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学者姓名：郑茜颖
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Abstract ：
Transporter proteins play a crucial role in maintaining ionic homeostasis inside and outside the cell, facilitating protein uptake, and enabling cellular communication with the external environment. Transporter proteins (TPs) are also significant targets for medical research and drug development. Accurately predicting novel TPs remains a major challenge in functional genomics. In this study, we propose TP prediction models, designated as TCS-TP, that utilize transformer and convolutional neural networks for multi-scale feature extraction of protein sequences. The transformer incorporates the GLU activation function, whereas the convolutional neural network (CNN) features three parallel subnetworks. Support vector machines are used as a classifier for TP classification. The test results demonstrate that TCS-TP could successfully recognize TPs, with AUROC of 0.89, AUPRC of 0.81, and accuracy of 91.6617%. Upon further comparison, it is determined that TCS-TP outperforms other methods. We hoped that TCS-TP will prove to be a valuable tool for predicting TPs in large-scale genomic projects and contribute to the discovery of new TPs.
Keyword ：
convolutional neural network (CNN) convolutional neural network (CNN) protein prediction protein prediction sequence information sequence information support vector machines (SVMs) support vector machines (SVMs) transformer transformer transporter protein transporter protein
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| GB/T 7714 | Yu, Fan , Zheng, Qianying , Fu, Qingwei et al. TCS-TP: Transporter Protein Prediction Based On Multi-Scale Feature Extraction [J]. | BIOTECHNOLOGY AND APPLIED BIOCHEMISTRY , 2025 . |
| MLA | Yu, Fan et al. "TCS-TP: Transporter Protein Prediction Based On Multi-Scale Feature Extraction" . | BIOTECHNOLOGY AND APPLIED BIOCHEMISTRY (2025) . |
| APA | Yu, Fan , Zheng, Qianying , Fu, Qingwei , Chen, Jiansen . TCS-TP: Transporter Protein Prediction Based On Multi-Scale Feature Extraction . | BIOTECHNOLOGY AND APPLIED BIOCHEMISTRY , 2025 . |
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High-resolution medical images contain more detailed pathological information than low-resolution medical images, and clarity of medical images is critical for doctors in the diagnosis of disease. Nevertheless, previous deep learning-based methods are deficient in terms of capturing high-frequency details and retaining edge information, so the present paper puts forth a distillation network based on the enhancement of frequency and spatial features as a means of achieving super-resolution reconstruction of medical CT images. Specifically, we propose a distillation module for spatial-frequency domain feature enhancement. This module combines the Fast Fourier Transform (FFT) to extract frequency information and utilizes edge operators to obtain spatial information, which enables the effective extraction of textures and details. Moreover, it reduces the large number of parameters brought by FFT through distillation. In addition, in order to expand the receptive field of the model, a spatial attention mechanism module based on large kernels is designed, which enables the model to focus more effectively on relevant spatial regions, thereby enhancing the extraction and utilization of spatial features. The experiment shows that the reconstructed images of the proposed method are superior to the comparison algorithms in both objective evaluation metrics and subjective perception, and the effect is even better when the scaling factor is large.
Keyword ：
Deep learning Deep learning Large kernel Large kernel Medical image processing Medical image processing Spatial-frequency fusion Spatial-frequency fusion Super-resolution reconstruction Super-resolution reconstruction
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| GB/T 7714 | Chen, Xiuhui , Zheng, Qianying , Chen, Jiansen et al. Distillation network based on spatial-frequency fusion for super-resolution of medical CT images [J]. | DIGITAL SIGNAL PROCESSING , 2025 , 160 . |
| MLA | Chen, Xiuhui et al. "Distillation network based on spatial-frequency fusion for super-resolution of medical CT images" . | DIGITAL SIGNAL PROCESSING 160 (2025) . |
| APA | Chen, Xiuhui , Zheng, Qianying , Chen, Jiansen , Yu, Fan , Fu, Qingwei . Distillation network based on spatial-frequency fusion for super-resolution of medical CT images . | DIGITAL SIGNAL PROCESSING , 2025 , 160 . |
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针对现有脑部医学图像融合算法存在的融合图像细节模糊和边缘性差等问题,设计一种扩张金字塔特征提取算法,由特征提取器、特征融合器和特征重构器3部分组成.特征提取器由扩张金字塔特征模块提取浅层和深层图像特征的结合,防止图像细节信息的丢失;特征融合器采用改进的功能能量比(Functional Energy Ratio,FER)特征融合策略增强融合图像边缘信息;特征重构器由4层卷积构成归一化图像.实验结果表明,相较于当前通用的脑部融合算法,所提出的算法具有较好的视觉效果和细节信息,客观评价指标有更好的表现.
Keyword ：
多模态医学图像 多模态医学图像 特征融合 特征融合 特征重构 特征重构 脑部医学图像融合 脑部医学图像融合 金字塔特征 金字塔特征
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| GB/T 7714 | 马为民 , 郑茜颖 . 结合扩张金字塔的脑部医学图像融合 [J]. | 电视技术 , 2024 , 48 (1) : 16-21,29 . |
| MLA | 马为民 et al. "结合扩张金字塔的脑部医学图像融合" . | 电视技术 48 . 1 (2024) : 16-21,29 . |
| APA | 马为民 , 郑茜颖 . 结合扩张金字塔的脑部医学图像融合 . | 电视技术 , 2024 , 48 (1) , 16-21,29 . |
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Object detection using unmanned aerial vehicle (UAV) remote sensing images is a challenging task due to varying object scales, dense distribution, and the predominance of small object. Directly using a generalized object detector that has not been specially designed makes it difficult to balance accuracy and model complexity. To address this challenge, we propose a lighter and more accurate network (LMANet) model. First, a more effective loss function called IMIoU has been developed by combining the concepts of minimum point distance bounding box regression-based loss with auxiliary edge-assisted regression. Second, the model output reconstruction (MOR) was used to optimize the structure for small target objects. Third, we have designed an efficient feature extraction module (EFEM) that can effectively enhance the feature extraction capability of the backbone network for complex environmental information. Finally, to reduce the computational overhead of the model, we have designed a feature fusion lightweight strategy (FFLS) in the neck part, which significantly reduces the computational and parametric quantities of the model. The results of the LMANet on the VisDrone-2021DET and HIT-UAV datasets demonstrate a 4.7% and 2.3% improvement in mean average precision (mAP), respectively, compared to the benchmark model. Additionally, the model's parameters and computation are reduced by 79.3% and 12.6%, respectively.
Keyword ：
Accuracy Accuracy Autonomous aerial vehicles Autonomous aerial vehicles Computational modeling Computational modeling Convolution Convolution Efficient feature extraction Efficient feature extraction Feature extraction Feature extraction Head Head lightweight lightweight model output reconstruction (MOR) model output reconstruction (MOR) multiobject detection multiobject detection Task analysis Task analysis unmanned aerial vehicle (UAV) unmanned aerial vehicle (UAV)
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| GB/T 7714 | Fu, Qingwei , Zheng, Qianying , Yu, Fan . LMANet: A Lighter and More Accurate Multiobject Detection Network for UAV Remote Sensing Imagery [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2024 , 21 . |
| MLA | Fu, Qingwei et al. "LMANet: A Lighter and More Accurate Multiobject Detection Network for UAV Remote Sensing Imagery" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 21 (2024) . |
| APA | Fu, Qingwei , Zheng, Qianying , Yu, Fan . LMANet: A Lighter and More Accurate Multiobject Detection Network for UAV Remote Sensing Imagery . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2024 , 21 . |
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Aiming at the problems of difficult segmentation and inaccurate segmentation of small lesions in lung Computed Tomography (CT) images, a multi-scale feature fusion deep learning prediction model that conforms to the observation of things by the human eye is proposed, and specific performance indicators and visualization results are given. The multi-scale feature fusion mechanism is used to effectively capture the long-distance characteristics of lesions. A joint loss function is proposed to make the training smoother and further improve the segmentation performance. The analysis is verified in the segmentation test set, and the results show that the overlap between the proposed model segmentation results and the real results has a Dice value of 83.29%, a sensitivity Sen of 82.66%, a cross-union ratio IoU of 73.15%, and a specificity Spec of 99.82%, which can better segment the lesion region compared with the existing algorithms. Therefore, using the proposed improved model to predict lung CT image lesions can be used clinically for doctors to diagnose diseases faster. © 2024 IEEE.
Keyword ：
Computerized tomography Computerized tomography Deep learning Deep learning Diagnosis Diagnosis Image segmentation Image segmentation Lung cancer Lung cancer Prediction models Prediction models
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| GB/T 7714 | Ma, Weimin , Zheng, Qianying . Segmentation of Lung CT Images Based on Multiscale Feature Fusion [C] . 2024 : 44-48 . |
| MLA | Ma, Weimin et al. "Segmentation of Lung CT Images Based on Multiscale Feature Fusion" . (2024) : 44-48 . |
| APA | Ma, Weimin , Zheng, Qianying . Segmentation of Lung CT Images Based on Multiscale Feature Fusion . (2024) : 44-48 . |
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针对医学断层图像层间分辨率较低的问题,提出了基于形变估计与运动补偿的医学CT图像层间超分辨率算法用于生成切片间图像,从而提高层间分辨率.首先利用U-Net对相邻两幅图像作多尺度特征提取与融合;其次,为了处理层间图像的复杂形变,使用基于自适应协作流的变形扭曲模块来实现相邻切片间的双向形变估计,设计层级信息递进融合模块对金字塔特征层进行特征聚合,对生成图进行运动补偿;最后经过后处理网络以减少异常像素点.该算法在两种CT数据集上进行验证,平均PSNR值分别达到了35.59 dB和30.76 dB,输出图能较好地恢复图像细节.与现有的一些方法对比,相关实验证明了该算法的有效性.
Keyword ：
三维医学图像 三维医学图像 卷积神经网络 卷积神经网络 层间超分辨率 层间超分辨率 形变估计 形变估计
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| GB/T 7714 | 郑智震 , 郑茜颖 , 俞金玲 . 基于形变估计与运动补偿的医学CT图像层间超分辨率算法 [J]. | 计算机应用研究 , 2024 , 41 (4) : 1234-1238 . |
| MLA | 郑智震 et al. "基于形变估计与运动补偿的医学CT图像层间超分辨率算法" . | 计算机应用研究 41 . 4 (2024) : 1234-1238 . |
| APA | 郑智震 , 郑茜颖 , 俞金玲 . 基于形变估计与运动补偿的医学CT图像层间超分辨率算法 . | 计算机应用研究 , 2024 , 41 (4) , 1234-1238 . |
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提出了一种基于深度学习技术的光伏板缺陷分类定位方法,用于快速准确地确定光伏板缺陷的位置和类型.为了克服传统单张图像缺陷检测方法的视角限制,采用图像配准、拼接等算法生成高分辨率的光伏全景图像,并使用深度学习技术对光伏板红外图像进行缺陷分类,通过与可见光图像进行对比,可以有效地确定光伏板缺陷的类型.光伏板缺陷分类的准确率、精确率、召回率和F1分数分别达到了 93.71%、93.13%、93.20%和 93.11%.与传统方法相比,该方法具有非接触、高效和快速等优点,适用于大规模光伏板缺陷的检测和定位,能够在短时间内获取准确、全面的光伏板缺陷信息.
Keyword ：
光伏板 光伏板 拼接 拼接 深度学习 深度学习 缺陷 缺陷 缺陷分类 缺陷分类
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| GB/T 7714 | 刘枭雄 , 郑茜颖 . 基于深度学习的光伏板缺陷分类定位算法研究 [J]. | 光电子技术 , 2024 , 44 (1) : 54-60 . |
| MLA | 刘枭雄 et al. "基于深度学习的光伏板缺陷分类定位算法研究" . | 光电子技术 44 . 1 (2024) : 54-60 . |
| APA | 刘枭雄 , 郑茜颖 . 基于深度学习的光伏板缺陷分类定位算法研究 . | 光电子技术 , 2024 , 44 (1) , 54-60 . |
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近年来,高血压患者的比例不断上升,如何在血压值异常前发出警报提早治疗成为广泛关注的研究课题.为解决这一问题,提出了一种基于多层长短期记忆网络(LSTM)结合上下文信息层的组合预测模型.使用双向LSTM结构添加了从负时间方向的时序信息对当前状态的影响,加入LSTM残差连接解决多层网络带来的梯度消失和梯度爆炸问题.在输出层之前增加了一个额外的加入了用户的基本信息数据的全连接层.额外层的激活函数为修正线性单元(ReLU),使用多个时序数据对不同时段的血压进行预测.实验结果表明使用24个时序的实验结果最佳.在24个时序的数据集上,进行不同时段1h、6h、12 h、24h的血压预测,预测误差和预测偏差对于收缩压分别为 0.002 644、0.003 952、0.004216、0.005 528 和 0.037 796、0.047931、0.049 879、0.057 454,对于舒张压分别为 0.001 226、0.001 554、0.001 706、0.001 955 和 0.024293、0.028 369、0.030190、0.032668,实验误差与其他模型相比,所提模型预测误差和预测偏差都得到降低.
Keyword ：
上下文信息 上下文信息 时间序列 时间序列 深度循环网络 深度循环网络 血压预测 血压预测
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| GB/T 7714 | 孙永莹 , 郑茜颖 . 基于深度循环网络结合上下文信息的血压预测 [J]. | 微电子学与计算机 , 2023 , 40 (7) : 10-17 . |
| MLA | 孙永莹 et al. "基于深度循环网络结合上下文信息的血压预测" . | 微电子学与计算机 40 . 7 (2023) : 10-17 . |
| APA | 孙永莹 , 郑茜颖 . 基于深度循环网络结合上下文信息的血压预测 . | 微电子学与计算机 , 2023 , 40 (7) , 10-17 . |
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针对肺部新冠病灶在医学成像中具有大小不均匀、多集中于肺部边缘且灰度与胸腔灰度相近的特点,提出一种基于U-Net改进的用于肺部轮廓分割和新冠病灶分割的网络模型.所提出的方法采用加深的编解码路径,使用带有残差连接的编码器子模块代替原始U-Net的标准卷积单元.为了提高高级特征的表征能力,在编码器和解码器中间加入自注意力机制,来学习特征的内在关系.整理一个用于分割训练的数据集,共2973张新冠肺炎患者的肺部CT图片.实验结果表明,所提出的网络在肺部轮廓分割实验的Dice系数和F1系数分别达到了98.70％和98.89％,在新冠病灶分割实验中分别达到了87.47％和87.81％,优于其他对比模型.
Keyword ：
U-Net U-Net 医学图像分割 医学图像分割 深度学习 深度学习 自注意力机制 自注意力机制
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| GB/T 7714 | 林培阳 , 郑茜颖 . 基于U-Net改进的肺部轮廓与新冠病灶分割网络 [J]. | 电视技术 , 2023 , 47 (1) : 8-15 . |
| MLA | 林培阳 et al. "基于U-Net改进的肺部轮廓与新冠病灶分割网络" . | 电视技术 47 . 1 (2023) : 8-15 . |
| APA | 林培阳 , 郑茜颖 . 基于U-Net改进的肺部轮廓与新冠病灶分割网络 . | 电视技术 , 2023 , 47 (1) , 8-15 . |
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As photovoltaic (PV) arrays are exposed to the outdoors year-round, they are susceptible to various faults. The shading condition, degradation or dust coverage can make fault signals more complex, forming compound faults. These faults can lead to a large loss of power generation or irreversible damage to the PV modules, and even fires in severe cases. Moreover, unknown fault types that have never been seen in the training set may occur at actual working conditions. Therefore, accurate diagnosis of various types of single and compound faults (closed-set faults) by considering the identification of unknown faults, namely open-set faults diagnosis, is crucial to improve the efficiency of operation and maintenance. A 1D VoVNet-SVDD based open-set fault diagnosis model for PV arrays is proposed. The model is a two-stage network model consisting of a 1D VoVNet network and a multi-classification Support Vector Data Description (SVDD) in series. The 1D VoVNet network automatically extracts fault features from the input original I-V curve data. These extracted fault features are then combined with environmental parameters to construct the SVDD model. The SVDD identifies known fault types by con-structing a hypersphere for each fault type. Fault types that are not classified into any of the hyperspheres are considered as unknown faults, enabling open-set diagnosis. The experimental results show that the proposed model can accurately classify the closed-set faults among the three designed testing tasks while identify unknown type faults. The comparison demonstrates that the proposed algorithm is superior to the compared models.
Keyword ：
Compound faults Compound faults Deep learning Deep learning Fault diagnosis Fault diagnosis Open -set Open -set Photovoltaic arrays Photovoltaic arrays
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| GB/T 7714 | Lin, Peijie , Guo, Feng , Lu, Xiaoyang et al. A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults [J]. | SOLAR ENERGY , 2023 , 267 . |
| MLA | Lin, Peijie et al. "A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults" . | SOLAR ENERGY 267 (2023) . |
| APA | Lin, Peijie , Guo, Feng , Lu, Xiaoyang , Zheng, Qianying , Cheng, Shuying , Lin, Yaohai et al. A compound fault diagnosis model for photovoltaic array based on 1D VoVNet-SVDD by considering unknown faults . | SOLAR ENERGY , 2023 , 267 . |
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