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| 1 | Individual consistency and sex differences in migration strategies of Scopoli's shearwaters Calonectris diomedea despite year differences显示文摘 | Martina S. MULLER Bruno MASSA Richard A. PHILLIPS Giacomo DELL | 2014 | Current Zoology2014，60，5： | 1 |
| 2 | A comprehen- sive study of polymorphisms in ABCB1, ABCC2 and ABCG2 and lung cancer chemotherapy response and prognosis 显示文摘 | Daniele Campa Phillip Muller Lutz Edler | 2012 | Int J Canc- er2012，31，12： | 1 |
| 3 | Congenital sodium diarrhea is an autosomal recessive disorder of sodium/proton exchange but unrelated to known candidate genes显示文摘 | Muller T Wijmenga C Phillips AD | 2000 | Gastroenterology2000，119，6： | 1 |
| 4 | Severe neuromuscular complications possibly associated with amlodipine 显示文摘 | Phillips BB Muller BA | 1998 | Ann Pharmacother1998，32，11： | 1 |
| 5 | Severe neuromuscular complications possibly associated with amlodipine 显示文摘 | Phillips BB Muller BA | 1998 | Ann Pharmaeother1998，3201，： | 1 |
| 6 | Congenital sodium diarrhea is an autosomal recessive disorder of sodium/proton exchange but unrelated to known candidate genes 显示文摘 | Muller T Wijmenga C Phillips A D | 2000 | Gastroenterol2000，119，6： | 1 |
| 7 | Congenital sodium diarrhea is an autosomal recessive disorder of sodium/proton exchange but unrelated to known candidate genes显示文摘 | Muller T Wijmenga C Phillips AD | 2000 | Gastroenterology2000，119，6： | 1 |
| 8 | Safe operation of online learning data driven model predictive control of building energy systems显示文摘Model predictive control is a promising approach to reduce the CO 2 emissions in the building sector.However,the vast modeling effort hampers the widescale practical application.Here,data-driven process models,like artificial neural networks,are well-suited to automatize the modeling.However,the underlying data set strongly determines the quality and reliability of artificial neural networks.In general,the validity domain of a machine learning model is limited to the data that was used to train it.Predictions based on system states outside that domain,so-called extrapolations,are unreliable and can negatively influence the control quality.We present a safe operation approach combined with online learning to deal with extrapolation in data-driven model predictive control.Here,the k-nearest neighbor algorithm is used to detect extrapolation to switch to a robust fallback controller.By continuously retraining the artificial neural networks during operation,we successively increase the validity domain of the artificial neural networks and the control quality.We apply the approach to control a building energy system provided by the BOPTEST framework.We compare controllers based on two data sets,one with extensive system excitation and one with baseline operation.The system is controlled to a fixed temperature set point in baseline operation.Therefore,the artificial neural networks trained on this data set tend to extrapolate in other operating points.We show that safe operation in combination with online learning significantly improves performance. | Phillip Stoffel Patrick Henkel Martin Ratz Alexander Kumpel Dirk Muller | 2023 | Energy and AI2023，14，4： | 0 |
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