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学者姓名：张勇
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摘要 ：
The traffic data corrupted by noise and missing entries often lead to the poor performance of Intelligent Transportation Systems (ITS), such as the bad congestion prediction and route guidance. How to efficiently impute the traffic data is an urgent problem. As a classic deep learning method, Generative Adversarial Network (GAN) achieves remarkable success in image recovery fields, which opens up a new way for the traffic data imputation. In this paper, we propose a novel spatio-temporal GAN model for the traffic data imputation (STGAN). Firstly, we design the generative loss and center loss, which not only minimizes the reconstructed errors of the imputed entries, but also ensures each imputed entry and its neighbors conform to the local spatio-temporal distribution. Then, the discriminator uses the convolution neural network classifier to judge whether the imputed matrix conforms to the global spatio-temporal distribution. As for the network architecture of the generator, we introduce the skip-connection to keep all well preserved data unchanged, and employ the dilated convolution to capture the spatio-temporal correlation in the traffic data. The experimental results show that our proposed method obviously outperforms other competitive traffic data imputation methods.
关键词 ：
Generative adversarial networks Generative adversarial networks Task analysis Task analysis Data models Data models Generators Generators Image reconstruction Image reconstruction Matrix decomposition Matrix decomposition traffic data imputation traffic data imputation Data mining Data mining Correlation Correlation generative adversarial network generative adversarial network
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| GB/T 7714 | Yuan, Ye , Zhang, Yong , Wang, Boyue et al. STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation [J]. | IEEE TRANSACTIONS ON BIG DATA , 2023 , 9 (1) : 200-211 . |
| MLA | Yuan, Ye et al. "STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation" . | IEEE TRANSACTIONS ON BIG DATA 9 . 1 (2023) : 200-211 . |
| APA | Yuan, Ye , Zhang, Yong , Wang, Boyue , Peng, Yuan , Hu, Yongli , Yin, Baocai . STGAN: Spatio-Temporal Generative Adversarial Network for Traffic Data Imputation . | IEEE TRANSACTIONS ON BIG DATA , 2023 , 9 (1) , 200-211 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Accurately estimating the number of individuals contained in an image is the purpose of the crowd counting. It has always faced two major difficulties: uneven distribution of crowd density and large span of head size. Focusing on the former, most CNN-based methods divide the image into multiple patches for processing, ignoring the connection between the patches. For the latter, the multi-scale feature fusion method using feature pyramid ignores the matching relationship between the head size and the hierarchical features. In response to the above issues, we propose a crowd counting network named CCST based on swin transformer, and tailor a feature adaptive fusion regression head called FAFHead. Swin transformer can fully exchange information within and between patches, and effectively alleviate the problem of uneven distribution of crowd density. FAFHead can adaptively fuse multi-level features, improve the matching relationship between head size and feature pyramid hierarchy, and relief the problem of large span of head size available. Experimental results on common datasets show that CCST has better counting performance than all weakly supervised counting works and great majority of popular density map-based fully supervised works.
关键词 ：
Large span of head size Large span of head size Crowd counting Crowd counting Uneven distribution of crowd density Uneven distribution of crowd density Feature adaptive fusion Feature adaptive fusion Transformer Transformer
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| GB/T 7714 | Li, Bo , Zhang, Yong , Xu, Haihui et al. CCST: crowd counting with swin transformer [J]. | VISUAL COMPUTER , 2022 , 39 (7) : 2671-2682 . |
| MLA | Li, Bo et al. "CCST: crowd counting with swin transformer" . | VISUAL COMPUTER 39 . 7 (2022) : 2671-2682 . |
| APA | Li, Bo , Zhang, Yong , Xu, Haihui , Yin, Baocai . CCST: crowd counting with swin transformer . | VISUAL COMPUTER , 2022 , 39 (7) , 2671-2682 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Finding students at high risk of poor academic performance as early as possible plays an important role in improving education quality. To do so, most existing studies have used the traditional machine learning algorithms to predict students' achievement based on their behavior data, from which behavior features are extracted manually thanks to expert experience and knowledge. However, owing to an increase in the varieties and overall volume of behavioral data, it has become more and more challenging to identify high-quality handcrafted features. In this paper, we propose an end-to-end deep learning model that automatically extracts features from students' multi-source heterogeneous behavior data to predict academic performance. The key innovation of this model is that it uses long short-term memory networks to capture inherent time-series features for each type of behavior, and it takes two-dimensional convolutional networks to extract correlation features among different behaviors. We conducted experiments with four types of daily behavior data from students of the university in Beijing. The experimental results demonstrate that the proposed deep model method outperforms several machine learning algorithms.
关键词 ：
Academic performance prediction Academic performance prediction LSTM LSTM 2DCNN 2DCNN Time-series features Time-series features Correlation features Correlation features
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| GB/T 7714 | Li, Xiaoyong , Zhang, Yong , Cheng, Huimin et al. Student achievement prediction using deep neural network from multi-source campus data [J]. | COMPLEX & INTELLIGENT SYSTEMS , 2022 , 8 (6) : 5143-5156 . |
| MLA | Li, Xiaoyong et al. "Student achievement prediction using deep neural network from multi-source campus data" . | COMPLEX & INTELLIGENT SYSTEMS 8 . 6 (2022) : 5143-5156 . |
| APA | Li, Xiaoyong , Zhang, Yong , Cheng, Huimin , Li, Mengran , Yin, Baocai . Student achievement prediction using deep neural network from multi-source campus data . | COMPLEX & INTELLIGENT SYSTEMS , 2022 , 8 (6) , 5143-5156 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
随着信息技术的高速发展,各种数字档案数据量出现了爆炸式的增长。如何合理地挖掘分析档案数据,提升对新收录档案智能管理的效果已成为一个亟需解决的问题。现有的档案数据分类方法是面向管理需求的人工分类,这种人工分类的方式效率低下,忽略了档案固有的内容信息。此外,对于档案信息发现和利用来说,需进一步挖掘分析档案数据内容之间的关联性。面向档案智能管理的需求,从档案数据的文本内容角度出发,对人工分类的档案进行进一步分析。采用LDA模型提取文档的主题特征向量,进而用K-means算法对档案的主题特征进行聚类,得到档案间的关联。针对新收录档案数据的分类问题,采用现有档案数据,有监督的训练FastText深度学习...
关键词 ：
FastText文本分类 FastText文本分类 档案管理 档案管理 LDA特征表示 LDA特征表示 文本聚类 文本聚类
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| GB/T 7714 | 霍光煜 , 张勇 , 孙艳丰 et al. 基于语义的档案数据智能分类方法研究 [J]. | 计算机工程与应用 , 2021 , 57 (06) : 247-253 . |
| MLA | 霍光煜 et al. "基于语义的档案数据智能分类方法研究" . | 计算机工程与应用 57 . 06 (2021) : 247-253 . |
| APA | 霍光煜 , 张勇 , 孙艳丰 , 尹宝才 . 基于语义的档案数据智能分类方法研究 . | 计算机工程与应用 , 2021 , 57 (06) , 247-253 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
基于语义的档案数据智能分类方法研究
关键词 ：
文本聚类 文本聚类 档案管理 档案管理 FastText文本分类 FastText文本分类 LDA特征表示 LDA特征表示
引用：
复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 霍光煜 , 张勇 , 孙艳丰 et al. 基于语义的档案数据智能分类方法研究 [J]. | 霍光煜 , 2021 , 57 (6) : 247-253 . |
| MLA | 霍光煜 et al. "基于语义的档案数据智能分类方法研究" . | 霍光煜 57 . 6 (2021) : 247-253 . |
| APA | 霍光煜 , 张勇 , 孙艳丰 , 尹宝才 , 计算机工程与应用 . 基于语义的档案数据智能分类方法研究 . | 霍光煜 , 2021 , 57 (6) , 247-253 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于空间自注意力图卷积循环神经网络的交通数据修复方法，全连接层作为输入层将输入映射到一个高维空间提高模型的表达能力；双向图卷积门控循环单元是将门控循环单元中的全连接层替换为图卷积得到的，它能够同时建模局部空间相关性和时间相关性；多头空间自注意力模块用于捕获路网的隐含空间相关性，同时还能从全局聚合各个节点的信息；卷积层作为输出层用于对特征维度进行衰减。本发明利用图卷积建模局部空间相关性；利用门控循环单元学习交通数据的动态变化，捕获时间相关性；此外，考虑到交通状况受到许多潜在因素的影响，本发明采用多头空间自注意力机制从全局来建模交通数据的隐含空间相关性。
引用：
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| GB/T 7714 | 张勇 , 林锋 , 胡永利 et al. 一种基于空间自注意力图卷积循环神经网络的交通数据修复方法 : CN202110182167.4[P]. | 2021-02-09 . |
| MLA | 张勇 et al. "一种基于空间自注意力图卷积循环神经网络的交通数据修复方法" : CN202110182167.4. | 2021-02-09 . |
| APA | 张勇 , 林锋 , 胡永利 , 尹宝才 . 一种基于空间自注意力图卷积循环神经网络的交通数据修复方法 : CN202110182167.4. | 2021-02-09 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
一种基于任意角度人体图像的正面姿态估计方法属于计算机视觉领域，本发明包括一种多角度人体图像数据集的制作以及针对所提出数据集进行二维人体图像的正面姿态估计的算法设计两部分。数据集制作部分主要通过设计一整套数据的采集和数据的处理的方法，通过数据集的制作为算法设计提供数据支持。算法设计部分主要是通过对目前主流的深度学习算法进行改进，以实现任意角度人体图像的正面姿态估计。本发明可以完成任意角度图像的正面姿态估计，即使对人体自遮挡非常严重的背面图像，或者有部分人体缺失侧面图像也可以有较好的表现。
引用：
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| GB/T 7714 | 尹宝才 , 唐永正 , 张勇 et al. 一种基于任意角度人体图像的正面姿态估计方法 : CN202110210298.9[P]. | 2021-02-11 . |
| MLA | 尹宝才 et al. "一种基于任意角度人体图像的正面姿态估计方法" : CN202110210298.9. | 2021-02-11 . |
| APA | 尹宝才 , 唐永正 , 张勇 , 陈路飞 , 尹禹化 . 一种基于任意角度人体图像的正面姿态估计方法 : CN202110210298.9. | 2021-02-11 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Vehicle reidentification refers to the mission of matching vehicles across nonoverlapping cameras, which is one of the critical problems of the intelligent transportation system. Due to the resemblance of the appearance of the vehicles on road, traditional methods could not perform well on vehicles with high similarity. In this paper, we utilize hypergraph representation to integrate image features and tackle the issue of vehicles re-ID via hypergraph learning algorithms. A feature descriptor can only extract features from a single aspect. To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. The method proposed in this paper uses hypergraph optimization to learn about the similarity between the query image and images in the library. By using the pair and higher-order relationship between query objects and image library, the similarity measurement method is improved compared to direct matching. The experiments conducted on the image library constructed in this paper demonstrates the effectiveness of using multifeature hypergraph fusion and the spatiotemporal correlation model to address issues in vehicle reidentification. © 2021 Wang Li et al.
关键词 ：
Vehicles Vehicles Cameras Cameras Intelligent systems Intelligent systems Learning algorithms Learning algorithms Image enhancement Image enhancement
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| GB/T 7714 | Li, Wang , Yong, Zhang , Wei, Yuan et al. Vehicle Reidentification via Multifeature Hypergraph Fusion [J]. | International Journal of Digital Multimedia Broadcasting , 2021 , 2021 . |
| MLA | Li, Wang et al. "Vehicle Reidentification via Multifeature Hypergraph Fusion" . | International Journal of Digital Multimedia Broadcasting 2021 (2021) . |
| APA | Li, Wang , Yong, Zhang , Wei, Yuan , Hongxing, Shi . Vehicle Reidentification via Multifeature Hypergraph Fusion . | International Journal of Digital Multimedia Broadcasting , 2021 , 2021 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
针对理工科课程在教学内容、方式上实施"润物细无声"式课程思政的难度,以计算机图形学为例,提出课程思政建设总体思路,在阐述具体课程思政建设过程及结果的基础上,凝练总体建设原则,给出一般化的理工科课程思政建设策略,为在理工科院校更广泛、更有效地开展课程思政建设提供思路。
关键词 ：
内涵与外延 内涵与外延 理工科课程思政建设 理工科课程思政建设 科学方法论 科学方法论 计算机图形学 计算机图形学 课程思政 课程思政
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| GB/T 7714 | 孔德慧 , 李敬华 , 王立春 et al. 基于计算机图形学教学实践的理工科课程思政建设研究 [J]. | 计算机教育 , 2021 , (09) : 15-18 . |
| MLA | 孔德慧 et al. "基于计算机图形学教学实践的理工科课程思政建设研究" . | 计算机教育 09 (2021) : 15-18 . |
| APA | 孔德慧 , 李敬华 , 王立春 , 张勇 , 孙艳丰 . 基于计算机图形学教学实践的理工科课程思政建设研究 . | 计算机教育 , 2021 , (09) , 15-18 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明涉及一种基于多任务学习的实时人脸检测及头部姿态估计方法，用于解决头部姿态估计模型在同时估计多人头部姿态时的低效性，以及存在大量冗余计算的问题。具体包括特征提取网络以及四个支路组成，特征提取网络用于提取输入图片的4个不同层级的语义信息，每个层级的语义信息送入到对应支路当中。每个支路用于对不同层级的语义信息进行人脸检测及头部姿态估计，四条支路输出结果即为最终的人脸检测及对应的头部位姿估计结果。同时设计了多任务损失函数来评判模型的收敛，包含人脸检测的损失及头部姿态估计损失两部分。本发明在估计多人头部姿态时，效率有了巨大的提升。
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| GB/T 7714 | 尹宝才 , 陈世存 , 张勇 et al. 一种基于多任务学习的实时人脸检测及头部姿态估计方法 : CN202110093339.0[P]. | 2021-01-22 . |
| MLA | 尹宝才 et al. "一种基于多任务学习的实时人脸检测及头部姿态估计方法" : CN202110093339.0. | 2021-01-22 . |
| APA | 尹宝才 , 陈世存 , 张勇 , 唐永正 , 苗壮壮 . 一种基于多任务学习的实时人脸检测及头部姿态估计方法 : CN202110093339.0. | 2021-01-22 . |
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