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学者姓名：沈英
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Abstract ：
Image super-resolution (SR) has recently gained traction in various fields, including remote sensing, biomedicine, and video surveillance. Nonetheless, the majority of advancements in SR have been achieved by scaling the architecture of convolutional neural networks, which inevitably increases computational complexity. In addition, most existing SR models struggle to effectively capture high-frequency information, resulting in overly smooth reconstructed images. To address this issue, we propose a lightweight Progressive Feature Aggregation Network (PFAN), which leverages Progressive Feature Aggregation Block to enhance different features through a progressive strategy. Specifically, we propose a Key Information Perception Module for capturing high-frequency details from cross-spatial-channel dimension to recover edge features. Besides, we design a Local Feature Enhancement Module, which effectively combines multi-scale convolutions for local feature extraction and Transformer for long-range dependencies modeling. Through the progressive fusion of rich edge details and texture features, our PFAN successfully achieves better reconstruction performance. Extensive experiments on five benchmark datasets demonstrate that PFAN outperforms state-of-the-art methods and strikes a better balance across SR performance, parameters, and computational complexity. Code can be available at https://github.com/handsomeyxk/PFAN.
Keyword ：
CNN CNN Key information perception Key information perception Local feature enhancement Local feature enhancement Progressive feature aggregation network Progressive feature aggregation network Super-resolution Super-resolution Transformer Transformer
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| GB/T 7714 | Chen, Liqiong , Yang, Xiangkun , Wang, Shu et al. PFAN: progressive feature aggregation network for lightweight image super-resolution [J]. | VISUAL COMPUTER , 2025 . |
| MLA | Chen, Liqiong et al. "PFAN: progressive feature aggregation network for lightweight image super-resolution" . | VISUAL COMPUTER (2025) . |
| APA | Chen, Liqiong , Yang, Xiangkun , Wang, Shu , Shen, Ying , Wu, Jing , Huang, Feng et al. PFAN: progressive feature aggregation network for lightweight image super-resolution . | VISUAL COMPUTER , 2025 . |
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针对现有图像超分辨率重建方法存在模型复杂度过高和参数量过大等问题,文中提出基于多尺度空间自适应注意力网络(Multi-scale Spatial Adaptive Attention Network,MSAAN)的轻量级图像超分辨率重建方法.首先,设计全局特征调制模块(Global Feature Modulation Module,GFM),学习全局纹理特征.同时,设计轻量级的多尺度特征聚合模块(Multi-scale Feature Aggregation Module,MFA),自适应聚合局部至全局的高频空间特征.然后,融合GFM和MFA,提出多尺度空间自适应注意力模块(Multi-scale Spatial Adaptive Attention Module,MSAA).最后,通过特征交互门控前馈模块(Feature Interactive Gated Feed-Forward Module,FIGFF)增强局部信息提取能力,同时减少通道冗余.大量实验表明,MSAAN能捕捉更全面、更精细的特征,在保证轻量化的同时显著提升图像的重建效果.
Keyword ：
Transformer Transformer 卷积神经网络 卷积神经网络 多尺度空间自适应注意力 多尺度空间自适应注意力 轻量级图像超分辨率重建 轻量级图像超分辨率重建
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| GB/T 7714 | 黄峰 , 刘鸿伟 , 沈英 et al. 基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法 [J]. | 模式识别与人工智能 , 2025 , 38 (1) : 36-50 . |
| MLA | 黄峰 et al. "基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法" . | 模式识别与人工智能 38 . 1 (2025) : 36-50 . |
| APA | 黄峰 , 刘鸿伟 , 沈英 , 裘兆炳 , 陈丽琼 . 基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法 . | 模式识别与人工智能 , 2025 , 38 (1) , 36-50 . |
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The pedestrian detection network utilizing a combination of infrared and visible image pairs can improve detection accuracy by fusing their complementary information, especially in challenging illumination conditions. However, most existing dual-modality methods only focus on the effectiveness of feature maps between different modalities while neglecting the issue of redundant information in the modalities. This oversight often affects the detection performance in low illumination conditions. This paper proposes an efficient attention feature fusion network (EAFF-Net), which suppresses redundant information and enhances the fusion of features from dualmodality images. Firstly, we design a dual-backbone network based on CSPDarknet53 and combine with an efficient partial spatial pyramid pooling module (EPSPPM), improving the efficiency of feature extraction in different modalities. Secondly, a feature attention fusion module (FAFM) is built to adaptively weaken modal redundant information to improve the fusion effect of features. Finally, a deep attention pyramid module (DAPM) is proposed to cascade multi-scale feature information and obtain more detailed features of small targets. The effectiveness of EAFF-Net in pedestrian detection has been demonstrated through experiments conducted on two public datasets.
Keyword ：
Deep learning Deep learning Feature attention Feature attention Multiscale features Multiscale features Pedestrian detection Pedestrian detection Visible and infrared images Visible and infrared images
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| GB/T 7714 | Shen, Ying , Xie, Xiaoyang , Wu, Jing et al. EAFF-Net: Efficient attention feature fusion network for dual-modality pedestrian detection [J]. | INFRARED PHYSICS & TECHNOLOGY , 2025 , 145 . |
| MLA | Shen, Ying et al. "EAFF-Net: Efficient attention feature fusion network for dual-modality pedestrian detection" . | INFRARED PHYSICS & TECHNOLOGY 145 (2025) . |
| APA | Shen, Ying , Xie, Xiaoyang , Wu, Jing , Chen, Liqiong , Huang, Feng . EAFF-Net: Efficient attention feature fusion network for dual-modality pedestrian detection . | INFRARED PHYSICS & TECHNOLOGY , 2025 , 145 . |
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Attention mechanisms have been introduced to exploit deep-level information for image restoration by capturing feature dependencies. However, existing attention mechanisms often have limited perceptual capabilities and are incompatible with low-power devices due to computational resource constraints. Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA). Specifically, ESA enhances high-frequency texture features in the feature maps, and MLSKA executes the further extraction. The rich and fine-grained high-frequency information are extracted and fused from multiple perceptual layers, thus improving super-resolution (SR) performance. To validate FECAN's effectiveness, we evaluate it with different complexities by stacking different numbers of high-frequency enhancement modules (HFEM) that contain FECA. Extensive experiments on benchmark datasets demonstrate that FECAN outperforms state-of-the-art lightweight SR networks in terms of objective evaluation metrics and subjective visual quality. Specifically, at a x 4 scale with a 121 K model size, compared to the second-ranked MAN-tiny, FECAN achieves a 0.07 dB improvement in average peak signal-to-noise ratio (PSNR), while reducing network parameters by approximately 19% and FLOPs by 20%. This demonstrates a better trade-off between SR performance and model complexity.
Keyword ：
Convolution neural network Convolution neural network Enhanced shuffle attention Enhanced shuffle attention Lightweight image super-resolution Lightweight image super-resolution Multi-scale large separable kernel attention Multi-scale large separable kernel attention
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| GB/T 7714 | Huang, Feng , Liu, Hongwei , Chen, Liqiong et al. Feature enhanced cascading attention network for lightweight image super-resolution [J]. | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
| MLA | Huang, Feng et al. "Feature enhanced cascading attention network for lightweight image super-resolution" . | SCIENTIFIC REPORTS 15 . 1 (2025) . |
| APA | Huang, Feng , Liu, Hongwei , Chen, Liqiong , Shen, Ying , Yu, Min . Feature enhanced cascading attention network for lightweight image super-resolution . | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
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Fuel cell air compressor is a key enabler of hydrogen-based renewable energy systems as it improves air supply efficiency and stability. This paper first simplifies the gas flow equations, compares ideal gas path elements with circuit elements, and converts the air compressor system transfer function into an interpretable control-oriented energy-circuit-based model, and derives the transfer matrix relating flow and pressure at any position to the initial position using Laplace transforms. The reconstructed model then converts the complex frequency domain system transfer functions into the time-domain form, generating a control-oriented energy-circuit-based model for the proton exchange membrane fuel cell air compressor system to describe the dynamical and dimensional features. Under the current step condition, the energy-circuit-based air compressor system model achieves less than 5 % mean relative error (MRE) in intake manifold pressure and flow. The model-based pressure distribution has a root-mean-squared error (RMSE) and an MRE of 26.5 Pa and 0.01246 % compared to finite element results. Additionally, the energy-circuit-based air compressor system model with surge constraints has been used to develop the model predictive controller which has been further tested under typical simulation conditions. The proposed control strategy demonstrates enhanced transient response and enables the determination of pressure distribution along the pipeline. © 2025 Elsevier Ltd
Keyword ：
Air compressor control Air compressor control Energy-circuit-based modeling Energy-circuit-based modeling Pressure distribution along the pipeline Pressure distribution along the pipeline Proton exchange membrane fuel cell Proton exchange membrane fuel cell Surge-awareness predictive control Surge-awareness predictive control
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| GB/T 7714 | Ou, K. , Ye, W. , Zhang, X. et al. An energy-circuit-based fuel cell air compressor system model reconstruction and predictive control approach [J]. | Renewable Energy , 2025 , 254 . |
| MLA | Ou, K. et al. "An energy-circuit-based fuel cell air compressor system model reconstruction and predictive control approach" . | Renewable Energy 254 (2025) . |
| APA | Ou, K. , Ye, W. , Zhang, X. , Zhang, Q. , Shen, Y. , Wang, Y.-X. . An energy-circuit-based fuel cell air compressor system model reconstruction and predictive control approach . | Renewable Energy , 2025 , 254 . |
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Keyword ：
biomolecular interaction biomolecular interaction biosensing biosensing differential measurement differential measurement label-free detection label-free detection phase-sensitive interferometry phase-sensitive interferometry
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| GB/T 7714 | Shen, Ying , Huang, Zeyu , Huang, Feng et al. A self-reference interference sensor based on coherence multiplexing (vol 10, 880081, 2022) [J]. | FRONTIERS IN CHEMISTRY , 2024 , 12 . |
| MLA | Shen, Ying et al. "A self-reference interference sensor based on coherence multiplexing (vol 10, 880081, 2022)" . | FRONTIERS IN CHEMISTRY 12 (2024) . |
| APA | Shen, Ying , Huang, Zeyu , Huang, Feng , He, Yonghong , Ye, Ziling , Zhang, Hongjian et al. A self-reference interference sensor based on coherence multiplexing (vol 10, 880081, 2022) . | FRONTIERS IN CHEMISTRY , 2024 , 12 . |
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Abstract ：
叶黄素是天然的抗氧化剂,对人体健康有多种益处,异养小球藻具有叶黄素纯度和产量均较高的优势,而小球藻叶黄素产量主要取决于生物质产量和叶黄素含量两个因素.传统的光密度法测生物质产量和高效液相色谱法测叶黄素含量存在操作复杂、时效性低等不足.为了快速、无损测定小球藻生长过程中叶黄素含量变化,搭建可见-近红外双模式快照式多光谱成像检测系统,根据光谱响应区域,分别利用可见光相机获取叶黄素光谱信息,近红外相机获取生物质光谱信息,构建含有生物质量和叶黄素含量信息的可见-近红外双模式多光谱数据集.针对系统所使用的快照式多光谱相机光谱范围宽、波长数量少的特征波长选取问题,提出一种结合序列浮动前向选择的改进型连续投影算法(mSPA);将mSPA与常规的连续投影算法、遗传算法及随机蛙跳三种波长选择算法作对比分析后,构建了基于特征波长的多元线性回归和极限学习机模型;最后,利用生物质产量和叶黄素含量的最佳预测模型生成小球藻叶黄素产量的可视化分布图.结果表明,在利用近红外、可见光相机分别检测小球藻生物质、叶黄素量时,mSPA得到的特征波长数均较少,并具有最高的预测精度.生物质量与叶黄素含量的最佳模型均为mSPA筛选特征波长后建立的极限学习机模型,对应的预测集决定系数分别为0.947和0.907,预测集均方根误差分别为0.698 g·L-1和0.077 mg·g-1,剩余预测偏差分别为3.535和3.338,模型的预测能力较好.可视化分布实现了直观监测小球藻叶黄素产量的变化,有助于后续实际生产中在线检测叶黄素产量.mSPA在快照式多光谱检测小球藻生物质含量及叶黄素含量中,通过对排序波长逐个评估以选择出最佳特征波长组合,有效地避免了特征波长的错选、漏选,提高了模型的预测精度,为快照式多光谱成像技术应用提供新的波长选择思路.
Keyword ：
叶黄素产量 叶黄素产量 小球藻 小球藻 快照式多光谱 快照式多光谱 特征波长 特征波长
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| GB/T 7714 | 沈英 , 占秀兴 , 黄春红 et al. 基于快照式多光谱特征波长的小球藻叶黄素产量快速测定 [J]. | 光谱学与光谱分析 , 2024 , 44 (8) : 2216-2223 . |
| MLA | 沈英 et al. "基于快照式多光谱特征波长的小球藻叶黄素产量快速测定" . | 光谱学与光谱分析 44 . 8 (2024) : 2216-2223 . |
| APA | 沈英 , 占秀兴 , 黄春红 , 谢友坪 , 郭翠霞 , 黄峰 . 基于快照式多光谱特征波长的小球藻叶黄素产量快速测定 . | 光谱学与光谱分析 , 2024 , 44 (8) , 2216-2223 . |
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Polarization imaging technology can be applied to unveil the interaction between light and matter by harnessing the transverse vector wave attributes of light, thus to accentuate target characteristics amidst complex weather conditions. This technology has the potential to be widely used in road target detection. However, polarization detection is significantly affected by illumination and detection angles, as well as the considerable variation in the scale of road targets. The optimal polarization parameters should be adaptively adjusted to weather conditions, angles and target features, whereas most existing research employs handcrafted polarization parameters without considering actual complex detection requirements, which are unable to adaptively adjust the polarization feature enhancement methods. In this paper, we propose a road target detection algorithm based on an end-to-end adaptive polarization coding method, named YOLO-Polarization of Road Target Detection (YOLO-PRTD). To enhance the polarized features of targets under complex weather conditions, an Adaptive Polarization Coding Module (APCM) is designed. This module integrates channel-wise global self-attention and small kernel convolution to adaptively adjust the polarization enhancement method using dynamically extracted global and local polarization feature information. A multi-scale detection network is also designed to fully extract and fuse multi-scale feature information from receptive fields, channels, and spaces in different dimensions. Additionally, a dataset of Polarized Images of Road Targets in Complex Weather conditions (PIRT-CW) is proposed for training and evaluation. Experimental results on the PIRT-CW show that the YOLO-PRTD algorithm achieves a mAP0.5 of 89.83%, reducing the error rate by 15.54% compared to the baseline network YOLOX.
Keyword ：
Complex weather conditions Complex weather conditions Multi-scale Multi-scale Polarization feature enhancement Polarization feature enhancement Road target detection Road target detection
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| GB/T 7714 | Huang, Feng , Zheng, Junlong , Liu, Xiancai et al. Polarization of road target detection under complex weather conditions [J]. | SCIENTIFIC REPORTS , 2024 , 14 (1) . |
| MLA | Huang, Feng et al. "Polarization of road target detection under complex weather conditions" . | SCIENTIFIC REPORTS 14 . 1 (2024) . |
| APA | Huang, Feng , Zheng, Junlong , Liu, Xiancai , Shen, Ying , Chen, Jinsheng . Polarization of road target detection under complex weather conditions . | SCIENTIFIC REPORTS , 2024 , 14 (1) . |
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The efficiency and dynamic response of air compressors are crucial for stability and lifespan of hydrogen fuel cells. A predictive control scheme with surge- and choke-constrained awareness is proposed to ensure safe and efficient operation of air compressors in this study. The proposed scheme consists of an efficiency enhancement model predictive control (EE-MPC), and an improved active disturbance rejection control (IADRC). Surge- and choke-constrained awareness is achieved by comparing predicted air flow with surge and choke limitations. Simultaneously, the EE-MPC is constrained with oxygen excess ratio (OER) and obtains optimal solution by searching active set. The reference flow and supply manifold pressure trajectories for IADRC are generated by EE-MPC. A designed piecewise differentiable nonlinear smoothing function is embedded in IADRC. The disturbances are estimated for coordinating flow and pressure control. Under China heavy-duty commercial vehicle test cycle for bus conditions, root-mean-squared errors (RMSEs) of flow and pressure are 3.27 g s-1 and 1.88 x 103 Pa, respectively, and the mean efficiency can be enhanced by 13.4% compared to the MPC with fixed OER. Finally, a controller hardware-in-the-loop test is conducted, with flow and pressure RMSEs of 2.48 g s-1 and 4.28 x 103 Pa between the test and simulation, respectively. This study proposes a predictive control scheme with surge- and choke-constrained awareness to guarantee safety and efficiency of air compressors. The reference flow and pressure trajectories are formulated by efficiency enhancement model predictive control, and further tracked by improved active disturbance rejection control. The proposed scheme can efficiently improve fuel cell air compressor isentropic efficiency and avoid surge and choke. image
Keyword ：
air compressor predictive control air compressor predictive control compressor isentropic efficiency enhancement compressor isentropic efficiency enhancement coordinated control coordinated control fuel cell fuel cell surge- and choke-constrained awareness surge- and choke-constrained awareness
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| GB/T 7714 | Ye, Wangcheng , Zhong, Shunbin , Shen, Ying et al. Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge- and Choke-Constrained Awareness [J]. | ADVANCED THEORY AND SIMULATIONS , 2024 , 7 (6) . |
| MLA | Ye, Wangcheng et al. "Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge- and Choke-Constrained Awareness" . | ADVANCED THEORY AND SIMULATIONS 7 . 6 (2024) . |
| APA | Ye, Wangcheng , Zhong, Shunbin , Shen, Ying , Zhang, Xuezhi , Wang, Ya-Xiong . Predictive Control Scheme for Fuel Cell Air Compressor Efficiency Enhancement with Surge- and Choke-Constrained Awareness . | ADVANCED THEORY AND SIMULATIONS , 2024 , 7 (6) . |
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为了提高车辆视觉感知能力,针对交通场景运用提出一种改进的轮廓角方向(contour angle orientation,CAO)算法用于实现红外与可见光图像配准.通过模拟不同的交通场景,对成熟算法进行性能检测对比,选出CAO算法这一优势算法,并对其粗匹配参数和图像预处理图像缩放程序做了改进.实验表明,改进后的CAO算法细匹配更精准,马赛克拼接图拼接处衔接更加自然,线条更加顺滑,效果更好.与原来CAO算法相比,改进后的算法均方根误差值RMSE下降 3.29%,查准率Precision提高 2.13%,平均运算耗时减少 0.11 s,在配准精度和配准实时性方面均证明了算法的改进效果.
Keyword ：
图像配准 图像配准 特征提取 特征提取 红外和可见光图像 红外和可见光图像 轮廓角方向 轮廓角方向
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| GB/T 7714 | 苏建彬 , 沈英 , 黄磊 et al. 车载红外和可见光图像配准方法 [J]. | 红外技术 , 2024 , 46 (10) : 1209-1217 . |
| MLA | 苏建彬 et al. "车载红外和可见光图像配准方法" . | 红外技术 46 . 10 (2024) : 1209-1217 . |
| APA | 苏建彬 , 沈英 , 黄磊 , 沈元兴 . 车载红外和可见光图像配准方法 . | 红外技术 , 2024 , 46 (10) , 1209-1217 . |
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