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Resonance mechanism of flapping wing based on fluid structure interaction simulation

Chinese Journal of Aeronautics 2024年第5期37卷 243-262页

作者： Yueyang GUO Wenqing YANG Yuanbo DONG Dong XUE National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of AeronauticsNorthwestern Polytechnical UniversityXi'an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang 215400China

Certain insect species have been observed to exploit the resonance mechanism of their *** order to achieve resonance and optimize aerodynamic performance,the conventional approach is to set the flapping frequency of flexible wings based on the Traditional Structural Modal(TSM)***,there exists controversy among researchers regarding the relationship between frequency and aerodynamic *** that the structural response of wings can be influenced by the surrounding air vibrations,an analysis known as Acoustic Structure Interaction Modal(ASIM)is introduced to calculate the resonant *** this study,Fluid Structure Interaction(FSI)simulations are employed to investigate the aerodynamic performance of flapping wings at modal frequencies derived from both TSM and ASIM *** performance is evaluated for various mass ratios and frequency ratios,and the findings indicate that the deformation and changes in vortex structure exhibit similarities at mass ratios that yield the highest aerodynamic ***,the flapping frequency associated with the maximum time-averaged vertical force coefficient at each mass ratio closely aligns with the ASIM frequency,as does the frequency corresponding to maximum ***,the ASIM analysis can provide an effective means for predicting the optimal flapping frequency for flexible ***,it enables the prediction that flexible wings with varying mass ratios will exhibit similar deformation and vortex structure *** paper offers a fresh perspective on the ongoing debate concerning the resonance mechanism of Flexible Flapping Wings(FFWs)and proposes an effective methodology for predicting their aerodynamic performance.

关键词： Flexible Flapping Wing(FFW) Acoustic Structure Interaction Modal(ASIM) Fluid Structure Interaction(FSI) Resonance mechanism Aerodynamic performance

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ViF-SD2E: a robust weakly-supervised framework for neural decoding

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Neural Computing and Applications 2025年第9期37卷 6645-6661页

作者： Feng, Jingyi Luo, Yong Song, Shuang Hu, Han National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural decoding solutions primarily perform some preprocessing operations on neural data before feeding them into existing models (such as LSTM) for decoding. However, these solutions either are prone to overfitting or cannot well exploit the spatial and temporal information. In our previous observations, there is a symmetrical phenomenon between the unsupervised decoded trajectory and the ground truth trajectory within the activity space. This precisely motivates us to propose (or derive) a robust weakly-supervised framework (or model structure), called ViF-SD2E, for neural decoding. In particular, it consists of a space-division (SD) module and an exploration–exploitation (2E) strategy, to effectively exploit both the spatial information of the outside world and the temporal information of neural activity, where the SD2E output is analogized with the weak 0/1 vision feedback (ViF) label for training. Extensive experiments demonstrate the effectiveness of our method, which can sometimes be comparable to supervised counterparts. Therefore, we redirect our attention to the information (hidden in data) ViF-SD2E conveys to us. In other words, we believe that the advantage of ViF-SD2E lies in the fact that its processing steps are objectively determined by the inherent attributes (i.e., symmetry) of the neural data, or rather, the model structure is fixed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Space division multiple access

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Multiplex Networks and Pan-Cancer Multiomics-Based Driver Gene Identification Using Graph Neural Networks

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Big Data Mining and Analytics 2024年第4期7卷 1262-1272页

作者： Xingyi Li Junming Li Jun Hao Xingyu Liao Min Li Xuequn Shang School of Computer Science Northwestern Polytechnical UniversityXi’an 710072Chinaand with Research&Development Institute of Northwestern Polytechnical University in ShenzhenShenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072Chinaand with Research&Development Institute of Northwestern Polytechnical University in ShenzhenShenzhen 518063China School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science and Engineering Central South UniversityChangsha 410083China

Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic *** omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning ***,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of ***,samples with imbalanced classes in driver gene identification hamper the performance of *** address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model *** reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.

关键词： cancer driver gene multiplex networks pan-cancer multiomics data graph neural networks negative sample inference

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Impact of dissolution and precipitation on pore structure in CO2 sequestration within tight sandstone reservoirs

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Petroleum science 2025年第2期22卷 868-883页

作者： Hui Gao Kai-Qing Luo Chen Wang Teng Li Zhi-Lin Cheng Liang-Bin Dou Kai Zhao Nan Zhang Yue-Liang Liu School of Petroleum Engineering Xi'an Shiyou University Engineering Research Center of Development and Management for Low to Ultra-Low Permeability Oil & Gas Reservoirs in West China Ministry of Education Xi'an Key Laboratory of Tight Oil (shale Oil) Development Department of Electrical Engineering and Computer Science University of Stavanger School of Petroleum Engineering China University of Petroleum (Beijing)

Complex physical and chemical reactions during CO2sequestration alter the microscopic pore structure of geological formations,impacting sequestration *** investigate CO2sequestration dynamics,comprehensive physical simulation experiments were conducted under varied pressures,coupled with assessments of changes in mineral composition,ion concentrations,pore morphology,permeability,and sequestration capacity before and after ***,a method using NMR T2spectra changes to measure pore volume shift and estimate CO2sequestration is *** quantifies CO2needed for mineralization of soluble ***,when CO2dissolves in crude oil,the precipitation of asphaltene compounds impairs both seepage and storage ***,the impact of dissolution and precipitation is closely associated with storage pressure,with a particularly pronounced influence on smaller *** pressure levels rise,the magnitude of pore alterations progressively *** a pressure threshold of 25 MPa,the rate of change in small pores due to dissolution reaches a maximum of 39.14%,while precipitation results in a change rate of-58.05% for small *** observed formation of dissolution pores and micro-cracks during dissolution,coupled with asphaltene precipitation,provides crucial insights for establishing CO2sequestration parameters and optimizing strategies in low permeability reservoirs.

关键词： Dissolution Precipitation Pore structure CO2 sequestration Unconventional reservoirs

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A Unified Model Fusing Region of Interest Detection and Super Resolution for Video Compression

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computers, Materials & Continua 2024年第6期79卷 3955-3975页

作者： Xinkun Tang Feng Ouyang Ying Xu Ligu Zhu Bo Peng School of Computer and Cyber Sciences Communication University of ChinaBeijing100024China Cable Television Technology Research Institute Academy of Broadcasting ScienceBeijing100866China

High-resolution video transmission requires a substantial amount of *** this paper,we present a novel video processing methodology that innovatively integrates region of interest(ROI)identification and super-resolution *** method commences with the accurate detection of ROIs within video sequences,followed by the application of advanced super-resolution techniques to these areas,thereby preserving visual quality while economizing on data *** validate and benchmark our approach,we have curated a new gaming dataset tailored to evaluate the effectiveness of ROI-based super-resolution in practical *** proposed model architecture leverages the transformer network framework,guided by a carefully designed multi-task loss function,which facilitates concurrent learning and execution of both ROI identification and resolution enhancement *** unified deep learning model exhibits remarkable performance in achieving super-resolution on our custom *** implications of this research extend to optimizing low-bitrate video streaming *** selectively enhancing the resolution of critical regions in videos,our solution enables high-quality video delivery under constrained bandwidth *** results demonstrate a 15%reduction in transmission bandwidth compared to traditional super-resolution based compression methods,without any perceivable decline in visual *** work thus contributes to the advancement of video compression and enhancement technologies,offering an effective strategy for improving digital media delivery efficiency and user experience,especially in bandwidth-limited *** innovative integration of ROI identification and super-resolution presents promising avenues for future research and development in adaptive and intelligent video communication systems.

关键词： Super resolution region of interest detection video compression

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Progressive Retinal Image Registration via Global and Local Deformable Transformations

Progressive Retinal Image Registration via Global and Local ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yepeng Yu, Baosheng Chen, Tian Gu, Yuliang Du, Bo Xu, Yongchao Cheng, Jun Wuhan University School of Computer Science Wuhan China The University of Sydney School of Computer Science Sydney Australia A∗star Institute for Infocomm Research Singapore

ISBN: (纸本)9798350386226

Retinal image registration plays an important role in the ophthalmological diagnosis process. Since there exist variances in viewing angles and anatomical structures across different retinal images, keypoint-based approaches become the mainstream methods for retinal image registration thanks to their robustness and low latency. These methods typically assume the retinal surfaces are planar, and adopt feature matching to obtain the homography matrix that represents the global transformation between images. Yet, such a planar hypothesis inevitably introduces registration errors since retinal surface is approximately curved. This limitation is more prominent when registering image pairs with significant differences in viewing angles. To address this problem, we propose a hybrid registration framework called HybridRetina, which progressively registers retinal images with global and local deformable transformations. For that, we use a keypoint detector and a deformation network called GAMorph to estimate the global transformation and local deformable transformation, respectively. Specifically, we integrate multi-level pixel relation knowledge to guide the training of GAMorph. Additionally, we utilize an edge attention module that includes the geometric priors of the images, ensuring the deformation field focuses more on the vascular regions of clinical interest. Experiments on two widely-used datasets, FIRE and FLoRI21, show that our proposed HybridRetina significantly outperforms some state-of-the-art methods. The code is available at https://***/lyp-deeplearning/awesome-retinal-registration. © 2024 IEEE.

关键词： Image registration

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Optimized Resource Allocation in Vehicle Edge Computing through Platoon Collaboration

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IEEE Internet of Things Journal 2025年第11期12卷 16129-16141页

作者： Zhao, Liang Feng, Yuhang Hawbani, Ammar Xu, Lexi Liu, Zhi Bi, Yuanguo Shenyang Aerospace University School of Computer Science Shenyang110136 China China Unicom China United Network Communications Corporation Research Institute Beijing100048 China University of Electro-Communications Department of Computer and Network Engineering Japan Northeastern University School of Computer Science and Engineering Shenyang110819 China

In modern vehicular networks, the absence of infrastructure support such as Roadside Units (RSUs) presents significant challenges for efficient task offloading and allocation. Limited computational capabilities of individual vehicles, combined with task allocation imbalances caused by varying task complexity and vehicle capacities, further complicate the process. Additionally, the formation of vehicular platoons requires accurate future route and destination information to ensure stable collaboration and effective coordination. However, such information can be challenging to obtain due to dynamic and unpredictable road environments, hindering the reliability of platoon formation. To address these challenges, we propose a platoon-based offloading strategy that integrates Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks to enhance task allocation efficiency. This approach also leverages the Convoy Formation Algorithm considering Future Positions (CFA-FP) to manage platoon constraints effectively. Experimental results demonstrate that our method significantly improves key performance metrics, including total computation cost, latency, and offloading success rate, compared to other task offloading strategies. © 2014 IEEE.

关键词： Deep reinforcement learning

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Ph.D. Project: Achieving Low-Latency Acceleration on Multi-FPGA for GPT Application 32

Ph.D. Project: Achieving Low-Latency Acceleration on Multi-F...

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32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2024

作者： Zheng, Zhendong Wang, Teng Wang, Chao School of Computer Science and Technology Suzhou Institute for Advanced Research University of Science and Technology of China China

ISBN: (纸本)9798350372434

This paper proposes a latency-oriented recurrent architecture for GPT on multi-FPGA with communication optimization. We devise an efficient communication scheme that overlaps part of the computation and communication delay to improve the latency and scalability of our platform. Then, we deploy recurrent structures on each FPGA to accelerate the different phases of GPT. A preliminary experiment shows that our method can reduce the synchronization overhead and increase the computing intensity, resulting in an average 11.8 x speedup over NVIDIA V100 GPU and 3.0x speedup over existing multi-FPGA accelerator appliance on the GPT-2 model. © 2024 IEEE.

关键词： Program processors

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NAND-SPIN-based processing-in-MRAM architecture for convolutional neural network acceleration

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science China(Information sciences) 2023年第4期66卷 244-260页

作者： Yinglin ZHAO Jianlei YANG Bing LI Xingzhou CHENG Xucheng YE Xueyan WANG Xiaotao JIA Zhaohao WANG Youguang ZHANG Weisheng ZHAO School of Electronic and Information Engineering Beihang University Qingdao Research Institute Beihang University School of Computer Science and Engineering Beihang University Academy for Multidisciplinary Studies Capital Normal University School of Integrated Circuit Science and Engineering Beihang University

The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing “power wall” and “memory wall” problems. To resolve those problems, processing-in-memory(PIM) architectures are developed to bring computation logic in or near memory to alleviate the bandwidth limitations during data transmission. NAND-like spintronics memory(NAND-SPIN) is one kind of promising magnetoresistive random-access memory(MRAM) with low write energy and high integration density, and it can be employed to perform efficient in-memory computation operations. In this study, we propose a NAND-SPIN-based PIM architecture for efficient convolutional neural network(CNN) acceleration. A straightforward data mapping scheme is exploited to improve parallelism while reducing data movements. Benefiting from the excellent characteristics of NAND-SPIN and in-memory processing architecture, experimental results show that the proposed approach can achieve ～2.6× speedup and ～1.4× improvement in energy efficiency over state-of-the-art PIM solutions.

关键词： processing-in-memory convolutional neural network NAND-like spintronics memory nonvolatile memory magnetic tunnel junction

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Modelling and analysis of initial icing roughness with fixed-grid enthalpy method based on DPM-VOF algorithm

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Chinese Journal of Aeronautics 2022年第7期35卷 168-178页

作者： Jie LIU Peng KE Department of Civil Helicopter Research and Development China Helicopter Research and Development InstituteTianjin 300000China School of Transportation Science and Engineering Beihang UniversityBeijing 100191China

Ice particles could form under the continuous impingement of incoming supercooled droplets in icing conditions,which will change the surface roughness to enhance the further heat and mass transfer during icing process.A fixed-grid porous enthalpy method based on the improved Discrete Phase Model(DPM)and Volume of Fluid(VOF)integrated algorithm is developed to solve the multiphase heat transfer problem to give more detailed demonstration of the formation of initial ice *** algorithms to determine the criterion of transformation from DPM to VOF and the allocation of source items during transformation are improved to the general DPM-VOF *** verification cases,namely two glycerine-solution droplets impact and single droplet freeze,are conducted to verify the accuracy and reliability of the enthalpy-DPMVOF method,where the simulation results match well with experiment *** roughness on a NACA0012 airfoil is precisely captured and the effects on convective heat transfer characteristics are preliminarily *** results illustrate that the enthalpy-DPM-VOF method could successfully capture the characteristics of motion and the phase change process of droplet,as well as balance the calculation accuracy and efficiency.

关键词： Discrete phase model Fixed-grid porous enthalpy method Ice roughness Icing modelling Integrated algorithm Multiphase heat transfer Volume of fluid

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