Literatura académica sobre el tema "Multivariate Response Surface"
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Artículos de revistas sobre el tema "Multivariate Response Surface"
Moslemi, Amir y Mirmehdi Seyyed-Esfahani. "A novel robust multivariate regression approach to optimize multiple surfaces". RAIRO - Operations Research 52, n.º 4-5 (octubre de 2018): 1233–43. http://dx.doi.org/10.1051/ro/2018016.
Texto completoBratchell, N. "Multivariate response surface modelling by principal components analysis". Journal of Chemometrics 3, n.º 4 (septiembre de 1989): 579–88. http://dx.doi.org/10.1002/cem.1180030406.
Texto completoMoslemi, Amir y Mirmehdi Seyyed-Esfahani. "Robust optimization of multistage process: response surface and multi-response optimization approaches". International Journal of Nonlinear Sciences and Numerical Simulation 23, n.º 2 (26 de noviembre de 2021): 163–75. http://dx.doi.org/10.1515/ijnsns-2017-0003.
Texto completoKumar, Rupak y Meega Reji. "Response surface methodology (RSM): An overview to analyze multivariate data". Indian Journal of Microbiology Research 9, n.º 4 (15 de enero de 2023): 241–48. http://dx.doi.org/10.18231/j.ijmr.2022.042.
Texto completoPatel, Trina, Donatello Telesca, Saji George y André E. Nel. "Toxicity profiling of engineered nanomaterials via multivariate dose-response surface modeling". Annals of Applied Statistics 6, n.º 4 (diciembre de 2012): 1707–29. http://dx.doi.org/10.1214/12-aoas563.
Texto completoFlandrois, C., C. Lahet, D. Feldmann, J. M. Gabastou, A. Gonnon y I. Maire. "Urinary alanine aminopeptidase assay improved as result of multivariate response-surface analysis." Clinical Chemistry 34, n.º 5 (1 de mayo de 1988): 954–57. http://dx.doi.org/10.1093/clinchem/34.5.954.
Texto completoGatley-Montross, Caitlyn M., John A. Finlay, Nick Aldred, Harrison Cassady, Joel F. Destino, Beatriz Orihuela, Michael A. Hickner et al. "Multivariate analysis of attachment of biofouling organisms in response to material surface characteristics". Biointerphases 12, n.º 5 (diciembre de 2017): 051003. http://dx.doi.org/10.1116/1.5008988.
Texto completoLi, Yingjie, Xiangliang Liu, Biaojun Zhang, Qun Zhao, Ping Ning y Senlin Tian. "Aquatic photochemistry of sulfamethazine: multivariate effects of main water constituents and mechanisms". Environmental Science: Processes & Impacts 20, n.º 3 (2018): 513–22. http://dx.doi.org/10.1039/c7em00548b.
Texto completoGhattas, Badih y Diane Manzon. "Machine Learning Alternatives to Response Surface Models". Mathematics 11, n.º 15 (4 de agosto de 2023): 3406. http://dx.doi.org/10.3390/math11153406.
Texto completoMurakami, Kenya, Tatsuya Iizaka, Tomoji Kanno, Tetsuro Matsui, Makoto Shimosawa y Akihiro Takano. "Improvement of Solar Cells Efficiency by Response Surface Method based on Multivariate Analysis Models". IEEJ Transactions on Electronics, Information and Systems 131, n.º 8 (2011): 1424–30. http://dx.doi.org/10.1541/ieejeiss.131.1424.
Texto completoTesis sobre el tema "Multivariate Response Surface"
Hässig, Fonseca Santiago. "Applications and optimization of response surface methodologies in high-pressure, high-temperature gauges". Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44902.
Texto completoLiggett, Rachel Esther. "Multivariate Approaches for Relating Consumer Preference to Sensory Characteristics". The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1282868174.
Texto completoKing, Adam C. "The Cressbrook Creek alluvial aquifer system, Southeast Queensland : hydrochemistry and isotopes to determine hydrological processes and response to floods". Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/78443/1/Adam_King_Thesis.pdf.
Texto completoMoberg, My. "Liquid Chromatography Coupled to Mass Spectrometry : Implementation of Chemometric Optimization and Selected Applications". Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7071.
Texto completoRodrigues, Marlon Casagrande. "Estudo da influência dos parâmetros de injeção de combustível no ruído emitido por motores diesel, fazendo uso do planejamento multivariado de experimentos". [s.n.], 2011. http://repositorio.unicamp.br/jspui/handle/REPOSIP/265284.
Texto completoDissertação (mestrado profissional) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica
Made available in DSpace on 2018-08-18T11:44:59Z (GMT). No. of bitstreams: 1 Rodrigues_MarlonCasagrande_M.pdf: 2084739 bytes, checksum: 4206c434cf21dfc145d73ca304a9981b (MD5) Previous issue date: 2011
Resumo: Nos últimos anos a emissão de ruído tem sido decisiva para aceitação de veículos no mercado, não somente devido a legislação, mas também no que diz respeito a satisfação do cliente. Por este motivo as empresas fabricantes de veículos e ou motores de combustão interna tem sido obrigadas a dar uma atenção especial as emissões de ruído para poderem competir com suas concorrentes. Neste trabalho realizou-se estudo da influencia dos parâmetros de injeção de combustível do motor MWM 6.12 TCE no nível de ruído emitido pelo motor Diesel na condição de marcha lenta, por meio da técnica de planejamento multivariado de experimentos. Foram escolhidas duas técnicas de medição indireta de ruído (ruído de combustão e aceleração na saia do bloco) para serem utilizadas como variável resposta do planejamento de experimentos. Para verificação da eficácia dos planejamentos realizou-se ensaios qualitativos e quantitativos de ruído propriamente dito. Foi feito um planejamento fatorial fracionário 28-4 para fazer uma triagem de oito fatores de acordo com seus efeitos nas respostas aceleração na saia do bloco e ruído de combustão. Os fatores com efeitos mais significativos, pressão do rail, ponto de injeção principal, ponto de pré-injeção 2 e debito da pré-injeção 2, foram investigados usando um planejamento composto central e superfícies de respostas foram determinadas para cada uma das respostas. Os resultados mostraram que apenas a variável resposta vibração na saia do bloco apresentou resultados satisfatórios para esta condição especifica do motor tanto nos testes quantitativos como qualitativos (redução de 2 dB e melhora na qualidade sonora respectivamente). Apos verificação da influencia dos parâmetros de injeção no ruído emitido verificou-se também a influencia destas alterações no consumo de combustível e emissões dos gases de escape e foi observado que existem influencias significativas nas emissões dos gases de escape
Abstract: In recent years the noise level has been decisive for acceptance of vehicles on the market, not only because of legislation but also with regard to customer satisfaction. For this reason the manufacturers of vehicles and internal combustion engines have been forced to give special attention to the emission of noise to compete with their competitors. In this work, the influences of fuel injection parameters on the noise level of the MWM 6.12 TCE diesel engine emitted under low idle condition were determined using multivariate statistical design of experiments. Two techniques for the indirect measurement of noise, combustion noise and engine crankcase vibration, were chosen as the response variables for the experimental design. To check design effectiveness both qualitative and quantitative noise measurements were carried out. A 28-4 fractional factorial design was performed to screen eight factors according to their effects on engine crankcase vibration, and combustion noise. The factors with the most significant effects, rail pressure, pre-injection point, main injection point and the pre-injection delivery, were investigated using a central composite design and response surfaces were determined for each response. The results showed that only the engine crankcase vibration showed satisfactory results for this particular engine condition in both the quantitative and qualitative analyses (reduction of 2 dB and an improvement in sound quality, respectively). After verification of the influences of the injection parameters on the noise the influences of these changes on fuel consumption and exhaust emissions were also analyzed. Significant influences were observed on the exhaust gas emissions
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Williamson, Martin Rodney. "Multivariate Optimization of Neutron Detectors Through Modeling". 2010. http://trace.tennessee.edu/utk_graddiss/924.
Texto completoLibros sobre el tema "Multivariate Response Surface"
1951-, Christensen Ronald, ed. Advanced linear modeling: Multivariate, time series, and spatial data; nonparametric regression and response surface maximization. 2a ed. New York: Springer, 2001.
Buscar texto completoChristensen, Ronald. Advanced Linear Modeling: Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization. Springer London, Limited, 2013.
Buscar texto completoChristensen, Ronald. Advanced Linear Modeling: Multivariate, Time Series, and Spatial Data; Nonparametric Regression and Response Surface Maximization. Springer New York, 2010.
Buscar texto completoLee, Li-Chu. Empirical Bayes estimation of the response function and multivariate regression model. 1989.
Buscar texto completoSurrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences. Taylor & Francis Group, 2020.
Buscar texto completoGramacy, Robert B. Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences. Taylor & Francis Group, 2020.
Buscar texto completoGramacy, Robert B. Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences. Taylor & Francis Group, 2020.
Buscar texto completoGramacy, Robert B. Surrogates. Taylor & Francis Group, 2020.
Buscar texto completoGramacy, Robert B. Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences. Taylor & Francis Group, 2020.
Buscar texto completoCapítulos de libros sobre el tema "Multivariate Response Surface"
Carter, C. W. "Experimental Design, Quantitative Analysis, and the Cartography of Crystal Growth". En Crystallization of Nucleic Acids and Proteins. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780199636792.003.0008.
Texto completoArnold, Stevan J. "The Selection Surface and Adaptive Landscape for Multiple Traits". En Evolutionary Quantitative Genetics, 58–82. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780192859389.003.0005.
Texto completoSharma, Pankaj. "Applications of Statistical Tools for Optimization and Development of Smart Drug Delivery System". En Smart Drug Delivery [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.99632.
Texto completoActas de conferencias sobre el tema "Multivariate Response Surface"
Morelli, Eugene y Richard DeLoach. "Response surface modeling using multivariate orthogonal functions". En 39th Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-168.
Texto completoLuchino, Federico, Martin Ordonez, German G. Oggier y John E. Quaicoe. "MOSFET power loss characterization: Evolving into multivariate response surface". En 2011 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2011. http://dx.doi.org/10.1109/ecce.2011.6064020.
Texto completoGalvez, Juan, Martin Ordonez, Samuel Cove y John Quaicoe. "Nonlinearity in small planar inductors: experimental characterization using multivariate response surface". En 2012 25th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). IEEE, 2012. http://dx.doi.org/10.1109/ccece.2012.6857451.
Texto completoVaccari, David A. "Multivariate Polynomial Response Surface Analysis - Combining Advantages of Multilinear Regression and Artificial Neural Networks". En Modelling, Simulation and Identification. Calgary,AB,Canada: ACTAPRESS, 2018. http://dx.doi.org/10.2316/p.2018.857-021.
Texto completoWidodo, Edy y Kariyam. "Estimating multivariate response surface model with data outliers, case study in enhancing surface layer properties of an aircraft aluminium alloy". En PROCEEDINGS FROM THE 14TH INTERNATIONAL SYMPOSIUM ON THERAPEUTIC ULTRASOUND. Author(s), 2017. http://dx.doi.org/10.1063/1.4978127.
Texto completoNelson, Donald D. y Elaine Cohen. "Algebraic Surface Derivatives for Rendering Virtual Contact Force". En ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-2426.
Texto completoZhang, T., K. K. Choi y S. Rahman. "A Hybrid Method Using Response Surface and Pattern Search for Design Optimization". En ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-85146.
Texto completoSomayasa, Wayan, Ruslan Ruslan y Desak Ketut Sutiari. "Assessing the optimum condition of multivariate second order response surface model through the asymptotic inference of the eigenvalues". En THE 3RD INTERNATIONAL CONFERENCE ON MATHEMATICS AND SCIENCES (THE 3RD ICMSc): A Brighter Future with Tropical Innovation in the Application of Industry 4.0. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0112594.
Texto completoWahab, M. M. A., V. J. Kurian, M. S. Liew, Z. Nizamani y D. K. Kim. "Structural Reliability Analysis Using Quadratic Polynomial Response Surface and Finite Element in MATLAB". En ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/omae2016-54543.
Texto completoBranagan, Michael, Neal Morgan, Brian Weaver y Houston Wood. "Response Surface Mapping and Multi-Objective Optimization of Tilting Pad Bearing Designs". En ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-64949.
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