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2024 International Conference on Image Processing, Intelligent Control and Computer Engineering, IPICE 2024

作者： Tang, Kang Sun, Yu Zhong, Xiaoyang Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University China

ISBN: (纸本)9798400710285

With the development of artificial intelligence technology, urban traffic management has become increasingly convenient, and the task of illegal parking detection has become a major research focus. Currently, most illegal parking detection schemes use fixed-point cameras, which not only waste resources but also have the limitation of a small detection range. To overcome this issue, we have designed a highly mobile system for detecting illegally parked vehicles. Considering the different relative positions of the tires and parking lines in roadside parking areas, we use an optimized You Only Look Once(YOLOv5) algorithm to classify the tires and determine whether the vehicle is illegally parked based on different category of tires combinations. Subsequently, we applied the above-mentioned illegal parking detection strategy to a embedded device which mounted on the mobile vehicle to realize the real-time mobile detection and information collection of illegal vehicles in the roadside parking area. © 2024 Copyright held by the owner/author(s).

关键词： Tires

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Enhancing Fish Counting in Sonar Images With Multi-Task Learning and Local-Global Feature Interaction

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IEEE Sensors Journal 2025年第12期25卷 21775-21791页

作者： Wang, Yuhang Wu, Qunyong Yang, Shiyu Li, Mengmeng Wang, keyue Chen, Xuanyu Fuzhou350128 China Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350128 China

Accurate fish counting is crucial for environmental monitoring and management, with sonar imaging providing a non-intrusive way to gather data in previously inaccessible underwater environments. However, the strong visual similarity between fish and noise in sonar images presents significant challenges in achieving high counting accuracy. Existing methods rely on attention maps to emphasize fish regions but do not fully capture the discriminative features between fish and noise, limiting counting accuracy. To address this, we propose the local-global multi-task transformer (LGMFormer) to enhance fish counting in sonar images. The model employs an encoder-decoder architecture, with density map regression as the primary task and multi-class semantic segmentation as an auxiliary task. By predicting multi-class segmentation maps, the shared network layers fully learn discriminative features between fish and noise. We also develop a segmentation-enhanced density head to further strengthen the connection between tasks. Within LGMFormer, the local-global feature interaction module is designed to fuse local spatial detail features with global correlation features for more precise fish counting. Additionally, a high-level feature guidance module is developed to retain more detail during the feature fusion process between the encoder and decoder. We also develop an automatic image segmentation method based on Otsu's thresholding to create multi-class segmentation labels for sonar images. Extensive experiments on a public sonar fish counting dataset demonstrate that LGMFormer outperforms state-of-the-art methods in counting accuracy, reducing the mean absolute error of count prediction by 17.6% and the false recognition rate by 42.9%. The source code will be available at https://***/camerayuhang/LGMFormer. © 2001-2012 IEEE.

关键词： Semantic Segmentation

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MSTMN: a novel meta-attention-based multi-task spatiotemporal network for traffic flow prediction

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Neural Computing and Applications 2024年第36期36卷 23195-23222页

作者： Zhou, Qianqian Chen, Nan College of Computer and Data Science Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China Fuzhou University Fuzhou350108 China

Traffic flow prediction in a given area is often influenced by the interactions with complex dependencies among multiple areas. By far, it remains unexplored to obtain interactive information. To address the issue, MSTMN was proposed, a multi-task learning framework that jointly learns interactive information and spatiotemporal dependencies across tasks. MSTMN consists of a node network, an edge network, and a prediction network. The node network and edge network were trained using the proposed meta-fully convolutional blocks to extract interactive features and generalizable features. The prediction network employed the meta-gated fusion and the recalibration block to both integrate these learned features and external factors. This ensures that the features capture optimal interaction information during the training phase. The proposed model was validated on two real-world movement-on-demand traffic datasets collected in Xiamen, China. Experimental results showed that MSTMN improved performance by 38.42% and 31.77% for one-step and multi-step prediction compared to the state-of-the-art baseline. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Multi-task learning

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IDENTIFYMIX: AN EFFICIENT TWO-STAGE LEARNING APPROACH TO COMBATING LABEL NOISE

Journal of Applied and Numerical Optimization

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Journal of Applied and Numerical Optimization 2023年第2期5卷 237-253页

作者： Tong, Kai Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou350003 China

Deep neural networks require correct label annotation during supervised learning. It is inevitable, however, that some labels are noisy during the labeling process. A deep neural network retains incorrect labels during training, resulting in a degradation of performance. Therefore, it is essential to identify samples with potentially correct labels. In state-of-the-art methods, small-loss samples are chosen for subsequent training through a sample selection strategy. Howerver, it typically ignores the imbalance in noise ratios between mini-batches when performing sample selection within each minibatch. Further, numerous valuable samples with high losses are discarded, which adversely affects the generalization performance of the model, particularly under conditions of high noise ratios. To this end, this paper proposes IdentifyMix, an effective two-stage learning approach for noisy robust learning that combines an unique sample selection strategy and the semi-supervised learning technique. By observing how the dynamics of network training are changing, AUM (Area Under the Margin) provides a criterion that is applied in this research to identify mislabeled data. Moreover, by combining semi-supervised learning with contrastive learning and data augmentation, it is possible to extract more useful information from mislabeled samples. Experiments on several synthetic and real-world noise benchmarks demonstrate the effectiveness of IdentifyMix compared with state-of-the-art methods. © 2023 Journal of Applied and Numerical Optimization.

关键词： Image classification

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Quantitatively analyzing loess landforms from the perspective of peak intervisibility

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Journal of Mountain Science 2025年第5期22卷 1748-1767页

作者： WANG Chen CHEN Nan SUN Zhenzhen Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou UniversityFuzhou 350116China The Academy of Digital China(Fujian) Fuzhou UniversityFuzhou 350108China

Loess landforms in the Loess Plateau are typical landforms in arid and semiarid areas and have a significant impact on the environment and soil *** analyses on loess landform have been employed from various *** intervisibility can provide the potential topographic information implied in the visual connectivity of peaks,however,its application in loess landform analysis remains *** this study,the interwoven sightlines among peaks,representing peak intervisibility,were extracted from the digital elevation model and simulated into a peak intervisibility network(PIN).Nine indices were proposed to quantify the *** a case study in Northern Shaanxi,China,three tasks were conducted,including,landform interpretation,spatial pattern mining,and landform *** main findings are as follows:(1)PIN responds to terrain morphology and is beneficial for loess landform interpretation.(2)The spatial patterns of PIN indices are heterogeneous and strongly coupled with the terrain morphologies,showing anisotropy and autocorrelation in spatial variations.(3)Using the light gradient boost machine classifier,the PIN index-based classification reaches a mean accuracy of 86.09%,an overall accuracy of 86%and a kappa coefficient of *** findings shed light on the applicability of PIN in loess landform *** intervisibility not only enriches the theories and methodologies of relation-based digital terrain analysis,but also enhances our comprehension of loess landform genesis,morphology,distribution,and evolution.

关键词： Loess landform Intervisibility DEM Digital terrain analysis Geomorphology

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PSTCGCN: Principal spatio-temporal causal graph convolutional network for traffic flow prediction

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Neural Computing and Applications 2024年 1-14页

作者： Yang, Shiyu Wu, Qunyong Li, Ziwei Wang, keyue Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education Fuzhou University Fuzhou350108 China National Engineering Research Centre of Satellite Geospatial Information Technology Fuzhou University Fuzhou350108 China

Traffic prediction is indispensable for constructing transportation networks in smart cities. Due to the complex spatio-temporal correlations of traffic data, this task presents challenges. Recent studies mainly use graph neural networks to simulate complex spatio-temporal relationships through fixed or adaptive graphs. While fixed graphs may not adapt to data drift caused by changes in road network structures, adaptive graphs overlook critical information of the original roads. To address this challenge, we propose a principal spatio-temporal causal graph convolutional network (PSTCGCN) to accurately predict traffic flow. In response to the data drift problem, we introduce a data-driven semi-principal generated graph embedding (SPGGE) that first extracts the principal features of the original roads to model the spatio-temporal sequence distribution and then remodels the data after drift through adaptive transformation. Traffic flow data, while showcasing fundamental spatial relationships, also exhibit temporal dynamics. We propose an effective temporal causal convolution component comprising SPGGE, graph convolution, both local and global causal learning models to jointly learn short-term and long-term spatio-temporal correlations. PSTCGCN was evaluated using two actual highway datasets, PEMS03 and PEMS07, achieving a notable improvement of 6.12% in RMSE on PEMS03 compared to STGATRGN. Our code is available at https://***/OvOYu/PSTCGCN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Graph embeddings

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Improved particle swarm optimization LSSVM spatial location trajectory data prediction model in health care monitoring system

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Personal and Ubiquitous Computing 2022年第3期26卷 795-805页

作者： Chen, Guobin Li, Zhang Chongqing Key Laboratory of Spatial Information Data Mining and Big Data Integration for Ecological Environment Rongzhi College of Chongqing Technology and Business University Chongqing401320 China

With the real-time recording of the health care system, real-time monitoring of user trajectory data can effectively protect the health and health of the body. In order to accurately establish the spatial position trajectory data prediction model, a space trajectory data prediction model based on variable space chaotic particle swarm optimization (CPSO)-optimized least squares support vector machine (LSSVM) is proposed. Firstly, aiming at the problem that particle swarm optimization (PSO) is easy to fall into local optimum, the global optimization performance of PSO algorithm is improved by using variable space chaos search strategy and particle out-of-bounds mirror processing strategy. Numerical simulation experiments verify the superiority of improved particle swarm optimization algorithm. Based on the CPSO algorithm, the hyper parameters of the CPSO-optimized least squares support vector machine (CPSO-LSSVM) are proposed to improve the prediction accuracy of the model. Finally, using GeoLife and T-Drive dataset as the research object, the CPSO-LSSVM spatial position prediction model is established by using the trajectory data of the dataset. The simulation experiment verifies that the CPSO-LSSVM positional space prediction model has higher prediction accuracy and more strong generalization ability to accurately and effectively predict spatial location. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Health care

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Accelerating Unsupervised Federated Graph Neural Networks via Semi-asynchronous Communication 18th

Accelerating Unsupervised Federated Graph Neural Networks vi...

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18th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Liao, Yuanming Wu, Duanji Lin, Pengyu Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819996360

Graph neural networks have shown excellent performance in many fields owing to their powerful processing ability of graph data. In recent years, federated graph neural network has become a reasonable solution due to the enactment of privacy-related regulations. However, frequent communication between the coordinator and participants in federated graph neural network results in longer model training time and consumes many communication resources. To address this challenge, in this paper, we propose a novel semi-asynchronous federated graph learning communication protocol that simultaneously alleviates the negative impact of stragglers(slow participants) and accelerate the training process in the unsupervised federated graph neural network scenario. First, the weighted enforced synchronization strategy is intended to preserve the information carried by stragglers while preventing their stale models from harming the global model update. Second, the adaptive local update strategy is developed to make the local model of the participant with poor computing performance as close as possible to the global model. Experiments combine federated learning with graph contrastive learning. The results demonstrate that our proposed protocol outperforms the existing protocols in real-world networks. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet protocols

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Detection and interpretation of the time-varying seasonal signals in China with multi-geodetic measurements

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Geodesy and Geodynamics 2025年第1期16卷 42-54页

作者： Liansheng Deng Yugang Xiao Qusen Chen Wei Peng Zhao Li Hua Chen Zhiwen Wu School of Electrical and Electronic Information Engineering Hubei Polytechnic UniversityHuangshi 435003China Hubei Luojia Laboratory GNSS Research CenterWuhan UniversityWuhan 430079China Changjiang Spatial Information Technology Engineering Co. Ltd.Wuhan 430010China Key Laboratory of Spatial Data Mining&Information Sharing of the Ministry of Education Fuzhou UniversityFuzhou 350108China School of Geodesy and Geomatics Wuhan UniversityWuhan 430079China The First Surveying and Mapping Institute of Hunan Province Changsha 410114China

The time-varying periodic variations in Global Navigation Satellite System(GNSS)stations affect the reliable time series analysis and appropriate geophysical *** this study,we apply the singular spectrum analysis(SSA)method to characterize and interpret the periodic patterns of GNSS deformations in China using multiple geodetic *** include 23-year observations from the Crustal Movement Observation Network of China(CMONOC),displacements inferred from the Gravity Recovery and Climate Experiment(GRACE),and loadings derived from Geophysical models(GM).The results reveal that all CMONOC time series exhibit seasonal signals characterized by amplitude and phase modulations,and the SSA method outperforms the traditional least squares fitting(LSF)method in extracting and interpreting the time-varying seasonal signals from the original time *** decrease in the root mean square(RMS)correlates well with the annual cycle variance estimated by the SSA method,and the average reduction in noise amplitudes is nearly twice as much for SSA filtered results compared with those from the LSF *** SSA analysis,the time-varying seasonal signals for all the selected stations can be identified in the reconstructed components corresponding to the first ten ***,both RMS reduction and correlation analysis imply the advantages of GRACE solutions in explaining the GNSS periodic variations,and the geophysical effects can account for 71%of the GNSS annual amplitudes,and the average RMS reduction is 15%.The SSA method has proved to be useful for investigating the GNSS timevarying seasonal *** could be applicable as an auxiliary tool in the improvement of nonlinear variations investigations.

关键词： GNSS coordinate time series Singularspectrumanalysis Time-varying seasonal signals Loading effects GRACE

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Mapping upland crop–rice cropping systems for targeted sustainable intensification in South China

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The Crop Journal 2024年第2期12卷 614-629页

作者： Bingwen Qiu Linhai Yu Peng Yang Wenbin Wu Jianfeng Chen Xiaolin Zhu Mingjie Duan Key Laboratory of Spatial Data Mining&Information Sharing of Ministry of Education Academy of Digital China(Fujian)Fuzhou UniversityFuzhou 350116FujianChina Key Laboratory of Agricultural Remote Sensing Ministry of Agriculture and Rural AffairsBeijing 100081China Department of Land Surveying and Geo-Informatics The Hong Kong Polytechnic UniversityKowloon 999077Hong KongChina

Upland crop-rice cropping systems(UCR)facilitate sustainable agricultural *** UCR cultivation mapping is needed to ensure food security,sustainable water management,and rural ***,datasets describing cropping systems are limited in spatial coverage and crop *** UCR is more challenging than crop identification and most existing approaches rely heavily on accurate phenology calendars and representative training samples,which limits its applications over large *** describe a novel algorithm(RRSS)for automatic mapping of upland crop-rice cropping systems using Sentinel-1 Synthetic Aperture Radar(SAR)and Sentinel-2 Multispectral Instrument(MSI)*** indicator,the VV backscatter range,was proposed to discriminate UCR and another two indicators were designed by coupling greenness and pigment indices to further discriminate tobacco or oilseed *** RRSS algorithm was applied to South China characterized by complex smallholder rice cropping systems and diverse topographic *** study developed 10-m UCR maps of a major rice bowl in South China,the Xiang-Gan-Min(XGM)*** performance of the RRSS algorithm was validated based on 5197 ground-truth reference sites,with an overall accuracy of 91.92%.There were7348 km^(2) areas of UCR,roughly one-half of them located in *** UCR was represented mainly by oilseed-UCR and tobacco-UCR,which contributed respectively 69%and 15%of UCR *** patterns accounted for only one-tenth of rice production,which can be tripled by intensification from single rice *** to complex and fragmented subtropical regions suggested the spatiotemporal robustness of the RRSS algorithm,which could be further applied to generate 10-m UCR datasets for application at national or global scales.

关键词： Cropping-pattern mapping Paddy rice Sentinel-1/2 China Sustainable intensification

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