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学者姓名：丁治明
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摘要 ：
基于元路径嵌入的移动应用需求偏好分析方法
关键词 ：
概念模型 概念模型 嵌入学习 嵌入学习 元路径 元路径 移动应用 移动应用 用户需求偏好 用户需求偏好
引用：
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| GB/T 7714 | 宋蕊 , 李童 , 董鑫 et al. 基于元路径嵌入的移动应用需求偏好分析方法 [J]. | 宋蕊 , 2021 , 58 (4) : 749-762 . |
| MLA | 宋蕊 et al. "基于元路径嵌入的移动应用需求偏好分析方法" . | 宋蕊 58 . 4 (2021) : 749-762 . |
| APA | 宋蕊 , 李童 , 董鑫 , 丁治明 , 计算机研究与发展 . 基于元路径嵌入的移动应用需求偏好分析方法 . | 宋蕊 , 2021 , 58 (4) , 749-762 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Emergency path planning technology is one of the research hotspots of intelligent transportation systems. Due to the complexity of urban road networks and congested road conditions, emergency path planning is very difficult. Road congestion caused by urban emergencies directly affects the original road network structure. In this way, the static weight of the original road network is no longer suitable as the basis for path recommendation. To handle the dynamic situational road network, an equidistant grid emergency path planning framework will be designed. A novel situation grid road network model, based on situation information, is proposed and applied to an equidistant grid emergency path planning framework. A situational grid heuristic search will be proposed methodology based on this model, which can be used to detect the vehicles passing around the congestion area grid and the road to the destination in the shortest time. In the path planning methodology, a grid inspired search strategy based on quaternion function is included, which can make the algorithm converge to the target grid quickly. Three graph acceleration algorithms are proposed to improve the search efficiency of path planning algorithm. Finally, this paper will set up three experiments to verify our proposed method.
关键词 ：
emergency path emergency path heuristic search heuristic search path planning path planning transportation network transportation network
引用：
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| GB/T 7714 | Yang, Bowen , Yan, Jin , Cai, Zhi et al. A Novel Heuristic Emergency Path Planning Method Based on Vector Grid Map [J]. | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2021 , 10 (6) . |
| MLA | Yang, Bowen et al. "A Novel Heuristic Emergency Path Planning Method Based on Vector Grid Map" . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 10 . 6 (2021) . |
| APA | Yang, Bowen , Yan, Jin , Cai, Zhi , Ding, Zhiming , Li, Dongze , Cao, Yang et al. A Novel Heuristic Emergency Path Planning Method Based on Vector Grid Map . | ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION , 2021 , 10 (6) . |
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摘要 ：
How to detect and predict the critical situation in large-scale activities is a very important research issue. The existing researches of emergency prediction are mainly focus on the micro events in some specific fields. Applying existing results directly to predict the critical situation in large-scale activity is a big challenge. In this paper, we show a novel method to predict emergency based on historical data analysis. We integrate relevant research results into a unified spatiotemporal model. Firstly, constructing the historical spatiotemporal context time series based on historical activity data. Then, dividing the time series into time period and time window. Finally, exploiting the time series’ spatiotemporal patterns to predict the emergency of current activity. Experimental results show that the proposed method can achieve better prediction of large-scale activity emergencies in a specific venue. © 2021, Springer Nature Switzerland AG.
关键词 ：
Forecasting Forecasting Time series Time series
引用：
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| GB/T 7714 | Zhao, Zilin , Ding, Zhiming , Cao, Yang . A Method of Emergency Prediction Based on Spatiotemporal Context Time Series [C] . 2021 : 14-28 . |
| MLA | Zhao, Zilin et al. "A Method of Emergency Prediction Based on Spatiotemporal Context Time Series" . (2021) : 14-28 . |
| APA | Zhao, Zilin , Ding, Zhiming , Cao, Yang . A Method of Emergency Prediction Based on Spatiotemporal Context Time Series . (2021) : 14-28 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
本发明公开了一种基于图卷积的遥感图像道路提取方法，图卷积可以聚合相邻节点之间的特征信息，在节点较大邻域内提取特征，有效解决局部位置信息丢失问题。所设计的方法可以视为多任务学习，首先，利用CNN实现对遥感图像的特征提取，其次，在基于CNN所提取的道路特征基础上构建图结构模型，主要由节点和相应的边关系组成，将CNN分支所提取的道路特征信息视为节点，节点之间的差异度视作边，通过获取节点之间的关系来获取局部位置信息，本发明通过利用图卷积来解决卷积神经网络因为泛化效果而造成的道路局部位置信息丢失问题，从而能有效提高道路分割精度。
引用：
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| GB/T 7714 | 陶敏玉 , 迟远英 , 丁治明 et al. 一种基于图卷积的遥感图像道路提取方法 : CN202110058757.6[P]. | 2021-01-16 . |
| MLA | 陶敏玉 et al. "一种基于图卷积的遥感图像道路提取方法" : CN202110058757.6. | 2021-01-16 . |
| APA | 陶敏玉 , 迟远英 , 丁治明 , 杨博文 . 一种基于图卷积的遥感图像道路提取方法 : CN202110058757.6. | 2021-01-16 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
随着互联网和移动应用平台的快速发展,围绕移动应用所产生的海量用户数据已经成为精确分析用户需求偏好的重要数据源.尽管已有不少学者从这些数据中分析和挖掘用户需求,但现有的方法通常只研究了数据的少数维度的特征,未能有效地挖掘多维移动应用信息以及他们之间的关联.提出一种基于元路径嵌入的移动应用需求偏好分析方法,能够为用户进行个性化移动应用推荐.具体地,首先分析移动应用的文本信息中的语义主题,挖掘用户需求偏好的分析维度.其次,将移动应用信息的语义特征构建了一个融合移动应用多维信息的概念模型,涵盖了能够表征用户需求偏好的多维度数据.基于概念模型的语义,设计了一组有意义的元路径集合,以精确地捕捉用户需求偏好...
关键词 ：
用户需求偏好 用户需求偏好 概念模型 概念模型 嵌入学习 嵌入学习 移动应用 移动应用 元路径 元路径
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 | 宋蕊 , 李童 , 董鑫 et al. 基于元路径嵌入的移动应用需求偏好分析方法 [J]. | 计算机研究与发展 , 2021 , 58 (04) : 749-762 . |
| MLA | 宋蕊 et al. "基于元路径嵌入的移动应用需求偏好分析方法" . | 计算机研究与发展 58 . 04 (2021) : 749-762 . |
| APA | 宋蕊 , 李童 , 董鑫 , 丁治明 . 基于元路径嵌入的移动应用需求偏好分析方法 . | 计算机研究与发展 , 2021 , 58 (04) , 749-762 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
The reliable road network plays a vital role in many applications. Owing to the development of remote sensing technology and the success of deep learning in computer vision, automatic road extraction from remote sensing images is a research hotspot in recent years. However, due to the complicated image background and special road structure, the results of automatic road extraction are still far from perfect. In this paper, we propose a road segmentation network that is designed based on improved U-Net, which contains an encoder and a decoder. First, the recurrent criss-cross attention module (CCA) is introduced into the encoder to obtain long-range contextual dependencies with a relatively small number of computations and parameters, which results in better understanding and expression of image information. Second, we propose the attention-based multi-scale feature fusion module (AMS) to resolve the problem of different shapes and widths of the roads, which is placed between the encoder and decoder and uses attention mechanisms to guide multi-scale information fusion. Experimental on the Massachusetts Roads Dataset show that the proposed method achieves better performance in road extraction than other methods in terms of precision, recall, F1-score, and accuracy. © 2021, Springer Nature Switzerland AG.
关键词 ：
Decoding Decoding Extraction Extraction Feature extraction Feature extraction Image processing Image processing Recurrent neural networks Recurrent neural networks Remote sensing Remote sensing Roads and streets Roads and streets Signal encoding Signal encoding
引用：
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| GB/T 7714 | Tao, Minyu , Ding, Zhiming , Cao, Yang . Attention U-Net for Road Extraction in Remote Sensing Images [C] . 2021 : 153-164 . |
| MLA | Tao, Minyu et al. "Attention U-Net for Road Extraction in Remote Sensing Images" . (2021) : 153-164 . |
| APA | Tao, Minyu , Ding, Zhiming , Cao, Yang . Attention U-Net for Road Extraction in Remote Sensing Images . (2021) : 153-164 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Urban rail transit stations are the key nodes of urban rail transit network. Identifying and analyzing land use characteristics around urban rail transit stations can significantly contribute to urban rail transportation operation and management. Therefore, a visualization method of land use characteristics around urban rail transit stations based on POI is proposed in this paper. In the proposed method, first, the Voronoi diagram is used to determine coverage of urban rail transit stations and each POI is put in a coverage area based on their physical location. Then, topic-oriented hierarchical POIs of each urban rail transit station are extracted based on skyline idea. Finally, the land use characteristics around an urban rail transit station are visualized based on the extracted hierarchical POIs. We carried out two case studies and a quality evaluation. By using realistic data from Beijing rail transit in order to validate the method proposed in this paper. Results show that our method can clarify various situations of land use of urban rail transit stations and may provide support for the application of transportation model technology.
关键词 ：
Correlation Correlation Data visualization Data visualization land use land use Public transportation Public transportation Rails Rails skyline query skyline query Urban areas Urban areas urban rail transit station urban rail transit station Visual analysis Visual analysis Visualization Visualization Voronoi diagram Voronoi diagram
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| GB/T 7714 | Cai, Zhi , Sun, Gongyu , Su, Xing et al. Visual Analysis of Land Use Characteristics Around Urban Rail Transit Stations [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2021 , 22 (10) : 6221-6231 . |
| MLA | Cai, Zhi et al. "Visual Analysis of Land Use Characteristics Around Urban Rail Transit Stations" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22 . 10 (2021) : 6221-6231 . |
| APA | Cai, Zhi , Sun, Gongyu , Su, Xing , Li, Tong , Guo, Limin , Ding, Zhiming . Visual Analysis of Land Use Characteristics Around Urban Rail Transit Stations . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2021 , 22 (10) , 6221-6231 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Accurate traffic flow prediction is becoming increasingly important for transportation planning, control, management, and information services of successful. Numerous existing models focus on short-term traffic forecasts, but effective long-term forecasting of traffic flows have become a challenging issue in recent years. To solve this problem, this paper proposes a deep learning architecture which consisting of two parts: the long short-term memory encoder-decoder structure at the bottom and the calibration layer at the top. In the encoder-decoder model, we propose an hard attention mechanism based on learning similar patterns to enhance neuronal memory and reduce the accumulation of error propagation. To correct some of the missing details, we design a control gate in the calibration layer to learn the predicted data in groups according to different forms. The proposed method is evaluated on real-world datasets and compared with other state-of-the-art methods. It is verified that our model can accurately learn local feature and long-term dependence, and has better accuracy and stability in long-term sequence prediction.
关键词 ：
attention attention Calibration Calibration Deep learning Deep learning encoder-decoder encoder-decoder Forecasting Forecasting Freeway traffic flow Freeway traffic flow long-term prediction long-term prediction Market research Market research Neural networks Neural networks Prediction algorithms Prediction algorithms Predictive models Predictive models similar pattern similar pattern
引用：
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| GB/T 7714 | Wang, Zhumei , Su, Xing , Ding, Zhiming . Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture [J]. | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2021 , 22 (10) : 6561-6571 . |
| MLA | Wang, Zhumei et al. "Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture" . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22 . 10 (2021) : 6561-6571 . |
| APA | Wang, Zhumei , Su, Xing , Ding, Zhiming . Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture . | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS , 2021 , 22 (10) , 6561-6571 . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Emergency path planning technology is one of the hot research points in intelligent transportation systems. There are many methodologies and applications in emergency path planning. However, due to the complexity of the urban network and crowded road conditions, the difficulty of emergency path planning. The objective of emergency path planning is to get the vehicle out of the emergency areas and to its destination in the shortest time. Road congestion caused by emergency situations in cities directly affects the original road network structure. Then the weight of the original road network is no longer suitable as a basis for path recommendation and the value of edges of weight will change over time. To handle the dynamic road network, a novel situational time-stamp heuristic search algorithm (STH) is introduced for the situation space. This algorithm can effectively solve the problem of diversity of situational networks. STH can build a heuristic that adapts to time changes based on the map refresh time, and ensures that the path given in the time window T is optimal. Moreover, STH can give a pruning strategy according to the search time window T, which significantly improves the efficiency of the algorithm. Finally, the path planned by STH is better than the baseline algorithm. © Published under licence by IOP Publishing Ltd.
关键词 ：
Big data Big data Heuristic algorithms Heuristic algorithms Heuristic methods Heuristic methods Intelligent systems Intelligent systems Intelligent vehicle highway systems Intelligent vehicle highway systems Motor transportation Motor transportation Roads and streets Roads and streets
引用：
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| GB/T 7714 | Yang, Bowen , Yuan, Lei , Yan, Jin et al. A Novel Heuristic Method for Emergency Path Planning Based on Dynamic Spatial-Temporal Characteristics Map [C] . 2021 . |
| MLA | Yang, Bowen et al. "A Novel Heuristic Method for Emergency Path Planning Based on Dynamic Spatial-Temporal Characteristics Map" . (2021) . |
| APA | Yang, Bowen , Yuan, Lei , Yan, Jin , Ding, Zhiming , Cai, Zhi , Guo, Limin . A Novel Heuristic Method for Emergency Path Planning Based on Dynamic Spatial-Temporal Characteristics Map . (2021) . |
| 导入链接 | NoteExpress RIS BibTex |
摘要 ：
Students have produced a large number of data in the teaching life of colleges and universities. At present, the development trend of university data is to gradually form a high-dimensional data storage system composed of student status information, educational administration information, behavior information, etc. It is of great significance to make use of the existing data of students in Colleges and universities to carry out deep-seated and personalized data mining for college education decision-making, implementation of education and teaching programs, and evaluation of education and teaching. Student portrait is the extension of user portrait in the application of education data mining. According to the data of students’ behavior in school, a labeled student model is abstracted. To address above problems, a hybrid neural network model is designed and implemented to mine the data of college students and build their portraits, so as to help students’ academic development and improve the quality of college teaching. In this paper, experiments are carried out on real datasets (the basic data of a college’s students in Beijing and the behavior data in the second half of 2018–2019 academic year). The results show that the hybrid neural network model is effective. © 2021, Springer Nature Switzerland AG.
关键词 ：
Clustering algorithms Clustering algorithms Data mining Data mining Decision making Decision making Digital storage Digital storage Education computing Education computing Neural networks Neural networks Students Students
引用：
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| GB/T 7714 | Ding, Zhiming , Li, Xuyang . College Students’ Portrait Technology Based on Hybrid Neural Network [C] . 2021 : 165-183 . |
| MLA | Ding, Zhiming et al. "College Students’ Portrait Technology Based on Hybrid Neural Network" . (2021) : 165-183 . |
| APA | Ding, Zhiming , Li, Xuyang . College Students’ Portrait Technology Based on Hybrid Neural Network . (2021) : 165-183 . |
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