Relevant bibliographies by topics / Multivariate location models

Academic literature on the topic 'Multivariate location models'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Multivariate location models"

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Ahmed, S. E., and A. K. Md. E.Saleh. "Improved nonparametric estimation of location vectors in multivariate regression models." Journal of Nonparametric Statistics 11, no. 1-3 (January 1999): 51–78. http://dx.doi.org/10.1080/10485259908832775.

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McCluskey, William J., and Richard A. Borst. "Specifying the effect of location in multivariate valuation models for residential properties." Property Management 25, no. 4 (August 21, 2007): 312–43. http://dx.doi.org/10.1108/02637470710775185.

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Arnold, Barry C., and Robert J. Beaver. "Alternative constructions of skewed multivariate distributions." Acta et Commentationes Universitatis Tartuensis de Mathematica 8 (December 31, 2004): 73–81. http://dx.doi.org/10.12697/acutm.2004.08.03.

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A review of the construction of skewed multivariate normal distributions is presented. The review considers construction via (1) hidden truncation, (2) threshold models, (3) additive components and (4) a location and scale change for k variables beginning with k−1 independent standard normal variates and one univariate skew normal density. Extensiom to non-normal distributions have mainly used the hidden truncation approach. Unlike the normal case, the use of the three remaining techniques in constructing non-normal multivariate distributions leads to models distinct from those found using the hidden truncation approach. Examples of several tractable multivariate distributions using methods (1) and (3) are also presented.
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Akpootu, D. O., M. I. Iliyasu, B. M. Olomiyesan, S. A. Fagbemi, S. B. Sharafa, M. Idris, Z. Abdullahi, and N. O. Meseke. "MULTIVARIATE MODELS FOR PREDICTING GLOBAL SOLAR RADIATION IN JOS, NIGERIA." Matrix Science Mathematic 6, no. 1 (2022): 05–12. http://dx.doi.org/10.26480/msmk.01.2022.05.12.

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This study developed two to six multivariate regression equations that reliably predict global radiation in Jos (Latitude 9.87 °𝑁 and Longitude 8.75 °𝐸). Thirty-one years (1980 – 2010) observed monthly mean daily global solar radiation, sunshine hours, maximum and minimum temperatures, cloud cover, rainfall, relative humidity and wind speed data were used in this study with the clearness index as the response variable and other variables as predictors. The seven validation indices employed are the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to determine the reliability, suitability and applicability of the developed models. The results in this study revealed that all the developed multivariate models were found reliable for global solar radiation estimation in Jos depending on the obtainable meteorological data measured in the location. The correlation between the measured and predicted (developed) global solar radiation shows a perfect correlation as depicted from the figures.
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Chandra, Srinivasa Ravi, and Haitham Al-Deek. "Cross-Correlation Analysis and Multivariate Prediction of Spatial Time Series of Freeway Traffic Speeds." Transportation Research Record: Journal of the Transportation Research Board 2061, no. 1 (January 2008): 64–76. http://dx.doi.org/10.3141/2061-08.

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Short-term traffic prediction on freeways is one of the critical components of the advanced traveler information system (ATIS). The traditional methods of prediction have used univariate ARIMA time-series models based on the autocorrelation function of the time series of traffic variables at a location. However, the effect of upstream and downstream location information has been largely neglected or underused in the case of freeway traffic prediction. The purpose of this study is to demonstrate the effect of upstream as well as downstream locations on the traffic at a specific location. To achieve this goal, a section of five stations extending over 2.5 mi on I-4 in the downtown region of Orlando, Florida, was selected. The speeds from a station at the center of this location were then checked for cross-correlations with stations upstream and downstream. The cross-correlation function is analogous to the autocorrelation function extended to two variables. It indicates whether the past values of an input series influence the future values of a response series. It was found in this study that the past values of upstream as well as downstream stations influence the future values at a station and therefore can be used for prediction. A vector autoregressive model was found appropriate and better than the traditional ARIMA model for prediction at these stations.
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Cheng, I., X. Xu, and L. Zhang. "Overview of receptor-based source apportionment studies for speciated atmospheric mercury." Atmospheric Chemistry and Physics 15, no. 14 (July 17, 2015): 7877–95. http://dx.doi.org/10.5194/acp-15-7877-2015.

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Abstract. Receptor-based source apportionment studies of speciated atmospheric mercury are not only concerned with source contributions but also with the influence of transport, transformation, and deposition processes on speciated atmospheric mercury concentrations at receptor locations. Previous studies applied multivariate receptor models including principal components analysis and positive matrix factorization, and back trajectory receptor models including potential source contribution function, gridded frequency distributions, and concentration–back trajectory models. Combustion sources (e.g., coal combustion, biomass burning, and vehicular, industrial and waste incineration emissions), crustal/soil dust, and chemical and physical processes, such as gaseous elemental mercury (GEM) oxidation reactions, boundary layer mixing, and GEM flux from surfaces were inferred from the multivariate studies, which were predominantly conducted at receptor sites in Canada and the US. Back trajectory receptor models revealed potential impacts of large industrial areas such as the Ohio River valley in the US and throughout China, metal smelters, mercury evasion from the ocean and the Great Lakes, and free troposphere transport on receptor measurements. Input data and model parameters specific to atmospheric mercury receptor models are summarized and model strengths and weaknesses are also discussed. Multivariate models are suitable for receptor locations with intensive air monitoring because they require long-term collocated and simultaneous measurements of speciated atmospheric Hg and ancillary pollutants. The multivariate models provide more insight about the types of Hg emission sources and Hg processes that could affect speciated atmospheric Hg at a receptor location, whereas back trajectory receptor models are mainly ideal for identifying potential regional Hg source locations impacting elevated Hg concentrations. Interpretation of the multivariate model output to sources can be subjective and challenging when speciated atmospheric Hg is not correlated with ancillary pollutants and when source emissions profiles and knowledge of Hg chemistry are incomplete. The majority of back trajectory receptor models have not accounted for Hg transformation and deposition processes and could not distinguish between upwind and downwind sources effectively. Ensemble trajectories should be generated to take into account the trajectory uncertainties where possible. One area of improvement that applies to all the receptor models reviewed in this study is the greater focus on evaluating the accuracy of the models at identifying potential speciated atmospheric mercury sources, source locations, and chemical and physical processes in the atmosphere. In addition to receptor model improvements, the data quality of speciated atmospheric Hg plays an equally important part in producing accurate receptor model results.
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Cranshaw, Justin, Jonathan Mugan, and Norman Sadeh. "User-Controllable Learning of Location Privacy Policies With Gaussian Mixture Models." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 1146–52. http://dx.doi.org/10.1609/aaai.v25i1.8097.

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With smart-phones becoming increasingly commonplace, there has been a subsequent surge in applications that continuously track the location of users. However, serious privacy concerns arise as people start to widely adopt these applications. Users will need to maintain policies to determine under which circumstances to share their location. Specifying these policies however, is a cumbersome task, suggesting that machine learning might be helpful. In this paper, we present a user-controllable method for learning location sharing policies. We use a classifier based on multivariate Gaussian mixtures that is suitably modified so as to restrict the evolution of the underlying policy to favor incremental and therefore human-understandable changes as new data arrives. We evaluate the model on real location-sharing policies collected from a live location-sharing social network, and we show that our method can learn policies in a user-controllable setting that are just as accurate as policies that do not evolve incrementally. Additionally, we highlight the strength of the generative modeling approach we take, by showing how our model easily extends to the semi-supervised setting.
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Teräsvirta, Timo. "Mathematical and Quantitative Methods: Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series." Journal of Economic Literature 51, no. 4 (December 1, 2013): 1190–92. http://dx.doi.org/10.1257/jel.51.4.1183.r4.

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Timo Terasvirta of Aarhus University reviews, “Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series” by Andrew C. Harvey. The Econlit abstract of this book begins: “Presents a theory for a class of nonlinear time series models that can deal with dynamic distributions, with an emphasis on models in which the conditional distribution of an observation may be heavy-tailed and the location and/or scale changes over time. Discusses statistical distributions and asymptotic theory; location; scale; location/scale models for nonnegative variables; dynamic kernel density estimation and time-varying quantiles; multivariate models, correlation, and association; and further directions in dynamic models. Harvey is Professor of Econometrics at the University of Cambridge and Fellow of Corpus Christi College, the Econometric Society, and the British Academy.”
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Yates, A. R., and K. A. Yeomans. "The selection of food shopping locations by UK suburban households: A multivariate analysis." South African Journal of Business Management 16, no. 4 (December 31, 1985): 171–80. http://dx.doi.org/10.4102/sajbm.v16i4.1092.

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In this article a behavioural model of retail location is discussed which differs from established models in two respects. Firstly, in the behavioural model the demand for retail outlets is emphasized rather than the supply of retail outlets. Secondly, psychological and sociological constructs are used to help explain store selection behaviour. The behavioural model differs from established location theory in that an attempt is made to study the act of shopping in relation to other human activities. The basic postulate of the model is that people are constrained in their shopping behaviour. People can then be rated on a scale of constraints, which can be used to predict store selection behaviour.
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Chandrasekar, B., and S. Amala Revathy. "Equivariant estimation for the parameters of location-scale multivariate exponential models and its application in reliability analysis." Communications in Statistics - Theory and Methods 45, no. 18 (December 17, 2015): 5550–59. http://dx.doi.org/10.1080/03610926.2014.948195.

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Dissertations / Theses on the topic "Multivariate location models"

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Eisman, Elyktra. "GIS-integrated mathematical modeling of social phenomena at macro- and micro- levels—a multivariate geographically-weighted regression model for identifying locations vulnerable to hosting terrorist safe-houses: France as case study." FIU Digital Commons, 2015. http://digitalcommons.fiu.edu/etd/2261.

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Adaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge for societies throughout the world. Innovations in computer science have incorporated applied mathematics to develop a wide array of predictive models to support the variety of approaches to counterterrorism. Predictive models are usually designed to forecast the location of attacks. Although this may protect individual structures or locations, it does not reduce the threat—it merely changes the target. While predictive models dedicated to events or social relationships receive much attention where the mathematical and social science communities intersect, models dedicated to terrorist locations such as safe-houses (rather than their targets or training sites) are rare and possibly nonexistent. At the time of this research, there were no publically available models designed to predict locations where violent extremists are likely to reside. This research uses France as a case study to present a complex systems model that incorporates multiple quantitative, qualitative and geospatial variables that differ in terms of scale, weight, and type. Though many of these variables are recognized by specialists in security studies, there remains controversy with respect to their relative importance, degree of interaction, and interdependence. Additionally, some of the variables proposed in this research are not generally recognized as drivers, yet they warrant examination based on their potential role within a complex system. This research tested multiple regression models and determined that geographically-weighted regression analysis produced the most accurate result to accommodate non-stationary coefficient behavior, demonstrating that geographic variables are critical to understanding and predicting the phenomenon of terrorism. This dissertation presents a flexible prototypical model that can be refined and applied to other regions to inform stakeholders such as policy-makers and law enforcement in their efforts to improve national security and enhance quality-of-life.
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Gwes, Manyama Georges. "Uvärdering av Lagerlokalisering och Lagerutformning." Thesis, Linköping University, Department of Production Economics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2339.

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Armatec tillhandahåller system, produkter och funktionella lösningar inom värme, kyla och process. Företaget har lager i Sverige, Norge och Danmark. Under senare tid har företaget sett att lagret i Norge utgör en stor kostnad. Armatec har därför börjat fundera på om de skulle tjäna på att centralisera logistikstrukturen dvs. stänga lagret i Norge och utöka centrallagret i Göteborg. Syftet meddetta examensarbete är att undersöka huruvida lagret behövs i Norge eller ej. Den metod som arbetet använder sig av är Dupont - modellen eller avkastningsmodellen. Genom att använda denna modell beräknas lagerkostnaderna, transportkostnaderna, lagerstorlek, kapacitetsutnyttjande osv. Dessutom gör man en analys av leveransprecision, leveranssäkerhet, leveranstid och leverensflexibilitet. Arbetet har gått genom dessa punkter och finner att lagerräntabiliteten ökar med 13% om företaget Armatec stänger sitt lager i Norge. Detta under villkor att leveransservicenivån förbättras. Transportkostnaderna ökar något men inte markant i det här sammanhanget. Arbetet berör också andra typer av metoder som behandlar lagerlokaliserings fråga nämligen tyndpunktsmetoden, matematiska modeller samt Analytic Hierarchy Process (AHP) Uppsatsen använder sig av en differentierad lagerhållningsränta för att beräkna lagerkostnaderna. Man har dock utgått ifrån en multivariabel ABC analys för att klassificera produkterna. En kvalitativ metod nämligen SWOT- analysen dvs. Styrka ,Svaghet, möjligheter och Hot avslutar detta arbete. Slutstaten med detta examensarbete är att Armatec skulle tjäna på en centralisering av lagerstrukturerna. Författaren vill påminna läsaren att Företaget vill behålla sitt lager i Köpenhamn. Det är därför Köpenhamn inte igår i arbetet. Företaget har inget lager i Finland. Arbetet visar också att den finska marknaden inte kommer att påverkas i samband med centralisering

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Books on the topic "Multivariate location models"

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Thompson, Norris B., and SreyRam Kuy. Multivariable Predictors of Postoperative Surgical Site Infection after General and Vascular Surgery. Edited by SreyRam Kuy. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199384075.003.0013.

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This landmark study proposed a model for predicting surgical site infections (SSI). Using logistic regression analysis, variables independently associated with increased risk of SSI were identified, which included smoking, alcohol use, comorbidities, disseminated cancer, weight loss greater than 10%, emergency surgery, and length of operative time. This chapter describes the basics of the study, including funding, year study began, year study was published, study location, who was studied, who was excluded, how many patients, study design, study intervention, follow-up, endpoints, results, and criticism and limitations. The chapter briefly reviews other relevant studies and information, gives a summary and discusses implications, and concludes with a relevant clinical case.
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Jenset, Gard B., and Barbara McGillivray. A new methodology for quantitative historical linguistics. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198718178.003.0007.

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Chapter 7 summarizes the book and discusses how the proposed framework provides researchers with the methodological guidance to answer new questions, as well as answering existing questions in new ways. A section summarizes the core steps of the quantitative research process based on the principles and best practices of Chapter 2. The importance of open data and open research processes for transparent research is highlighted. An extended case study on morphological change in early modern English is used to exemplify a research process that includes exploratory data analysis and different types of multivariate statistical techniques. The case study highlights that quantitative studies still require interpretation, and that judgements must be made about the adequacy of the statistical models, an important point that is not always sufficiently emphasized in existing methodological introductions. The chapter ends with a summary locating the framework of the book in the historical linguistics landscape.
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Book chapters on the topic "Multivariate location models"

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Oja, Hannu. "Multivariate location and scatter models." In Multivariate Nonparametric Methods with R, 5–13. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0468-3_2.

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Sen, Pranab Kumar, Jana Jurečková, and Jan Picek. "Affine Equivariant Rank-Weighted L-Estimation of Multivariate Location." In Modern Nonparametric, Robust and Multivariate Methods, 307–23. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22404-6_18.

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Hayoz, Stefanie, and Jürg Hüsler. "Nonparametric Location Estimators in the Randomized Complete Block Design." In Modern Nonparametric, Robust and Multivariate Methods, 47–68. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22404-6_4.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Bayesian Causal Network for Discrete Variables." In Intelligent Control and Learning Systems, 233–49. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_13.

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AbstractEnsuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.
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"Multivariate location model." In Robust Statistical Methods with R. Chapman and Hall/CRC, 2005. http://dx.doi.org/10.1201/9781420035131.ch5.

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"Multivariate location model." In Robust Statistical Methods with R, 141–52. Chapman and Hall/CRC, 2005. http://dx.doi.org/10.1201/9781420035131-11.

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Kopstein, Jeffrey S., and Jason Wittenberg. "Measuring Threat and Violence." In Intimate Violence, 43–56. Cornell University Press, 2018. http://dx.doi.org/10.7591/cornell/9781501715259.003.0003.

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This chapter describes our data and methods. Our analysis is based on an original dataset of census returns, electoral results, and pogrom location information. We gathered these data at the lowest geographical unit for which they could be merged, yielding observations for over 2,000 localities. We use census data on religion and electoral data on support for Jewish and non-Jewish nationalist parties to measure the degree of perceived political threat prior to the outbreak of war. We establish the characteristics of those localities where pogroms occurred using a variety of methods, including multivariate statistical models and ecological inference.
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Webb, Paul, and Tim Bale. "How parties compete (3)." In The Modern British Party System, 175–204. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780199217236.003.0006.

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Parties in the UK know that party competition is about far more than strategic shifts in ideology calculated to maximize voter appeal. It is also about the day-to-day business of projecting and protecting their reputations in the country’s print, broadcast, and social media, all of which are increasingly interrelated, even inseparable. This chapter explores those images, looking at their potential impact on voters and at how party leaders, the media, and parties’ own marketing efforts help to create and maintain them. It concludes with further multivariate models which broadly confirm the significance for voting behaviour and party competition of the range of factors identified in the first six chapters of this book: ideology, social location, leaders, and the competence and cohesion of political parties all play a part.
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"Location Model." In Statistical Inference for Models with Multivariatet-Distributed Errors, 39–68. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118853931.ch3.

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Patil, Apurva, and Rajesh Kumar K. V. "Forecasting the Space Utilization Trend in Corporate Offices." In Advances in Business Information Systems and Analytics, 137–66. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-4246-3.ch009.

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The research is mainly focused on forecasting office space utilization trends in the organization using information such as office space count, space occupancy count, holidays, leaves. Space occupancy data is collected using PIR sensors. Descriptive analytics is done using creative visualizations, and model building is done using univariate and multivariate time series methods. Descriptive analytics explains that there is a positive autocorrelation in the data with no outliers and randomness. There exists a pattern of space occupancy for different office locations at different times of the day. Univariate time series models are suitable for forecasting space occupancy for single office locations, whereas multivariate time series model VAR is suitable when considering multiple office locations of a client or multiple office locations of different clients at the same time. Empirical research has exhibited that out of tested models, SARIMAX has shown better performance on multiple test datasets.
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Conference papers on the topic "Multivariate location models"

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Galvan-Tejada, Carlos E., Juan P. Garcia-Vazquez, and Ramon Brena. "Magnetic-Field Feature Reduction for Indoor Location Estimation Applying Multivariate Models." In 2013 12th Mexican International Conference on Artificial Intelligence (MICAI). IEEE, 2013. http://dx.doi.org/10.1109/micai.2013.22.

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Sikka, Raghav, Maths Halstensen, and Joachim Lundberg. "Spray drop size characterization in an external-mixing bluff-body atomizer based on acoustics and Multivariate Analysis." In 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Trondheim, Norway, September 20-21, 2022. Linköping University Electronic Press, 2022. http://dx.doi.org/10.3384/ecp192027.

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Air-assist atomizers have been widely used in various applications such as the aerospace industry, internal combustion engines, molten metal, food processing, etc. The mean drop size for these atomizers was obtained through the Shadowgraph imaging technique. This study aims to assess the feasibility of the acoustic chemometrics approach for classifying the atomizer types and predicting the mean drop size, such as Sauter mean diameter (SMD), for a two-phase spray atomizer employed. The droplet size measurements were carried out at three radial locations and one axial location for various air and liquid (water) flow rates. The acoustic signals were recorded through two different sensors: accelerometers and microphones. The main objective of this work is to implement prediction models for the mean drop sizes (SMD) measured at various locations. The model prediction is based on the dimensionless number B, whose unique values correspond to different two-phase flow working conditions. This analysis will further cater to the question that whether the acoustics chemometrics approach, including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLS-R), is suitable for extracting valuable information such as predicting mean drop size (SMD) in two-phase flows through recorded acoustic signals.
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Mercelis, Peter, Marc Dufour, Ariel Alvarez Gebelin, Vincent Gruwez, Sarah Doorme, Marc Sas, and Gert Leyssen. "Generation of Multivariate Wave Conditions as Input for a Probabilistic Level III Breakwater Design." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-24143.

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For an offshore LNG project situated in the estuary of the Rio de la Plata nearby Montevideo, Uruguay, it was required to verify the deterministic design of the protective rubble mound breakwater and the jetty infrastructure with a level three probabilistic design. Therefore, in first instance extreme site conditions were required both in front of and behind the breakwater. To obtain these conditions, the first step is to extrapolate the offshore variables in order to translate them to the breakwater location. All the possible combinations of extreme wind, water level and waves are quantified with a probability of occurrence. A combination of univariate extreme value distributions, copula’s and regression is used to describe the multivariate statistical behaviour of the offshore variables. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The secondary variable is water level. Wind velocity and water levels are only correlated for some wind directions. For these directions, wind velocity and water level extreme value distributions are linked through a multivariate Gumbel Copula. The wave height at the model boundaries was taken into account by a regression function with the extreme wind velocity at the offshore location and the wave period by a regression function with the wave height. This way 1515 synthetic events were selected and simulated with the spectral wave model SWAN, each of which a frequency of occurrence is calculated for. However, due to refraction and diffraction effects of the approach channel (in the area of concern water depths are limited to about 7 m and the navigation channel has a depth of about 14 m), the port basin and the breakwater itself, the spectral wave model SWAN is not sufficient to accurately calculate the local wave conditions in the entire area of interest. Therefore a non-linear Boussinesq wave model (i.e. Mike 21 BW) was set up in addition, using input from the spectral model at the boundary and including the navigation channel of more than 12 km long. Combining both models, significant wave heights are obtained on both the seaward side and the leeside of the breakwater with corresponding frequencies of occurrence. This approach allows the determination of conditional return periods and generates the site conditions required for a probabilistic level three design of the breakwater and the jetty infrastructure taking for example the joint probabilities between waves and water levels fully into account as needed for overtopping or failure calculations.
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Thakur, Nikita, Andy Keane, and Prasanth B. Nair. "Probabilistic Analysis of Manufacturing Uncertainty in Turbine Blades." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86190.

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An important part in the efficient and robust design of turbine blades is to capture the details of any manufacturing uncertainty. However, the data available detailing the manufacturing uncertainty inevitably contains variability due to inherent errors in any measurement process. The presented work proposes a methodology that employs existing probabilistic data analysis techniques, namely, Principal Component Analysis (PCA), Multivariate Analysis of Variance (MANOVA) and Fast Fourier Transform (FFT) analysis for separation of the measurement error from measurement data to obtain the underlying manufacturing uncertainty. This manufacturing uncertainty is further segregated in terms of the manufacturing uncertainty with time and the blade to blade manufacturing error. A method for dimensionality reduction is employed which utilizes prior information available on the variance of the measurement error for each measurement location. The application of the proposed methodology leads to reconstruction of new datasets that may be used for generating 3-d models of the manufactured blade shapes. These 3-d models may then be used further for Finite Element Analysis (FEA) in standard FEA tools.
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Wan, Y. P., V. Gupta, H. Zhang, A. Varshney, S. Sampath, V. Prasad, and J. R. Fincke. "Modeling and Visualization of Plasma Spray Process for Depositing Functionally Graded Materials." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-1096.

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Abstract Recently, several models have been developed to simulate the plasma spraying process. The present paper extends our previous model (Wan et al., 1999a) to the plasma spray system of two-component materials with two different feed nozzles. It accounts for plasma-particle interaction, particle heating/melting/evaporation and solidification on the substrate. A special visualization algorithm has been developed to demonstrate the effects of various parameters on particle conditions while in flight, growth of functionally graded materials and distribution of the two components in the coating. Visualization of thermal processes is a challenging task if it has to be used for materials design and system development. It requires special schemes for data management in a multivariate system that includes at least velocity, temperature and species in four co-ordinates (space and time). Our effort is focused on developing a visualization scheme which goes far beyond the process animation and can be ultimately used for virtual prototyping of the processes, an area that needs special research efforts. Simulation and visualization have been performed for spraying of zirconia and NiCrAlY powders, with many combinations of powder injection features, e.g., number of nozzles, nozzle location and injection velocity. The fluctuation of the voltage is also simulated and animated to show its effect on both plasma gas and particle behavior. The optimized operating parameters are deduced from the distribution of these two materials in the coating layer. Issues related to visualization are also discussed.
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Holcomb, William, Randy F. LaFollette, and Ming Zhong. "The Third Dimension: Productivity Effects From Spatial Placement and Well Architecture in Eagle Ford Shale Horizontal Wells." In SPE Hydraulic Fracturing Technology Conference. SPE, 2015. http://dx.doi.org/10.2118/spe-173343-ms.

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Abstract Eagle Ford shale formation exhibits highly variability hydrocarbon production rates and EUR within small areas, indicating a highly heterogeneous reservoir. Attempts to determine performance drivers among geological, production or completion data points have produced inconclusive results. For example, production for one cluster of nearby wells may be strongly correlated with proppant quantity, while the trend is not valid for a group of similar wells a short distance away. A more widely valid set of correlations could improve engineering efficiency and productivity across the play. Multivariate statistical modeling has indicated that wellbore architecture factors influence well performance. Such models have determined relative influence of such factors as fracturing fluid type and volumes, proppant sizes and volumes, etc. On the wellbore architecture side, prior studies found that surface latitude and longitude are among the strongest drivers. However, these studies largely omitted consideration of the third dimension (relative vertical location in the reservoir). This study evaluates productivity influences from azimuth, dip, porpoising, and TVD from heel to toe (vertical zone coverage). It also reconsiders previously studied factors (such as fracturing fluids and proppants) with a considerably larger body of data—especially longer-term production data—than was available for the prior studies. The goal is to determine geological, wellbore architecture, and completion factors that show statistical significance as performance drivers, and where they are applicable if the results vary across the play.
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Galvan-Tejada, Carlos E., Juan P. Garcia-Vazquez, Jorge I. Galvan-Tejada, and Ramon Brena. "Multivariate or univariate model analysis for indoor location systems: A comparison." In 2015 International Conference on Electronics, Communications and Computers (CONIELECOMP). IEEE, 2015. http://dx.doi.org/10.1109/conielecomp.2015.7086936.

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Robey, R. E., J. S. Pope, O. Y. Vorobiev, S. M. Torres, M. J. Hargather, J. Kimberley, and D. Mann. "Fracture Detection of Lab-Scale Energetic Stimulation." In 56th U.S. Rock Mechanics/Geomechanics Symposium. ARMA, 2022. http://dx.doi.org/10.56952/arma-2022-2051.

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ABSTRACT: Stimulation of lab-scale boreholes was studied using small explosives for improving the development of fracture networks in engineered rock surrogates. The experimental series examines the confluence of initial stress states, orientation of induced discontinuities, and their interaction with source generated fracture growth. Density and stress response to the energetic was measured using high-speed schlieren imaging through the transparent polymethyl methacrylate (PMMA) sample. Outer surfaces were instrumented with an acoustic emissions array to detect 3D location of fracture evolution between wellbores. Prior to testing, the experiments were simulated to predict the generation of a shock-induced fracture network between single and multiple wellbores in a variety of stress states. The quantification of wave arrivals, fracture growth, and development of the fracture network in transparent PMMA material is used as further validation against computational models. Understanding the conditions under which fractures propagate in the multivariate environment with small energetics results in improved modeling capability of larger scale wellbores and sources. The present work is part of a broader effort to improve the accuracy of computational models necessary to predict formation interconnectivity established with energetics in low permeability reservoirs typical of enhanced geothermal systems. 1. INTRODUCTION Acoustic emission (AE) sensing has become a pertinent technology to observe flaws in a variety of media. Investigators can use arrays of omni-directional probes to observe energy radiating in the form of compression waves released from fracture tips, voids or features as they are distorted under load. Patterns in radiated energy may be correlated to spatial-temporal formation of microfractures. This method has been applied to the field of geomechanics where fractures grow due to an induced stress state, or when energy is radiated from an existing feature such as a slip plane. In this study, stress was induced for the purposes of studying lab-scale stimulation of geothermal wellbores using energetic sources so that computational models could be further informed by the acoustic emissions of fractures driven by energetics.
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Wang, Hui, Saumuy Suriano, Liang Zhou, and S. Jack Hu. "High-Definition Metrology Based Spatial Variation Pattern Analysis for Machining Process Monitoring and Diagnosis." In ASME 2009 International Manufacturing Science and Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/msec2009-84154.

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Non-contact high-definition measurement technology, such as laser holographic interferometry, makes it feasible to quickly inspect dimensional variation at micron level, providing up to 2 million data points over a surface area of up to 300×300 mm2. Such high-definition metrology (HDM) data contain rich spatial variation information but it is challenging to utilize this information for process monitoring and control. The spatial distribution of the data is in high-dimensional form and may show nonlinear patterns. Conventional statistical process monitoring and diagnostic schemes based on simple test statistics and linear statistical process models are incapable of capturing the complex surface characteristics as reflected by large amounts of spatial data. This paper develops a framework for efficient monitoring of spatial variation in HDM data using principal curves and quality control charts. Since large scale surface variation patterns (caused by fixturing and part bending) may camouflage those in the smaller scale (generally associated with tooling conditions), it is essential to separate the patterns in these scales and monitor them individually. At each scale, process monitoring is implemented in a sequential manner by monitoring the overall spatial features followed by localized variation identification if an out-of-control condition is detected. To examine the overall spatial characteristics, a principal-component-analysis (PCA) filtered principal curve regression is proposed in conjunction with multivariate control charts whereby nonlinear patterns of spatial data are extracted and monitored. When the overall monitoring indicates a problem, the identification of a surface variation change can be achieved through localized monitoring over each surface region based on variogram pattern analysis and control charts. The location of surface region change provides clues for variation source diagnosis. The proposed method is illustrated using simulated HDM data.
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Sannah, Muhammad, and Ahmad Smaili. "Analytical Investigation on Elastodynamic Vibration Suppression of a Flexible Four-Bar Mechanism System Featuring a Smart Coupler Link and Multivariable Optimal Regulator." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/vib-3779.

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Abstract This paper presents an analytical investigation on active control of the elastodynamic response of a four-bar (4R) mechanism system using “smart” materials featuring piezoelectric sensor/actuator (S/A) pairs and multivariable optimal control. The 4R mechanism consists of a flexible coupler link, relatively flexible follower link, and a relatively rigid crank. Two thin plate-type piezoceramic S/A pairs are bonded to the flanks of the coupler link at high strain locations corresponding to the first and second vibration modes. Based on the optimal multivariable control theory, a controller which consists of a linear quadratic regulator (LQR) and a Luenberger observer as a state estimator is designed and implemented. As the mechanism changes configuration, its modal characteristics are recalculated, and the controller is redesigned. The dynamic model used for the controller design includes the second and fourth vibration modes of the mechanism system. These modes are predominated by the first two bending modes of the mechanism’s coupler link. The results showed that while the proposed active control strategy is successful in reducing the amplitudes of vibrations about the quasistatic response, it has no effect on the quasistatic deflections due to steady state loading.
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