共被期刊论文引用了3次
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| 1 | 智慧城市综合感知与智能决策的进展及应用显示文摘城市感知与决策是实现城市智慧化的首要前提。针对当前的最新研究进展,分别从基于空天地集成化传感网的城市综合感知、城市时空大数据的管理与分析、面向多层次用户的智能决策等3方面进行了分析论述,对比分析了当前城市时空信息平台及应用,在此基础上,提出了未来智慧城市感知决策面临的“三高”挑战,即高度融合的物理感知与社会感知,高度智能化的城市管理分析能力,以及高置信度的城市信物融合系统决策。 | 龚健雅 张翔 向隆刚 陈能成 | 2019 | 测绘学报2019，48，12： | 39 |
| 2 | 斜拉桥监测索力的时空评估方法显示文摘为更准确地反映监测索力的综合状态,提出一种考虑索力时间和空间特征的评估方法.借鉴桥梁风险矩阵决策方法,将时间指标和空间指标置于一个矩阵中进行评估决策.时空评估等级分为四级,分别为蓝色Ⅰ级、黄色Ⅱ级、橙色Ⅲ级和红色Ⅳ级.时间指标同时考虑索力变化速率和变化幅度,其中,索力变化速率通过时间序列分析得到,变化幅度通过索力变化率绝对值计算得到.空间指标主要反映由偏载、单索损伤等因素造成的斜拉桥两侧索力的偏差程度,采用Canberra距离表征.以南京长江三桥7 a监测数据为例,对20根长索进行索力时间和空间的综合评估,得到群索评估表和评估等级分布图.结果表明:监测索力符合时间序列特性,可采用一元线性回归模型提取趋势项;斜拉索的时空评估等级在Ⅱ级及以上,主要原因有索力变化趋势过快和上下游索力差异较大.评估分布图为斜拉索的人工检查提供更有针对性的指导. | 刘小玲 汪炳 任远 黄侨 | 2018 | 哈尔滨工业大学学报2018，50，9： | 1 |
| 3 | Large scale air pollution prediction with deep convolutional networks显示文摘Although considerable success has been achieved in urban air quality prediction(AQP) with machine learning techniques, accurate and long-term prediction is still challenging. One of the key issues for existing AQP approaches is that air quality monitoring stations are sparsely distributed, typically with around ten monitoring stations per city. As air quality may change abruptly in a local area, it is difficult to perform AQP accurately in areas that are far away from observation points. In addition, due to the large distance between every two monitoring stations, we cannot effectively leverage spatial relations among them to improve the AQP accuracy. In this paper, thanks to the development of low-cost air quality sensors, we are now able to collect a large-scale air quality dataset with 393 deployed air quality monitoring stations in a 120 km × 70 km region, which is more than ten times denser than existing AQP datasets. Further,we present a novel method to handle the data effectively. Specifically, we first convert the observed data from irregularly distributed monitoring stations into a regular image-like pollution map, which can then be processed with advanced deep convolutional networks. The experimental results show that the proposed approach can simultaneously model the temporal and spatial relations in our large-scale densely-observed dataset, leading to significantly improved AQP results. | Gao HUANG Chunjiang GE Tianyu XIONG Shiji SONG Le YANG Baoxian LIU Wenjun YIN Cheng WU | 2021 | Science China(Information Sciences)2021，64，9： | 0 |
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