Showing 801 - 820 results of 53,088 for search '((structures OR structures) OR (structure OR structural)) data', query time: 0.45s Refine Results
  1. 801

    Hierarchical Information-Extreme Machine Learning of Hand Prosthesis Control System Based on Decursive Data Structure by Anatolii Dovbysh, Vladyslav Piatachenko, Mykyta Myronenko, Mykyta Suprunenko, Julius Simonovskiy

    Published 2024-11-01
    “…The distinctiveness of the developed method from known machine learning methods was in applying a hierarchical data structure as a decursive binary tree, which allowed for transitioning from multi-class machine learning to two-class learning for each stratum of the decursive tree. …”
    Get full text
    Article
  2. 802

    SREDNEKEDROVAYA PALEOSEISMODISLOCATION IN THE BAIKAL RIDGE: ITS STRUCTURE AND THROWS ESTIMATED FROM GROUND‐PENETRATING RADAR DATA by O. V. Lunina, A. S. Gladkov, A. A. Gladkov, I. A. Denisenko

    Published 2018-07-01
    “…Our study aimed to clarify the seismic potential of the Severobaikalsk fault and to discover the structural features of active faults on the NW shores of Lake Baikal. …”
    Get full text
    Article
  3. 803

    Revealing the Hidden Social Structure of Pigs with AI-Assisted Automated Monitoring Data and Social Network Analysis by Saif Agha, Eric Psota, Simon P. Turner, Craig R. G. Lewis, Juan Pedro Steibel, Andrea Doeschl-Wilson

    Published 2025-03-01
    “…This proof-of-concept study addresses, for the first time, the hypothesis that applying social network analysis (SNA) on AI-automated monitoring data could potentially facilitate the analysis of social structures of farm animals. …”
    Get full text
    Article
  4. 804
  5. 805
  6. 806

    Geological structure identification in geothermal manifestation of Lamongan Volcano Complex: A magnetic data analysis approach by Ulumuddin Faqih, Fajar Miftakhul Haris M., Warnana Dwa Desa, Rafi Muhammad Erfand Dzulfiqar, Rahayu K Helda usuma

    Published 2024-01-01
    “…However, the identification of geo - thermal manifestations in this location is limited, so magnetic data can help identify subsurface geological structures to confirm the geo thermal potential in this area. …”
    Get full text
    Article
  7. 807

    Fractal Characteristics of the Auroral Oval Structure According to the All-Sky Camera Data in Apatity for 2013–2020 by Kozelov Boris

    Published 2025-04-01
    “…The spatial structure of polar auroras is described by the fractal dimension of glow fluctuations and its anisotropy from direction. …”
    Get full text
    Article
  8. 808

    Study of the structure and development of oil deposits in carbonate reservoirs using field data and X-ray microtomography by D. A. Martyushev, I. N. Ponomareva, B. M. Osovetsky, K. P. Kazymov, E. M. Tomilina, A. S. Lebedeva, A. S. Chukhlov

    Published 2024-04-01
    “…At present, a large number of scientific works devoted to the study of the features of the geological structure and the development of oil deposits in complex carbonate reservoirs are based on the use of any one research method. …”
    Get full text
    Article
  9. 809

    UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data by Lijun Fan, Shichao Yi, Wenrui Guan, Pingxin Wang

    Published 2025-01-01
    “…Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. In recent years, numerous researchers have utilized GCNs for data with Euclidean structures. such as image data and language data, and achieved good results, expanding the application scope of GCN. …”
    Get full text
    Article
  10. 810
  11. 811

    A Cloud Vertical Structure Optimization Algorithm Combining FY-4A and DSCOVR Satellite Data by Zhuowen Zheng, Jie Yang, Taotao Lv, Yulu Yi, Zhiyong Lin, Jiaxin Dong, Siwei Li

    Published 2025-07-01
    “…Clouds are important for Earth’s energy budget and water cycles, and precisely characterizing their vertical structure is essential for understanding their impact. …”
    Get full text
    Article
  12. 812

    Conserved genome structure and phylogenetic insights for the heterogeneous subfamily of Convallarioideae (Asparagaceae) revealed from plastome data by Shao-De Wu, Ran Meng, Ze-Long Nie, Ming-Yang Song, Xing-Ru Chen, Jun Wen, Ying Meng

    Published 2025-05-01
    “…Despite its importance, chloroplast genome data for Convallarioideae remain limited, hindering a comprehensive understanding of their genome structural evolution and phylogenetic relationships. …”
    Get full text
    Article
  13. 813

    Structural Tectonic Scheme Creation Based on Seismic-Gravity Models and Isostasy Usage: Ural Case by Martyshko Petr, Ladovskii Igor, Byzov Denis, Tsidaev Aleksandr

    Published 2024-02-01
    “…They contain solution of non-linear (structural) inverse problem and the solution of the linear three-dimensional inverse problem taking note of the side sources. …”
    Get full text
    Article
  14. 814

    Des structures inconciliables ? Cartographie comparée des chartes et des édifices « romans » (Xe –XIIIe siècles) by Nicolas Perreaux

    Published 2016-02-01
    “…This article uses digital mapping, geolocation and data mining to compare these two structures at European level. …”
    Get full text
    Article
  15. 815
  16. 816

    Theoretical study of the structures of Schiff base compounds and thermodynamic study of the tautomerism reactions by ab initio calculations by Ali Hossein Kianfar, Roghayeh Hashemi Fath

    Published 2017-12-01
    “…The optimized molecular geometry and atomic charges were calculated using MP2 method with 6-31G(d) basis set and compared with the reported X-ray data. Nickel and copper complexes have a planar structure while the zinc structure shows a distorted square-planar N2O2 coordination geometry. …”
    Get full text
    Article
  17. 817

    Multi-View Cluster Structure Guided One-Class BLS-Autoencoder for Intrusion Detection by Qifan Yang, Yu-Ang Chen, Yifan Shi

    Published 2025-07-01
    “…Specifically, a multi-view co-association matrix optimization objective function with doubly-stochastic constraints is first designed to capture the cross-view cluster structure. Then, a multi-view cluster structure guided one-class BLS-autoencoder (MOCBLSAEs) is proposed, which learns the discriminative patterns of normal traffic data by preserving the cross-view clustering structure while minimizing the intra-view sample reconstruction errors, thereby enabling the identification of unknown intrusion data. …”
    Get full text
    Article
  18. 818
  19. 819
  20. 820