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学者姓名：毛政元
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Abstract ：
As an indispensible component in understanding spatial processes, direction has been concerned by researchers of related disciplines for a long time. Challenges still remain in modeling directional uncertainties due to randomness of positional information in the input data. By formulating involving cumulative distribution functions (CDFs) and corresponding probability density functions (PDFs), this paper modeled the directional uncertainty between an accurate point and an uncertain one as well as that between two uncertain points respectively in 2D spaces on the condition of that the real position corresponding to the observed one of an uncertain point follows either Poisson distribution or the kernel function decreasing from the center to periphery within its error circle. The established models accurately quantify the correlation of the directional uncertainty with r (the radius of the error circle of the uncertain point), L (the observed distance containing one or two uncertain points), and their ratio (r/L) for the four different situations respectively, which opens up a new way for related studies including spatial query, analysis and reasoning, etc. Experimental results indicate that the proposed methods in this article are more efficient, robust than the corresponding Monte Carlo simulation. Their effectiveness has also been demonstrated with practical cases.
Keyword ：
2D spaces 2D spaces CDFs CDFs Direction Direction Modeling Modeling PDFs PDFs Uncertainties Uncertainties
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| GB/T 7714 | Mao, Zhengyuan . Modeling directional uncertainties in 2D spaces [J]. | GEOINFORMATICA , 2025 . |
| MLA | Mao, Zhengyuan . "Modeling directional uncertainties in 2D spaces" . | GEOINFORMATICA (2025) . |
| APA | Mao, Zhengyuan . Modeling directional uncertainties in 2D spaces . | GEOINFORMATICA , 2025 . |
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针对目前电商行业基础设施布局和空间利用不合理的问题,提出考虑设施规模决策的两级选址-路径问题(2E-LRP)求解模型.首先,在传统2E-LRP中引入差异性设施规模约束,通过识别客户群设计不同设施规模组合,利用规模弹性变化调整总成本组成,并以最小运营成本为目标建立顾及设施规模弹性变化的2E-LRP模型;其次,提出两阶段混合迭代局部搜索启发式算法求解模型;最后,分析所提模型和优化算法,并以Prodhon等不同数据集为实例进行验证.实验结果表明,所提模型具有针对区域差异和不同数据规模的普适性,且设施规模的弹性变化范围值与总成本呈负相关;与拉格朗日松弛粒度禁忌搜索(LRGTS)等算法的最优成本相比,所提算法对所有算例的最优成本平均值降低了6.67％,可以有效节约运行成本.
Keyword ：
两级选址-路径问题 两级选址-路径问题 偏随机化 偏随机化 城市物流 城市物流 设施规模决策 设施规模决策 迭代局部搜索 迭代局部搜索
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| GB/T 7714 | 冷琴 , 毛政元 . 考虑设施规模决策的两级选址-路径优化 [J]. | 计算机应用 , 2024 , 44 (11) : 3513-3520 . |
| MLA | 冷琴 等. "考虑设施规模决策的两级选址-路径优化" . | 计算机应用 44 . 11 (2024) : 3513-3520 . |
| APA | 冷琴 , 毛政元 . 考虑设施规模决策的两级选址-路径优化 . | 计算机应用 , 2024 , 44 (11) , 3513-3520 . |
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Models based on convolutional neural networks (CNNs) have achieved remarkable advances in high-resolution remote sensing (HRRS) images scene classification, but there are still challenges due to the high similarity among different categories and loss of local information. To address this issue, a multigranularity alternating feature mining (MGA-FM) framework is proposed in this article to learn and fuse both global and local information for HRRS scene classification. First, a region confusion mechanism is adopted to guide network's shallow layers to adaptively learn the salient features of distinguishing regions. Second, an alternating comprehensive training strategy is designed to capture and fuse shallow local feature information and deep semantic information to enhance feature representation capabilities. In particular, the MGA-FM framework can be flexibly embedded in various CNN backbone networks as a training mechanism. Extensive experimental results and visualization analysis on three remote sensing scene datasets indicated that the proposed method can achieve competitive classification performance.
Keyword ：
Convolutional neural network (CNN) Convolutional neural network (CNN) feature mining feature mining local detailed information local detailed information remote sensing image remote sensing image scene classification scene classification
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| GB/T 7714 | Weng, Qian , Huang, Zhiming , Lin, Jiawen et al. Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining [J]. | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 : 318-330 . |
| MLA | Weng, Qian et al. "Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining" . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 16 (2023) : 318-330 . |
| APA | Weng, Qian , Huang, Zhiming , Lin, Jiawen , Jian, Cairen , Mao, Zhengyuan . Remote Sensing Scene Classification Via Multigranularity Alternating Feature Mining . | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING , 2023 , 16 , 318-330 . |
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With the popularization of smartphones, online car-hailing has become a common travel alternative and plays an important role in meeting public travel demand. Therefore, online car-hailing operation platforms have been a major component of Intelligent Transportation Systems in which passenger demand prediction is one of the core problems to be solved. However, models proposed in the existing literature usually ignore the long-term temporal correlation and multiple spatial correlations. This paper presented a Spatio-Temporal Multi-Graph Convolutional Network Fused With Global Features (GST-MGCN) to address the limitations of existing research achievements, taking full account of the unique spatiotemporal correlations of the travel demand of online car-hailing passengers. Following the Closeness, Period, and Trend (CPT) paradigm, the model fitted temporal dependencies with time series information. By identifying multiple spatial semantic correlations, the corresponding relational graph structure was constructed, and a multi-graph convolutional model was built in which the global features fusion module employed gated fusion and sum fusion methods to capture sudden and gradual changes of passenger demand, respectively. Taking the Haikou city dataset as an example, our experimental results show that the values of the three indicators, MAE, RMSE, and MAPE of the GST-MGCN model proposed in this paper were 2.269, 3.917, and 21.447, respectively, which were lower than those derived from other similar mainstream models. This study demonstrated that the proposed model GST-MGCN can effectively mine the spatio-temporal pattern of online car hailing passenger travel demand, extract the impact of global features, and accurately predict it. © 2023 Journal of Geo-Information Science. All rights reserved.
Keyword ：
Convolution Convolution Convolutional neural networks Convolutional neural networks Deep learning Deep learning E-learning E-learning Forecasting Forecasting Graph neural networks Graph neural networks Intelligent systems Intelligent systems Online systems Online systems Semantics Semantics
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| GB/T 7714 | Huang, Xin , Mao, Zhengyuan . Prediction of Passenger Demand for Online Car-hailing based on Spatio-temporal Multigraph Convolution Network [J]. | Journal of Geo-Information Science , 2023 , 25 (2) : 311-323 . |
| MLA | Huang, Xin et al. "Prediction of Passenger Demand for Online Car-hailing based on Spatio-temporal Multigraph Convolution Network" . | Journal of Geo-Information Science 25 . 2 (2023) : 311-323 . |
| APA | Huang, Xin , Mao, Zhengyuan . Prediction of Passenger Demand for Online Car-hailing based on Spatio-temporal Multigraph Convolution Network . | Journal of Geo-Information Science , 2023 , 25 (2) , 311-323 . |
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地理对象关系网尤其是邻近关联生成的网络是客观存在的,有着基本的拓扑结构,最重要的拓扑性质是其度及度分布.在进行空间数据计算分析时,人们往往进行邻近关联操作(分析).为了进一步加深对地理对象邻近关联的认识,以生态斑块邻近关系网为案例,近似测算客观地理对象邻近关系网的度分布.结果表明:度分布在尺度、时间和地域上是保持一致的;在特定范围,度分布服从幂律分布,地理对象邻近关联网络具有无标度特性.另外,度与邻近对象的属性具有一定相关性.
Keyword ：
地理对象 地理对象 度 度 度分布 度分布 邻近关系网络 邻近关系网络
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| GB/T 7714 | 韦思亮 , 毛政元 . 地理对象邻近关联网络度分布测算及其意义 [J]. | 测绘地理信息 , 2023 , 48 (1) : 107-111 . |
| MLA | 韦思亮 et al. "地理对象邻近关联网络度分布测算及其意义" . | 测绘地理信息 48 . 1 (2023) : 107-111 . |
| APA | 韦思亮 , 毛政元 . 地理对象邻近关联网络度分布测算及其意义 . | 测绘地理信息 , 2023 , 48 (1) , 107-111 . |
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Current mainstream deep learning network models have many problems such as inner cavity, discontinuity, missed periphery, and irregular boundaries when applied to building extraction from high spatial resolution remote sensing images. This paper proposed the RMAU-Net model by designing a new activation function (Activate Customized or Not, ACON) and integrating residuals block with channel-space and criss-cross attention module based on the U-Net model structure. The ACON activation function in the model allows each neuron to be activated or not activated adaptively, which helps improve the generalization ability and transmission performance of the model. The residual module is used to broaden the depth of the network, reduce the difficulty in training and learning, and obtain deep semantic feature information. The channel-spatial attention module is used to enhance the correlation between encoding and decoding information, suppress the influence of irrelevant background region, and improve the sensitivity of the model. The cross attention module aggregates the context information of all pixels on the cross path and captures the global context information by circular operation to improve the global correlation between pixels. The building extraction experiment using the Massachusetts dataset as samples shows that among all the 7 comparison models, the proposed RMA-UNET model is optimal in terms of intersection of union and F1-score, as well as indexes of precision and recall, and the overall performance of RMAU-Net is better than similar models. Each module is added step by step to further verify the validity of each module and the reliability of the proposed method. ©2022, Science Press. All right reserved.
Keyword ：
Chemical activation Chemical activation Convolutional neural networks Convolutional neural networks Deep learning Deep learning Extraction Extraction Pixels Pixels Remote sensing Remote sensing Semantics Semantics Space optics Space optics
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| GB/T 7714 | Wu, Xinhui , Mao, Zhengyuan , Weng, Qian et al. A Neural Network based on Residual Multi-attention and ACON Activation Function for Extract Buildings [J]. | Journal of Geo-Information Science , 2022 , 24 (4) : 792-801 . |
| MLA | Wu, Xinhui et al. "A Neural Network based on Residual Multi-attention and ACON Activation Function for Extract Buildings" . | Journal of Geo-Information Science 24 . 4 (2022) : 792-801 . |
| APA | Wu, Xinhui , Mao, Zhengyuan , Weng, Qian , Shi, Wenzao . A Neural Network based on Residual Multi-attention and ACON Activation Function for Extract Buildings . | Journal of Geo-Information Science , 2022 , 24 (4) , 792-801 . |
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Short-term traffic flow prediction with high accuracy and efficiency plays an important role in Intelligent Transportation Systems, which is a prerequisite for traffic guidance, management, and control. Due to the time-varying and non-stationary characteristics of the dynamic change of traffic flow, it is difficult to predict traffic flow with high accuracy, which needs to be resolved urgently in the transportation field. In order to improve the accuracy and efficiency of short-term traffic flow prediction, the paper develops a short-term traffic flow predicting algorithm based on adaptive time slice and the improved KNN model (A-TS-KNN), which is then implemented successfully in short-term traffic flow predicting experiments. In the first, the Dynamic Time Warping (DTW) algorithm is used to dynamically slice the daytime sequence of traffic flow into different traffic patterns. Secondly, the mutual information method is used to solve the maximum threshold of the time delays of traffic flow at each time in different traffic patterns. Then the traffic flow state vectors of different time delays is constructed, which generates a history database of traffic flow. Thirdly, the method of ten times ten-fold cross-validation is used to solve the orthogonal error distribution of different time delays and K values of traffic flow at each time. The orthogonal result with the smallest error is selected, and the parameters combination of adaptive time delay and K value are obtained. In the end, the weighted value of the reciprocal Euclidean distance of the K most similar neighbors is used for predicting traffic flow of next time. The forecasting accuracies of the improved A-TS-KNN and other four models including K-Nearest Neighbors (KNN) model, Support Vector Regression (SVR) model, Long-Short Term Memory (LSTM) neural networks, and Gate Recurrent Unit (GRU) neural networks are compared. The experimental results indicate that the improved A-TS-KNN model is more appropriate for short-term traffic flow forecasting than the other models. In addition, the A-TS-KNN algorithm is used for short-term traffic flow predicting at other four different intersections in the urban road network of Fuzhou, which has been shown good generalization ability. © 2022, Science Press. All right reserved.
Keyword ：
Efficiency Efficiency Forecasting Forecasting Intelligent systems Intelligent systems Long short-term memory Long short-term memory Nearest neighbor search Nearest neighbor search Street traffic control Street traffic control Time delay Time delay Timing circuits Timing circuits
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| GB/T 7714 | Qi, Duo , Mao, Zhengyuan . Short-term Traffic Flow Prediction based on Adaptive Time Slice and KNN [J]. | Journal of Geo-Information Science , 2022 , 24 (2) : 339-351 . |
| MLA | Qi, Duo et al. "Short-term Traffic Flow Prediction based on Adaptive Time Slice and KNN" . | Journal of Geo-Information Science 24 . 2 (2022) : 339-351 . |
| APA | Qi, Duo , Mao, Zhengyuan . Short-term Traffic Flow Prediction based on Adaptive Time Slice and KNN . | Journal of Geo-Information Science , 2022 , 24 (2) , 339-351 . |
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Distances are functions of spatial positions, therefore distance uncertainties should be resulted from transmission of spatial position uncertainties via the functional relationships accordingly. How to model the transmission precisely, a challenging problem in GIScience, has increasingly drawn attentions during the past several decades. Aiming at the limitations of presently available solutions to the problem, this article derived probability dis-tribution functions and corresponding density functions of distance uncertainties in two-dimensional space related to one or two uncertain endpoints respectively, under the premise that real positions, corresponding with the observed position of an uncertain point, follow the kernel function within its error circle. The density functions were employed to explore the diffusing law of uncertainty information from point positions to dis-tances, which opened up a new way for thoroughly solving problems of measuring distance uncertainties. It turns out that the proposed methods in this article are more efficient, robust than the corresponding Monte Carlo ones, which verifies their effectiveness and advantages.
Keyword ：
Distance Distance Modeling Modeling Probability Probability Two-dimensional space Two-dimensional space Uncertainties Uncertainties
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| GB/T 7714 | Mao, Zhengyuan , Fan, Linna , Dong, Pinliang . Modeling distance uncertainties in two-dimensional space [J]. | MEASUREMENT , 2022 , 202 . |
| MLA | Mao, Zhengyuan et al. "Modeling distance uncertainties in two-dimensional space" . | MEASUREMENT 202 (2022) . |
| APA | Mao, Zhengyuan , Fan, Linna , Dong, Pinliang . Modeling distance uncertainties in two-dimensional space . | MEASUREMENT , 2022 , 202 . |
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It is significant to study the vegetation of protected areas in rugged mountains where the vegetation grows naturally with minimal eco-society environmental stress from anthropogenic activities. The shadow-eliminated vegetation index (SEVI) was used to monitor the vegetation of protected areas, since it successfully removes topographic shadow effects. In order to auto achieve the best adjustment factor for SEVI calculation from regional area images, we developed a new calculation algorithm using block information entropy (BIE-algorithm). The BIE-algorithm auto-detected typical blocks (subareas) from slope images and achieved the best adjustment factor from a block where the SEVI obtained the highest information entropy in an entire scene. Our obtained regional SEVI result from two scenes of Landsat 8 OLI images using the BIE-algorithm exhibited an overall flat feature with the impression of the relief being drastically removed. It achieved balanced values among three types of samples: Sunny area, self-shadow, and cast shadow, with SEVI means of 0.73, 0.77, and 0.75, respectively, and the corresponding SEVI relative errors of self-shadow and cast shadow were only 4.99% and 1.84%, respectively. The linear regression of SEVI vs. the cosine of the solar incidence angle was nearly horizontal, with an inclination of -0.0207 and a coefficient of determination of 0.0042. The regional SEVI revealed that the vegetation growth level sequence of three protected areas was Wuyishan National Park (SEVI mean of 0.718) > Meihuashan National Nature Reserve (0.672) > Minjiangyuan National Nature Reserve (0.624) > regional background (0.572). The vegetation growth in the protected areas was influenced by the terrain slope and years of establishment of the protected area and by the surrounding buffer zone. The homogeneous distribution of vegetation in a block is influenced by many factors, such as the actual vegetation types, block size, and shape, which need consideration when the proposed BIE-algorithm is used.
Keyword ：
adjustment factor adjustment factor calculation algorithm calculation algorithm information entropy information entropy slope slope topographic effect topographic effect
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| GB/T 7714 | Jiang, Hong , Yao, Maolin , Guo, Jia et al. Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI) [J]. | REMOTE SENSING , 2022 , 14 (4) . |
| MLA | Jiang, Hong et al. "Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI)" . | REMOTE SENSING 14 . 4 (2022) . |
| APA | Jiang, Hong , Yao, Maolin , Guo, Jia , Zhang, Zhaoming , Wu, Wenting , Mao, Zhengyuan . Vegetation Monitoring of Protected Areas in Rugged Mountains Using an Improved Shadow-Eliminated Vegetation Index (SEVI) . | REMOTE SENSING , 2022 , 14 (4) . |
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Semantic segmentation in high-resolution aerial images is a fundamental and challenging task with a wide range of applications. Although many segmentation methods with convolutional neural networks have achieved inspiring results, it is still difficult to distinguish regions with similar spectral features only using high-resolution data. Besides, the traditional data-independent upsampling methods may lead to suboptimal results. This letter proposes a multisensor data fusion model (MSDFM). Following the classical encoder-decoder structure, MSDFM regards colored digital surface models (colored-DSMs) data as a complementary input for further detailed feature extraction. A data-dependent upsampling (DUpsampling) method is adopted in the decoder stage instead of the common upsampling approaches to improve the classification accuracy of pixels of the small objects. Extensive experiments on Vaihingen and Potsdam datasets demonstrate that our proposed MSDFM outperforms most related models. Significantly, segmentation performance for the car category surpasses state-of-the-art methods over the International Society of Photogrammetry and Remote Sensing (ISPRS) Vaihingen dataset.
Keyword ：
Automobiles Automobiles Decoding Decoding Deconvolution Deconvolution Digital surface model (DSM) Digital surface model (DSM) Feature extraction Feature extraction high-resolution aerial images high-resolution aerial images Image segmentation Image segmentation Semantics Semantics semantic segmentation semantic segmentation Vegetation Vegetation
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| GB/T 7714 | Weng, Qian , Chen, Hao , Chen, Hongli et al. A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images [J]. | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
| MLA | Weng, Qian et al. "A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images" . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 19 (2022) . |
| APA | Weng, Qian , Chen, Hao , Chen, Hongli , Guo, Wenzhong , Mao, Zhengyuan . A Multisensor Data Fusion Model for Semantic Segmentation in Aerial Images . | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS , 2022 , 19 . |
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