Relevant bibliographies by topics / Extended multivariate analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Extended multivariate analysis'

Author: Grafiati
Published: 9 October 2021
Last updated: 18 July 2025

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Journal articles on the topic "Extended multivariate analysis"

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Ohlhausen, JA. "Advanced Multivariate Analysis Tools Applied to Surface Analysis." Microscopy and Microanalysis 16, S2 (2010): 382–83. http://dx.doi.org/10.1017/s1431927610055558.

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Keenan, Michael R., and Paul G. Kotula. "Multivariate Statistical Analysis of EEL-Spectral Images." Microscopy and Microanalysis 10, S02 (2004): 874–75. http://dx.doi.org/10.1017/s1431927604880620.

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Lavine, Barry K., Charles E. Davidson, Jason Ritter, David J. Westover, and Thomas Hancewicz. "Varimax extended rotation applied to multivariate spectroscopic image analysis." Microchemical Journal 76, no. 1-2 (2004): 173–80. http://dx.doi.org/10.1016/s0026-265x(03)00159-0.

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Keenan, MR, VS Smentkowski, RM Ulfig, E. Oltman, DJ Larson, and TF Kelly. "Multivariate Statistical Analysis of Atom Probe Tomography Data." Microscopy and Microanalysis 16, S2 (2010): 270–71. http://dx.doi.org/10.1017/s143192761005436x.

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Parish, C., C. Capdevila, and MK Miller. "Applying Multivariate Statistical Analysis to Atom Probe Tomography." Microscopy and Microanalysis 16, S2 (2010): 1858–59. http://dx.doi.org/10.1017/s1431927610056205.

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Van Benthem, M., P. Kotula, and P. Lu. "Multivariate Statistical Analysis Strategies of EELS Spectral Images." Microscopy and Microanalysis 17, S2 (2011): 784–85. http://dx.doi.org/10.1017/s143192761100479x.

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Sarahan, M., F. de la Peña, Q. Ramasse, and M. Walls. "Novel Multivariate Statistical Analysis Methods for STEM/EELS." Microscopy and Microanalysis 17, S2 (2011): 1306–7. http://dx.doi.org/10.1017/s1431927611007409.

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Hakim, H. A. Nazmul, ANM Nure Azam, Md Tuhin Talukder, et al. "Independent Predictors of Extended Hospitalization Following Cholecystectomy: A Multivariate Analysis." Scholars Journal of Applied Medical Sciences 12, no. 12 (2024): 1742–46. https://doi.org/10.36347/sjams.2024.v12i12.008.

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Introduction: Cholecystectomy, the surgical removal of the gallbladder, is one of the most commonly performed surgical procedures worldwide, particularly in patients with symptomatic cholelithiasis and cholecystitis. This study aimed to assess the independent predictors of extended hospitalization following cholecystectomy. Methods: This prospective observational study was conducted at the various surgical units of the inpatient Department of Surgery, Dhaka Medical College Hospital, Dhaka, from July 2018 to June 2019. Patients with cholecystitis attending in the outdoor or emergency, admitted with cholecystitis in Dhaka Medical College Hospital, Dhaka were considered as the study population. A total of 50 patients were selected as study subjects by purposive sampling technique. Statistical Package for Social Science (SPSS) version 21 for Windows was used to analyze the data. A p-value < 0.05 was considered to be significant. Result: The median length of stay (LOS) was 5.0 days (range: 1–21 days), with the 80th percentile at 6 days. Patients were classified into two groups: control (LOS <6 days) and prolonged LOS (≥6 days). Univariate analysis revealed that factors such as patient age (p <0.002), male gender (p =0.026), preoperative leukocyte count (p =0.002), preoperative NLR (p <0.002), and admission through the ED (p <0.002) were linked to prolonged LOS. Multivariate analysis identified three independent predictors of prolonged LOS: age ≥50 years (OR 2.212, 95% CI 1.372 – 3.530, p <0.002), preoperative NLR ≥3.0 (OR 1.776, 95% CI 1.146–2.725, p =0.004), and admission via the ED (OR 1.664, 95% CI 1.070–2.560, p =0.008). Conclusion: This study identifies key independent predictors of extended hospitalization (≥6 days) following cholecystectomy, highlighting the roles of advanced age (≥50 years), elevated neutrophil-lymphocyte ratio (NLR ≥3), and admission through the emergency department (ED). Through multivariate analysis, these factors were shown ...
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Kotula, P. G., та M. A. Rodriguez. "Multivariate Statistical Analysis Strategies of μ-XRF Spectral Images". Microscopy and Microanalysis 18, S2 (2012): 948–49. http://dx.doi.org/10.1017/s1431927612006599.

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Artyushkova, K., S. Pylypenko, J. Fenton, K. Archuleta, L. Williams, and J. Fulghum. "Multi-technique, Multivariate Analysis Methods for Enhanced Sample Characterization." Microscopy and Microanalysis 12, S02 (2006): 1402–3. http://dx.doi.org/10.1017/s1431927606069492.

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Dissertations / Theses on the topic "Extended multivariate analysis"

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Dyberg, Johanna. "Osäkerhetsbedömning av skjuvhållfasthet i lera längs med Göta älv." Thesis, KTH, Jord- och bergmekanik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302656.

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Vid geotekniska arbeten är osäkerheter oundvikliga, men i stället för att hantera dessa som okända parametrar kan geotekniker med hjälp av bayesiansk statistik hantera geotekniska osäkerheter som slumpmässiga variabler med en sannolikhetsfördelning. I detta arbete har den bayesianska metoden utökad multivariabelanalys (EMA) tillämpats för bedömning av odränerad skjuvhållfasthet (𝑐u) i leror längs med Göta älv. Analysen har genomförts för områdena Smådala och Sörängen lokaliserade i lilla Edet längs med Göta älv, med mätdata från fem geotekniska mätmetoder i fält- och laboratorium: CPT-sondering (CPT), CRS-försök (CRS), direkta skjuvförsök (DSS), vingförsök (Vb) och fallkonförsök (Kon). Beräkningarna har genomförts i två steg: först har den totala osäkerheten för skattningen av 𝑐u bestämts separat för varje metod med hjälp av varianskoefficienten (𝐶𝑂𝑉tot) och sedan har samtliga 𝐶𝑂𝑉tot viktats med en EMA för en uppdaterad, mer tillförlitlig, total osäkerhet (𝐶𝑂𝑉tot,viktad). Resultaten visar 𝐶𝑂𝑉tot,viktat ≈ 2– 3,5 % för Smådala och 𝐶𝑂𝑉tot,viktat ≈ 1– 2 % för Sörängen. Slutsatsen är att bayesiansk statistik kan bidra till att kvantifiera geotekniska osäkerheter och därmed öka förståelsen dessa. Dock har osäkerheter vid bedömningen av vissa parametrar i 𝐶𝑂𝑉tot uppmärksammats, vilket kan innebära att osäkerheten från modellfelet (𝜗) kan öka osäkerheten vid skattningen av 𝑐u. Därför rekommenderas att storleken på 𝜗 vid bedömning av 𝐶𝑂𝑉tot bör undersökas och adderas till modellen för att möjliggöra användning av EMA i praktiken.<br>Uncertainties are inevitable in geotechnical investigations. However, instead of viewing these uncertainties as unknown parameters they could be managed with Bayesian statistics where the uncertainties are viewed as random variables with a statistical distribution. In this master thesis, the Bayesian method extended multivariate analysis (EMA) has been used for evaluation of the undrained shear strength (𝑐u) in clay along the Göta älv river. The analysis has been applied for the areas Smådala and Sörängen in the region Lilla Edet along the river, and with data from five geotechnical investigations methods from field- and laboratory testing: cone penetration test (CPT), constant rate of strain test (CRS), direct simple shear test (DSS), vane shear test (Vb) and fall cone test (Kon). The calculations were performed in two steps: first the calculation of the total uncertainty from the estimation of 𝑐u for each investigation method with the coefficient of variation (𝐶𝑂𝑉tot) and secondly the weighting of all the different 𝐶𝑂𝑉tot with an EMA to achieve an updated estimation of the uncertainties (𝐶𝑂𝑉tot,viktad). The results show that 𝐶𝑂𝑉tot,viktat ≈ 2– 3,5 % in Smådala and 𝐶𝑂𝑉tot,viktat ≈ 1– 2 % in Sörängen. The conclusion is that the usage of Bayesian statistics could increase the understanding of geotechnical uncertainties as well as give tools to quantify them. Although, there were uncertainties with the estimation of some parameters within 𝐶𝑂𝑉tot and thus the uncertainty from the so-called model error (𝜗) could increase the uncertainty in the estimation of 𝑐u. Therefore, it is suggested that the magnitude of 𝜗 when estimating 𝐶𝑂𝑉tot should be investigated and added to the model to enable the usage of EMA in practice.
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Mieloch, Krzysztof. "Hierarchically linked extended features for fingerprint processing." Doctoral thesis, 2008. http://hdl.handle.net/11858/00-1735-0000-0006-B3BC-A.

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Books on the topic "Extended multivariate analysis"

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Neumann, Ludmila. Extended Performance Evaluation Based on DEA: A Multidimensional Point of View. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2017.

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Neumann, Ludmila. Extended Performance Evaluation Based on DEA: A Multidimensional Point of View. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2017.

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Neumann, Ludmila. Extended Performance Evaluation Based on DEA: A Multidimensional Point of View. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2017.

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Neumann, Ludmila. Extended Performance Evaluation Based on DEA: A Multidimensional Point of View. Lang GmbH, Internationaler Verlag der Wissenschaften, Peter, 2017.

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Halperin, Sandra, and Oliver Heath. 17. A Guide to Multivariate Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/hepl/9780198702740.003.0017.

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This chapter extends the principles of bivariate analysis to multivariate analysis, which takes into account more than one independent variable and the dependent variable. With multivariate analysis, it is possible to investigate the impact of multiple factors on a dependent variable of interest, and to compare the explanatory power of rival hypotheses. Multivariate analysis can also be used to develop and test multi-causal explanations of political phenomena. After providing an overview of the principles of multivariate analysis, and the different types of analytical question to which they can be applied, the chapter shows how multivariate analysis is carried out for statistical control purposes. More specifically, it explains the use of OLS regression and logistic regression, the latter of which builds on cross-tabulation, to carry out multivariate analysis. It also discusses the use of multivariate analysis to debunk spurious relationships and to illustrate indirect causality.
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Walsh, Bruce, and Michael Lynch. Measuring Multivariate Selection. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0030.

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This chapter extends many of the results from Chapter 29 on single trait-fitness associations to the multiple trait setting. It examines the estimate of multivariate fitness surfaces, starting with quadratic surfaces and then moving to nonparametric versions (which assume no a prior functional form). It also examines path analysis, the analysis of missing data, and multilevel selection.
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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 "Extended multivariate analysis"

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Hwang, Heungsun, and Yoshio Takane. "Structural Equation Modeling by Extended Redundancy Analysis." In Measurement and Multivariate Analysis. Springer Japan, 2002. http://dx.doi.org/10.1007/978-4-431-65955-6_12.

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Hamid, Jemila S., and Sayantee Jana. "Some Tests for the Extended Growth Curve Model and Applications in the Analysis of Clustered Longitudinal Data." In Recent Developments in Multivariate and Random Matrix Analysis. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56773-6_7.

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Lorenzi, Marco, Marie Deprez, Irene Balelli, Ana L. Aguila, and Andre Altmann. "Integration of Multimodal Data." In Machine Learning for Brain Disorders. Springer US, 2012. http://dx.doi.org/10.1007/978-1-0716-3195-9_19.

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AbstractThis chapter focuses on the joint modeling of heterogeneous information, such as imaging, clinical, and biological data. This kind of problem requires to generalize classical uni- and multivariate association models to account for complex data structure and interactions, as well as high data dimensionality.Typical approaches are essentially based on the identification of latent modes of maximal statistical association between different sets of features and ultimately allow to identify joint patterns of variations between different data modalities, as well as to predict a target modality conditioned on the available ones. This rationale can be extended to account for several data modalities jointly, to define multi-view, or multi-channel, representation of multiple modalities. This chapter covers both classical approaches such as partial least squares (PLS) and canonical correlation analysis (CCA), along with most recent advances based on multi-channel variational autoencoders. Specific attention is here devoted to the problem of interpretability and generalization of such high-dimensional models. These methods are illustrated in different medical imaging applications, and in the joint analysis of imaging and non-imaging information, such as -omics or clinical data.
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Burghardt, Dirk, Alexander Dunkel, Eva Hauthal, et al. "Extraction and Visually Driven Analysis of VGI for Understanding People’s Behavior in Relation to Multifaceted Context." In Volunteered Geographic Information. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35374-1_12.

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AbstractVolunteered Geographic Information in the form of actively and passively generated spatial content offers great potential to study people’s activities, emotional perceptions, and mobility behavior. Realizing this potential requires methods which take into account the specific properties of such data, for example, its heterogeneity, subjectivity, and spatial resolution but also temporal relevance and bias.The aim of the chapter is to show how insights into human behavior can be gained from location-based social media and movement data using visual analysis methods. A conceptual behavioral model is introduced that summarizes people’s reactions under the influence of one or more events. In addition, influencing factors are described using a context model, which makes it possible to analyze visitation and mobility patterns with regard to spatial, temporal, and thematic-attribute changes. Selected generic methods are presented, such as extended time curves and the co-bridge metaphor to perform comparative analysis along time axes. Furthermore, it is shown that emojis can be used as contextual indicants to analyze sentiment and emotions in relation to events and locations.Application-oriented workflows are presented for activity analysis in the field of urban and landscape planning. It is shown how location-based social media can be used to obtain information about landscape objects that are collectively perceived as valuable and worth preserving. The mobility behavior of people is analyzed using the example of multivariate time series from football data. Therefore, topic modeling and pattern analyzes were utilized to identify average positions and area of movements of the football teams.
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Anton, Cristina, and Iain Smith. "Model Based Clustering of Functional Data with Mild Outliers." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_2.

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AbstractWe propose a procedure, called CFunHDDC, for clustering functional data with mild outliers which combines two existing clustering methods: the functional high dimensional data clustering (FunHDDC) [1] and the contaminated normal mixture (CNmixt) [3] method for multivariate data. We adapt the FunHDDC approach to data with mild outliers by considering a mixture of multivariate contaminated normal distributions. To fit the functional data in group-specific functional subspaces we extend the parsimonious models considered in FunHDDC, and we estimate the model parameters using an expectation-conditional maximization algorithm (ECM). The performance of the proposed method is illustrated for simulated and real-world functional data, and CFunHDDC outperforms FunHDDC when applied to functional data with outliers.
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Steiber, Nadia, and Barbara Haas. "Working Conditions and Retirement Preferences: The Role of Health and Subjective Age as Mediating Variables in the Association of Poor Job Quality with Early Retirement." In Older Workers and Labour Market Exclusion Processes. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11272-0_8.

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AbstractThis chapter presents a theoretical model that links working conditions with men’s and women’s retirement preferences via their physical and psychological health and their subjective age and longevity expectations. The model is based on the assumption that ‘subjective age’ is a central variable in retirement decisions that mediates the relationship between working conditions and individuals’ preferred retirement timing. The theoretical model is tested using survey data from a representative sample of older workers in Austria. Based on findings from multivariate regression analyses, we conclude that improved working conditions – directly and via improved health and feelings of youthfulness – can help delaying the timing of labour market exit. Improvements in working conditions would help to extend working life, because workers who enjoy ‘good working conditions’ tend to feel healthier and younger and would be willing to work until a higher age. Job attributes that help workers to maintain a sense of youthfulness and encourage them to stay part of the active work force until a higher age include high intrinsic job quality (e.g. learning and development opportunities at work, task variety) and employee-led time flexibility. Older workers in ‘bad jobs’ that involve physical work strain and time pressure tend to feel older and to prefer an earlier retirement.
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Nwaogu, Chukwudi. "Improving Food Security by Adapting and Mitigating Climate Change-Induced Crop Pest: The Novelty of Plant-Organic Sludge in Southern Nigeria." In African Handbook of Climate Change Adaptation. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_135.

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AbstractClimate change is a global issue threatening food security, environmental safety, and human health in tropical and developing countries where people depend mainly on agriculture for their livelihood. Nigeria ranks among the top in the global yam production. It has the largest population in Africa and has been able to secure food for its growing population through food crops especially yam. Unfortunately, the recent increase in termites’ colonies due to climate change threatens yam yield. Besides harming man and environment, pesticides are expensive and not easily accessible to control the pests. This prompted a study which aimed at applying a biotrado-cultural approach in controlling the termites, as well as improving soil chemical properties and yam production. The study hypothesized that Chromolaena odorata and Elaeis guineensis sludge improved soil nutrient and yam yield and consequently decreased termites’ outbreak. In a randomized design experiment of five blocks and five replicates, five different treatments including unmanaged (UM), Vernonia amygdalina (VA), Chromolaena odorata (CO), Elaeis guineensis (EG) liquid sludge, and fipronil (FP) were applied in termites-infested agricultural soil. Data were collected and measured on the responses of soil chemical properties, termites, and yam yield to treatments using one-way ANOVA, regression, and multivariate analyses. The result showed that Chromolaena odorata (CO) and EG treatments were the best treatments for controlling termites and increase yam production. Termites were successfully controlled in VA and FP treatments, but the control was not commensurate with yam production. The experiment needs to be extended to other locations in the study region. It also requires an intensive and long-term investigation in order to thoroughly understand (i) the influence of climate change on the termites’ outbreak, (ii) the extent of termite damage to the crops, (iii) the impacts of climate change and variability on yam yields, (iii) the agricultural and economic benefits of the applied treatments, and (iv) the ecological and human health safety of the treatments.
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Tauler, Romà, Marcel Maeder, and Anna de Juan. "Multiset Data Analysis: Extended Multivariate Curve Resolution." In Comprehensive Chemometrics. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-409547-2.14702-x.

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Tauler, R., M. Maeder, and A. de Juan. "Multiset Data Analysis: Extended Multivariate Curve Resolution." In Comprehensive Chemometrics. Elsevier, 2009. http://dx.doi.org/10.1016/b978-044452701-1.00055-7.

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Arellano-Valle, Reinaldo B., Pilar L. Iglesias, and Ignacio Vidal. "Bayesian Inference for Elliptical Linear Models: Conjugate Analysis and Model Comparison." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0001.

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Abstract Bayesian inference for normal regression models, including sensitivity analysis, model comparison and error in variables under non-informative and conjugate prior for the model parameters has received considerable attention in the last decades. From a distributional point of view the results can be extended in several directions. One is by considering a wider class of prior distributions for the parameters of the model. Another, is by considering alternative distributions for the error terms. Usually, the results with non-conjugate priors rely heavily on MCMC methods. On the other hand, many extensions has been obtained by considering the so called dependent elliptical model, which is often used in linear regression analysis to accommodate the kurtosis of the error terms and to accommodate outliers. Bayesian inference with multivariate elliptical models was initially presented in Chu (1973). Posteriorly, Meinhold and Singpurwalla (1989) consider a robustification of Kalman filters by using the multivariate Student-t distribution. This distribution was also used by Zellner (1976), who considered a Bayesian treatment of linear regression models under non-informative prior distributions.
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Conference papers on the topic "Extended multivariate analysis"

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Tassi, Emma, Federica Goffi, Carolina Bonivento, et al. "Multivariate Developmental Pattern of Cortical Thickness, Brain Functional Connectivity and Behaviour: a Multi-Block Partial Least Square Discriminant Analysis." In 2024 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE, 2024. https://doi.org/10.1109/metroxraine62247.2024.10796151.

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KIRK, J. "The use of multivariate analysis to optimise design parameters for extended-range combat aircraft." In 4th Symposium on Multidisciplinary Analysis and Optimization. American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4707.

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Hettiarachchi, Imali T., Shady Mohamed, Saeid Nahavandi, and Sofia Nahavandi. "Application of Extended Multivariate Modeling for Information Flow Analysis of Event Related Responses." In 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2015. http://dx.doi.org/10.1109/smc.2015.323.

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Hettiarachchi, Imali T., Shady Mohamed, Luke Nyhof, and Saeid Nahavandi. "An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network." In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2013. http://dx.doi.org/10.1109/embc.2013.6610408.

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Zhang, Qi, David A. Steinman, and Morton H. Friedman. "Prediction of Disturbed Flow by Factor Analysis of Carotid Bifurcation Geometry." In ASME 2009 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2009. http://dx.doi.org/10.1115/sbc2009-204798.

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Hemodynamics plays an important role in the development and progression of carotid artery atherosclerotic lesions. Certain aspects of vascular geometry, which mediates local hemodynamics, might be risk factors that increase a vessel’s atherosusceptibility [1]. To further evaluate this “geometric risk factor” hypothesis, the relationship between geometric features and hemodynamic quantities thought to typify “disturbed flow” was recently investigated [2]. Fourteen intercorrelated geometric features were initially extracted from MR images of 50 carotid bifurcations, and multivariate regression based on an a priori selection of a subset of four of these geometric features was used to identify two that were predictors of disturbed hemodynamics. Here, this work is extended to simultaneously analyze the combined role of all geometric variables using factor analysis.
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Rabbani, Majid, and Richard l. Vanmetter. "Analysis of signal and noise propagation for several imaging mechanisms." In OSA Annual Meeting. Optica Publishing Group, 1988. http://dx.doi.org/10.1364/oam.1988.tuo5.

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We have previously shown1 that multivariate moment-generating functions provide a facile method for analyzing the influence of stochastic amplifying and scattering mechanisms on the transfer of signal and noise through multistage imaging systems. The method is general in that it can be applied to stationary as well as nonstationary processes. In the special case of stationary processes, relationships between the input and output noise power spectra (NPS) can be derived. For the case of photon-limited inputs, one can also readily express the detective quantum efficiency. We have extended application of the method to five cases of amplifying and scattering mechanisms in which the parameters are themselves stochastic variables and in which random variables are correlated between stages. Expressions for the NPS and DQE are derived for each case, and a physical example drawn from the study of radio-graphic intensifying screens is given. In several of these examples, previously published theoretical results have been shown to follow as special cases of these more general results.
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Drago, Michele, Matteo Mattioli, and Federico Quondamatteo. "Metocean Design Criteria for Deep Water Offshore Systems." In ASME 2015 34th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/omae2015-41108.

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In the last decades the off-shore hydrocarbon extraction industry has extended its field of activities in very deep waters up to more than 2000 m. Extraction and production systems can vary between complete subsea development with export pipelines to on-shore treatment plants and surface development by means of surface units (SSFU) connected to subsea wells by risers and anchored by mooring systems which extend through the whole water column. For exclusively subsea developments, including sealines, the metocean design data and criteria to be developed and the applicable methodologies to derive them are well established. Univariate theory is usually applied in order to quantify the risk of failure due to (extreme) sea conditions. The surface developments and the connections through the water column (e.g. risers, moorings) are newly challenging aspects. They could suffer from severe damages due to the occurrence of critical combinations of different variables during a single sea storm:: thus, it may be important to consider the joint occurrence of different forcing conditions (i.e. multivariate analysis). The present manuscript provides a simplified methodology in order to carry out a sensible multivariate analysis of the contemporary data such as wind, waves and current. Three different cases are analyzed: i) the correlation of extremes of different variables (wind, wave and current), ii) the extreme profiles of current and iii) the current profile climate. A practical case study is illustrated throughout the paper.
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Wu, Hao, and Xiaoping Du. "Time-Dependent System Reliability Analysis With Second Order Reliability Method." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22214.

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Abstract System reliability is quantified by the probability that a system performs its intended function in a period of time without failure. System reliability can be predicted if all the limit-state functions of the components of the system are available, and such a prediction is usually time consuming. This work develops a time-dependent system reliability method that is extended from the component time-dependent reliability method that uses the envelop method and second order reliability method. The proposed method is efficient and is intended for series systems with limit-state functions whose input variables include random variables and time. The component reliability is estimated by the existing second order component reliability method, which produces component reliability indexes. The covariance between components responses are estimated with the first order approximations, which are available from the second order approximations of the component reliability analysis. Then the joint probability of all the component responses is approximated by a multivariate normal distribution with its mean vector being component reliability indexes and covariance being those between component responses. The proposed method is demonstrated and evaluated by three examples.
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Rusanov, Vyacheslav, Ilya Gubanov, Sergey Agafonov, and Alexey Daneev. "Simulation of analytical calculations when optimizing integrative properties and composite metal coatings." In Intelligent Human Systems Integration (IHSI 2023) Integrating People and Intelligent Systems. AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1002869.

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In this work, a nonlinear multidimensional regression-tensor (valence 3) model is constructed and investigated for the analytical substantiation of the necessary / sufficient conditions for optimizing the technological calculation of the multifactor physicochemical process of hardening complex composite media of metal coatings. An adaptive-a posteriori procedure for the parametric formation of the target functional of the quality of the integrative physical and mechanical properties of the designed metal coating is proposed. The results of the study can serve as the basic elements of the mathematical language in the creation of automated design of precision nanotechnologies for hardening the surfaces of complex composite metal coatings on the basis of group accounting of multifactorial tribological, as well as anti-corrosion, tests. In this case, the main goal is not so much the formal accuracy of inferences, but rather the clarity of concepts in the development of general problems of tribology associated with precision modeling of nanostructures of complex composite metal coatings. The multivariate regression-tensor model for tribological / anti-corrosion tests is substantiated by means of the least squares identification of multivariate nonlinear regression equations with the minimum tensor norm. This approach, due to the abundance of available computational problems, as well as due to the possibilities that it opens up for applications of nonlinear multivariate regression tensor analysis, can acquire great (extended) significance in the problems of precision multifactorial nonlinear optimization of physicochemical processes. strengthening of complex composite metal coatings and metamaterials.
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Wang, Junzhe, Shyam Kareepadath Sajeev, Evren Ozbayoglu, Silvio Baldino, Yaxin Liu, and Haorong Jing. "Reducing NPT Using a Novel Approach to Real-Time Drilling Data Analysis." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/215028-ms.

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Abstract Early detection and characterization of anomalous events during drilling operations are critical to avoid costly downtime and prevent hazardous events, such as a stuck pipe or a well control event. A key aspect of real-time drilling data analysis is the capability to make precise predictions of specific drilling parameters based on past time series information. The ideal models should be able to deal with multivariate time series and perform multi-step predictions. The recurrent neural network with a long short-term memory (LSTM) architecture is capable of the task, however, given that drilling is a long process with high data sampling frequency, LSTMs may face challenges with ultra-long-term memory. The transformer-based deep learning model has demonstrated its superior ability in natural language processing and time series analysis. The self-attention mechanism enables it to capture extremely long-term memory. In this paper, transformer-based deep learning models have been developed and applied to real-time drilling data prediction. It comprises an encoder and decoder module, along with a multi-head attention module. The model takes in multivariate real-time drilling data as input and predicts a univariate parameter in advance for multiple time steps. The proposed model is applied to the Volve field data to predict real-time drilling parameters such as mud pit volume, surface torque, and standpipe pressure. The predicted results are observed and evaluated. The predictions of the proposed models are in good agreement with the ground truth data. Four Transformer-based predictive models demonstrate their applicability to forecast real-time drilling data of different lengths. Transformer models utilizing non-stationary attention exhibit superior prediction accuracy in the context of drilling data prediction. This study provides guidance on how to implement and apply transformer-based deep learning models applied to drilling data analysis tasks, with a specific focus on anomaly detection. When trained on dysfunction-free datasets, the proposed model can predict real-time drilling data with high precision, whereas when a downhole anomaly starts to build, the significant error in the prediction can be used as an alarm indicator. The model can consider extremely long-term memory and serve as the alternative algorithm to LSTM. Furthermore, this model can be extended to a wide range of sequence data prediction problems in the petroleum engineering discipline.
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Reports on the topic "Extended multivariate analysis"

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Gong, Xuan, Zhou Chen, Kui Yang, et al. Endoscopic Transsphenoidal Surgery for Infra-Diaphragmatic Craniopharyngiomas: Impact of Diaphragm Sellae Competence on Hypothalamic Injury. International Journal of Surgery, 2024. http://dx.doi.org/10.60122/j.ijs.2024.20.03.

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Objective: Investigate the impact of diaphragm sellae competence on surgical outcomes and risk factors for postoperative hypothalamic injury (HI) in patients undergoing endoscopic transsphenoidal surgery (ETS) for infra-diaphragmatic craniopharyngiomas (ICs). Methods: A retrospective analysis of 54 consecutive patients (2016-2023) with ICs treated by ETS was conducted. All tumors originated from the sellar region inferior to the diaphragm sellae and were classified into two subtypes in terms of diaphragm sellae competence: IC with competent diaphragm sellae (IC-CDS) and IC with incompetent diaphragm sellae (IC-IDS). Clinical features, intraoperative findings, and follow-up data were compared between subtypes. Postoperative HI was assessed using a magnetic resonance imaging-based scoring system. Results: Fifty-four patients (29 males, 25 females) were included in this study, with 12 (22.2%) under 18 years old. Overall, 35 cases were IC-CDS, while 19 were IC-IDS. Compared with IC-CDS, patients with IC-IDS tended to have hormone hypofunction before surgery (p = 0.03). Tumor volume in IC-IDS group (9.0 ± 8.6 cm3) was also higher than that in IC-CDS group (3.3 ±3.4 cm, p = 0.011). Thirty-seven patients underwent standard endoscopic transsphenoidal approach (SEA) and 17 underwent an extended endoscopic transsphenoidal approach (EEA). Gross total resection (GTR) was achieved in 50 cases (92.6%). Postoperative CSF leak was observed in four patients (7.4%). Permanent diabetes insipidus (DI) occurred in 13 patients (27.7%), six in IC-CDS and seven in IC-IDS. Postoperative HI occurred in 38.9% of patients. Univariate analysis revealed that large tumor size (p = 0.014), prior hypopituitarism (p = 0.048) and IC-IDS (p &lt; 0.001) were significantly associated with postoperative HI. Multivariate analysis revealed that IC- IDS was the sole predictor of postoperative HI. Conclusion: To our knowledge, this is the largest case series in the literature to describe IC resected by endoscopic surgery in a single institution. Classification based on diaphragm sellae competence highlights distinct clinical features and surgical outcomes between IC-CDS and IC-IDS subtypes. Notably, IC-IDS is an independent risk factor for postoperative HI. Preoperative identification of subtype can guide surgical strategy and potentially minimize complications.
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