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Short-term solar eruptive activity prediction models based on machine learning approaches:A review

science China Earth sciences 2024年第12期67卷 3727-3764页

作者： Xin HUANG Zhongrui ZHAO Yufeng ZHONG Long XU Marianna B.KORSÓS R.ERDÉLYI Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China State Key Laboratory of Space Weather National Space Science CenterChinese Academy of SciencesBeijing 100190China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China Beijing Cellular Exploration Science and Technology Center Beijing 100190China Dipartimento di Fisica e Astronomia“Ettore Majorana” Universitàdi CataniaCatania I 95123Italy Department of Astronomy Eötvös Loránd UniversityBudapest H-1112Hungary Hungarian Solar Physics Foundation Gyula H-5700Hungary Solar Physics&Space Plasma Research Center(SP2RC) School of Mathematics and StatisticsUniversity of SheffieldSheffield S37RHUK

Solar eruptive activities,mainly including solar flares,coronal mass ejections(CME),and solar proton events(SPE),have an important impact on space weather and our *** short-term solar eruptive activity prediction is an active field of research in the space weather ***,statistical,and machine learning methods are proposed to build prediction models of the solar eruptive *** the development of space-based and ground-based facilities,a large amount of observational data of the Sun is accumulated,and data-driven prediction models of solar eruptive activities have made a significant *** this review,we briefly introduce the machine learning algorithms applied in solar eruptive activity prediction,summarize the prediction modeling process,overview the progress made in the field of solar eruptive activity prediction model,and look forward to the possible directions in the future.

关键词： Solar flare Coronal mass ejection Solar proton event Machine learning Prediction model

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Predicting the synthesizability of crystalline inorganic materials from the data of known material compositions

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npj Computational Materials 2023年第1期9卷 733-743页

作者： Evan R.Antoniuk Gowoon Cheon George Wang Daniel Bernstein William Cai Evan J.Reed Materials Science Division Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA Department of Chemistry Stanford UniversityStanfordCAUSA Department of Applied Physics Stanford UniversityStanfordCAUSA Google Research Mountain ViewCAUSA Department of Physics Stanford UniversityStanfordCAUSA Department of Mathematics Stanford UniversityStanfordCAUSA Department of Computer Science Stanford UniversityStanfordCAUSA Department of Materials Science and Engineering Stanford UniversityStanfordCAUSA

Reliably identifying synthesizable inorganic crystalline materials is an unsolved challenge required for realizing autonomous materials *** this work,we develop a deep learning synthesizability model(SynthNN)that leverages the entire space of synthesized inorganic chemical *** reformulating material discovery as a synthesizability classification task,SynthNN identifies synthesizable materials with 7×higher precision than with DFT-calculated formation *** a head-to-head material discovery comparison against 20 expert material scientists,SynthNN outperforms all experts,achieves 1.5×higher precision and completes the task five orders of magnitude faster than the best human ***,without any prior chemical knowledge,our experiments indicate that SynthNN learns the chemical principles of charge-balancing,chemical family relationships and ionicity,and utilizes these principles to generate synthesizability *** development of SynthNN will allow for synthesizability constraints to be seamlessly integrated into computational material screening workflows to increase their reliability for identifying synthetically accessible materials.

关键词： inorganic crystalline utilize

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Low-temperature spin Seebeck effect in nonmagnetic vanadium dioxide

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Physical Review B 2024年第2期110卷 024415-024415页

作者： Renjie Luo Tanner J. Legvold Liyang Chen Henry Navarro Ali C. Basaran Deshun Hong Changjiang Liu Anand Bhattacharya Ivan K. Schuller Douglas Natelson Department of Physics and Astronomy Applied Physics Graduate Program Rice Quantum Institute Department of Physics and Center for Advanced Nanoscience Materials Science Division Department of Electrical and Computer Engineering Department of Materials Science and NanoEngineering

The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low-temperature phase is expected to lack magnetic degrees of freedom, as vanadium atoms are thought to form singlets upon dimerization of the vanadium chains. Instead, we find a paramagnetic SSE response in VO2 films that grows as the temperature decreases below 50 K. The field and temperature-dependent SSE voltage is qualitatively consistent with a general model of paramagnetic SSE response and inconsistent with triplet spin transport. Quantitative estimates find a spin Seebeck coefficient comparable in magnitude to that observed in strongly magnetic materials. The microscopic nature of the magnetic excitations in VO2 requires further examination.

关键词： Spin Seebeck effect Paramagnets Strongly correlated systems

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Normal with Occasional Anomalies: Feature Extraction for Detecting Non-Stationary Abnormal Events in Wind Turbines

Normal with Occasional Anomalies: Feature Extraction for Det...

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European Signal Processing Conference (EUSIPCO)

作者： Takuya Wakayama Taiki Inoue Jun Ogata Makoto Iida Tetsuji Ogawa Department of Communications and Computer Engineering Waseda University Artificial Intelligence Research Center National Institute of Advanced Industrial Science and Technology (AIST) Research Center for Advanced Science and Technology The University of Tokyo

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

We explored a feature representation method aimed at identifying behaviors that typically operate normally but occasionally transition to different states, a phenomenon referred to as “normal with occasional anomalies.” In wind turbine failures, observations deviating from normal operation are not consistently obtained; instead, they often resemble normal operation but occasionally produce different observations. Because the observed spectra typically show minimal differences from the normal state overall, detecting such pre-failure signs poses a challenge. In this study, a detection method was developed to accurately identify these pre-failure signs by focusing on transitions between normal and different states. Specifically, for anomaly detection, we introduced the M-measure, which represents temporal changes in posterior probability distributions, to capture the frequency of state transitions. Experimental comparisons using actual vibration data from wind turbine main bearings demonstrated the effectiveness of M-measure features in detecting pre-failure cases where detection is challenging due to significant overlap with normal operation. Additionally, we showed that explicitly capturing the frequency of state transitions using M-measure features enables robust anomaly detection that is resilient to variations in rotational speed.

关键词： Vibrations Switching frequency Posterior probability Velocity control Neural networks Focusing Signal processing Feature extraction Wind turbines Anomaly detection

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Development of High Temperature Resistant Soft Metal Sealing Components in Downhole Tools

Development of High Temperature Resistant Soft Metal Sealing...

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2024 International Petroleum Technology Conference, IPTC 2024

作者： Guo, Huijuan Li, Jianghao Song, Xiping Guodong, David Zhan Wang, Baodong Drilling Mechanical Department CNPC Engineering Technology R&D Company Limited China State Key Laboratory for Advanced Metals and Materials University of Science and Technology Beijing China Drilling Technology Division EXPEC Advanced Research Center Saudi Aramco Saudi Arabia

ISBN: (纸本)9781959025184

The sealing components plays the role of sealing and pressure resistance of downhole tools, which is a key component that determines the safe and reliable operation of downhole tools. With the development of oil and gas exploitation towards deep and ultra-deep wells, the performance requirements of sealing components for downhole tools are gradually improved. Rubber materials are prone to aging and deterioration in downhole high temperature and corrosive environment, resulting in sealing failure and difficulty in maintaining good sealing for a long time. Soft metal materials have the characteristics of high temperature resistance, high sealing pressure and corrosion resistance. The application of high temperature resistant soft metal materials in petroleum engineering sealing components can better solve the problem of aging failure of rubber sealing components, which is of great significance for ensuring the safety of downhole sealing. Based on the introduction of the application of soft metal materials in petroleum engineering, this paper studies the refining method of copper-based soft metal materials, and determines the formula of soft metal materials with excellent performance by analyzing the microstructure of soft metal materials. Based on the developed soft metal material, the high temperature tensile mechanical properties test, high temperature elastic modulus test were carried out. The feasibility of using copper-based soft metal as sealing element in petroleum engineering was analyzed, and the soft metal sealing element for expansion pipe sealing was designed. The research results show that: (1) Metal material seals have been widely studied at home and abroad, and some products have been maturely applied in the market, with good mechanical properties and sealing performance. (2) By adding Ga, Rh, Si and other alloying elements to copper metal, the metal wettability, temperature resistance and elastic properties of the material can be improved, and the seal

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A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

A Scalable Real-Time Data Assimilation Framework for Predict...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Yin, Junqi Liang, Siming Liu, Siyan Bao, Feng Chipilski, Hristo G. Lu, Dan Zhang, Guannan Oak Ridge National Laboratory National Center for Computational Science Oak RidgeTN United States Florida State University Department of Mathematics TallahasseeFL United States Oak Ridge National Laboratory Computational Science and Engineering Division Oak RidgeTN United States Florida State University Department of Scientific Computing TallahasseeFL United States Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States

ISBN: (纸本)9798350355543

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF);and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations. © 2024 IEEE.

关键词： data assimilation Diffusion models foundation models generative AI high-performance computing uncertainty quantification

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WindVibraTransformer: A Foundational Model for Precise and Robust Wind Turbine Condition Monitoring via Vibration Signals

WindVibraTransformer: A Foundational Model for Precise and R...

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International Conference on Machine Learning and Applications (ICMLA)

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

We aimed to develop WindVibraTransformer, a foundational model based on Transformers applied to vibration spectrograms, to derive precise and robust feature representations for wind turbine condition monitoring. In anomaly detection for wind turbine equipment, it is common to employ inlier modeling, where models are built solely on data from normal operation, with deviations classified as anomalies. Acquiring data for wind turbines, especially from the specific turbine being monitored, is challenging, often resulting in limited availability. However, this limitation can increase susceptibility to false alarms due to environmental variations. Thus, in this study, we explored the use of a vision Transformer trained with self-supervised learning to predict masked spectra conditioned on unmasked ones from vibration spectrograms, serving as the precise and robust feature extractor. Since this model aims to capture relationships within the context of the surroundings rather than solely the overview of the spectrogram, the resulting feature representations are expected to be complex yet less directly influenced by environmental variations. The complexity of these feature representations is addressed by constructing the normal state model using highly expressive normalizing flows. Experimental comparisons conducted on anomaly detection using real wind turbine vibration signals demonstrated that our approach enables highly reliable detection compared to existing unsupervised auto-encoder-based and supervised wind turbine classifier-based feature extraction methods.

关键词： Vibrations Condition monitoring Self-supervised learning Feature extraction Transformers Robustness Data models Wind turbines Anomaly detection Spectrogram

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An energy-aware routing scheme based on a virtual relay tunnel in flying ad hoc networks

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Alexandria engineering Journal 2024年 91卷 249-260页

作者： Hosseinzadeh, Mehdi Ali, Saqib Mohammed, Adil Hussein Lansky, Jan Mildeova, Stanislava Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Hassan Ahmed, Omed Rahmani, Amir Masoud Mehmood, Asif Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Engineering Gachon University Gyeonggi-do Seongnam-si13120 Korea Republic of

Flying ad hoc networks (FANETs) tackle diverse challenges, for example, dynamic topological structure, high mobility of nodes, low density, and energy restrictions. These challenges make problems in designing reliable and stable routing in FANETs. Hence, the design of a routing algorithm is essential for transferring data between drones. In this paper, an energy-aware routing scheme based on a virtual relay tunnel (EARVRT) is proposed in flying ad hoc networks. EARVRT includes a virtual relay tunnel (VRT) that manages relay nodes in the route discovery process and lowers network overhead. Moreover, three metrics, namely route energy, hop counts, and a new metric called path correlation are regarded in the path selection operation. Note that correlation is defined with regard to two scales, including the cosine similarity of two nodes and their distance. EARVRT is evaluated according to delay, network longevity, consumed energy, and packet delivery rate. Then, our scheme is compared with three methods, namely LoCaL, O-LAR, and PSO-GLFR. These results show the superiority and success of EARVRT in comparison with other algorithms. © 2024 The Author(s)

关键词： Drones

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SecureCut: Federated Gradient Boosting Decision Trees with Efficient Machine Unlearning 27th

SecureCut: Federated Gradient Boosting Decision Trees with ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Li, Bowen Zhang, Jian Li, Jie Wu, Chentao Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Yancheng Blockchain Research Institute Yancheng China Blockchain Advanced Research Center Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance and Web3 Shanghai China

ISBN: (纸本)9783031781681

In response to legislation, companies are now mandated to honor the right to be forgotten by erasing user data. Consequently, it has become imperative to enable data removal in Vertical Federated Learning (VFL), where multiple parties provide private features for model training. In VFL, data removal, i.e., machine unlearning, requires removing specific features across all samples, ensuring privacy in federated learning. To address this challenge, we propose SecureCut, a novel Gradient Boosting Decision Tree (GBDT) framework that effectively enables both instance unlearning and feature unlearning without the need for retraining from scratch. Leveraging a robust GBDT structure, we enable effective data deletion while reducing degradation of model performance. Extensive experimental results on popular datasets demonstrate that our method achieves superior model utility and forgetfulness compared to the state-of-the-art methods. To the best of our knowledge, this is the first work that investigates machine unlearning in VFL scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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DeeReCT-APA:Prediction of Alternative Polyadenylation Site Usage Through Deep Learning

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Genomics, Proteomics & Bioinformatics 2022年第3期20卷 483-495页

作者： Zhongxiao Li Yisheng Li Bin Zhang Yu Li Yongkang Long Juexiao Zhou Xudong Zou Min Zhang Yuhui Hu Wei Chen Xin Gao King Abdullah University of Science and Technology(KAUST) Computational Bioscience Research Center(CBRC)ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionThuwal 23955-6900Saudi Arabia Department of Biology Southern University of Science and Technology(SUSTech)Shenzhen 518055China Cancer Science Institute of Singapore Singapore 117599Singapore

Alternative polyadenylation(APA)is a crucial step in post-transcriptional *** bioinformatic studies have mainly focused on the recognition of polyadenylation sites(PASs)in a given genomic sequence,which is a binary classification ***,computational methods for predicting the usage level of alternative PASs in the same gene have been ***,all of them cast the problem as a non-quantitative pairwise comparison task and do not take the competition among multiple PASs into *** address this,here we propose a deep learning architecture,Deep Regulatory Code and Tools for Alternative Polyadenylation(DeeReCT-APA),to quantitatively predict the usage of all alternative PASs of a given *** accommodate different genes with potentially different numbers of PASs,DeeReCT-APA treats the problem as a regression task with a variable-length *** on a convolutional neural network-long short-term memory(CNN-LSTM)architecture,DeeReCT-APA extracts sequence features with CNN layers,uses bidirectional LSTM to explicitly model the interactions among competing PASs,and outputs percentage scores representing the usage levels of all PASs of a *** addition to the fact that only our method can quantitatively predict the usage of all the PASs within a gene,we show that our method consistently outperforms other existing methods on three different tasks for which they are trained:pairwise comparison task,highest usage prediction task,and ranking ***,we demonstrate that our method can be used to predict the effect of genetic variations on APA patterns and sheds light on future mechanistic understanding in APA *** code and data are available at https://***/lzx325/DeeReCT-APA-repo.

关键词： Polyadenylation Gene regulation Sequence analysis Deep learning Bioinformatics

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