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Auswahl der wissenschaftlichen Literatur zum Thema „FZG machine“
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Zeitschriftenartikel zum Thema "FZG machine"
Höhn, B. R., und H. Winter. „Laboratories at work: Institute for machine elements, Gear Research Centre (FZG)“. Tribotest 3, Nr. 3 (März 1997): 325–40. http://dx.doi.org/10.1002/tt.3020030306.
Der volle Inhalt der QuelleHargreaves, D. J., und Anton Planitz. „Assessing the energy efficiency of gear oils via the FZG test machine“. Tribology International 42, Nr. 6 (Juni 2009): 918–25. http://dx.doi.org/10.1016/j.triboint.2008.12.016.
Der volle Inhalt der QuelleWinter, H. „Integrating Universities and Industry—A German Approach“. Proceedings of the Institution of Mechanical Engineers, Part B: Management and engineering manufacture 202, Nr. 1 (Februar 1988): 9–17. http://dx.doi.org/10.1243/pime_proc_1988_202_041_02.
Der volle Inhalt der QuelleMassocchi, Davide, Marco Lattuada, Steven Chatterton und Paolo Pennacchi. „SRV Method: Lubricating Oil Screening Test for FZG“. Machines 10, Nr. 8 (28.07.2022): 621. http://dx.doi.org/10.3390/machines10080621.
Der volle Inhalt der QuelleAyel, J., Y. Kraus und J. P. Michel. „Séverisation de l'essai de capacité de charge des lubrifiants sur machine a engrenages FZG“. Revue de l'Institut Français du Pétrole 40, Nr. 6 (November 1985): 831–42. http://dx.doi.org/10.2516/ogst:1985049.
Der volle Inhalt der QuelleDurand de Gevigney, J., C. Changenet, F. Ville und P. Velex. „Thermal modelling of a back-to-back gearbox test machine: Application to the FZG test rig“. Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 226, Nr. 6 (16.01.2012): 501–15. http://dx.doi.org/10.1177/1350650111433243.
Der volle Inhalt der QuelleTao, J., T. G. Hughes, H. P. Evans, R. W. Snidle, N. A. Hopkinson, M. Talks und J. M. Starbuck. „Elastohydrodynamic Lubrication Analysis of Gear Tooth Surfaces From Micropitting Tests“. Journal of Tribology 125, Nr. 2 (19.03.2003): 267–74. http://dx.doi.org/10.1115/1.1510881.
Der volle Inhalt der QuelleHlebanja, Gorazd. „Gradual development of S-shaped gears“. MATEC Web of Conferences 366 (2022): 01001. http://dx.doi.org/10.1051/matecconf/202236601001.
Der volle Inhalt der QuelleArri, Harwant Singh, Ramandeep Singh, Sudan Jha, Deepak Prashar, Gyanendra Prasad Joshi und Ill Chul Doo. „Optimized Task Group Aggregation-Based Overflow Handling on Fog Computing Environment Using Neural Computing“. Mathematics 9, Nr. 19 (07.10.2021): 2522. http://dx.doi.org/10.3390/math9192522.
Der volle Inhalt der QuelleAlalibo, Belema P., Bing Ji und Wenping Cao. „Short Circuit and Broken Rotor Faults Severity Discrimination in Induction Machines Using Non-invasive Optical Fiber Technology“. Energies 15, Nr. 2 (14.01.2022): 577. http://dx.doi.org/10.3390/en15020577.
Der volle Inhalt der QuelleDissertationen zum Thema "FZG machine"
Grenet, de Bechillon Nicolas. „Approche multi-échelles pour l'étude du grippage des dentures d'engrenages“. Electronic Thesis or Diss., Lyon, INSA, 2023. http://www.theses.fr/2023ISAL0024.
Der volle Inhalt der QuelleEnvironmental concerns are driving the aerospace industry to innovate and develop new technologies to achieve sustainable aviation. Among these innovations, the next generation of civil engines requires the integration of gearboxes within them. In order to design a reliable product, different failure modes, such as gear scuffing, must be taken into account. Scuffing is a sudden gear failure where material is transferred from one surface to another. This transfer is caused by local surface welding during meshing. Scuffing leads to degradation of the tooth surface, which reduces gear efficiency. Although this mode of gear failure has been extensively studied, there are no commonly accepted initiation criteria. Therefore, physical understanding of scuffing initiation is needed. The first part of this study focused on the role of roughness. A numerical model was set up to evaluate the temperatures reached locally in the contact zone. The calculations carried out show that these last ones at the roughness scale do not seem able to explain the formation of micro-welds by fusion of the surface asperities in a lubricated contact. Scuffing therefore appear to be the consequence of a potential break in the lubricant film. In a second part, this film breakage was studied experimentally on a twin-disk machine. A procedure was developed to study the phenomenon by acting on the lubricant film thickness. The performed tests seem to show that the breakdown of the lubricating film is governed by its temperature, which depends directly on the operating conditions. Thus, a scuffing criterion was established on discs.In the last part, gear tests were carried out. It was shown, as for disc tests, that total temperature alone does not predict scuffing. However, the criterion developed on discs does not seem to be able to explain tooth scuffing. Since no criteria seem to be able to explain the scuffing, a new approach is proposed. Finally, conclusions and prospects are proposed. The chronology of the scuffing initiation mechanism are recalled. The prospects aim, on the one hand, to improve the representativeness of the tests on discs compared to gears, in particular with regard to the geometry of the surface roughness; and, on the other hand, to analyse in detail and experimentally the hypothesis of the lubricating film breakage as a mechanism of scuffing initiation
Badokhon, Alaa. „An Adaptable, Fog-Computing Machine-to-Machine Internet of Things Communication Framework“. Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1492450137643915.
Der volle Inhalt der QuelleHolas, Jiří. „Modernizace řízení frézky FNG“. Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442843.
Der volle Inhalt der QuelleGullo, Thomas W. „A Methodology to Evaluate the Dynamic Behavior of Back-to-back Test Machines“. The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555588592218025.
Der volle Inhalt der QuelleLu, Shen. „Early identification of Alzheimer's disease using positron emission tomography imaging and machine learning“. Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23735.
Der volle Inhalt der QuelleEgli, Sebastian [Verfasser], und Jörg [Akademischer Betreuer] Bendix. „Satellite-Based Fog Detection: A Dynamic Retrieval Method for Europe Based on Machine Learning / Sebastian Egli ; Betreuer: Jörg Bendix“. Marburg : Philipps-Universität Marburg, 2019. http://d-nb.info/1187443476/34.
Der volle Inhalt der QuelleDi, Donato Davide. „Sviluppo, Deployment e Validazione Sperimentale di Architetture Distribuite di Machine Learning su Piattaforma fog05“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19021/.
Der volle Inhalt der QuelleAnjum, Ayesha. „Differentiation of alzheimer's disease dementia, mild cognitive impairment and normal condition using PET-FDG and AV-45 imaging : a machine-learning approach“. Toulouse 3, 2013. http://thesesups.ups-tlse.fr/2238/.
Der volle Inhalt der QuelleWe used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes. The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set. We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease
Dukart, Jürgen. „Contribution of FDG-PET and MRI to improve Understanding, Detection and Differentiation of Dementia“. Doctoral thesis, Universitätsbibliothek Leipzig, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-66495.
Der volle Inhalt der QuelleCastellanos, Carlos. „Development of a validation shape sensing algorithm in Python with predictive and automatedanalysis“. Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-454942.
Der volle Inhalt der QuelleBücher zum Thema "FZG machine"
Free cash flow: Seeing through the accounting fog machine to find great stocks. Hoboken, N.J: Wiley, 2009.
Den vollen Inhalt der Quelle findenDie Entwicklung der Firma Kugelfischer, Georg Schäfer & Co.: Unter besonderer Berücksichtigung der Kontinuität als Familienunternehmen und die regionalen Auswirkungen ihrer Entwicklung aus betriebs- und industriebezogener Sicht. Würzburg: Creator, 1988.
Den vollen Inhalt der Quelle findenFandel, G. Modern Production Concepts: Theory and Applications Proceedings of an International Conference, Fernuniversität, Hagen, FRG, August 20-24, 1990. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991.
Den vollen Inhalt der Quelle findenMisra, Sudip, Subhadeep Sarkar und Subarna Chatterjee. Sensors Cloud and Fog. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenChristy, George C. Free Cash Flow: Seeing Through the Accounting Fog Machine to Find Great Stocks. Wiley & Sons, Limited, John, 2011.
Den vollen Inhalt der Quelle findenErgonomic Data for Equipment Design: Proceedings of the NATO ARI held in Munich, FRG, March 22-26, 1982 (Nato Conference Series III, Vol 25: Human Factors). Springer, 1985.
Den vollen Inhalt der Quelle findenMisra, Sudip, Subhadeep Sarkar und Subarna Chatterjee. Sensors, Cloud, and Fog: The Enabling Technologies for the Internet of Things. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "FZG machine"
Thomas, Priya, und Deepa V. Jose. „Edge/Fog Computing“. In Machine Intelligence, 47–64. Boca Raton: Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003424550-3.
Der volle Inhalt der QuelleLohani, Kaustubh, Prajwal Bhardwaj und Ravi Tomar. „Fog Computing and Machine Learning“. In Fog Computing, 133–51. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230-10.
Der volle Inhalt der QuelleJaiswal, Kabir, und Niharika Singh. „Application of Machine Learning in Fog Computing“. In Fog Computing, 41–50. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230-4.
Der volle Inhalt der QuelleGaba, Smriti, Susheela Dahiya, Samarth Vashisht und Avita Katal. „The Use of Machine Learning in Fog Computing“. In Fog Computing, 27–39. Boca Raton: Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003188230-3.
Der volle Inhalt der QuelleMoh, Melody, und Robinson Raju. „Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems“. In Fog and Edge Computing, 223–57. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119525080.ch10.
Der volle Inhalt der QuelleYan, Xuan, Xiaolong Xu, Yu Zheng und Fei Dai. „Fog Server Placement for Multimodality Data Fusion in Neuroimaging“. In Machine Learning for Cyber Security, 234–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7_20.
Der volle Inhalt der QuelleGutiérrez, Norma, Eva Rodríguez, Sergi Mus, Beatriz Otero und Ramón Canal. „Privacy Preserving Deep Learning Framework in Fog Computing“. In Machine Learning, Optimization, and Data Science, 504–15. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_45.
Der volle Inhalt der QuellePinto, Manuel, Nicola Roveri, Gianluca Pepe, Andrea Nicoletti, Gabriele Balconi und Antonio Carcaterra. „Extraction of the Beam Elastic Shape from Uncertain FBG Strain Measurement Points“. In Mechanisms and Machine Science, 362–69. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03320-0_39.
Der volle Inhalt der QuelleMartin, John Paul, Christina Terese Joseph, K. Chandrasekaran und A. Kandasamy. „Machine Learning Powered Autoscaling for Blockchain-Based Fog Environments“. In Blockchain and Applications, 281–91. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86162-9_28.
Der volle Inhalt der QuellePrasad, Devendra, Pradeep Singh Rawat und Neeraj Rathore. „Optimized Cloud Storage Data Analysis Using the Machine Learning Model“. In Bio-Inspired Optimization in Fog and Edge Computing Environments, 165–84. New York: Auerbach Publications, 2022. http://dx.doi.org/10.1201/9781003322931-10.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "FZG machine"
Michalczewski, Remigiusz, Marian Szczerek, Waldemar Tuszynski und Jan Wulczynski. „The Scuffing Resistance of the Coated Tribosystems Lubricated With Ecological Oils“. In World Tribology Congress III. ASMEDC, 2005. http://dx.doi.org/10.1115/wtc2005-63432.
Der volle Inhalt der QuelleGai, Yuxian, Huiying Liu und Shen Dong. „Vibration Control System for a Sub-Micro Ultra-Precision Turning Machine“. In 2007 First International Conference on Integration and Commercialization of Micro and Nanosystems. ASMEDC, 2007. http://dx.doi.org/10.1115/mnc2007-21040.
Der volle Inhalt der QuelleWu, Dazhong, Janis Terpenny, Li Zhang, Robert Gao und Thomas Kurfess. „Fog-Enabled Architecture for Data-Driven Cyber-Manufacturing Systems“. In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8559.
Der volle Inhalt der QuelleArandjelovic, Ognjen, und Roberto Cipolla. „Colour invariants for machine face recognition“. In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813306.
Der volle Inhalt der QuelleGoncalves, Diogo, Karima Velasquez, Marilia Curado, Luiz Bittencourt und Edmundo Madeira. „Proactive Virtual Machine Migration in Fog Environments“. In 2018 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2018. http://dx.doi.org/10.1109/iscc.2018.8538655.
Der volle Inhalt der QuelleBittencourt, Luiz Fernando, Marcio Moraes Lopes, Ioan Petri und Omer F. Rana. „Towards Virtual Machine Migration in Fog Computing“. In 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE, 2015. http://dx.doi.org/10.1109/3pgcic.2015.85.
Der volle Inhalt der QuelleLi-Xia Xie, Hong-Yu Yang und Yi Lu. „FUG based intelligent query for audit database“. In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4620837.
Der volle Inhalt der QuelleHu, Ching-Piao, und C. F. Liao. „Optical holographic Hough processor for machine vision“. In 15th Int'l Optics in Complex Sys. Garmisch, FRG, herausgegeben von F. Lanzl, H. J. Preuss und G. Weigelt. SPIE, 1990. http://dx.doi.org/10.1117/12.34930.
Der volle Inhalt der QuelleFoukalas, Fotis, und Athanasios Tziouvaras. „A Federated Machine Learning Protocol for Fog Networks“. In IEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2021. http://dx.doi.org/10.1109/infocomwkshps51825.2021.9484485.
Der volle Inhalt der QuelleIngistov, Steve. „Fog System Performance in Power Augmentation of Heavy Duty Power Generating Gas Turbines Model 7EA“. In ASME Turbo Expo 2000: Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/2000-gt-0305.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "FZG machine"
Michaelis, K., und H. Winter. Development of a High Temperature FZG-Ryder Gear Lubricant Load Capacity Machine. Fort Belvoir, VA: Defense Technical Information Center, Mai 1989. http://dx.doi.org/10.21236/ada210799.
Der volle Inhalt der QuelleAlber, Charlotte, Laura Dusl, Brigitte Ecker und Sabine Pohoryles-Drexel. Erfahrungen und Ergebnisse aus der begleitenden Erhebung zum Pilot w-fFORTE Innovatorinnen. BMDW, Juli 2021. http://dx.doi.org/10.22163/fteval.2021.523.
Der volle Inhalt der QuelleWarta, Katharina, Tobias Dudenbostel, María del Carmen Calatrava Moreno, Francesca Guadagno, Simon Zingerle, Sandra Skok und Harald Grill. Evaluierung des COMET-Programms. Technopolis Group - Austria, Juni 2021. http://dx.doi.org/10.22163/fteval.2022.524.
Der volle Inhalt der QuelleKirchhoff, Helmut, und Ziv Reich. Protection of the photosynthetic apparatus during desiccation in resurrection plants. United States Department of Agriculture, Februar 2014. http://dx.doi.org/10.32747/2014.7699861.bard.
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