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| 1 | Sialic acid is a receptor determinant for infection of cells by avian infectious bronchitis virus显示文摘 | Winter C Schwegmann W C Cavanagh D | | 0，，： | 1 |
| 2 | Identification of sugar residues involved in the binding of TGEV to porcine brush border membranes显示文摘 | Schwegmann Wessels C Herrler G | 2008 | Meth Mol Biol2008，454，： | 1 |
| 3 | The adsorption of atomic nitrogen on Ru(0001):geometry and energetics显示文摘 | Schwegmann S Seitsonen A P Dietrich H | 1997 | Chemical Physics Letters1997，264，： | 1 |
| 4 | 查看详情显示文摘 | KimYD Schwegmann S Seitsonnen A. P | | 0，，： | 1 |
| 5 | Host-directed drug targeting of fac-tors hijacked by pathogens显示文摘 | Schwegmann A Brombacher F | 2008 | Sci Signal2008，1，29： | 1 |
| 6 | Alternative mac- rophage activation is essential for survival during schistosomiasis and downmodulates T helper 1 responses and immunopathology 显示文摘 | Mohrs M Arendse B Schwegmann A et d | 2004 | hnmunity2004，20，5： | 1 |
| 7 | WY 16225 (dezocine),a new synthetic opiate agonist-antagonist and potent analgesic:comparison with morphine for relief of pain after lower abdominal surgery显示文摘 | Downing JW Brock-Utne JG Barclay A Schwegmann IL | | 0，，01： | 1 |
| 8 | Prediction of Earth orientation parameters by artificial neural networks显示文摘 | H. Schuh M. Ulrich D. Egger J. Müller W. Schwegmann | 2002 | Journal of Geodesy2002，，5： | 1 |
| 9 | Sialic acids as receptor determinants for coronaviruses 显示文摘 | Schwegmann W C Herrler G | 2006 | Glycoconj J2006，23，12： | 1 |
| 10 | Prediction of Earth orientation parameters by artificial neural networks显示文摘 | H. Schuh M. Ulrich D. Egger J. Müller W. Schwegmann | 2002 | Journal of Geodesy2002，，5： | 1 |
| 11 | Sialic acid is a receptor determinant for infection of cells by avian infectious bronchitis virus 显示文摘 | Winter C Schwegmann W C Cavanagh D | 2006 | Journal of General Virology2006，87，： | 1 |
| 12 | The rote of B-cells and IgM antibodies in parasitemia, anemia, and VSG switching in Trypanosoma brucei-infected mice显示文摘 | Magez S Schwegmann A Atkinson R | 2008 | Plos Pathog2008，4，8： | 1 |
| 13 | Sialic acids as receptor determi- nants for coronaviruses显示文摘 | Schwegmann W C Herder G | 2006 | Glycoconj J2006，23，12： | 1 |
| 14 | Prediction of Earth orientation parameters by artificial neural networks显示文摘 | H. Schuh M. Ulrich D. Egger J. Müller W. Schwegmann | | 0，，： | 1 |
| 15 | Enabling Virtual Met Masts for wind energy applications through machinelearning-methods显示文摘As wind is the basis of all wind energy projects, a precise knowledge about its availability is needed. For ananalysis of the site-specific wind conditions, Virtual Meteorological Masts (VMMs) are frequently used. VMMsmake use of site calibrated numerical data to provide precise wind estimates during all phases of a wind energyproject. Typically, numerical data are used for the long-term correlation that is required for estimating theyield of new wind farm projects. However, VMMs can also be used to fill data gaps or during the operationalphase as an additional reference data set to detect degrading sensors. The value of a VMM directly dependson its ability and precision to reproduce site-specific environmental conditions. Commonly, linear regressionis used as state of the art to correct reference data to the site-specific conditions. In this study, a frameworkof 10 different machine-learning methods is tested to investigated the benefit of more advanced methods ontwo offshore and one onshore site. We find significantly improving correlations between the VMMs and the reference data when using more advanced methods and present the most promising ones. The K-NearestNeighbors and AdaBoost regressors show the best results in our study, but Multi-Output Mixture of GaussianProcesses is also very promising. The use of more advanced regression models lead to decreased uncertainties;hence those methods should find its way into industrial applications. The recommended regression models canserve as a starting point for the development of end-user applications and services. | Sandra Schwegmann Janosch Faulhaber Sebastian Pfaffel Zhongjie Yu Martin Dörenkämper Kristian Kersting Julia Gottschall | 2023 | Energy and AI2023，11，1： | 0 |
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