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Developing a new red band- SEVI-blue band (RSB) enhancement method for recognition the extra-high-voltage transmission line corridor in green mountains

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International Journal of Digital Earth 2023年第1期16卷 806-824页

作者： Hong Jiang Yong Zhang Jinglan Lin Xiaogan Zheng Hui Yue Yunzhi Chen Key Laboratory of Spatial Data Mining&Information Sharing of Ministry of Education National&Local Joint Engineering Research Center of Satellite Geospatial Information TechnologyAcademy of Digital China(Fujian)Fuzhou UniversityFuzhouPeople’s Republic of China Fujian Soil and Water Conservation Experimental Station FuzhouPeople’s Republic of China Lab of Live Working Tech for Power Transmission Fuzhou Power Supply Company of State Grid Fujian Electric Power Co.Ltd.FuzhouPeople’s Republic of China Fujian(Changting)Soil and Water Conservation Research Center Soil and Water Conservation Center of Changting CountyLongyanPeople’s Republic of China

Monitoring the extra-high-voltage transmission line corridor(EHVTLC)in mountains is critical for safe smart-grid ***,the transmission lines are so narrow that they are difficult to recognize using multispectral satellite images with a spatial resolution of 10 *** this study,we developed a new method using the red band–shadow-eliminated vegetation index(SEVI)–blue band(RSB)composite image to enhance the EHVTLC in green mountains(named RSB-enhancement method).Using this method,the EHVTLC becomes evident in the false-color synthesis of the RSB composite of the Sentinel-2 ***,we recognized and extracted approximately 342.45 km of the EHVTLC in a mountainous region of Fuzhou City,China,including a 46.73 km three-parallel-lane segment of 1000 kV and a 295.72 km two-parallel-lane segment of 500 *** analysis shows that the SEVI mean difference between the EHVTLC and the buffer zone reaches approximately 10%,and three landslides and 2.66 km^(2) soil erosion reside in the buffer zone which area is approximately 73.67 km^(2).Finally,the RSB-enhancement method can be used in other satellite images with spatial resolutions of greater than 10 m for enhancement and recognition the transmission line corridors in green mountains.

关键词： Enhancement method shadow-eliminated vegetation index(SEVI) transmission line lane(TLL) green mountains soil erosion

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NMR Metabolite Spectrum Denoising with Improved U-Net++

NMR Metabolite Spectrum Denoising with Improved U-Net++

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IEEE/ICME International Conference on Complex Medical engineering, CME

作者： Wenjie Fang Hao Fu Di Guo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350316117

ISBN: (纸本)9798350316124

Nuclear Magnetic Resonance (NMR) spectroscopy serves as a significant tool for capturing essential information in chemical reactions. However, in practical applications, NMR signals are frequently afflicted by Gaussian noise, leading to challenges in sequence analysis and quantification. In this paper, we have devised a deep neural network named NMRDnet, with the Unet++ architecture at its core, incorporating modules such as CBAM and soft-thresholding. This network was trained using a simulated dataset based on the exponential characteristics of NMR spectroscopic signals. The experimental results show that the NMRDnet network can effectively remove the noise and restore the original signal. It is superior to the low-rank Hankel matrix denoising method in maintaining peak signal strength and reducing signal distortion.

关键词： Spectroscopy Sequences Gaussian noise Noise reduction Artificial neural networks Distortion Chemical reactions

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Fast Three-Dimensional NMR Spectrum Reconstruction Using a Block Hankel Matrix Based on Sliding Windows

Fast Three-Dimensional NMR Spectrum Reconstruction Using a B...

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IEEE/ICME International Conference on Complex Medical engineering, CME

作者： Hao Fu Di Guo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen China

ISBN: (数字)9798350316117

ISBN: (纸本)9798350316124

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most promising analytical chemistry techniques, although it takes a long time to acquire data. Three-dimensional NMR spectra offer higher resolution. The Low Rank Hankel Matrix (LRHM) method reconstructs three-dimensional undersampled NMR data by transforming each 2D indirect plain slice into a block Hankel matrix. However, this reconstruction process involves a considerable time overhead. In this work, we introduce a block Hankel matrix based on a sliding window method specifically designed for 3D NMR, to accelerate spectrum reconstruction from NUS signals. This approach can effectively reduce the size of the Hankel matrix for faster reconstruction. Experiments on both synthetic and real data demonstrate that the proposed method achieves the fastest reconstruction speed compared to other methods, without compromising the quality of the spectra.

关键词： Proteins Spectroscopy Three-dimensional displays Correlation Chemistry Graphics processing units Nuclear magnetic resonance

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Privacy risk estimation of online social networks

Privacy risk estimation of online social networks

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2022 International Conference on Networking and Network applications, NaNA 2022

作者： Fu, Shitong Yao, Zhiqiang College of Computer and Cyber Security Fujian Normal University Fuzhou China Fujian Provincial University Engineering Research Center Ich Digitalization and Multi-source Information Fusion Fujian Provincial University Engineering Research Center of Big Data Analysis and Application Fuzhou China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

With the growing risk of privacy breaches in online social networks, privacy protection has become a key issue. To increase users' privacy awareness and protect their data, there is a need for a simple and effective method of quantifying privacy risk. A user with a higher privacy risk score is more likely to face a serious privacy breach. In this paper, we propose an effective and reasonable privacy risk scoring method. Our method takes into account the granularity of the shared profile items, combines sensitivity and visibility, and generates a privacy risk score for each user. With the privacy risk score, users can acquire a more intuitive awareness of their privacy status and then defend it by altering privacy settings or lowering the granularity of shared data. In addition, our experiments analyzing real-world and synthetic datasets demonstrate that our method is capable of effectively assessing user privacy risks in online social networks. © 2022 IEEE.

关键词： Social networking (online)

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Novelty detection technology for Alzheimer's disease based on autoencoder feature extractor and MCD classification 7

Novelty detection technology for Alzheimer's disease based o...

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Real-time Photonic Measurements, data Management, and Processing VII 2023

作者： Zhang, Shuheng Zhang, Yan Pan, Jie Lin, Xintian Ding, Xuemei Yang, Hongqin Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education Provincial Key Laboratory for Photonics Technology Fujian Normal University Fuzhou350117 China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application Fujian Normal University Fuzhou350117 China Xiamen Jingyi zhikang Technology Co. Ltd. Xiamen361000 China

ISBN: (纸本)9781510667938

Alzheimer's disease (AD) is a neurodegenerative disorder that affects the life quality of millions of people worldwide. To diagnose new cases in a timely manner, we propose a new novelty detection technique that combines Autoencoder and Minimum Covariance Determinant (MCD). The technique consists of two steps: first, we use an Autoencoder to extract low-dimensional and discriminative features from the publicly available ADNI dataset, where we only train the Autoencoder with normal data, making the abnormal data more distinguishable in the feature space;second, based on the features of normal data, we use MCD to construct a decision boundary, and judge the degree of abnormality by the distance of the test point to the boundary. Compared with traditional methods without using Autoencoder, our technique has significant advantages in terms of accuracy and sensitivity, and can effectively deal with data imbalance problem. Experimental results show that our method can efficiently detect novel AD cases, and has a wide range of application prospects. © 2023 SPIE.

关键词： Neurodegenerative diseases

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information research Institute.

关键词： Long short-term memory

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research on Zenith Tropospheric Delay Model Based on TCN Improving HGPT2 Model 14th

Research on Zenith Tropospheric Delay Model Based on TCN Imp...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Shi, Danyang Zhao, Jumin Wu, Fanming Yan, Liangquan Feng, Ran Zhang, Xinfang Zhao, Jinhua College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

The zenith tropospheric delay (ZTD) obtained by global navigation satellite system (GNSS) atmospheric sounding is a pivotal data source for water vapor monitoring. Meteorological changes in Antarctica play an important role in analysis of the global climate, but factors such as complex climatic conditions can limit the collection of meteorological data needed for ZTD retrieval. Therefore, it is necessary to establish high-precision ZTD models that do not rely on measured meteorological data. The existing global pressure and temperature 3 (GPT3) model has limited ability to capture complex weather variations and cannot obtain high-precision GPT3_ZTD. To address the above issues, this research proposes a high-precision ZTD model through using temporal convolutional network (TCN) for improving the hourly global pressure and temperature 2 (HGPT2) model. The HGTP2 model based on Fourier analysis and the time-segmentation concept can consider the linear trend of climate variations, simultaneously the TCN is introduced to simulate the long-term temporal dependence of HGPT2_ZTD, so as to achieve the goal of obtaining high-precision ZTD without relying on measured meteorological data. The experimental results show that the precision of TCN_ZTD obtained from the proposed model is higher than GPT3_ZTD and HGPT2_ZTD when using the GNSS_ZTD observations obtained from 35 stations in Antarctica as a reference, and the improvement of the model is obvious. © 2024, Aerospace Information research Institute.

关键词： Fourier analysis

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Heavy Rainfall Prediction Model Using Sample Entropy Derived from GNSS-PWV and PSO-SVM 14th

Heavy Rainfall Prediction Model Using Sample Entropy Derived...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Wu, Fanming Li, Dengao Zhao, Jinhua Feng, Ran Shi, Danyang Zhang, Xinfang Zhao, Jumin College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

There is a growing interest to use Global Navigation Satellite System (GNSS) inversed PWV for heavy rainfall prediction. When heavy rainfall occurs, it requires the atmosphere to contain sufficient water vapour and undergo strong upward motion. However, existing models using GNSS-PWV for heavy rainfall prediction have not accounted for the effect of the complex motion of water vapour. In this paper, an hourly heavy rainfall prediction model using sample entropy derived from GNSS-PWV and PSO-SVM is proposed. The sample entropy of GNSS-PWV is used to measure the complexity of the water vapour movement process before heavy rainfall occurs. Meanwhile, combining GNSS-PWV time domain features and co-located meteorological data, the occurrence of hourly heavy rainfall events is predicted by support vector machine optimized with particle swarm. To verify the validity and feasibility of the proposed algorithm, Hong Kong's HKSC station in Sham Shui Po is used for the model to train and test. The result shows that probability of detection, false alarm rate and critical success index of the proposed algorithm in this paper have significantly improved over other heavy rainfall prediction models. © 2024, Aerospace Information research Institute.

关键词： Support vector machines

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Estimating China's poverty reduction efficiency by integrating multi-source geospatial data and deep learning techniques

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地球空间信息科学学报（英文版） 2024年第4期27卷 1000-1016页

作者： Yao Yao Jianfeng Zhou Zhenhui Sun Qingfeng Guan Zhiqiang Guo Yin Xu Jinbao Zhang Ye Hong Yuyang Cai Ruoyu Wang School of Geography and Information Engineering China University of GeosciencesWuhanChina Center for Spatial Information Science The University of TokyoChibaJapan Alibaba Group HangzhouChina Foshan Surveying Mapping and Geoinformation Research Institute FoshanChina Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities(Ministry of Natural Resources) School of Geographic SciencesEast China Normal UniversityShanghaChina School of Geography and Information Engineering China University of GeosciencesWuhanChina Institute of International Rivers and Eco-Security Yunnan UniversityKunmingChina Institute of Geography and Spatial Information School of Earth SciencesZhejiang UniversityHangzhouChina School of Geography and Planning Sun Yat-Sen UniversityGuangzhouChina Institute of Cartography and Geoinformation ETH ZurichZürichSwitzerland Department of Civil Environmental and Geomatic Engineering School of EngineeringUniversity College LondonLondonUK Centre for Public Health Queen's University BelfastBelfastNorthern IrelandUK

Poverty threatens human development especially for developing countries,so ending poverty has become one of the most important United Nations Sustainable Development Goals(SDGs).This study aims to explore China's progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning *** poverty reduction efficiency(PRE)is measured by the difference in the out-of-poverty rates(which measures the probability of being not poor)of 2016 and *** study shows that the probability of poverty in all regions of China has shown an overall decreasing trend(PRE=0.264),which indicates that the progress in poverty reduction during this period is *** Hu Huanyong Line(Hu Line)shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest *** 2016 to 2019,the centroid of China's out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line,indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to *** results imply that the government's future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor *** study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government's poverty reduction policy.

关键词： Poverty reduction efficiency(PRE) driving forces multisource big data deep learning random forest(RF)

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Energy Consumption-Driven UAV Dock Deployment Planning for Power Inspection

Energy Consumption-Driven UAV Dock Deployment Planning for P...

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2024 Symposium on spatial Information to Empower the Metaverse

作者： Fu, Jing Shao, Guiwei Wang, Zhiye Wu, Shaolong Cai, Huanqing Wu, Huaxi Chen, Chi Yang, Bisheng China Electric Power Research Institute Co. Ltd Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application Ministry of Education of China China Institute of Geo-spatial Intelligence Wuhan University Wuhan China

As an important energy in modern society, the stable operation of electricity is the guarantee of social and economic operation, and the loss caused by the damage of power facilities is immeasurable, so the regular monitoring of power facilities is indispensable. UAVs, with flexible flight mode, have played an important role in power inspection. With the development of UAV docks, UAV inspection has entered a new stage. However, due to the fixed location of the docks and limited coverage, how to achieve the optimal deployment of the docks with the lowest cost and full coverage of power facilities through reasonable deployment is one of the important issues that need to be solved at this stage of dock construction. To solve this problem, this paper proposes an energy-driven adaptive optimization method for dock location by combining geospatial analysis with UAV performance index. The experiment results show that our method can achieve an efficient, reasonable and feasible optimal location for UAV dock deployment. © Author(s) 2024.

关键词： Site selection

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