizes to each edge device, and adopt advanced technologies like knowledge distillation to aggregate their local models to update the large model. However, these methods assign sub-models in a coarse-grained manner and require a certain amount of edge devices to train the large model locally, thus cannot achieve efficient edge learning for large models in practice. In this paper, to make the utmost of edge devices to collaboratively train a large model faster and better, we propose a novel synchronous edge learning framework to achieve efficient large model training across heterogeneous resource-limited edge devices. Specifically, to reduce the waiting time in the training process while ensuring that every edge device can afford the local training, we design a capability-aware local model customization mechanism to granularly tailor personalized model structures for each edge device based on their memory and computing capabilities to make them have similar local training times. Then, to efficiently integrate the local training achievements of heterogeneous edge devices, we propose a layer augmentation-based heterogeneous model aggregation mechanism to align local models and quickly and effectively get the global update for the global large model. Besides, we propose a monument distillation-based model deployment mechanism to deploy the updated global large model at edge devices without the loss of learned knowledge. In this way, each edge device can fully contribute to the global large model training, thus achieving efficient edge learning for the large model. Experimental results demonstrate that our framework has better performance in accuracy and efficiency than the sta

关键词： Learning systems

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RGB-T Multi-Modal Crowd Counting Based on Transformer 33

RGB-T Multi-Modal Crowd Counting Based on Transformer

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Liu, Zhengyi Wu, Wei Tan, Yacheng Zhang, Guanghui School of Computer Science and Technology Anhui University Hefei China

Crowd counting aims to estimate the number of persons in a scene. Most state-of-the-art crowd counting methods based on color images can't work well in poor illumination conditions due to invisible objects. With the widespread use of infrared cameras, crowd counting based on color and thermal images is studied. Existing methods only achieve multi-modal fusion without count objective constraint. To better excavate multi-modal information, we use count-guided multi-modal fusion and modal-guided count enhancement to achieve the impressive performance. The proposed count-guided multi-modal fusion module utilizes a multi-scale token transformer to interact two-modal information under the guidance of count information and perceive different scales from the token perspective. The proposed modal-guided count enhancement module employs multi-scale deformable transformer decoder structure to enhance one modality feature and count information by the other modality. Experiment in public RGBT-CC dataset shows that our method refreshes the state-of-the-art results. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： computer vision

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Building Li-S batteries with enhanced temperature adaptability via a redox-active COF-based barrier-trapping electrocatalyst

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Journal of Energy Chemistry 2025年第2期101卷 702-712,I0015页

作者： Jie Xu Acheng Zhu Zhangyu Zheng Yiming Qi Yuwen Cheng Yongjie Cao Bo Peng Lianbo Ma Yonggang Wang School of Materials Science and Engineering Key Laboratory of Efficient Conversion and Solid-state Storage of Hydrogen&Electricity of Anhui ProvinceKey Laboratory of Green Fabrication and Surface Technology of Advanced Metal Materials of Ministry of EducationAnhui University of TechnologyMa'anshan 243002AnhuiChina Department of Chemistry and Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials Institute of New EnergyiChEM(Collaborative Innovation Center of Chemistry for Energy Materials)Fudan UniversityShanghai 200433China

Covalent organic frameworks(COFs)are promising materials for mitigating polysulfide shuttling in lithium-sulfur(Li-S)batteries,but enhancing their ability to convert polysulfides across a wide temperature range remains a challenge,Herein,we introduce a redox-active COF(RaCOF)that functions as both a physical barrier and a kinetic enhancer to improve the temperature adaptability of Li-S batteries,The RaCOF constructed from redox-active anthraquinone units accelerates polysulfide conversion kinetics through reversible C=O/C-OLi transformations within a voltage range of 1,7 to 2.8 V(***^(+)/Li),optimizing sulfur redox reactions in ether-based *** conventional COFs,RaCOF provides bidentate trapping of polysulfides,increasing binding energy and facilitating more effective polysulfide ***-situ XRD and ToF-SIMS analyses confirm that RaCOF enhances polysulfide adsorption and promotes the transformation of lithium sulfide(Li_(2)S),leading to better sulfur cathode ***,RaCOF-modified Li-S batteries demonstrate low self-discharge(4.0%decay over a 7-day rest),excellent wide-temperature performance(stable from-10 to+60℃),and high-rate cycling stability(94%capacity retention over 500 cycles at 5.0 C).This work offers valuable insights for designing COF structures aimed at achieving temperature-adaptive performance in rechargeable batteries.

关键词： Lithium-sulfur batteries Redox-active property Covalentorganic framework Wide-temperature adaptability

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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CEMA-LSTM:Enhancing Contextual Feature Correlation for Radar Extrapolation Using Fine-Grained Echo Datasets

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computer Modeling in engineering & sciences 2023年第4期135卷 45-64页

作者： Zhiyun Yang Qi Liu HaoWu Xiaodong Liu Yonghong Zhang School of Computer and Software Engineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK School of Automation Nanjing University of Information Science TechnologyNanjing210044China

Accurate precipitation nowcasting can provide great convenience to the public so they can conduct corresponding arrangements in advance to deal with the possible impact of upcoming heavy *** relevant research activities have shown their concerns on various deep learning models for radar echo extrapolation,where radar echo maps were used to predict their consequent moment,so as to recognize potential severe convective weather ***,these approaches suffer from an inaccurate prediction of echo dynamics and unreliable depiction of echo aggregation or dissipation,due to the size limitation of convolution filter,lack of global feature,and less attention to features from previous *** address the problems,this paper proposes a CEMA-LSTM recurrent unit,which is embedded with a Contextual Feature Correlation Enhancement Block(CEB)and a Multi-Attention Mechanism Block(MAB).The CEB enhances contextual feature correlation and supports its model to memorize significant features for near-future prediction;the MAB uses a position and channel attention mechanism to capture global features of radar *** practical radar echo datasets were used involving the FREM and CIKM 2017 *** quantification and visualization of comparative experimental results have demonstrated outperformance of the proposed CEMA-LSTMover recentmodels,e.g.,PhyDNet,MIM and PredRNN++,*** particular,compared with the second-rankedmodel,its average POD,FAR and CSI have been improved by 3.87%,1.65%and 1.79%,respectively on the FREM,and by 1.42%,5.60%and 3.16%,respectively on the CIKM 2017.

关键词： Radar echo extrapolation attention mechanism long short-term memory deep learning

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Dual-head alternating attention-based adaptive convolutional framework for improving low-resource and imbalanced text classification

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Neural Computing and Applications 2025年 1-21页

作者： Agbesi, Victor Kwaku Chen, Wenyu Hossin, Md Altab Ukwuoma, Chiagoziem C. School of Computer Science and Engineering University of Electronic Science and Technology of China No. 2006 Xiyuan Ave West Hi-Tech Zone Sichuan Chengdu China School of Innovation and Entrepreneurship Chengdu University No. 2025 Chengluo Avenue 610106 Sichuan Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Low-resource imbalance texts pose significant challenges due to class imbalance, adversely affecting the performance of deep learning approaches. To address this issue, we propose a dual-head alternating attention-based adaptive convolutional framework with label embedding (AltXLM-AdaCLE), a novel framework designed for imbalanced text classification in low-resourced datasets. Our framework features a new alternating attention model, which combines a dual-head attention mechanism with a bidirectional long short-term memory network, facilitating dynamic filter generation that adapts to the input data’s characteristics and enhances the capture of relevant features and long-range dependencies. A key innovation in this study is the dual-head attention mechanism, which utilizes an alternating reading sequence to extract information from multiple document segments, thereby minimizing information loss and improving the understanding of complex relationships. Furthermore, we introduce a method for embedding class labels in a shared vector space with the text, allowing the model to leverage label information throughout the classification process, which is particularly beneficial for imbalanced datasets. Our framework also incorporates an adaptive convolutional layer that processes text features and label embeddings, generating more representative features of the underlying data distribution and improving classification performance. Experimental results on six low-resourced imbalance text datasets validate the effectiveness of AltXLM-AdaCLE with an average AUC gain of 26.42%, demonstrating its superiority over existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Vector spaces

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Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction

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IEEE Transactions on Cloud Computing 2024年第1期12卷 186-199页

作者： Xiong, Lizhi Han, Xiao Yang, Ching-Nung Shi, Yun-Qing Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai200240 China National Dong Hwa University Department of Computer Science & Information Engineering Hualien974301 Taiwan New Jersey Institute of Technology ECE Department NewarkNJ07102 United States

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably using k-out-of-n marked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable. © 2013 IEEE.

关键词： Image reconstruction

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Bio-Inspired Optimal Dispatching of Wind Power Consumption Considering Multi-Time Scale Demand Response and High-Energy Load Participation

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computer Modeling in engineering & sciences 2023年第2期134卷 957-979页

作者： Peng Zhao Yongxin Zhang Qiaozhi Hua Haipeng Li Zheng Wen School of Information Technology Luoyang Normal UniversityLuoyang471022China Computer School Hubei University of Arts and ScienceXiangyang441000China Capinfo Company Ltd.Beijing100010China School of Fundamental Science and Engineering Waseda UniversityTokyo169-8050Japan

Bio-inspired computer modelling brings solutions fromthe living phenomena or biological systems to engineering *** overcome the obstruction problem of large-scale wind power consumption in Northwest China,this paper constructs a bio-inspired computer *** is an optimal wind power consumption dispatching model of multi-time scale demand response that takes into account the involved high-energy ***,the principle of wind power obstruction with the involvement of a high-energy load is examined in this *** this step,highenergy load model with different regulation characteristics is ***,considering the multi-time scale characteristics of high-energy load and other demand-side resources response speed,a multi-time scale model of coordination optimization is *** improved bio-inspired model incorporating particle swarm optimization is applied to minimize system operation and wind curtailment costs,as well as to find the most optimal energy configurationwithin the ***,we take an example of regional power grid in Gansu Province for simulation *** demonstrate that the suggested scheduling strategy can significantly enhance the wind power consumption level and minimize the system’s operational cost.

关键词： Biological system multi-time scale wind power consumption demand response bio-inspired computermodelling particle swarm optimization

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