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学者姓名：贾克斌
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摘要 ：
Stream -observing cameras have recently been deployed in stream systems to monitor water depth dynamics. However, most existing image -based water depth monitoring methods require additional gauging equipment, extensive manual annotations, or complex manual calibration. In this paper, we propose the hierarchical model, a novel multi -modal and multi -scale deep learning framework for monitoring water depth in headwater streams with only a field camera capable of night vision and no additional equipment. In particular, the hierarchical model integrates long-term dynamic patterns extracted from large-scale meteorological data with short-term dynamic patterns extracted from small-scale stream image data to jointly monitor water depth at a fine -level temporal resolution. In order to overcome the issue of limited availability of images, we introduce a transfer learning strategy and incorporate more accurate long-term patterns that enable the hierarchical model to perform competitively even with a small number of images. We evaluate our method on a real -world headwater stream monitoring dataset from the West Brook study area in western Massachusetts, United States. Our extensive experiments demonstrate that the hierarchical model outperforms several state-of-the-art methods for water depth monitoring, and that more accurate long-term patterns can better guide the monitoring of short-term patterns with excellent flexibility and less computational cost. The mean absolute error of our hierarchical model achieves a remarkable level of 4 . 9 cm at the study site with 0 . 89 m average water depths, and only 12 . 5 cm at more drastically varied site with 3 . 95 m average depths.
关键词 ：
Headwater streams Headwater streams Water depth monitoring Water depth monitoring Multi-modal time series Multi-modal time series Deep learning Deep learning Hierarchical model Hierarchical model
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| GB/T 7714 | Zhao, Xiaohu , Jia, Kebin , Letcher, Benjamin et al. Bringing vision to climate: A hierarchical model for water depth monitoring in headwater streams [J]. | INFORMATION FUSION , 2024 , 110 . |
| MLA | Zhao, Xiaohu et al. "Bringing vision to climate: A hierarchical model for water depth monitoring in headwater streams" . | INFORMATION FUSION 110 (2024) . |
| APA | Zhao, Xiaohu , Jia, Kebin , Letcher, Benjamin , Fair, Jennifer , Jia, Xiaowei . Bringing vision to climate: A hierarchical model for water depth monitoring in headwater streams . | INFORMATION FUSION , 2024 , 110 . |
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摘要 ：
Video quality is essential for many consumer electronic devices. However, existing quality enhancement methods do not pay enough attention to the video characteristics. The success of artificial intelligence (AI) can help improve the quality of compressed video. In this paper, we perform an analysis of compressed video. Based on this, we propose utilizing multi-frame information as input and using two adjacent high-quality frames to enhance the low-quality frames in between. Therefore, an efficient Multi-Frame Quality Recovery (MFQR) method is proposed. In MFQR, we first develop a forward feature extraction module to extract information from multi-frame input. Then, a bidirectional time information extraction module, consisting of a Bi-directional Long-Short Time Memory (Bi-LSTM) structure and channel attention mechanism, is developed to capture bi-directional temporal information and enhance spatiotemporal information expression of features. Finally, we construct a residual feature enhancement module to improve model performance. Extensive experiment results show that the proposed MFQR method achieves an average increase of 27% in PSNR and reduces the number of parameters by an average of 23% than the representative methods.
关键词 ：
Consumer electronics Consumer electronics Quality assessment Quality assessment Quantization (signal) Quantization (signal) Encoding Encoding Video recording Video recording Compressed video Compressed video quality recovery quality recovery multi-frame multi-frame Bi-LSTM Bi-LSTM Convolution Convolution Feature extraction Feature extraction
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| GB/T 7714 | Liu, Chang , Jia, Kebin . Multi-Frame Quality Recovery Model for Compressed Video Enhancement [J]. | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS , 2024 , 70 (3) : 6354-6362 . |
| MLA | Liu, Chang et al. "Multi-Frame Quality Recovery Model for Compressed Video Enhancement" . | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 70 . 3 (2024) : 6354-6362 . |
| APA | Liu, Chang , Jia, Kebin . Multi-Frame Quality Recovery Model for Compressed Video Enhancement . | IEEE TRANSACTIONS ON CONSUMER ELECTRONICS , 2024 , 70 (3) , 6354-6362 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于神经网络的用于压缩视频质量增强的方法，属于视频后处理领域。其特征在于：首先构建了包含多个具有不同分辨率和内容的压缩视频集用于训练；其次设计了时空信息预提取网络，通过3D卷积层对特征图在时空维度上进行编解码，同时在时空维度上提取特征图底层特征和深层特征；最后设计了时空信息融合网络，将连续视频帧分解，在时间域上利用2D卷积层对分解的视频帧单独进行信息提取，然后再融合分解的视频帧特征，有效的对视频帧的信息进行增强，达到对压缩视频质量增强的目的。
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| GB/T 7714 | 贾克斌 , 黄威威 , 刘鹏宇 . 一种基于神经网络的用于压缩视频质量增强的方法 : CN202310256616.4[P]. | 2023-03-16 . |
| MLA | 贾克斌 et al. "一种基于神经网络的用于压缩视频质量增强的方法" : CN202310256616.4. | 2023-03-16 . |
| APA | 贾克斌 , 黄威威 , 刘鹏宇 . 一种基于神经网络的用于压缩视频质量增强的方法 : CN202310256616.4. | 2023-03-16 . |
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摘要 ：
本发明公开了一种基于深度学习的扩散相关光谱无创血压连续监测方法，具体包括：首先，基于扩散相关光谱技术获取被测试者手臂部位的光强自相关函数数据，利用传统非线性拟合方法计算出组织血流指数；然后，基于所提出的U‑net网络将拟合出的组织血流指数数据进行训练，建立从组织血流指数到血压之间的端到端网络模型；最后，将测试集数据送入训练好的网络模型中，实现血压的预测，从而得到连续血压波形。本发明直接建立了组织血流指数与血压间的端到端关系，为无创血压连续监测提供了新方法，克服了现有无创血压连续监测方法操作繁琐、因袖带充气而导致不适等不足，为人们了解血压的起伏变化提供了方便。
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| GB/T 7714 | 李哲 , 白江涛 , 姜敏楠 et al. 一种基于深度学习的扩散相关光谱无创血压连续监测方法 : CN202310317145.3[P]. | 2023-03-26 . |
| MLA | 李哲 et al. "一种基于深度学习的扩散相关光谱无创血压连续监测方法" : CN202310317145.3. | 2023-03-26 . |
| APA | 李哲 , 白江涛 , 姜敏楠 , 冯金超 , 贾克斌 . 一种基于深度学习的扩散相关光谱无创血压连续监测方法 : CN202310317145.3. | 2023-03-26 . |
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摘要 ：
本发明公开了基于图卷积神经网络的近红外光谱层析成像重建方法，本发明提出一种基于图卷积的深度学习网络框架，该深度学习网络框架对具有不规则结构的成像域建立图模型，并将图结构信息加入到带有注意力机制的图卷积神经网络中，以提取图节点上的光学特征参数的特征，将采集到的光学信号作为网络输入进行端到端的训练，同时恢复出含氧血红蛋白，脱氧血红蛋白和水三种发色团的浓度。实验结果表明，本发明能够实现NIRST图像的准确重建。
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| GB/T 7714 | 冯金超 , 苏琳轩 , 魏承朴 et al. 基于图卷积神经网络的近红外光谱层析成像重建方法 : CN202310513333.3[P]. | 2023-05-09 . |
| MLA | 冯金超 et al. "基于图卷积神经网络的近红外光谱层析成像重建方法" : CN202310513333.3. | 2023-05-09 . |
| APA | 冯金超 , 苏琳轩 , 魏承朴 , 贾克斌 , 李哲 , 孙中华 . 基于图卷积神经网络的近红外光谱层析成像重建方法 : CN202310513333.3. | 2023-05-09 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Growing incidences of cardiovascular diseases have driven demand for continuous tissue oxygen metabolism monitoring. Diffuse optical spectroscopy (DOS) or near-infrared spectroscopy (NIRS) is typically employed to measure oxygenated and de-oxygenated hemoglobin, while diffuse correlation spectroscopy (DCS) provides a direct measure of tissue blood flow noninvasively. Traditionally, combined DCS and DOS/NIRS approaches are utilized for monitoring oxygen metabolism, which needs two different numerical models to calculate blood flow and oxygen saturation. In this paper, we evaluate the utility of multi-wavelength diffuse correlation spectroscopy (mwDCS) using a proposed deep learning approach, i.e., long short-term memory (LSTM) based recurrent neural network (RNN). Arterial occlusion experiments were performed in vivo to generate the data from 6 healthy individuals. Further, based on the dataset acquired by mwDCS, a LSTM-based RNN model was developed to directly assess changes of blood flow and oxygen saturation. Compared to conventional methods, the in vivo experiments in this study demonstrated a more portable system with promising results. Pearsons correlation coefficients of the proposed model reached 0.82 and 0.64 for the changes of blood flow and oxygen saturation, respectively. Our results shows that mwDCS using LSTM-based RNN model provides an alternative method for continuous tissue oxygen metabolism monitoring.
关键词 ：
Multi-wavelength diffuse correlation Multi-wavelength diffuse correlation Oxygen metabolism monitoring Oxygen metabolism monitoring Deep learning Deep learning Diffuse optical spectroscopy Diffuse optical spectroscopy Diffuse correlation spectroscopy Diffuse correlation spectroscopy spectroscopy spectroscopy
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| GB/T 7714 | Li, Zhe , Bai, Jiangtao , Jiang, Minnan et al. Continuous monitoring of tissue oxygen metabolism based on multi-wavelength diffuse correlation spectroscopy using LSTM-based RNN model [J]. | OPTICS AND LASER TECHNOLOGY , 2023 , 171 . |
| MLA | Li, Zhe et al. "Continuous monitoring of tissue oxygen metabolism based on multi-wavelength diffuse correlation spectroscopy using LSTM-based RNN model" . | OPTICS AND LASER TECHNOLOGY 171 (2023) . |
| APA | Li, Zhe , Bai, Jiangtao , Jiang, Minnan , Chen, Xing , Wei, Ran , Jia, Kebin . Continuous monitoring of tissue oxygen metabolism based on multi-wavelength diffuse correlation spectroscopy using LSTM-based RNN model . | OPTICS AND LASER TECHNOLOGY , 2023 , 171 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Significance: X-ray Cherenkov-luminescence tomography (XCLT) produces fast emission data from megavoltage (MV) x-ray scanning, in which the excitation location of molecules within tissue is reconstructed. However standard filtered backprojection (FBP) algorithms for XCLT sinogram reconstruction can suffer from insufficient data due to dose limitations, so there are limits in the reconstruction quality with some artifacts. We report a deep learning algorithm for XCLT with high image quality and improved quantitative accuracy.Aim: To directly reconstruct the distribution of emission quantum yield for x-ray Cherenkov luminescence tomography, we proposed a three-component deep learning algorithm that includes a Swin transformer, convolution neural network, and locality module model.Approach: A data-to-image model x-ray Cherenkov-luminescence tomography is developed based on a Swin transformer, which is used to extract pixel-level prior information from the sinogram domain. Meanwhile, a convolutional neural network structure is deployed to transform the extracted pixel information from the sinogram domain to the image domain. Finally, a locality module is designed between the encoder and decoder connection structures for delivering features. Its performance was validated with simulation, physical phantom, and in vivo experiments.Results: This approach can better deal with the limits to data than conventional FBP methods. The method was validated with numerical and physical phantom experiments, with results showing that it improved the reconstruction performance mean square error (> 94.1%), peak signal-tonoise ratio (> 41.7%), and Pearson correlation (> 19%) compared with the FBP algorithm. The Swin-CNN also achieved a 32.1% improvement in PSNR over the deep learning method AUTOMAP.Conclusions: This study shows that the three-component deep learning algorithm provides an effective reconstruction method for x-ray Cherenkov-luminescence tomography.(c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JBO.28.2.026004]
关键词 ：
x-ray Cherenkov-luminescence tomography x-ray Cherenkov-luminescence tomography Swin-transformer Swin-transformer image reconstruction image reconstruction Cherenkov imaging Cherenkov imaging deep learning deep learning
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| GB/T 7714 | Feng, Jinchao , Zhang, Hu , Geng, Mengfan et al. X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module [J]. | JOURNAL OF BIOMEDICAL OPTICS , 2023 , 28 (2) . |
| MLA | Feng, Jinchao et al. "X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module" . | JOURNAL OF BIOMEDICAL OPTICS 28 . 2 (2023) . |
| APA | Feng, Jinchao , Zhang, Hu , Geng, Mengfan , Chen, Hanliang , Jia, Kebin , Sun, Zhonghua et al. X-ray Cherenkov-luminescence tomography reconstruction with a three-component deep learning algorithm: Swin transformer, convolutional neural network, and locality module . | JOURNAL OF BIOMEDICAL OPTICS , 2023 , 28 (2) . |
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摘要 ：
Sleep is crucial for human health. Automatic sleep stage classification based on polysomnography (PSG) is meaningful for the diagnosis of sleep diseases, which has attracted extensive attention recently. Most existed works learn the representations of PSG based on the conventional grid data structure, ignoring the topological channel correlations among different body regions in non-Euclidean space. To this end, we propose a deep learning model named SAGSleepNet, which transforms PSG into a robust graph structure by neural networks and self-attention mechanism, utilizing graph convolution network (GCN) and bidirectional gated recurrent unit (BiGRU) to capture epoch-level topological channel correlations and sequence-level temporal sleep transitions of PSG, respectively. The final learned representation is fed into a softmax layer to train an end-to-end model for automatic sleep staging. Experimental results on three public datasets (i.e., SleepEDFx, UCD, CAP) show that SAGSleepNet obtains the best classification performance compared with several baselines, including accuracy of 0.807,0.765, and 0.746, F1-score of 0.744, 0.750, and 0.695, and Cohen's Kappa (kappa) of 0.721, 0.690, and 0.660. In general, our work provides a reasonable graph structure to model PSG epoch, and makes contribution to explore implicit channel-wise information of PSG by deep learning techniques.
关键词 ：
Sleep stage classification Sleep stage classification Graph convolution network Graph convolution network Polysomnography Polysomnography Self-attention mechanism Self-attention mechanism
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| GB/T 7714 | Jin, Zheng , Jia, Kebin . SAGSleepNet: A deep learning model for sleep staging based on self-attention graph of polysomnography [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 86 . |
| MLA | Jin, Zheng et al. "SAGSleepNet: A deep learning model for sleep staging based on self-attention graph of polysomnography" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 86 (2023) . |
| APA | Jin, Zheng , Jia, Kebin . SAGSleepNet: A deep learning model for sleep staging based on self-attention graph of polysomnography . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2023 , 86 . |
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摘要 ：
本发明公开了一种基于粗细粒度的地基云图分类方法，属于大气科学与计算机视觉领域。如何在不添加人工辅助信息和额外的物体位置信息标注的情况下提取更精细、更显著的纹理和形状特征仍是一个亟待解决的技术问题。本发明包含以下步骤：构建了一种弱监督学习的粗细粒度预测网络来提取云图具有辨别性的纹理特征，通过网络训练建立了云图全局特征与局部特征之间的联系；结合注意力学习和局部定位方法实现对云图显著性局部特征的定位和细化；最后，将粗细粒度预测结果相融合实现对目标的定位和分类并输出所属类别。实验结果表明本方法在地基云分类方面比强监督方法取得了更好的进展，达到98.58％的准确率，为设备集成与实际应用提供了可能性。
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| GB/T 7714 | 冯金超 , 邢乐园 , 孙中华 et al. 一种基于粗细粒度的地基云图分类方法 : CN202211117962.6[P]. | 2022-09-14 . |
| MLA | 冯金超 et al. "一种基于粗细粒度的地基云图分类方法" : CN202211117962.6. | 2022-09-14 . |
| APA | 冯金超 , 邢乐园 , 孙中华 , 贾克斌 . 一种基于粗细粒度的地基云图分类方法 : CN202211117962.6. | 2022-09-14 . |
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摘要 ：
本实用新型专利特别涉及一种MRI兼容的插拔式组织血流检测探头，用于解决目前扩散相关光谱技术组织血流仪测量过程中探头固定的问题。具体包括：探测光纤、光源光纤和柔性探头座。探头柔韧性高，可实现与被测组织的紧密贴合；探测光纤、光源光纤可与柔性固定探头座之间以插拔方式实现安装，探测光纤和光源光纤垂直于柔性探头底面；不同光源和探测间距，可实现不同深度的组织血流检测；探头各组成部分材制均不涉及金属，核磁共振(MRI)兼容。本实用新型专利以插拔方式解决了组织血流测量过程中探测光纤与光源光纤的固定问题，满足扩散相关光谱技术组织血流测量的理论模型要求，提高了组织血流测量的准确性和稳定性。
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| GB/T 7714 | 李哲 , 姜敏楠 , 冯金超 et al. 一种MRI兼容的插拔式组织血流检测探头 : CN202220215504.5[P]. | 2022-01-26 . |
| MLA | 李哲 et al. "一种MRI兼容的插拔式组织血流检测探头" : CN202220215504.5. | 2022-01-26 . |
| APA | 李哲 , 姜敏楠 , 冯金超 , 贾克斌 . 一种MRI兼容的插拔式组织血流检测探头 : CN202220215504.5. | 2022-01-26 . |
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