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6th International Conference on Energy Systems and Electrical Power, ICESEP 2024

作者： Jiala, Erge Wu, Wenting Fuzhou University Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology The Academy of Digital China Fuzhou China

ISBN: (纸本)9798350353563

In the context of increasingly serious depletion and security risks of fossil energy, environmental pollution, and challenges of climate change, tidal range energy, and tidal current energy have attracted considerable attention due to their high predictability, insensitivity to flood and drought periods, and minimal environmental impact. In this study, we aim to assess the status of tidal range energy and tidal current energy in the coastal areas of Fujian Province using a high-resolution tidal dynamic model developed from the state-of-the-art model, Delft3D FM. Our results indicate that tidal range energy resources are concentrated in the coastal port areas north of central Fujian, with the highest tidal range energy potential of 1.18×105 KWh/m2 in Xinghua Bay. Tidal range energy resources are primarily concentrated in the waters of Sansha Bay, Pingtan Island, and Quanzhou Bay. Among them, the potential annual average power in Sansha Bay can reach 2.60×105 kWh/m2, with an estimated exploitable capacity of 67.24MW after the installation of tidal current power stations at the inlet channels. These research findings provide data support for site selection in the development of tidal range energy resources in the offshore waters of Fujian. © 2024 IEEE.

关键词： Delft3D FM High-resolution modelling Potential resources assessment Tidal energy

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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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A Unified Stream and Batch Graph Computing Model for Community Detection 17th

A Unified Stream and Batch Graph Computing Model for Commu...

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17th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： Dai, Jinkun Wu, Ling Guo, Kun Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789819923557

An essential challenge in graph data analysis and mining is to simply and effectively deal with large-scale network data that is expanding dynamically. Although batch-based parallel graph computation frameworks have better accuracy, they cannot process incremental data on time and need to be recomputed. To process real-time data, stream processing applications need to be redeveloped, which increases the redundancy of work, and some existing dynamic graph computation schemes are not generalizable. This paper proposes a unified stream and batch graph computing model(USBGM). The model is compatible with both stream and batch graph computing. Graph operators and algorithms developed based on the model can handle stream and batch graph data in a unified manner. The experiments on real-world and artificial networks verified the effectiveness and efficiency of the model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Population dynamics

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An Improved Leaky-ESN for Electricity Load Forecasting 15th

An Improved Leaky-ESN for Electricity Load Forecasting

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Lin, Qiaoying Huang, Fangwan Yu, Zhiyong Li, Li College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

In the recent decade, recurrent neural networks become a hot research field, with the powerful capability to capture temporal information from time series. To avoid the vanishing/exploding gradient problem caused by the gradient descent algorithm involved in most recurrent networks, echo state network (ESN) was proposed to contain a large but sparse reservoir instead of traditional hidden layers. However, the performance of ESN is very sensitive to the parameters of the reservoir. In this paper, we focus on the improvements of ESN in the background of electricity load forecasting. With the goal of effectively and efficiently computation in the context of pervasive and cloud computing, two versions of adaptive echo state network (AESN) are designed to adopt a modular control strategy to automatically adjust some parameters of the reservoir. Applying AESN on two synthetic datasets and two real world electricity datasets, experimental results demonstrate that AESN is viable. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Unveiling GRACE-based estimation techniques: insights from multichannel singular spectrum analysis of geocentre motion

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Geophysical Journal International 2025年第1期242卷

作者： Yu, Hongjuan Zhang, Yong Sun, Yu Sośnica, Krzysztof School of Geomatics Liaoning Technical University Fuxin123000 China Key Lab of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou University Wulongjiang North Road Fujian Fuzhou350108 China Institute of Geodesy and Geoinformatics Wroclaw University of Environmental and Life Sciences Grunwaldzka 53 Wroclaw50-357 Poland

This study aims to provide valuable scientific insights into various estimation techniques of geocentre motion (GCM) from the perspective of signal analysis, thereby enhancing Gravity Recovery and Climate Experiment (GRACE) users' understanding and application of GCM. Initially, it utilizes the satellite laser ranging (SLR) technique with the network shift approach to estimate over 30 yr of weekly GCM time-series from 1994 to 2024. Subsequently, we employ two approaches to estimate three types of monthly GCM time-series spanning more than 20 yr from 2002 to 2023: combining GRACE data with an ocean bottom pressure model (GRACE-OBP approach), the fingerprint approach (FPA), and the fingerprint approach with satellite altimetry data (FPA-SA, up to 2022). The former is referred to as SLR-based GCM estimates, while the latter, which uses GRACE Earth's gravity field models, is termed GRACE-based GCM estimates. Furthermore, this study pioneers the use of multichannel singular spectrum analysis (MSSA) for GCM analysis, especially focusing on the latest GRACE-based GCM estimates from the GRACE-OBP and FPA/FPA-SA approaches, marking the first comprehensive analysis of GCM estimated by various techniques. The results show that MSSA can effectively extract common signals from the three components of the GCM time-series. The seasonal components extracted from GRACE-based GCM estimates using MSSA are consistent with those from SLR-based GCM estimates, although the former exhibit slightly larger amplitudes of the annual and semi-annual signals. After correcting the atmosphere-ocean dealiasing, the amplitudes of the SLR-based estimates correspondingly decrease, remaining slightly larger but becoming closer to those of the GRACE-based estimates. However, a periodic signal with an approximate 160-d period is detectable in all GRACE-based GCM estimates, but it is absent in SLR-based GCM estimates. Further investigation using MSSA into higher degree spherical harmonic (SH) coefficients

关键词： Geodetic satellites

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An Overlapping Local Community Detection Algorithm Based on Node Transitivity and Modularity Density 15th

An Overlapping Local Community Detection Algorithm Based on ...

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15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Huang, Xintong Wu, Ling Guo, Kun College of Mathematics and Computer Sciences Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350116 China

ISBN: (纸本)9789811625398

The local community detection (LCD) method can discover the local community structure in which the seed node is located. Compared with global community detection, local community detection is characterized by its low cost and high efficiency. However, most existing LCD methods only return a non-overlapping community. Individuals in the real world may participate in multiple communities, which can only be discovered by using overlapping local community detection methods. In this study, an overlapping local community detection algorithm based on modularity and node transitivity. First, the scope and structure information of the overlapping communities are obtained according to the node transitivity. Second, NMF is used to obtain the number of overlapping communities. Finally, the local modularity density based on edge weights is used to refine the detected local communities. The experimental results validate the high performance of our method to the other method in comparison. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Complex networks

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Using Deep Active Learning to Save Sensing Cost When Estimating Overall Air Quality 15th

Using Deep Active Learning to Save Sensing Cost When Estimat...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Lei, Dehao Yu, Zhiyong Li, Peiguan Han, Lei Huang, Fangwan College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

Air quality is widely concerned by the governments and people. To save cost, air quality monitoring stations are deployed at only a few locations, and the stations are actuated at partial time. Therefore, it is necessary to study how to actively collect a subset of air quality data to maximize the estimation accuracy of air quality at other locations and time. In order to solve this challenge, we propose the active variational adversarial model (AVAM) that selects the most valuable unlabeled samples through two iterative phases of active learning. In the first phase of our model, a candidate set with unlabeled samples is selected through traditional active learning. In the second phase, variational auto-encoder (VAE) is used to obtain the compressed representation of the candidate set and the training set with labeled samples, then a discriminator based on three-layer neural network is trained from the compressed representation. Finally the discriminator can output the most valuable unlabeled samples from the candidate set. The experimental results show that the AVAM proposed in this paper is superior to active learning models with the first or second phase only. © 2020, Springer Nature Switzerland AG.

关键词： Air quality

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Design and implementation of marine dynamic environmental data integrating and information sharing system of Fujian Province

Design and implementation of marine dynamic environmental da...

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9th AGILE International Conference on Geographic information Science: "Shaping the Future of Geographic information Science in Europe", AGILE 2006

作者： Fan, Ming-Hui Chen, Chong-Cheng Chi, Tian-He Lin, Jia-Xiang Key Laboratory of Data Mining and Information Sharing of Ministry of Education Spatial Information Research Center Fuzhou University Fuzhou 350002 China Institute of Remote Sensing Application Chinese Academy of Science Beijing 100101 China

A general information integrating and sharing architecture is discussed, the components of the architecture are put forward, a data integrating method based on distributed data warehouse together with a information sh... 详细信息

关键词： information services

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A Distributed Storage Middleware Based on HBase and Redis 15th

A Distributed Storage Middleware Based on HBase and Redis

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15th CCF Conference on Computer Supported Cooperative Work and Social Computing, Chinese CSCW 2020

作者： Xu, Lingling Chen, Yuzhong Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

In the era of big data, traditional relational databases face challenges in massive data storage. HBase is a non-relational database based on column storage which is used widely for big data storage. The writing performance of HBase is high, but the unbalanced load caused by its uneven data storage strategy is the bottleneck of its reading performance. HBase needs to access disks to get query results. Therefore, the efficiency of disk access has a great impact on the query performance of HBase. In view of the above problems, this paper proposes a storage middleware which utilizes HBase for load balancing and Redis for memory caching. Specifically, we improve HBase’s original load balancing algorithm for Regions and RegionServers and customize a Redis cache eviction algorithm according to the data’s query and update frequency. Furthermore, the coprocessor of HBase is used to synchronize data between HBase and Redis. Experiments on synthetic datasets show that the proposed storage middleware achieves better writing and reading performance than HBase. The load balancing algorithm employed in the middleware is better than HBase's original algorithm. The hit rate of the customized cache eviction algorithm is also higher than that of the LRU algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cache memory

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Echo State Network Based on L0 Norm Regularization for Chaotic Time Series Prediction 15th

Echo State Network Based on L0 Norm Regularization for Chaot...

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15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020

作者： Li, Li Huang, Fangwan Yu, Zhiyong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Spatial Data Mining & Information Sharing Ministry of Education Fuzhou China

ISBN: (纸本)9783030642426

The echo state network introduces a large and sparse reservoir to replace the hidden layer of the traditional recurrent neural network, which can solve the gradient-based problem of most recurrent neural networks in the training process. However, there may be an ill-posed problem when the least square method is used to calculate the output weight. In this paper, we proposed an echo state network based on L0 norm regularization. The main idea is to limit the number of output connections to compute the output effectively by removing unimportant ones. The simulation results of the chaotic time series prediction show the effectiveness of the proposed model. © 2020, Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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