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3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Zhu, Meijiang Wang, Lei Chen, Fang Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

Landslides pose a serious threat to the economy and human safety. Rapid and efficient landslide extraction is essential for post-disaster relief and reconstruction efforts. Current landslide extraction techniques still face challenges in accurately identifying landslides boundaries. To address this issue, we propose the Parallel Attention Change Detection Network (PACDNet), a network designed to extract landslides by calculating changes between pre-disaster and post-disaster images. Our method is an encoder-decoder network that utilizes a Siamese network structure with StarBlock from StarNet in the encoding stage, allowing for higher feature extraction capability with lower computational costs. In the decoding phase, the Parallel Attention Module (PAM) enhances the model's landslide boundaries extraction capability by simultaneously computing spatial attention and channel attention. To validate the effectiveness of our proposed PACDNet, we used the Global Very-High-Resolution Landslide Mapping (GVLM) dataset. This dataset includes pre-disaster and post-disaster images of 17 different landslide events. After comparing our model with four recently proposed models, PACDNet demonstrated improvements of 2.31% and 3.41% in Intersection over Union (IoU) on the Kaikoura and Tbilisi datasets, respectively. This indicates that PACDNet has a significant advantage in landslide extraction across different landslide events. © 2025 SPIE.

关键词： Network coding

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WTUNet: A U-shaped semantic segmentation network combined with wavelet convolution 3

WTUNet: A U-shaped semantic segmentation network combined wi...

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3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Shi, Guangyue Wang, Lei Chen, Fang Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

Landslide extraction plays a critical role in facilitating effective rescue operations and emergency response. However, existing methods face difficulties in accurately extracting landslide areas due to complex terrain conditions, where variable textures, shapes, and spectral features can obscure critical details. These challenges are particularly evident in texture features, which spatial-domain methods alone struggle to capture reliably under varying lighting and background conditions. To address these issues, this study introduces WTUNet, a novel U-shaped semantic segmentation model that integrates frequency-domain feature extraction to enhance texture representation. WTUNet’s key innovation is the integration of a wavelet transform layer following each convolutional layer, enabling the model to capture frequency-domain features through wavelet decomposition while retaining spatial features from the convolutional layers. This dual-domain feature extraction approach is further optimized by dynamic feature fusion technology, which merges these diverse feature sets to enrich the semantic representation within feature maps, thereby substantially enhancing extraction performance. To evaluate WTUNet’s effectiveness, we conducted training and testing on the Bijie and Jiuzhaigou datasets, benchmarking it against three leading semantic segmentation models. Results demonstrate that wavelet transform integration meaningfully strengthens semantic feature representation, yielding at least a 2% Mean Intersection over Union(mIoU) improvement on the Bijie dataset and a 3% improvement on the Jiuzhaigou dataset, underscoring WTUNet's superior landslide extraction capabilities. © 2025 SPIE.

关键词： Semantic Segmentation

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FRMT: RMT with Frequency Feature learning Framework for land cover classification 3

FRMT: RMT with Frequency Feature learning Framework for land...

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3rd International Conference on Environmental Remote Sensing and Geographic Information Technology, ERSGIT 2024

作者： Ou, Zhihui Chen, Fang Wang, Lei Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

The classification of land cover is of great significance in the management and sustainable development of land resources. In recent years, deep learning methods have made remarkable progress in the field of remote sensing image processing. However, due to the complex difference in structure and scale of surface objects in remote sensing images, most of the current methods are prone to the problem of unclear boundary extraction, and the classification results of different scale surface objects are poor. In this letter, we proposed FRMT by introducing frequency domain information into RMT framework. A frequency feature learning module (FFLM) is proposed. The frequency domain features are decomposed into high frequency and low frequency features, and the frequency features are learned by spiking convolution to strengthen the model's learning of boundary and different scale features. We performed experimental validation on the very challenging multi-class remote sensing dataset GID-15 and Huawei Ascend Cup competition dataset, and the experimental results show that our model achieves the best results compared to five other recently proposed models, which mIoU achieves 77.91% and 71.85%, respectively. © 2025 SPIE.

关键词： Frequency domain analysis

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The extent changes of China's wetlands

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Science Bulletin 2025年第11期70卷 1714-1715页

作者： Guo, Huadong International Research Center of Big Data for Sustainable Development Goals Beijing100094 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Academy of Sciences Beijing101408 China

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Research on performance optimization of virtual data space across WAN

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Frontiers of Computer Science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance Computing Virtual data Space”,a part of the National Key Research and development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

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development and validation of a nomogram model for predicting overall survival in patients with gastric carcinoma

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World Journal of Gastrointestinal Oncology 2025年第2期17卷 132-143页

作者： Guan-Zhong Liang Xiao-Sheng Li Zu-Hai Hu Qian-Jie Xu Fang Wu Xiang-Lin Wu Hai-Ke Lei Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment Chongqing University Cancer HospitalChongqing 400030China Chongqing Cancer Multi-omics Big Data Application Engineering Research Center Chongqing University Cancer HospitalChongqing 400030China Department of Health Statistics School of Public HealthChongqing Medical UniversityChongqing 400016China Research Center for Medicine and Social Development School of Public HealthChongqing Medical UniversityChongqing 400016China The Research Center of Big Data Chongqing University Cancer HospitalChongqing 400030China

BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health *** forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this *** To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric *** data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and *** absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient *** model's predictability and discriminative ability were evaluated via receiver operating characteristic *** evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were *** A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte *** area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,*** AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,*** model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding *** with TNM staging,the model demonstrated superior clinical ***,a nomogram was developed via a web-based *** This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrat

关键词： Gastric carcinoma Prediction Overall survival Nomogram Prospective

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Hydrogen Energy Demand Management in China:A Department Scenario Analysis Method

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Energy Engineering 2025年第3期122卷 971-983页

作者： Zhongxun Li Bing Wang Xiaolin Liu Center for Sustainable Development and Energy Policy Research School of Energy and Mining EngineeringChina University of Mining and Technology-BeijingBeijing100083China Research Institute of Decision-Making Science and Big Data School of ManagementChina University of Mining and Technology-BeijingBeijing100083China

The proposal of carbon neutrality target makes decarbonization and hydrogenation typical features of future energy development in *** a wide range of application scenarios,hydrogen energy will experience rapid growth in production and *** formulate an effective hydrogen energy development strategy for the future of China,this study employs the departmental scenario analysis method to calculate and evaluate the future consumption of hydrogen energy in China’s heavy industry,transportation,electricity,and other related *** technical parameters are selected and predicted accurately and reliably in combination with different development *** findings indicate that the period from 2030 to 2050 will enjoy rapid development of hydrogen energy,having an average annual growth rate of 2%to 4%.The technological progress and breakthroughs scenario has the greatest potential for hydrogen demand scale among the four development *** this scenario,the total demand for hydrogen energy is expected to reach 446.37Mt in *** sector will be the sector with the greatest potential for hydrogen deployment growth from 2023 to 2060,which is expected to rise from 0.038Mt to about 163.18Mt,with the ambitious growth in the ***,hydrogen energy has a considerable development potential in the steel sector,and the trend of de-refueling coke by hydrogenation in this sector will be imperative for this energy-intensive industries.

关键词： Hydrogen demand management department scenario analysis carbon neutrality

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Automatic 3D modeling and exploration of indoor structures from panoramic imagery 24

Automatic 3D modeling and exploration of indoor structures f...

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2024 SIGGRAPH Asia 2024 Courses, SA Courses 2024

作者： Gobbetti, Enrico Pintore, Giovanni Agus, Marco National Research Center in High-Performance Computing Big Data and Quantum Computing Cagliari Italy Visual and Data-intensive Computing Center for Advanced Studies Research and Development in Sardinia Cagliari Italy College of Science and Engineering Hamad Bin Khalifa University Doha Qatar

ISBN: (纸本)9798400711350

Surround-view panoramic imaging delivers extensive spatial coverage and is widely supported by professional and commodity capture devices. Research on inferring and exploring 3D indoor models from 360° images has recently flourished, resulting in highly effective solutions. Nevertheless, challenges persist due to the complexity and variability of indoor environments and issues with noisy and incomplete data. This course provides an up-to-date integrative view of the field. After introducing a characterization of input sources, we define the structure of output models, the priors exploited to bridge the gap between imperfect input and desired output, and the main characteristics of geometry reasoning and data-driven approaches. We then identify and discuss the main sub-problems in indoor reconstruction from panoramas and review and analyze state-of-the-art solutions for indoor capture, room modeling, integrated model computation, visual representation generation, and immersive exploration. Relevant examples of implemented pipelines are described, focusing on deep-learning solutions. We finally point out relevant research issues and analyze research trends. © 2024 Copyright held by the owner/author(s).

关键词： 3D reconstruction

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PPO Based Reinforcement Learning Horizontal Pods Auto-Scaling-A Case Study in Simulation Environment 4

PPO Based Reinforcement Learning Horizontal Pods Auto-Scalin...

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4th International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2023

作者： Peng, Fang Ji, Xin Zhang, Le Chen, Ruibo Liao, Xingchuang Zhang, Kui State Grid Corporation of China Big Data Center Beijing China Beihang University State Key Laboratory of Software Development Environment Beijing China

ISBN: (纸本)9798350308082

Microservice has become a state-of-art architecture in the software engineering domain. In the big data scenario, the data can be encapsulated by microservice, providing independent service or orchestrating the business procedure. Much research, such as threshold-based, rule-based, and even deep learning-based resource scaling, has been proposed to tackle this problem. However, the model trained on data like log or monitor index limited the intended dynamic resource adjusting, which caused high latency during adjusting. Moreover, the computation complexity and the data latency can still be a hindrance to the problem-solving. This paper proposed a reinforcement learning-based horizontal pods auto-scaling algorithm to study and respond in low latency. Furthermore, a case study to validate the effectiveness was carried out in a simulation environment. The results show that the proposed algorithm can converge after 0.3 million steps, and three groups of experiments were carried out, which shows that the trained reinforcement learning model can adjust the replica number in real-time, which indicates that the proposed algorithm can be applied in a scenario with low resource consumption. © 2023 IEEE.

关键词： Reinforcement learning

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Displacement prediction for mountain road slopes based on variational mode decomposition and LSTM

Displacement prediction for mountain road slopes based on va...

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2024 International Conference on Smart Transportation and City Engineering, STCE 2024

作者： Li, Jiahang Yan, Qi Yuan, Rui Wang, Linhan Tian, Le Zhang, Weiwei Hua, Liru China Communications Information Technology Group Co. Ltd. Research and Development Center of Transport Industry of Big Data Processing Technologies and Application for Comprehensive Transport Ministry of Transport Beijing China

ISBN: (纸本)9781510690585

Mountain roads serve as vital links connecting remote areas with the outside world. The stability of road slopes in these regions is particularly significant due to complex terrain and variable geological conditions. Accurately predicting slope displacement is crucial for disaster prevention and ensuring traffic safety. This paper proposes a predictive model that combines variational mode decomposition (VMD) and long short-term memory neural network (LSTM) to improve the accuracy and reliability of slope displacement prediction for mountain roads. The original slope displacement data is preprocessed using VMD, decomposing it into trend, periodic, and random modal components. Then, the LSTM model is utilized to predict each modal component. Experimental results show that the proposed VMD-LSTM model outperforms single-use LSTM, XGBoost, and ELM models in terms of prediction accuracy, with RMSE, MAE, and MAPE values of 0.102, 0.098, and 2.854%, respectively, demonstrating the effectiveness and superiority of the model. © 2025 SPIE.

关键词： Variational mode decomposition

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