Relevant bibliographies by topics / Response surface

Academic literature on the topic 'Response surface'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Response surface"

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Jin, J., X. Wang, Y. Han, Y. Cai, Y. Cai, H. Wang, L. Zhu, L. Xu, L. Zhao, and Z. Li. "Combined beef thawing using response surface methodology." Czech Journal of Food Sciences 34, No. 6 (December 21, 2016): 547–53. http://dx.doi.org/10.17221/138/2016-cjfs.

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Based on four thawing methods (still air, still water, ultrasonic wave, and microwave) and single-factor tests, we established a four-factor three-level response surface methodology for a regression model (four factors: pH, drip loss rate, cooking loss rate, protein content). The optimal combined thawing method for beef rib-eye is: microwave thawing (35 s work/10 s stop, totally 170 s) until beef surfaces soften, then air thawing at 15°C until the beef centre temperature reaches –8°C, and finally ultrasonic thawing at 220 W until the beef centre temperature rises to 0°C. With this method, the drip loss rate is 1.9003%, cooking loss rate is 33.3997%, and protein content is 229.603 μg, which are not significantly different from the model-predicted theoretical results (P ≥ 0.05).
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Dube, Vinitkumar Dilipkumar. "Optimization of Biodiesel (MOME) Using Response Surface Methodology (RSM)." International journal of Emerging Trends in Science and Technology 04, no. 11 (November 13, 2016): 4736–41. http://dx.doi.org/10.18535/ijetst/v3i11.02.

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Manuel, Jeremia, Raffi Paramawati, and Maria D. P. Masli. "UTILIZATION OF RESPONSE SURFACE METHODOLOGY IN THE OPTIMIZATION OF ROSELLE ICE CREAM MAKING [Penggunaan Response Surface Methodology dalam Optimisasi Pembuatan Es Krim Rosella]." Jurnal Teknologi dan Industri Pangan 25, no. 2 (December 2014): 125–33. http://dx.doi.org/10.6066/jtip.2014.25.2.125.

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Shibata, Mario. "Response Surface Methodology." Nippon Shokuhin Kagaku Kogaku Kaishi 60, no. 12 (2013): 728–29. http://dx.doi.org/10.3136/nskkk.60.728.

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Myers, Raymond H., and Douglas C. Montgomery. "Response Surface Methodology." IIE Transactions 28, no. 12 (December 1996): 1031–32. http://dx.doi.org/10.1080/15458830.1996.11770760.

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Copeland, Karen A. F. "Response Surface Methodology." Journal of Quality Technology 28, no. 2 (April 1996): 262. http://dx.doi.org/10.1080/00224065.1996.11979672.

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Ginebra, Josep, and Murray K. Clayton. "Response Surface Bandits." Journal of the Royal Statistical Society: Series B (Methodological) 57, no. 4 (November 1995): 771–84. http://dx.doi.org/10.1111/j.2517-6161.1995.tb02062.x.

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Khuri, André I., and Siuli Mukhopadhyay. "Response surface methodology." Wiley Interdisciplinary Reviews: Computational Statistics 2, no. 2 (March 2010): 128–49. http://dx.doi.org/10.1002/wics.73.

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PRAJINA N V, PRAJINA N. V., and T. D. JOHN T D JOHN. "Multi Response Optimization of Cutting Forces in End Milling Using Response Surface Methodology and Desirability Function." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 126–30. http://dx.doi.org/10.15373/22778179/may2013/45.

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Doti, Baqe, Daudi Nyaanga, Samwel Nyakach, Jane Nyaanga, and Oscar Ingasia. "Biochar production and quality optimization using response surface methodology technique." Applied Research Journal of Environmental Engineering 4, no. 1 (March 31, 2022): 1–16. http://dx.doi.org/10.47721/arjee20220401011.

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The dependency on fossil fuels can be reduced by the use of renewable energy sources like biomass and it can make a remarkable contribution to the reduction of CO2 emissions and as a result reducing the carbon footprint hence eliminating the greenhouse gas effect. Biomass materials that go to waste can be recovered through the pyrolysis process in order to produce biochar which can be used as a source of energy for cooking. The aim of this study was to carry out optimization of biochar production and quality using the Response Surface Methodology technique. The parameters varied were feedstock moisture content (FMC) (10%, 15% and 20%), pyrolysis residence time (PRT) (in minutes) 90, 135 and 180 and chimney inclination angle (CIA) (30o, 45o and 60o). An experimental insulated metallic carbonization kiln (1 m high and 0.5 m diameter) was developed and used. Response Surface Methodology technique by using Box-Behnken Design was used to develop a mathematical equation to predict the production and quality of the biochar with respect to varied parameters which was later optimized to determine the optimal conditions for biochar production and quality. The biochar quality was based on its moisture content (MC), volatile matter (VM), ash content (AC), fixed carbon (FC) and pH. The combined optimal conditions were 10% feedstock moisture content, 126.93 min pyrolysis residence time and 30o chimney inclination angle resulting to production of 44.35%, MC = 3.82%, VM = 23.52%, AC = 2.94%, FC = 67.89% and pH = 9.28. The mathematical equation developed had composite desirability (CD) of 0.9490 at a p-value≤0.05 which made it viable. These research findings are of importance since optimization reduces the wastage of resources resulting into increase in the efficiency of the pyrolysis system. Keywords: Renewable Energy, Pyrolysis, Biochar, Optimization, Response Surface Methodology
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Dissertations / Theses on the topic "Response surface"

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Trinca, Luzia A. "Blocking response surface designs." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308028.

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Plaisance, Marc Charles. "Cellular Response to Surface Wettability Gradient on Microtextured Surfaces." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/53730.

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Objective: Topography, chemistry, and energy of titanium (Ti) implants alter cell response through variations in protein adsorption, integrin expression, and downstream cell signaling. However, the contribution of surface energy on cell response is difficult to isolate because altered hydrophilicity can result from changes in surface chemistry or microstructure. Our aim was to examine a unique system of wettability gradients created on microstructured Ti on osteoblast maturation and phenotype. Method: A surface energy gradient was created on sand-blasted/acid-etched (SLA) Ti surfaces. Surfaces were treated with oxygen plasma for 2 minutes, and then allowed to age for 1, 12, 80, or 116 hours to generate a wettability gradient. Surfaces were characterized by contact angle and SEM. MG63 cells were cultured on SLA or experimental SLA surfaces to confluence on TCPS. Osteoblast differentiation (IBSP, RUNX2, ALP, OCN, OPG) and integrin subunits (ITG2, ITGA5, ITGAV, ITGB1) measured by real-time PCR (n=6 surfaces per variable analyzed by ANOVA/Bonferroni’s modified Student’s t-test). Result: After plasma treatment, SLA surface topography was retained. A gradient of wettability was obtained, with contact angles of 32.0° (SLA116), 23.3° (SLA80), 12.5° (SLA12), 7.9° (SLA1). All surfaces were significantly more hydrophilic than the original SLA surface (126.8°). Integrin expression was affected by wettability. ITGA2 was higher on wettable surfaces than on SLA, but was highest on SLA1. ITGAV and ITGB1 were decreased on hydrophilic surfaces, but ITGA5 was not affected. IBSP, RUNX2, and ALP increased and OPG decreased with increasing wettability. OCN decreased with increasing wettability, but levels on the most wettable surface were similar to SLA. Conclusion: Here we elucidated the role of surface energy on cell response using surfaces with the same topography and chemistry. The results show that osteoblastic maturation was regulated in a wettability-dependent manner and suggest that the effects are mediated by integrins.
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Towashiraporn, Peeranan. "Building Seismic Fragilities Using Response Surface Metamodels." Diss., Georgia Institute of Technology, 2004. http://hdl.handle.net/1853/4793.

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Building fragility describes the likelihood of damage to a building due to random ground motions. Conventional methods for computing building fragilities are either based on statistical extrapolation of detailed analyses on one or two specific buildings or make use of Monte Carlo simulation with these models. However, the Monte Carlo technique usually requires a relatively large number of simulations in order to obtain a sufficiently reliable estimate of the fragilities, and it quickly becomes impractical to simulate the required thousands of dynamic time-history structural analyses for physics-based analytical models. An alternative approach for carrying out the structural simulation is explored in this work. The use of Response Surface Methodology in connection with the Monte Carlo simulations simplifies the process of fragility computation. More specifically, a response surface is sought to predict the structural response calculated from complex dynamic analyses. Computational cost required in a Monte Carlo simulation will be significantly reduced since the simulation is performed on a polynomial response surface function, rather than a complex dynamic model. The methodology is applied to the fragility computation of an unreinforced masonry (URM) building located in the New Madrid Seismic Zone. Different rehabilitation schemes for this structure are proposed and evaluated through fragility curves. Response surface equations for predicting peak drift are generated and used in the Monte Carlo simulation. Resulting fragility curves show that the URM building is less likely to be damaged from future earthquakes when rehabilitation is properly incorporated. The thesis concludes with a discussion of an extension of the methodology to the problem of computing fragilities for a collection of buildings of interest. Previous approaches have considered uncertainties in material properties, but this research incorporates building parameters such as geometry, stiffness, and strength variabilities as well as nonstructural parameters (age, design code) over an aggregation of buildings in the response surface models. Simulation on the response surface yields the likelihood of damage to a group of buildings under various earthquake intensity levels. This aspect is of interest to governmental agencies or building owners who are responsible for planning proper mitigation measures for collections of buildings.
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Villanova, Laura. "Response surface optimization for high dimensional systems with multiple responses." Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3421551.

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This thesis is about the optimization of physical systems (or processes) characterized by a high number of input variables (e.g., operations, machines, methods, people, and materials) and multiple responses (output characteristics). These systems are of interest because they are common scenarios in real-world studies and they present many challenges for practitioners in a wide range of applicative fields (e.g., science, engineering). The first objective of the study was to develop a model-based approach to support the practitioners in planning the experiments and optimizing the system responses. Of interest was the creation of a methodology capable of providing a feedback to the practitioner while taking into account his/her point of view. The second objective was to identify a procedure to select the most promising model, to be combined with the model-based approach, on the basis of the features of the applicative problem of interest. To cope with the first objective, experimental design, modeling and optimization techniques have been combined in a sequential procedure that interacts with the practitioner at each stage. The developed approach has roots in nonparametric and semiparametric response surface ethodology (NPRSM), design and analysis of computer experiments (DACE), multi-objective optimization and swarm intelligence computation. It consists of augmenting an initial experimental design (set of experiments) by sequentially identifying additional design points (experiments) with expected improved performance. The identification of new experimental points is guided by a particle swarm optimization (PSO) algorithm that minimizes a distance-based function. In particular, the distance between the measured response values and a target is minimized. The target is composed of ideal values of the responses and is selected using a multivariate adaptive regression splines (MARS) model, which is updated as soon as new experiments are implemented and the corresponding response values are measured. The developed approach resulted in a sequential procedure named Evolutionary Model-based Multiresponse Approach (EMMA). When tested on a set of benchmark functions, EMMA was shown to overcome the potential problem of premature convergence to a local optimum and to correctly identify the true global optimum. Furthermore, EMMA is distribution-free and it allows the automatic selection of the target, in contrast to the trial-and-error procedures usually employed for this purpose. Finally, EMMA was applied to a real-world chemical problem devoted to the functionalization of a substrate for possible biomedical studies. With respect to the method typically employed by the scientists, improvements of the responses of up to 380% were detected. The proposed approach was thus shown to hold much promise for the optimization of multiresponse high dimensional systems. Moreover, EMMA turned out to be a valuable methodology for industrial research. Indeed, by means of a preliminary simulation study, it gave an initial estimate of the number of experiments and time necessary to achieve a specific goal, thus providing an indication of the budget required for the research. To deal with the second objective of the research, a meta-learning approach for model selection was adopted. Interest in model selection strategies arose from questions such as ‘Is MARS the best model we could have used?’ and ‘Given an applicative problem, how can we select the most promising modeling technique to be combined with EMMA?’. Indeed, it is now generally accepted that no single model can outperform some other models over all possible regression problems. Furthermore, the model performance ‘... may depend on the detailed nature of the problem at hand in terms of the number of observations, the number of response variables, their correlation structure, signal-to-noise ratio, collinearity of the predictor variables, etc.’ (Breiman & Friedman 1997). The meta-learning approach was adopted to select the most promising model on the basis of measurable characteristics of the investigated problem. The basic idea was to study a set of multiresponse regression models and evaluate their performance on a broad class of problems, that were characterized by various degrees of complexity. By matching the problem characteristics and the models’ performance, the aim was to discover the conditions under which a model outperforms others as well as to acquire some rules to be used as a guidance when faced with a new application. The procedures to simulate the datasets were developed, the metrics to measure the problems characteristics were identified, and the R code to evaluate the models’ performances was generated. The foundations for a large computational study was therefore established. Implementation of such study is part of ongoing research, and future works will aim to examine the obtained empirical rules from a theoretical perspective with a view to confirm their validity, as well as generating insights into each model’s behaviour.
La tesi riguarda l’ottimizzazione di sistemi (o processi) fisici caratterizzati da un elevato numero di variabili in ingresso (operazioni, macchine, metodi, persone, materiali) e da più variabili risposta, impiegate per misurare le proprietà del prodotto finale. Questa tipologia di sistemi è molto frequente in un ampio spettro di campi applicativi, che spaziano dalla scienza all’ingegneria, e pone lo sperimentatore di fronte a delle problematiche di non sempre facile risoluzione. Il primo obiettivo di questo studio era di sviluppare un approcio, basato su un modello statistico, che fosse in grado di supportare lo sperimentatore nella pianificazione degli esperimenti e nell’ottimizzazione delle risposte del sistema. Fondamentale era lo sviluppo di una procedura capace di tenere in considerazione il punto di vista dello sperimentatore e fornirgli continuamente un feedback. Il secondo obiettivo della ricerca era l’identificazione di un metodo volto a selezionare il miglior modello statistico, da integrare all’approcio proposto, sulla base delle caratteristiche del problema applicativo investigato. Il primo obiettivo ha portato allo sviluppo di una procedura sequenziale che impiega tecniche di disegno sperimentale, modellazione e ottimizzazione, e che interagisce, ad ogni passo, con lo sperimentatore. La metodologia proposta è stata denominata EMMA e coinvolge varie aree di ricerca scientifica e computazionale, quali superfici di risposta nonparametriche e semiparametriche, disegno e analisi di esperimenti a computer, ottimizzazione multiobiettivo e computazione ispirata al comportamento degli sciami in natura. EMMA prevede l’identificazione di un disegno sperimentale (insieme di esperimenti) che viene successivamente integrato con dei punti sperimentali (esperimenti), identificati in modo sequentiale. Il processo di identificazione dei nuovi punti sperimentali è guidato da un algoritmo di ottimizzazione particle swarm, che minimizza la distanza fra i valori di risposta osservati e un target. Il target è un insieme di valori ottimali, uno per ogni risposta, che vengono selezionati usando un modello di regressione multivariata basato su spline (MARS). Tale target viene aggiornato non appena i nuovi esperimenti vengono implementati e le corrispondenti risposte vengono misurate. Quando testato su un insieme di funzioni standard, EMMA ha dimostrato di poter superare il potenziale problema di convergenza prematura verso un ottimo locale e di poter identificare correttamente il vero ottimo globale. Inoltre, EMMA non richiede nessuna assunzione sulla distribuzione dei dati e, diversamente da altre procedure, permette di selezionare automaticamente il target. Infine, EMMA è stata applicata ad un problema chimico volto alla funzionalizzazione di un substrato per possibili applicazioni biomediche. Rispetto al metodo generalmente usato dagli scienziati, EMMA ha permesso di migliorare le risposte del sistema di vari punti percentuali, e incrementi fino al 380% sono stati osservati. L’approccio proposto costituisce pertanto un metodologia con elevate potenzialità per l’ottimizzazione di sistemi multirisposta ad alta dimensionalità. Inoltre, grazie a degli studi di simulazione, EMMA permette di ottenere una stima iniziale del numero di esperimenti e del tempo necessario per raggiungere il miglioramento desiderato. Di conseguenza, potendo fornire un’indicazione del budget richiesto per lo studio di interesse, la metodologia risulta essere di interesse specialmente nel settore della ricerca industriale. Il secondo obiettivo ha portato allo sviluppo di un approcio di meta-apprendimento per la selezione del modello. L’interesse nella selezione del modello deriva da domande quali ‘E’ MARS il miglior modello che avremmo potuto usare?’ e ‘Dato un problema applicativo, come possiamo selezionare la tecnica di modellazione più promettente da combinare con EMMA?’. Infatti, `e ormai riconosciuto che non esiste un modello le cui performance sono migliori, rispetto ad altre tecniche di modellazione, per tutti i possibili problemi di regressione. Inoltre, le performance di un modello ‘... possono dipendere dalla natura del problema investigato in termini di numero di osservazioni, numero di variabili risposta, struttura di correlazione delle variabili, rapporto segnale-rumore, grado di collinearity dei predittori, etc.’ (Breiman & Friedman 1997). L’approcio di meta-apprendimento è stato adottato per identificare il modello statistico più promettente, sulla base delle caratteristiche del problema investigato. L’idea consisteva nello studiare un insieme di modelli di regressione multirisposta e valutare la loro performance su un’ampia classe di problemi caratterizzati da diversi gradi di complessità. Studiando la relazione fra le caratteristiche del problema e la performance dei modelli, lo scopo è di scoprire sotto quali condizioni un modello è migliore di altri e simultaneamente acquisire alcune regole da poter usare come linee guida nello studio di nuove applicazioni. A tale scopo sono state sviluppate le procedure per simulare i dati, le metriche per misurare le caratteristiche dei problemi, e il codice R necessario per la valutazione delle performance dei modelli. Questo ha permesso di gettare le fondamenta di un ampio studio di simulazione, la cui implementazione fa parte della ricerca attualmente in corso. Lo scopo della ricerca futura è di esaminare, da un punto di vista teorico, le regole empiriche ottenute in modo da poterne confermare la validità, oltre che favorire una migliore comprensione del comportamento delle tecniche di modellazione investigate.
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LaBute, Gerard Joseph. "Pseudo-Bayesian response surface analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0001/MQ34971.pdf.

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DeFeo, Patrick A. "Sequential robust response surface strategy." Diss., Virginia Polytechnic Institute and State University, 1988. http://hdl.handle.net/10919/53687.

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General Response Surface Methodology involves the exploration of some response variable which is a function of other controllable variables. Many criteria exist for selecting an experimental design for the controllable variables. A good choice of a design is one that may not be optimal in a single sense, but rather near optimal with respect to several criteria. This robust approach can lend well to strategies that involve sequential or two stage experimental designs. An experimenter that fits a first order regression model for the response often fears the presence of curvature in the system. Experimental designs can be chosen such that the experimenter who fits a first order model will have a high degree of protection against potential model bias from the presence of curvature. In addition, designs can also be selected such that the experimenter will have a high chance for detection of curvature in the system. A lack of fit test is usually performed for detection of curvature in the system. Ideally, an experimenter desires good detection capabilities along with good protection capabilities. An experimental design criterion that incorporates both detection and protection capabilities is the A₂* criterion. This criterion is used to select the designs which maximize the average noncentrality parameter of the lack of fit test among designs with a fixed bias. The first order rotated design class is a new class of designs that offers an improvement in terms of the A₂* criterion over standard first order factorial designs. In conjunction with a sequential experimental strategy, a class of second order rotated designs are easily constructed by augmenting the first order rotated designs. These designs allow for estimation of second order model terms when a significant lack of fit is observed. Two other design criteria, that are closely related, and incorporate both detection and protection capabilities are the JPCA, and JPCMAX criterion. JPCA, considers the average mean squared error of prediction for a first order model over a region where the detection capabilities of the lack of fit test are not strong. JPCMAX considers the maximum mean squared error of prediction over the region where the detection capabilities are not strong. The JPCA and JPCMAX criteria are used within a sequential strategy to select first order experimental designs that perform well in terms of the mean squared error of prediction when it is likely that a first order model will be employed. These two criteria are also adopted for nonsequential experiments for the evaluation of first order model prediction performance. For these nonsequential experiments, second order designs are used and constructed based upon JPCA and JPCMAX for first order model properties and D₂ -efficiency and D-efficiency for second order model properties.
Ph. D.
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Song, Qingtao. "Surface wind response to oceanic fronts /." View online ; access limited to URI, 2006. http://0-wwwlib.umi.com.helin.uri.edu/dissertations/dlnow/3225330.

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Thompson, Nicolas Ray. "Cylindrical designs for response surface studies." Thesis, Montana State University, 2011. http://etd.lib.montana.edu/etd/2011/thompson/ThompsonN0511.pdf.

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Central Composite Designs (CCDs) with cuboidal and spherical regions are among the most popular experimental designs for studying response surfaces. Cuboidal regions are typically used when the experimenter believes the levels of one or more of the factors are bounded while a spherical region is employed when there are no restrictions on the levels of any of the factors. We propose what we call a cylindrical design in which the levels of some factors are restricted while the other factors' levels need not be. Assuming the use of a second-order model, we give the general form for the model matrix X of such a design and give a closed form for the determinant of the X 0X matrix as well as its inverse. We use the results for the determinant and inverse of X 0X to compare designs using the alphabetic design optimality criteria. D-efficiencies, A-efficiencies, G-efficiencies, and IV-efficiencies for CCDs will be compared with those of the cylindrical design. Graphical assessment of the maximum spherical prediction variance will also be done. It will be shown that the cylindrical design is an excellent alternative when some but not all factors have restricted levels.
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Pickle, Stephanie M. "Semiparametric Techniques for Response Surface Methodology." Diss., Virginia Tech, 2006. http://hdl.handle.net/10919/28517.

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Many industrial statisticians employ the techniques of Response Surface Methodology (RSM) to study and optimize products and processes. A second-order Taylor series approximation is commonly utilized to model the data; however, parametric models are not always adequate. In these situations, any degree of model misspecification may result in serious bias of the estimated response. Nonparametric methods have been suggested as an alternative as they can capture structure in the data that a misspecified parametric model cannot. Yet nonparametric fits may be highly variable especially in small sample settings which are common in RSM. Therefore, semiparametric regression techniques are proposed for use in the RSM setting. These methods will be applied to an elementary RSM problem as well as the robust parameter design problem.
Ph. D.
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Akhtar, Munir. "Response surface designs robust to missing observations." Thesis, University of Southampton, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356685.

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Books on the topic "Response surface"

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1940-, Khuri André I., ed. Response surface methodology and related topics. New Jersey: World Scientific, 2005.

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A, Cornell John. How to apply response surface methodology. Milwaukee, WI: ASQC, 1990.

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Canada. Dept. of Fisheries and Oceans. User's Guide to Nonlinear Response Surface Analysis Software: Part 2: Plotting Response Surface Contours. S.l: s.n, 1987.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Effect of design selection on response surface performance. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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Çelikel, A. Kaan. Parametrics of near surface response of submersible vehicles. Monterey, Calif: Naval Postgraduate School, 1996.

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Institute of Materials, Minerals, and Mining., ed. Optimisation of manufacturing processes: A response surface approach. London: Maney for the Institute of Materials, Minerals and Mining, 2003.

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United States. National Aeronautics and Space Administration. Scientific and Technical Information Program., ed. Effect of design selection on response surface performance. [Washington, D.C.]: National Aeronautics and Space Administration, Office of Management, Scientific and Technical Information Program, 1993.

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Marti, Kurt. Semi-stochastic approximation by the response surface methodology (RSM). Neubiberg: Universität der Bundeswehr, 1990.

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Whitcomb, Pat. Response surface methods for process optimization: Ppt Version 27.04. Minneapolis, MN: Stat-Ease, Inc., 2006.

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Schuman, G. E. Vegetation response to soil surface modification in mined land reclamation. S.l: s.n, 1986.

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Book chapters on the topic "Response surface"

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Barton, Russell R. "Response Surface Methodology." In Encyclopedia of Operations Research and Management Science, 1307–13. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_1143.

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Christensen, Ronald. "Response Surface Maximization." In Springer Texts in Statistics, 344–76. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3847-6_8.

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Dean, Angela, Daniel Voss, and Danel Draguljić. "Response Surface Methodology." In Springer Texts in Statistics, 565–614. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52250-0_16.

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Kleijnen, Jack P. C. "Response Surface Methodology." In Handbook of Simulation Optimization, 81–104. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1384-8_4.

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Cavazzuti, Marco. "Response Surface Modelling." In Optimization Methods, 43–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31187-1_3.

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Khuri, André I. "Response Surface Methodology." In International Encyclopedia of Statistical Science, 1229–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_492.

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Selvamuthu, Dharmaraja, and Dipayan Das. "Response Surface Methodology." In Introduction to Statistical Methods, Design of Experiments and Statistical Quality Control, 319–51. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1736-1_9.

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Korondi, Péter Zénó, Mariapia Marchi, and Carlo Poloni. "Response Surface Methodology." In Optimization Under Uncertainty with Applications to Aerospace Engineering, 387–409. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60166-9_12.

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Zhang, Jie, Te Xiao, Jian Ji, Peng Zeng, and Zijun Cao. "Response Surface Methods." In Geotechnical Reliability Analysis, 127–72. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6254-7_4.

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Selvamuthu, Dharmaraja, and Dipayan Das. "Response Surface Methodology." In Introduction to Probability, Statistical Methods, Design of Experiments and Statistical Quality Control, 493–525. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9363-5_14.

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Conference papers on the topic "Response surface"

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Forrester, Alexander, Neil Bressloff, and Andy Keane. "Response Surface Model Evolution." In 16th AIAA Computational Fluid Dynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2003. http://dx.doi.org/10.2514/6.2003-4089.

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Chang, Kuo-Hao, and Hong Wan. "Stochastic Trust Region Response Surface Convergent Method for generally-distributed response surface." In 2009 Winter Simulation Conference - (WSC 2009). IEEE, 2009. http://dx.doi.org/10.1109/wsc.2009.5429426.

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Ahmad, N. A., K. Kidam, and R. Mohsin. "Community-based emergency response for toxic gas release: Conceptual framework." In THE PHYSICS OF SURFACES: Aspects of the Kinetics and Dynamics of Surface Reaction. AIP, 2023. http://dx.doi.org/10.1063/5.0114240.

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KIM, YOUNG JIN. "SEPARATE RESPONSE SURFACE MODELING FOR MULTIPLE RESPONSE OPTIMIZATION." In Proceedings of the 2nd International Workshop (AIWARM 2006). WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812773760_0086.

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Papila, Melih, and Raphael Haftka. "Uncertainty and response surface approximations." In 19th AIAA Applied Aerodynamics Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-1680.

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van Keulen, Fred, and Koen Vervenne. "Gradient-Enhanced Response Surface Building." In 9th AIAA/ISSMO Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-5455.

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Lin, Ke, Haobo Qiu, Liang Gao, and Yifei Sun. "Comparison of Stochastic Response Surface Method and Response Surface Method for Structure Reliability Analysis." In 2009 Second International Conference on Intelligent Computation Technology and Automation. IEEE, 2009. http://dx.doi.org/10.1109/icicta.2009.509.

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Krenz, Robert A. "Vehicle Response to Throttle Tip-In/Tip-Out." In SAE Surface Vehicle Noise and Vibration Conference. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1985. http://dx.doi.org/10.4271/850967.

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Wei Zhao and Nan Wang. "Probability Collectives using Response Surface estimation." In 2013 International Conference on Communications and Information Technology (ICCIT). IEEE, 2013. http://dx.doi.org/10.1109/iccitechnology.2013.6579512.

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Sobieski, I., and I. Kroo. "Collaborative optimization using response surface estimation." In 36th AIAA Aerospace Sciences Meeting and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1998. http://dx.doi.org/10.2514/6.1998-915.

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Reports on the topic "Response surface"

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Carley, Kathleen M., Natalia Y. Kamneva, and Jeff Reminga. Response Surface Methodology. Fort Belvoir, VA: Defense Technical Information Center, October 2004. http://dx.doi.org/10.21236/ada459032.

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Miller, Michael. Global Resource Management of Response Surface Methodology. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1620.

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Crosier, Ronald B. Some New Three-Level Response Surface Designs. Fort Belvoir, VA: Defense Technical Information Center, October 1991. http://dx.doi.org/10.21236/ada243964.

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Khuri, Andre I. Response Surface Analysis of Experiments with Random Blocks. Fort Belvoir, VA: Defense Technical Information Center, September 1988. http://dx.doi.org/10.21236/ada200829.

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Dressel, M., O. Klein, S. Bruder, G. Gruener, K. D. Carlson, H. H. Wang, and J. M. Williams. Surface impedance studies on the electrodynamical response of organic superconductors. Office of Scientific and Technical Information (OSTI), November 1994. http://dx.doi.org/10.2172/10194728.

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Oakey, Neil S. Horizontal Variability in Surface Mixing in Response to Wind Forcing. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada629422.

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Wong, E. K., and G. L. Richmond. Examination of the Surface Second Harmonic Response at Infrared Wavelengths. Fort Belvoir, VA: Defense Technical Information Center, May 1993. http://dx.doi.org/10.21236/ada265206.

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A.L. Cundy. Use of Response Surface Metamodels in Damage Identification of Dynamic Structures. Office of Scientific and Technical Information (OSTI), May 2003. http://dx.doi.org/10.2172/812182.

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Weller, Robert A., and Richard P. Trask. Mixed Layer Response to Monsoonal Surface Forcing in the Arabian Sea. Fort Belvoir, VA: Defense Technical Information Center, September 1997. http://dx.doi.org/10.21236/ada628604.

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Zappa, Christopher J. Ocean Surface Temperature Response to Atmosphere-Ocean Interaction of the MJO. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557074.

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