机构

• 自动化研究所 [7]
• 地理科学与资源研究所 [1]
• 计算技术研究所 [1]
• 长春光学精密机械与物... [1]
• 心理研究所 [1]

采集方式

• OAI收割 [11]

内容类型

• 期刊论文 [10]
• 会议论文 [1]

发表日期

• 2025 [1]
• 2024 [2]
• 2023 [2]
• 2022 [2]
• 2021 [1]
• 2020 [1]
• 更多

学科主题

筛选

浏览/检索结果: 共11条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择题名升序题名降序提交时间升序提交时间降序作者升序作者降序发表日期升序发表日期降序 DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection 期刊论文  OAI收割 REMOTE SENSING, 2025, 卷号: 17, 期号: 2, 页码: 30 作者:  Lin, Shaofu;  Yang, Yang;  Liu, Xiliang;  Tian, Li   |  收藏  |  浏览/下载：20/0  |  提交时间：2025/03/31 high-resolution images  photovoltaic  swin-transformer  dynamic spatial-frequency attention   DGSD: Dynamical graph self-distillation for EEG-based auditory spatial attention detection 期刊论文  OAI收割 NEURAL NETWORKS, 2024, 卷号: 179, 页码: 12 作者:  Fan, Cunhang;  Zhang, Hongyu;  Huang, Wei;  Xue, Jun;  Tao, Jianhua   |  收藏  |  浏览/下载：23/0  |  提交时间：2024/09/09 Auditory attention detection  Electroencephalography (EEG)  Dynamical graph convolutional network  Self-distillation  Frequency domain   Dual Frequency Transformer for Efficient SDR-to-HDR Translation 期刊论文  OAI收割 Machine Intelligence Research, 2024, 卷号: 21, 期号: 3, 页码: 538-548 作者:  Gang Xu;  Qibin Hou;  Ming-Ming Cheng   |  收藏  |  浏览/下载：16/0  |  提交时间：2024/05/23 Standard-dynamic-range to high-dynamic-range (SDR-to-HDR) translation, Transformer, dual frequency attention (DFA), frequency-aware feature decomposition, efficient model   BSS-TFNet: Attention-Enhanced Background Signal Suppression Network for Time-Frequency Spectrum in Magnetic Particle Imaging 期刊论文  OAI收割 IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE, 2023, 页码: 15 作者:  Wei, Zechen;  Liu, Yanjun;  Zhu, Tao;  Yang, Xin;  Tian, Jie   |  收藏  |  浏览/下载：39/0  |  提交时间：2024/02/22 Magnetic particle imaging  deep learning  self-attention mechanism  time-frequency spectrum  background signal   Illumination Guided Attentive Wavelet Network for Low-Light Image Enhancement 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2023, 卷号: 25, 页码: 6258-6271 作者:  Xu, Jingzhao;  Yuan, Mengke;  Yan, Dong-Ming;  Wu, Tieru   |  收藏  |  浏览/下载：20/0  |  提交时间：2024/02/22 Lighting  Wavelet transforms  Image enhancement  Frequency modulation  Wavelet coefficients  Noise reduction  Discrete wavelet transforms  Attention mechanism  illumination guidance  low-light image enhancement  wavelet transform   Age-related positivity effect of emotional attention: time and frequency domain 期刊论文  OAI收割 PSYCHOLOGY HEALTH & MEDICINE, 2022, 页码: 12 作者:  Liu, Haining;  Dong, Xianling;  Wang, Jingjing;  Liu, Haihong;  Liu, Yanli   |  收藏  |  浏览/下载：78/0  |  提交时间：2022/04/08 Positivity effect  emotional attention  facial expression  dot-probe task  time and frequency domain dynamics   Tactile-Based Fabric Defect Detection Using Convolutional Neural Network With Attention Mechanism 期刊论文  OAI收割 IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2022, 卷号: 71, 页码: 9 作者:  Bin Fang;  Long, Xingming;  Sun, Fuchun;  Liu, Huaping;  Zhang, Shixin   |  收藏  |  浏览/下载：52/0  |  提交时间：2022/12/07 Fabrics  Feature extraction  Frequency-domain analysis  Tactile sensors  Cameras  Visualization  Sensors  Attention mechanism  defect detection  vision-based tactile sensor   A time-frequency channel attention and vectorization network for automatic depression level prediction 期刊论文  OAI收割 Neurocomputing, 2021, 期号: 450, 页码: 208-218 作者:  Mingyue Niu;  Bin Liu;  Jianhua Tao;  Qifei Li   |  收藏  |  浏览/下载：47/0  |  提交时间：2021/06/01 Sphere embedding normalization  DenseNet  Transition layer  Time-frequency channel attention block  Time-frequency vectorization block  Depression detection   Joint Learning in the Spatio-Temporal and Frequency Domains for Skeleton-Based Action Recognition 期刊论文  OAI收割 IEEE TRANSACTIONS ON MULTIMEDIA, 2020, 卷号: 22, 期号: 9, 页码: 2207-2220 作者:  Hu, Guyue;  Cui, Bo;  Yu, Shan;  Guyue, Hu   |  收藏  |  浏览/下载：51/0  |  提交时间：2020/09/28 Frequency-domain analysis  Transforms  Frequency synchronization  Semantics  Training  Skeleton  Data mining  Action recognition  frequency attention  synchronous local and non-local learning  soft-margin focal loss  multi-task learning   An information theoretic approach to model reduction based on frequency-domain Cross-Gramian information (EI CONFERENCE) 会议论文  OAI收割 2010 8th World Congress on Intelligent Control and Automation, WCICA 2010, July 7, 2010 - July 9, 2010, Jinan, China 作者:  Zhou J. 收藏  |  浏览/下载：37/0  |  提交时间：2013/03/25 We focused our attention on model reduction for linear time invariant (LTI) continuous time systems with single input and single output (SISO). We analyzed the strongpoint and shortcoming of an information theoretic approach called minimum information loss method for model reduction. We explained relationships between the steady-state information entropy and controllability information for the Gaussian systems. The controllability  observability and Cross-Gramian information were analyzed in the time-domain. The frequency-domain Cross-Gramian information (FCGI) was defined based on Cross-Gramian matrix containing information associated with both controllability and observability. Furthermore  a valuable application of the frequency-domain Cross-Gramian information to model reduction (FCGMIL) was developed by using the concept and properties of frequency-domain Cross-Gramian information. The performance index of FCGMIL is to minimize the frequency-domain Cross-Gramian information loss caused by eliminating the state variables with weak contributions to Cross-Gramian information over some frequency band. Two numerical examples are illustrated to verify the efficiency of FCGMIL. 2010 IEEE.