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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Linhan Wu Jianxiao Wang Jie Yan Jie Song College of Engineering Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Ordos Research Institute of Energy Beijing China School of New Energy North China Electric Power University Beijing China College of Engineering Peking University Peking University Ordos Research Institute of Energy Beijing China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

Enhancing the resilience of power systems against the threats posed by typhoon events has become increasingly critical. Evaluating the potential damage to specific transmission lines prior to a typhoon’s arrival and implementing targeted reinforcement measures, along with achieving optimal economic dispatch during the typhoon, are essential challenges that must be addressed. To tackle these issues, this paper presents a novel economic dispatch strategy tailored for typhoon scenarios. This strategy utilizes Long Short-Term Memory (LSTM) neural networks to construct a power generation model for wind farms under typhoon scenarios and evaluates the power line fault conditions in the system at various time intervals during these scenarios. Furthermore, it incorporates an economic dispatch model that operates post-fault to schedule the power output of generation resources at different times. Finally, the efficacy of this strategy is demonstrated through an enhanced IEEE 14-node case study, showcasing its significant potential for improving the system’s adaptability to typhoon-related challenges.

关键词： Adaptation models Costs Tropical cyclones Wind speed Wind farms Power systems Wind turbines Power generation Long short term memory Load modeling

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Graph Condensation for Inductive Node Representation Learning

arXiv

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arXiv 2023年

作者： Gao, Xinyi Chen, Tong Zang, Yilong Zhang, Wentao Hung Nguyen, Quoc Viet Zheng, Kai Yin, Hongzhi The University of Queensland Brisbane Australia Kaiserslautern Germany Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Griffith University Gold Coast Australia University of Electronic Science and Technology of China Chengdu China

Graph neural networks (GNNs) encounter significant computational challenges when handling large-scale graphs, which severely restricts their efficacy across diverse applications. To address this limitation, graph condensation has emerged as a promising technique, which constructs a small synthetic graph for efficiently training GNNs while retaining performance. However, due to the topology structure among nodes, graph condensation is limited to condensing only the observed training nodes and their corresponding structure, thus lacking the ability to effectively handle the unseen data. Consequently, the original large graph is still required in the inference stage to perform message passing to inductive nodes, resulting in substantial computational demands. To overcome this issue, we propose mapping-aware graph condensation (MCond), explicitly learning the one-to-many node mapping from original nodes to synthetic nodes to seamlessly integrate new nodes into the synthetic graph for inductive representation learning. This enables direct information propagation on the synthetic graph, which is much more efficient than on the original large graph. Specifically, MCond employs an alternating optimization scheme with innovative loss terms from transductive and inductive perspectives, facilitating the mutual promotion between graph condensation and node mapping learning. Extensive experiments demonstrate the efficacy of our approach in inductive inference. On the Reddit dataset, MCond achieves up to 121.5× inference speedup and 55.9× reduction in storage requirements compared with counterparts based on the original graph. Copyright © 2023, The Authors. All rights reserved.

关键词： Condensation

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Graph Condensation for Inductive Node Representation Learning

Graph Condensation for Inductive Node Representation Learnin...

引用

International Conference on data engineering

作者： Xinyi Gao Tong Chen Yilong Zang Wentao Zhang Quoc Viet Hung Nguyen Kai Zheng Hongzhi Yin The University of Queensland Brisbane Australia German Research Centre for Artificial Intelligence (DFKI) Kaiserslautern Germany Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Griffith University Gold Coast Australia University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

关键词： Training Representation learning Social networking (online) Network topology Message passing data engineering Robustness

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Externality Value-Based Pricing Mechanism for Energy and Ancillary Service Markets

Externality Value-Based Pricing Mechanism for Energy and Anc...

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Energy, Electrical and Power engineering (CEEPE), International Conference on

作者： Yiyang Song Tiance Zhang Lin Wen Jianxiao Wang Dan Zeng Lei Cui School of Electrical and Electronic Engineering North China Electric Power University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Power Automation Department China Electric Power Research Institute State Grid Nanjing China

The high proportion of renewable energy integration and the sharp fluctuation of load bring huge backup demand to the power system, which requires the auxiliary service of thermal power units to ensure the safety of the power system. However, the widely used marginal price mechanism is difficult to identify the externalities value of market members and the value of ancillary services provided by generators. To accurately identify the externality value of market members, the Vickey-Clarke-Groves (VCG) mechanism based on incremental costs was introduced. Compared with the marginal price mechanism, the VCG mechanism requires removing the generators sequentially and re-optimizing, which is computationally inefficient. An accelerated algorithm based on optimal base replacement is proposed, which uses the dual simplex method to perform optimal basis replacement based on the initial optimal basis to obtain the optimal solution of the new problem after removing generators. Case studies based on modified IEEE30-bus system demonstrate the significant improvement of the acceleration algorithm on the computational efficiency of the VCG mechanism.

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INFORMATION GAIN PROPAGATION: A NEW WAY TO GRAPH ACTIVE LEARNING WITH SOFT LABELS

arXiv

引用

arXiv 2022年

作者： Zhang, Wentao Wang, Yexin You, Zhenbang Cao, Meng Huang, Ping Shan, Jiulong Yang, Zhi Cui, Bin School of CS Peking University China Apple United States National Engineering Laboratory for Big Data Analysis and Applications Institute of Computational Social Science Peking University Qingdao China

Graph Neural Networks (GNNs) have achieved great success in various tasks, but their performance highly relies on a large number of labeled nodes, which typically requires considerable human effort. GNN-based Active Learning (AL) methods are proposed to improve the labeling efficiency by selecting the most valuable nodes to label. Existing methods assume an oracle can correctly categorize all the selected nodes and thus just focus on the node selection. However, such an exact labeling task is costly, especially when the categorization is out of the domain of individual expert (oracle). The paper goes further, presenting a soft-label approach to AL on GNNs. Our key innovations are: i) relaxed queries where a domain expert (oracle) only judges the correctness of the predicted labels (a binary question) rather than identifying the exact class (a multi-class question), and ii) new criteria of maximizing information gain propagation for active learner with relaxed queries and soft labels. Empirical studies on public datasets demonstrate that our method significantly outperforms the state-of-the-art GNN-based AL methods in terms of both accuracy and labeling cost. Copyright © 2022, The Authors. All rights reserved.

关键词： Graph neural networks

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Learning Superconductivity from Ordered and Disordered Material Structures 38

Learning Superconductivity from Ordered and Disordered Mater...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Pin Peng, Luoxuan Jiao, Rui Mo, Qing Wang, Zhen Huang, Wenbing Liu, Yang Lu, Yutong National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University China Dept. of Comp. Sci. & Tech. Institute for AI BNRist Center Tsinghua University China Institute for AIR Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Superconductivity is a fascinating phenomenon observed in certain materials under certain conditions. However, some critical aspects of it, such as the relationship between superconductivity and materials' chemical/structural features, still need to be understood. Recent successes of data-driven approaches in material science strongly inspire researchers to study this relationship with them, but a corresponding dataset is still lacking. Hence, we present a new dataset for data-driven approaches, namely SuperCon3D, containing both 3D crystal structures and experimental superconducting transition temperature (Tc) for the first time. Based on SuperCon3D, we propose two deep learning methods for designing high Tc superconductors. The first is SODNet, a novel equivariant graph attention model for screening known structures, which differs from existing models in incorporating both ordered and disordered geometric content. The second is a diffusion generative model DiffCSP-SC for creating new structures, which enables high Tc-targeted generation. Extensive experiments demonstrate that both our proposed dataset and models are advantageous for designing new high Tc superconducting candidates. © 2024 Neural information processing systems foundation. All rights reserved.

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Translational analysis of data science and causal learning in real-world clinical evaluation of traditional Chinese medicine

Science of Traditional Chinese Medicine

引用

Science of Traditional Chinese Medicine 2024年第1期2卷 57-65页

作者： Wei Yang Danhui Yi XiaoHua Zhou Yuanming Leng Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijingChina School of Statistics Renmin University of ChinaBeijingChina Beijing International Center for Mathematical Research Peking UniversityBeijingChina Department of Biostatistics School of Public HealthPeking UniversityBeijingChina National Engineering Laboratory of Big Data Analysis and Applied Technology Peking UniversityBeijingChina Department of Biostatistics School of Public HealthBoston UniversityBostonMAUSA

Real-world clinical evaluation of traditional Chinese medicine(RWCE-TCM)is a method for comprehensively evaluating the clinical effects of TCM,with the aim of delving into the causality between TCM intervention and clinical *** study explored data science and causal learning methods to transform RWD into reliable real-world evidence,aiming to provide an innovative approach for *** study proposes a 10-step data science methodology to address the challenges posed by diverse and complex data in *** methodology involves several key steps,including data integration and warehouse building,high-dimensional feature selection,the use of interpretable statistical machine learning algorithms,complex networks,and graph network analysis,knowledge mining techniques such as natural language processing and machine learning,observational study design,and the application of artificial intelligence tools to build an intelligent engine for translational *** goal is to establish a method for clinical positioning,applicable population screening,and mining the structural association of TCM characteristic *** addition,the study adopts the principle of real-world research and a causal learning method for TCM clinical *** constructed a multidimensional clinical knowledge map of“disease-syndrome-symptom-prescription-medicine”to enhance our understanding of the diagnosis and treatment laws of TCM,clarify the unique therapies,and explore information conducive to individualized *** causal inference process of observational data can address confounding bias and reduce individual heterogeneity,promoting the transformation of TCM RWD into reliable clinical *** data science improves efficiency and accuracy for implementing *** proposed data science methodology for TCM can handle complex data,ensure high-quality RWD acquisition and analysis,and provide in-depth insights into clinical benefits of *** method supp

关键词： Traditional Chinese medicine Real-world study data science Causal learning Health information system Machine learning Artificial intelligence Treatment effects

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Fast extraction of winter wheat planting area in Huang-Huai-Hai Plain using high-resolution satellite imagery on a cloud computing platform

引用

International Journal of Agricultural and Biological engineering 2022年第1期15卷 241-250页

作者： Dongyan Zhang Mengru Zhang Fenfang Lin Zhenggao Pan Fei Jiang Liang He Hang Yang Ning Jin National Engineering Research Center for Agro-Ecological Big Data Analysis&Application Anhui UniversityHefei 230601China Key Laboratory of Geospatial Technology for Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475004HenanChina School of Informatics and Engineering Suzhou UniversitySuzhou 234000AnhuiChina National Meteorological Center Beijing 100081China Aerospace Information Research Institute Chinese Academy of ScienceBeijing 100101China

To extract regional winter wheat planting area using higher-resolution satellite imagery still faces many challenges due to large data size and long processing time in traditional remote sensing *** Earth Engine(GEE),a cloud computing analysis platform based on global geospatial analysis,provides a new opportunity for rapid analysis of remote sensing *** this study,high-quality Landsat-8 imagery was used to extract the winter wheat planting area from the Huang-Huai-Hai Plain in *** random forest algorithm was used to identify and map the winter wheat sown in 2019 and harvested in 2020,and Sentinel-2 imagery was used to verify the *** spectral indices,texture,and terrain features of the image were derived,and their contribution to the classification accuracy of winter wheat was evaluated by *** the top nine features were selected to form an optimal feature *** the set of thirty-four features and the optimized feature subset as the input variables of the random forest classifier,the results show that the accuracy difference between the two feature classification schemes is small,but the classification effect of all feature sets is slightly better than the optimal feature *** overall classification accuracy of sample plots verification was 86%-95%,the Kappa coefficient was between 0.7 and 0.85,and the percentage error of the total area was 5.42%.The research demonstrates a reliable method for mapping a wide range of winter wheat planting area,and provides a good prospect for exploring the precise mapping of other crops,which is of great significance to crop monitoring and agricultural development.

关键词： Google Earth Engine regional scale winter wheat rapid mapping

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PropNet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans

arXiv

引用

arXiv 2023年

作者： Chen, Zifan Li, Jiazheng Zhao, Jie Liu, Yiting Li, Hongfeng Dong, Bin Tang, Lei Zhang, Li Center for Data Science Peking University Beijing China Radiology Department Peking University Cancer Hospital & Institute Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Center for Health and Medical Data Science Peking University Beijing China Peking University Beijing China

Background: Accurate segmentation of gastric tumors on 3D CT scans is essential for diagnosis and treatment planning. However, irregular shapes and blurred boundaries of tumors make automatic segmentation challenging. Existing 2D-based methods do not model 3D morphology effectively, and 3D-based methods are computationally expensive and require annotated samples. Some studies have used few-shot learning (FSL) to train models with prior knowledge, but these methods cannot handle irregular shapes and blurred boundaries. Purpose: In this pilot study, we anticipate that the human-guided prior knowledge and specially designed modules will address the challenges of 3D segmentation of gastric tumors described above. Drawing inspiration from FSL, we aim to enable the model to learn from information propagation between slices rather than from specific segmentation annotation. Methods: We propose the PropNet framework to achieve 3D segmentation of gastric tumors by propagating the radiologist's prior knowledge from the 2D annotated slice into the entire 3D CT space. The proposing stage uses an inter-slice context module to extract contextual information between adjacent slices and generate a coarse segmentation. The refining stage then corrects difficult-to-segment pixels for improved segmentation. We use two-way branches for boundary and region in each stage to enhance the segmentation performance at tumor boundaries. Furthermore, we adopt an up-down parallel propagation strategy to speed up our model's predictions. Results: Our study utilizes 98 patients (173 CT scans) as a training set and 30 patients (59 CT scans) as a validation set. The proposed method achieves a comparable agreement with manual annotation (Dice of 0.803 with a p-value of 0.563) and radiologists (Dice of 0.797) on the validation set while significantly improving efficiency with only 40 seconds for annotation and segmentation of a tumor. Furthermore, we test our proposed method on an independent external

关键词： Refining

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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022

引用

China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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