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学者姓名：郑茜颖
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Abstract ：
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 等. "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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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 等. "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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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 等. "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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针对VINS-Mono在室内移动机器人场景中定位精度发散较快的问题,在原系统中融合了轮式里程计的数据,提出了基于VINS-Mono的室内移动机器人定位系统.系统对轮式里程计和陀螺仪数据积分,提供精确运动约束;结合预积分和运动恢复结构(SFM)的运算结果,用松耦合方法计算单目尺度和陀螺仪偏差;用非线性最小二乘优化算法融合预积分数据和相机数据,获得机器人位姿、特征点深度及陀螺仪偏差的最优估计.在公开数据集上进行仿真实验,结果表明:改进后的系统定位精度优于原系统.
Keyword ：
同时定位与建图 同时定位与建图 多传感器融合 多传感器融合 运动估计 运动估计
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| GB/T 7714 | 曾超 , 郑茜颖 , 程树英 . 基于VINS-Mono的室内机器人定位系统 [J]. | 传感器与微系统 , 2022 , 41 (11) : 85-88 . |
| MLA | 曾超 et al. "基于VINS-Mono的室内机器人定位系统" . | 传感器与微系统 41 . 11 (2022) : 85-88 . |
| APA | 曾超 , 郑茜颖 , 程树英 . 基于VINS-Mono的室内机器人定位系统 . | 传感器与微系统 , 2022 , 41 (11) , 85-88 . |
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三维点云数据的采集过程受到仪器精度、人为扰动和复杂环境等因素影响,存在大量的噪点并且分布不均匀,造成基于点云数据的重建模型尖锐特征模糊.对此,提出一种保持细节特征的点云去噪算法以提高点云模型的精度.该算法首先通过八叉树建立点云的拓扑关系,加快点云的邻域搜索速度;其次,采用主成分分析法估算点云的法向量;最后,将双边滤波器与加权局部最优投影算法结合,实现对点云的去噪均匀化处理.实验结果表明,所提算法不仅能很好地去除点云数据中的噪点,而且同时保留了点云重建模型的细节特征.
Keyword ：
主成分分析 主成分分析 保持特征 保持特征 八叉树 八叉树 加权局部最优投影 加权局部最优投影 点云去噪 点云去噪
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| GB/T 7714 | 郑一帆 , 郑茜颖 , 程树英 . 保持细节特征的点云去噪算法 [J]. | 电视技术 , 2022 , 46 (12) : 29-34 . |
| MLA | 郑一帆 et al. "保持细节特征的点云去噪算法" . | 电视技术 46 . 12 (2022) : 29-34 . |
| APA | 郑一帆 , 郑茜颖 , 程树英 . 保持细节特征的点云去噪算法 . | 电视技术 , 2022 , 46 (12) , 29-34 . |
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Objective: Medical image analysis is particularly important for doctors to differential diagnosis of diseases. Due to the outbreak of COVID-19, how to diagnose COVID-19 accurately has become a key issue. High-resolution lung CT images can provide more diagnostic information, so there is an urgent need to develop a super-resolution method to improve the resolution of medical images. Methods: In this paper, a method based on double paths with residual information distillation for medical images super resolution (DRIDSR) is established. In the low-frequency path, shallow convolutional network is used to get low-frequency features, while in the high-frequency path, a residual information distillation module (RIDM) is designed to obtain clearer high-frequency features. RIDM cascades multiple residual blocks, and uses the output of each residual block as the input of IDB for further information distillation. Finally, it merges the information left by multiple IDBs as output. Results: The proposed method is tested on the public dataset COVID-CT. The DRIDSR reconstruction quality of the algorithm is higher than that of the SRCNN, ESPCN, VDSR, IMDN and PAN method (+2.21 dB, +2.41 dB, +1.42 dB, +0.43 dB, +0.54 dB improvement, respectively) at x 3 upscale factor and (+2.35 dB, +2.17 dB, +1.59 dB, +0.48 dB, +0.56 dB increase, respectively) at x4 upscale factor. While the number of parameters and analysis time of our model are reduced. Conclusions: It is demonstrated that DRIDSR network can obtain better performance and better HR medical images than several state-of-the-art SR methods in terms of objective indicators and subjective evaluation.
Keyword ：
Convolutional neural networks Convolutional neural networks COVID-19 COVID-19 CT image CT image Information distillation Information distillation Super resolution Super resolution
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| GB/T 7714 | Chen, Yihan , Zheng, Qianying , Chen, Jiansen . Double paths network with residual information distillation for improving lung CT image super resolution [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2022 , 73 . |
| MLA | Chen, Yihan et al. "Double paths network with residual information distillation for improving lung CT image super resolution" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 73 (2022) . |
| APA | Chen, Yihan , Zheng, Qianying , Chen, Jiansen . Double paths network with residual information distillation for improving lung CT image super resolution . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2022 , 73 . |
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本发明涉及一种基于卷积神经网络和二维气象矩阵的光伏功率预测方法。该方法提出了一种由一维卷积神经网络和二维卷积神经网络构成的混合卷积神经网络模型，以此模型进行光伏发电功率的预测。以待测小时的气象参数为气象特征值通过灰色关联分析算法在电站的历史数据集中寻找待测小时的相似小时数据。然后将这些数据中的多元气象因素转化为二维气象矩阵，便于卷积神经网络深度挖掘气象因素和光伏功率输出的非线性关系。最后，将这些二维气象矩阵作为模型的输入，预测各个小时的发电功率。本发明能够快速准确对光伏电站的发电功率进行预测。
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| GB/T 7714 | 林培杰 , 程树英 , 陈振祥 et al. 基于卷积神经网络和二维气象矩阵的光伏功率预测方法 : CN202110330964.2[P]. | 2021-03-29 . |
| MLA | 林培杰 et al. "基于卷积神经网络和二维气象矩阵的光伏功率预测方法" : CN202110330964.2. | 2021-03-29 . |
| APA | 林培杰 , 程树英 , 陈振祥 , 陈志聪 , 吴丽君 , 郑茜颖 . 基于卷积神经网络和二维气象矩阵的光伏功率预测方法 : CN202110330964.2. | 2021-03-29 . |
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针对多晶硅太阳电池片裂纹检测中背景干扰和对比度较低等检测难点,提出基于微分匹配均值和匹配滤波器的裂纹检测算法.首先用基于高斯函数的匹配滤波器从多角度、多方向提取裂纹空间特征的算子,增强图像;同时用微分匹配均值滤波器区分裂纹和晶粒结构,有效提取裂纹;然后根据阈值公式筛选裂纹结构;最后用Hough变换去除栅线,并采用形态学方法提取出完整裂纹结构.实验结果表明,提出的裂纹检测算法能够精确、有效地检测出多晶硅太阳电池片的裂纹,解决了紊乱的晶粒背景干扰检测的问题,相比其他几种算法兼具鲁棒性和快速性.
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| GB/T 7714 | 张玉婷 , 郑茜颖 , 俞金玲 . 基于匹配滤波器的多晶硅电池片裂纹检测方法 [J]. | 电源技术 , 2021 , 45 (11) : 1486-1489 . |
| MLA | 张玉婷 et al. "基于匹配滤波器的多晶硅电池片裂纹检测方法" . | 电源技术 45 . 11 (2021) : 1486-1489 . |
| APA | 张玉婷 , 郑茜颖 , 俞金玲 . 基于匹配滤波器的多晶硅电池片裂纹检测方法 . | 电源技术 , 2021 , 45 (11) , 1486-1489 . |
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类比生成是计算机生成自然和创造性音乐作品的一种关键方法.使用类比生成能够将高层次的音乐特征从一个作品转移到另一个.为了在进行高效类比的同时也能够控制音乐的特征属性,提出了一种新型的显式特征解耦的编解码模型,由编码器解开以和弦为条件的音乐片段的音高和节奏表示,并用解码器还原成原始的音乐.在进行音乐类比生成时,该模型能够使一个作品借用其他作品的表现形式,用不同的音高轮廓、节奏模式进行创作.另外,得益于可视化的特征编码方式,该模型可以对不同的特征属性进行直观控制.
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| GB/T 7714 | 黄润泽 , 郑茜颖 , 周海芳 . 交互式音乐类比生成 [J]. | 计算机应用研究 , 2021 , 38 (9) : 2609-2613 . |
| MLA | 黄润泽 et al. "交互式音乐类比生成" . | 计算机应用研究 38 . 9 (2021) : 2609-2613 . |
| APA | 黄润泽 , 郑茜颖 , 周海芳 . 交互式音乐类比生成 . | 计算机应用研究 , 2021 , 38 (9) , 2609-2613 . |
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