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学者姓名：於志勇
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Abstract ：
Accurate monitoring of urban waterlogging contributes to the city's normal operation and the safety of residents' daily travel. However, due to feedback delays or high costs, existing methods make large-scale, fine-grained waterlogging monitoring impossible. A common method is to forecast the city's global waterlogging status using its partial waterlogging data. This method has two challenges: first, existing predictive algorithms are either driven by knowledge or data alone; and second, the partial waterlogging data is not collected selectively, resulting in poor predictions. To overcome the aforementioned challenges, this paper proposes a framework for large-scale and fine-grained spatiotemporal waterlogging monitoring based on the opportunistic sensing of limited bus routes. This framework follows the Sparse Crowdsensing and mainly comprises a pair of iterative predictor and selector. The predictor uses the collected waterlogging status and the predicted status of the uncollected area to train the graph convolutional neural network. It combines both knowledge-driven and data-driven approaches and can be used to forecast waterlogging status in all regions for the upcoming term. The selector consists of a two-stage selection procedure that can select valuable bus routes while satisfying budget constraints. The experimental results on real waterlogging and bus routes in Shenzhen show that the proposed framework could easily perform urban waterlogging monitoring with low cost, high accuracy, wide coverage, and fine granularity.
Keyword ：
active learning active learning graph convolutional network graph convolutional network route selection route selection sparse crowdsensing sparse crowdsensing waterlogging prediction waterlogging prediction
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| GB/T 7714 | Wang, Jingbin , Zhang, Weijie , Yu, Zhiyong et al. Route selection for opportunity-sensing and prediction of waterlogging [J]. | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (4) . |
| MLA | Wang, Jingbin et al. "Route selection for opportunity-sensing and prediction of waterlogging" . | FRONTIERS OF COMPUTER SCIENCE 18 . 4 (2024) . |
| APA | Wang, Jingbin , Zhang, Weijie , Yu, Zhiyong , Huang, Fangwan , Zhu, Weiping , Chen, Longbiao . Route selection for opportunity-sensing and prediction of waterlogging . | FRONTIERS OF COMPUTER SCIENCE , 2024 , 18 (4) . |
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随着城市汽车数量的持续增长，街道停车难已经成为一个热点问题。解决街道停车问题的关键在于准确预测街道未来的停车位信息。移动群智感知方式(CrowdSensing)通过在车辆上安装声呐以感知路边的停车位情况，是一种低成本、高效益的感知停车位的方式，然而这种方式感知的停车位数据在时间上存在高稀疏性问题，传统模型无法直接用于预测。针对此问题，提出了一种基于Transformer的停车位序列补全和预测网络，此网络通过编码器生成缺失停车位序列的记忆，进而解码器以自回归的方式补全停车位序列中缺失的部分，同时预测出未来的停车位信息。实验结果表明，所提方法在两个高缺失的街道停车位数据集上的补全和预测效果都优于传统的机器学习和深度学习方法。
Keyword ：
数据补全 数据补全 时序预测 时序预测 机器学习 机器学习 深度学习 深度学习 街道停车位 街道停车位
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| GB/T 7714 | 林滨伟 , 於志勇 , 黄昉菀 et al. 基于Transformer的街道停车位数据补全和预测 [J]. | 计算机科学 , 2024 , 51 (04) : 165-173 . |
| MLA | 林滨伟 et al. "基于Transformer的街道停车位数据补全和预测" . | 计算机科学 51 . 04 (2024) : 165-173 . |
| APA | 林滨伟 , 於志勇 , 黄昉菀 , 郭贤伟 . 基于Transformer的街道停车位数据补全和预测 . | 计算机科学 , 2024 , 51 (04) , 165-173 . |
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随着物联网的发展，众多传感器采集到大量具有丰富数据相关性的时间序列，为各种数据挖掘应用提供强大的数据支持。然而，一些客观或主观原因(如设备故障、稀疏感知等)往往会造成采集到的数据出现不同程度的缺失。虽然已有很多方法被提出用于解决这一问题，但这些方法在数据相关性方面或考虑不够全面，或计算成本过高。而且，现有方法仅关注对缺失值的补全，未能兼顾下游应用。针对上述不足，设计了一种兼顾补全与预测任务的双通道回声状态网络。两个通道的网络虽共用输入层，但具有各自的储备池和输出层。两者最大的区别是左/右通道的输出层分别表示输入层前/后一个时刻对应的目标值或预补值。最后将两个通道的估计值进行融合，充分利用来自缺失时刻之前和之后的数据相关性以进一步提升性能。两种缺失现象下(随机缺失和分段缺失)不同缺失率的实验结果表明，所提模型无论是在补全精度还是预测精度上都优于目前流行的各类方法。
Keyword ：
单步预测 单步预测 双通道ESN 双通道ESN 外生变量 外生变量 数据相关性 数据相关性 时间序列 时间序列 缺失补全 缺失补全
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| GB/T 7714 | 郑伟楠 , 於志勇 , 黄昉菀 . 基于双通道回声状态网络的时间序列补全及单步预测 [J]. | 计算机科学 , 2024 , 51 (03) : 128-134 . |
| MLA | 郑伟楠 et al. "基于双通道回声状态网络的时间序列补全及单步预测" . | 计算机科学 51 . 03 (2024) : 128-134 . |
| APA | 郑伟楠 , 於志勇 , 黄昉菀 . 基于双通道回声状态网络的时间序列补全及单步预测 . | 计算机科学 , 2024 , 51 (03) , 128-134 . |
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Discovering communities in attributed networks is an important research topic in complex network analysis. Community detection based on multi-objective evolutionary computing (MOEA) models community detection as a multi-objective optimization problem and searches the optimal solutions by simulating the evolution of a biological population. However, the existing multi-objective evolutionary algorithms for community detection faces two challenges: their encoding schemes are designed based on network topology and neglects the information in node attributes; and they are easy to fall into local optimum. In this article, we propose a community detection algorithm empowered by multi-objective evolutionary computing, named ECEVO-MOEA, which conducts edge closeness encoding and embedding vector optimization alternately. On the one hand, the evolution of a biological population is completed by employing a new edge closeness encoding scheme and multiple attribute-aware objective functions. On the other hand, the update of embedding vectors is used to calculate similarity matrix and communities to improve solution quality, avoiding it from early convergence. Experiments on real networks demonstrate that ECEVO-MOEA achieves higher accuracy than the baseline algorithms.
Keyword ：
Community networks Community networks Complex networks Complex networks Detection algorithms Detection algorithms Encoding Encoding Evolutionary computation Evolutionary computation Image edge detection Image edge detection Pareto optimization Pareto optimization Search problems Search problems Social factors Social factors Statistics Statistics
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| GB/T 7714 | Guo, Kun , Chen, Zhanhong , Yu, Zhiyong et al. Evolutionary Computing Empowered Community Detection in Attributed Networks [J]. | IEEE COMMUNICATIONS MAGAZINE , 2024 , 62 (5) : 22-26 . |
| MLA | Guo, Kun et al. "Evolutionary Computing Empowered Community Detection in Attributed Networks" . | IEEE COMMUNICATIONS MAGAZINE 62 . 5 (2024) : 22-26 . |
| APA | Guo, Kun , Chen, Zhanhong , Yu, Zhiyong , Chen, Kai , Guo, Wenzhong . Evolutionary Computing Empowered Community Detection in Attributed Networks . | IEEE COMMUNICATIONS MAGAZINE , 2024 , 62 (5) , 22-26 . |
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Sparse mobile crowdsensing (MCS) is a cost-effective data collection paradigm that aims to recruit users to collect data from a part of sensing subareas and infer the rest. In a more realistic scenario, users participate in real-time and collect data along the way. For missing data inference, the significance of data collected from different subareas often varies over time. However, since users' trajectories are uncertain, recruiting users who can cover important spatio-temporal subareas presents a challenge. Additionally, how to segment the budget wisely during recruitment is another challenge. To tackle these challenges, we propose a dual reinforcement learning (RL)-based online user recruitment strategy with adaptive budget segmentation, called DualRL-U, which consists of two alternating decision steps, i.e., the user recruitment decision and the budget retention decision. Specifically, for the user recruitment decision, we use RL to connect the user with data inference accuracy to estimate their contributions. For the budget retention decision, we use RL to connect the budget with the number of times the user can sense to evaluate the cost effectiveness. In this way, a dual RL model is constructed to achieve effective recruitment by alternately executing user recruitment decisions and budget retention decisions. Extensive experiments on real-world sensing data sets show the effectiveness of DualRL-U.
Keyword ：
Adaptive budget segmentation Adaptive budget segmentation Costs Costs Crowdsensing Crowdsensing online user recruitment online user recruitment Recruitment Recruitment reinforcement learning (RL) reinforcement learning (RL) Sensors Sensors sparse mobile crowd-sensing (MCS) sparse mobile crowd-sensing (MCS) Task analysis Task analysis Trajectory Trajectory Uncertainty Uncertainty
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| GB/T 7714 | Guo, Xianwei , Tu, Chunyu , Hao, Yongtao et al. Online User Recruitment With Adaptive Budget Segmentation in Sparse Mobile Crowdsensing [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (5) : 8526-8538 . |
| MLA | Guo, Xianwei et al. "Online User Recruitment With Adaptive Budget Segmentation in Sparse Mobile Crowdsensing" . | IEEE INTERNET OF THINGS JOURNAL 11 . 5 (2024) : 8526-8538 . |
| APA | Guo, Xianwei , Tu, Chunyu , Hao, Yongtao , Yu, Zhiyong , Huang, Fangwan , Wang, Leye . Online User Recruitment With Adaptive Budget Segmentation in Sparse Mobile Crowdsensing . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (5) , 8526-8538 . |
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随着城市化进程的不断加快,工业发展、人口聚集使得空气质量问题日益严峻.出于对采集成本的考虑,对空气质量的主动采样正受到越来越多的关注.但现有模型要么只能迭代选择采样位置,要么难以实时更新采样算法.基于此,提出了一种基于压缩感知自适应测量矩阵的空气质量主动采样方法,将采样位置的选择问题转化为矩阵的列子集选择问题.该方法首先利用历史完整数据进行字典学习,然后将学习后的字典经过列子集选择后得到能够指导批量采样的自适应测量矩阵,最后结合利用空气质量数据特性构建的稀疏基矩阵恢复出未采样的数据.该方法使用压缩感知模型一体化实现采样和推断,避免了使用多个模型的不足.此外,考虑到空气质量的时序变动问题,在每一次的主动采样后,字典还会利用最新数据进行在线更新以指导下一次的采样.两个真实数据集上的实验结果表明,经过字典学习后得到的自适应测量矩阵在低于20％的多个采样率下,恢复性能优于所有基线.
Keyword ：
主动采样 主动采样 压缩感知 压缩感知 字典学习 字典学习 群智感知 群智感知 自适应测量矩阵 自适应测量矩阵
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| GB/T 7714 | 黄伟杰 , 郭贤伟 , 於志勇 et al. 基于压缩感知自适应测量矩阵的空气质量主动采样 [J]. | 计算机科学 , 2024 , 51 (7) : 116-123 . |
| MLA | 黄伟杰 et al. "基于压缩感知自适应测量矩阵的空气质量主动采样" . | 计算机科学 51 . 7 (2024) : 116-123 . |
| APA | 黄伟杰 , 郭贤伟 , 於志勇 , 黄昉菀 . 基于压缩感知自适应测量矩阵的空气质量主动采样 . | 计算机科学 , 2024 , 51 (7) , 116-123 . |
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Sparse crowdsensing collects data from a subset of the sensing area and infers data for unsensed areas, reducing data collection costs. Previous works have primarily focused on independently collecting and inferring single types of data. However, real-world scenarios often involve multiple types of data that can complement each other by providing missing spatiotemporal distribution information. In this paper, we fully consider both intra-data correlations among data of the same type and inter-data correlations among data of different types, enabling collaborative execution of various tasks. In addition, we enhance the adaptability in practical application scenarios by utilizing real-time collected sparse data to guide task execution. For this purpose, we propose a multi-task adaptive budgeting framework for online sparse crowdsensing, called MTAB-SC. This framework consists of three parts: training data updating, data inference, and data collection. First, we propose a multi-task data updating method to keep models up-to-date. Second, we design a data inference network for multi-task data joint inference. Finally, to allocate suitable budgets for each task and facilitate collaborative data collection across multiple tasks, we propose an Adaptive Budgeting for Collaborative Data Collection model (AB-CoDC). The effectiveness of our proposals is demonstrated through extensive experiments on two real-world datasets.
Keyword ：
Collaboration Collaboration Correlation Correlation Crowdsensing Crowdsensing Data collection Data collection model updates model updates multi-agent reinforcement learning multi-agent reinforcement learning multi-task collaboration multi-task collaboration Multitasking Multitasking Online sparse crowdsensing Online sparse crowdsensing Sensors Sensors Task analysis Task analysis
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| GB/T 7714 | Tu, Chunyu , Yu, Zhiyong , Han, Lei et al. Adaptive Budgeting for Collaborative Multi-Task Data Collection in Online Sparse Crowdsensing [J]. | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (7) : 7983-7998 . |
| MLA | Tu, Chunyu et al. "Adaptive Budgeting for Collaborative Multi-Task Data Collection in Online Sparse Crowdsensing" . | IEEE TRANSACTIONS ON MOBILE COMPUTING 23 . 7 (2024) : 7983-7998 . |
| APA | Tu, Chunyu , Yu, Zhiyong , Han, Lei , Guo, Xianwei , Huang, Fangwan , Guo, Wenzhong et al. Adaptive Budgeting for Collaborative Multi-Task Data Collection in Online Sparse Crowdsensing . | IEEE TRANSACTIONS ON MOBILE COMPUTING , 2024 , 23 (7) , 7983-7998 . |
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Efficiently obtaining the up-to-date information in the disaster-stricken area is the key to successful disaster response. Unmanned aerial vehicles (UAVs), workers and cars can collaborate to accomplish sensing tasks, such as life detection task in disaster-stricken areas. In this paper, we explicitly address the route planning for a group of agents, including UAVs, workers, and cars, with the goal of maximizing the sensing task completion rate. we propose a MARL-based heterogeneous multi-agent route planning algorithm called MANF-RL-RP. The algorithm has made targeted designs in terms of global-local dual information processing and model structure for heterogeneous multi-agent, making it effectively considers the collaboration among heterogeneous agents and the long-term impact of current decisions. Finally, we conducted detailed experiments based on the rich simulation data. In comparison to the baseline algorithms, namely Greedy-SC-RP and MANF-DNN-RP, MANF-RL-RP has exhibited a significant performance improvement. Compared to MANF-DNN-RP and Greedy-SC-RP, the task completion rate based on MANF-RL-RP increased by an average of 8.82% and 56.8%, respectively.
Keyword ：
collaborative route planning collaborative route planning disaster response disaster response Mobile crowdsensing Mobile crowdsensing mulit-agent reinforcement learning mulit-agent reinforcement learning
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| GB/T 7714 | Han, Lei , Tu, Chunyu , Yu, Zhiwen et al. Collaborative Route Planning of UAVs, Workers, and Cars for Crowdsensing in Disaster Response [J]. | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 32 (4) : 3606-3621 . |
| MLA | Han, Lei et al. "Collaborative Route Planning of UAVs, Workers, and Cars for Crowdsensing in Disaster Response" . | IEEE-ACM TRANSACTIONS ON NETWORKING 32 . 4 (2024) : 3606-3621 . |
| APA | Han, Lei , Tu, Chunyu , Yu, Zhiwen , Yu, Zhiyong , Shan, Weihua , Wang, Liang et al. Collaborative Route Planning of UAVs, Workers, and Cars for Crowdsensing in Disaster Response . | IEEE-ACM TRANSACTIONS ON NETWORKING , 2024 , 32 (4) , 3606-3621 . |
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Missing data estimation (MDE) for time series is a crucial issue concerned with various applications based on the Internet of Things (e.g. sparse mobile crowdsensing). Although many approaches have been proposed to address this issue, they are either insufficiently considered or computationally expensive for data correlations. Motivated by this, an echo state network (ESN) with bidirectional-feedback connections is first proposed to skillfully encode temporal, cross-domain, and lagging correlations into the high-dimensional state of the reservoir. Meanwhile, the output weight (the only unknown parameter of the network) can be trained quickly by reservoir computing to reduce computation overhead. On this basis, an improved version with multiple reservoirs is designed to further integrate data correlations from information flows in different directions. Finally, a general process for MDE based on ESN architecture is developed for popularization and application. Experimental results of various missing rates under different missing mechanisms show that the proposed models perform better than the current methods in estimation accuracy. © 2022, China Computer Federation (CCF).
Keyword ：
Time series Time series
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| GB/T 7714 | Huang, Fangwan , Zheng, Weinan , Guo, Wenzhong et al. Estimating missing data for sparsely sensed time series with exogenous variables using bidirectional-feedback echo state networks [J]. | CCF Transactions on Pervasive Computing and Interaction , 2023 , 5 (1) : 45-63 . |
| MLA | Huang, Fangwan et al. "Estimating missing data for sparsely sensed time series with exogenous variables using bidirectional-feedback echo state networks" . | CCF Transactions on Pervasive Computing and Interaction 5 . 1 (2023) : 45-63 . |
| APA | Huang, Fangwan , Zheng, Weinan , Guo, Wenzhong , Yu, Zhiyong . Estimating missing data for sparsely sensed time series with exogenous variables using bidirectional-feedback echo state networks . | CCF Transactions on Pervasive Computing and Interaction , 2023 , 5 (1) , 45-63 . |
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近年来,日益严重的空气污染正成为影响人们身体健康的危险因素之一.空气质量指数数据可以为政府提供大气环境变化的规律,也可以用于对大气污染的控制和管理.但该数据在采集的过程中不可避免地存在缺失,导致了对其进行数据挖掘的难度升高.为了更加充分地利用已经搜集到的数据,对缺失数据进行补全是非常必要的.然而,现有的补全方法往往在高缺失率情况下表现不佳.基于此提出将缺失矩阵补全问题转换为稀疏矩阵重构问题,并设计了一种基于多维稀疏表示的数据补全方法.该方法首先利用训练数据模拟各种随机缺失情况并用于过完备字典的学习,然后利用学习后字典的上半部分获得具有缺失值的矩阵的稀疏表示,最后将该稀疏表示与字典的下半部分相结合得到重构后的估计矩阵.实验结果表明,所提方法在多维时序空气质量指数数据补全问题上优于传统的矩阵补全方法,尤其是在数据缺失比较严重的情况下具有明显的优势.
Keyword ：
多维稀疏表示 多维稀疏表示 字典学习 字典学习 矩阵补全 矩阵补全 空气质量指数 空气质量指数 缺失数据 缺失数据
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| GB/T 7714 | 蔡启铨 , 卢举鸿 , 於志勇 et al. 基于多维稀疏表示的空气质量指数数据补全 [J]. | 计算机科学 , 2023 , 50 (8) : 52-57 . |
| MLA | 蔡启铨 et al. "基于多维稀疏表示的空气质量指数数据补全" . | 计算机科学 50 . 8 (2023) : 52-57 . |
| APA | 蔡启铨 , 卢举鸿 , 於志勇 , 黄昉菀 . 基于多维稀疏表示的空气质量指数数据补全 . | 计算机科学 , 2023 , 50 (8) , 52-57 . |
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