Готові списки джерел за темами / Coefficient of confidence

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Добірка наукової літератури з теми "Coefficient of confidence"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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Статті в журналах з теми "Coefficient of confidence"

Padilla, Miguel A., and Jasmin Divers. "Coefficient Omega Bootstrap Confidence Intervals." Educational and Psychological Measurement 73, no. 6 (July 2, 2013): 956–72. http://dx.doi.org/10.1177/0013164413492765.

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Jeyaratnam, S. "Confidence intervals for the correlation coefficient." Statistics & Probability Letters 15, no. 5 (December 1992): 389–93. http://dx.doi.org/10.1016/0167-7152(92)90172-2.

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Pitts, Susan M., Rudolf Grübel, and Paul Embrechts. "Confidence bounds for the adjustment coefficient." Advances in Applied Probability 28, no. 03 (September 1996): 802–27. http://dx.doi.org/10.1017/s0001867800046504.

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Let ?(u) be the probability of eventual ruin in the classical Sparre Andersen model of risk theory if the initial risk reserve is u. For a large class of such models ?(u) behaves asymptotically like a multiple of exp (–Ru) where R is the adjustment coefficient; R depends on the premium income rate, the claim size distribution and the distribution of the time between claim arrivals. Estimation of R has been considered by many authors. In the present paper we deal with confidence bounds for R. A variety of methods is used, including jackknife estimation of asymptotic variances and the bootstrap. We show that, under certain assumptions, these procedures result in interval estimates that have asymptotically the correct coverage probabilities. We also give the results of a simulation study that compares the different techniques in some particular cases.

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Pitts, Susan M., Rudolf Grübel, and Paul Embrechts. "Confidence bounds for the adjustment coefficient." Advances in Applied Probability 28, no. 3 (September 1996): 802–27. http://dx.doi.org/10.2307/1428182.

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YE, Baojuan, and Zhonglin WEN. "Estimating Homogeneity Coefficient and Its Confidence Interval." Acta Psychologica Sinica 44, no. 12 (April 17, 2013): 1687–94. http://dx.doi.org/10.3724/sp.j.1041.2012.01687.

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Padilla, Miguel A., Jasmin Divers, and Matthew Newton. "Coefficient Alpha Bootstrap Confidence Interval Under Nonnormality." Applied Psychological Measurement 36, no. 5 (June 18, 2012): 331–48. http://dx.doi.org/10.1177/0146621612445470.

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Angus, John E. "Confidence coefficient of approximate two-sided confidence intervals for the binomial probability." Naval Research Logistics 34, no. 6 (December 1987): 845–51. http://dx.doi.org/10.1002/1520-6750(198712)34:6<845::aid-nav3220340609>3.0.co;2-d.

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Griffin, Norma S., and Michael E. Crawford. "Measurement of Movement Confidence with a Stunt Movement Confidence Inventory." Journal of Sport and Exercise Psychology 11, no. 1 (March 1989): 26–40. http://dx.doi.org/10.1123/jsep.11.1.26.

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The purposes of this study were (a) to construct and validate a Stunt Movement Confidence Inventory (SMCI) that would reliably discriminate between high- and low-confidence children and (b) to examine perceived confidence in light of assumptions from the movement confidence model. Interaction of three components postulated in the model (competence, potentials for enjoyment, and harm) was studied by analyzing the response patterns of 356 children. Reliability coefficients for item, subscale, total scale, and subject stability ranged from r=.79 to .93. SMCI subscales successfully classified 88% of all subjects with a 52.3% improvement over chance and a validity coefficient of .98. The factor matrix accounted for 49% of the total variance and verified the dominance of the competence subscale and the multivariate nature of the harm variable (subscale). Response profiles of low- and high-confidence groups validated the identity and separability of the model's theoretical components—competence, enjoyment, and harm. The SMCI was reliable and valid in discriminating between high- and low-confidence children.

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Andersson, Björn, and Tao Xin. "Large Sample Confidence Intervals for Item Response Theory Reliability Coefficients." Educational and Psychological Measurement 78, no. 1 (June 22, 2017): 32–45. http://dx.doi.org/10.1177/0013164417713570.

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In applications of item response theory (IRT), an estimate of the reliability of the ability estimates or sum scores is often reported. However, analytical expressions for the standard errors of the estimators of the reliability coefficients are not available in the literature and therefore the variability associated with the estimated reliability is typically not reported. In this study, the asymptotic variances of the IRT marginal and test reliability coefficient estimators are derived for dichotomous and polytomous IRT models assuming an underlying asymptotically normally distributed item parameter estimator. The results are used to construct confidence intervals for the reliability coefficients. Simulations are presented which show that the confidence intervals for the test reliability coefficient have good coverage properties in finite samples under a variety of settings with the generalized partial credit model and the three-parameter logistic model. Meanwhile, it is shown that the estimator of the marginal reliability coefficient has finite sample bias resulting in confidence intervals that do not attain the nominal level for small sample sizes but that the bias tends to zero as the sample size increases.

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Tonda, Tetsuji, and Kenichi Satoh. "Improvement of Confidence Interval for Linear Varying Coefficient." Japanese Journal of Applied Statistics 42, no. 1 (2013): 11–21. http://dx.doi.org/10.5023/jappstat.42.11.

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Дисертації з теми "Coefficient of confidence"

Fridline, Mark M. "Almost Sure Confidence Intervals for the Correlation Coefficient." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1258999665.

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Thesis(Ph.D.)--Case Western Reserve University, 2010
Title from PDF (viewed on 2009-12-22) Department of Statistics Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center

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Turner, Heather Jean. "A Performance Evaluation of Confidence Intervals for Ordinal Coefficient Alpha." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801899/.

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Ordinal coefficient alpha is a newly derived non-parametric reliability estimate. As with any point estimate, ordinal coefficient alpha is merely an estimate of a population parameter and tends to vary from sample to sample. Researchers report the confidence interval to provide readers with the amount of precision obtained. Several methods with differing computational approaches exist for confidence interval estimation for alpha, including the Fisher, Feldt, Bonner, and Hakstian and Whalen (HW) techniques. Overall, coverage rates for the various methods were unacceptably low with the Fisher method as the highest performer at 62%. Because of the poor performance across all four confidence interval methods, a need exists to develop a method which works well for ordinal coefficient alpha.

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Ukoumunne, Obioha Chukwunyere. "Confidence intervals for the intraclass correlation coefficient in cluster randomised trials." Thesis, King's College London (University of London), 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418293.

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Liu, Huayu. "Modified Profile Likelihood Approach for Certain Intraclass Correlation Coefficient." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_theses/96.

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In this paper we consider the problem of constructing confidence intervals and lower bounds forthe intraclass correlation coefficient in an interrater reliability study where the raters are randomly selected from a population of raters.The likelihood function of the interrater reliability is derived and simplified, and the profile likelihood based approach is readily available for computing the confidence intervals of the interrater reliability. Unfortunately, the confidence intervals computed by using the profile likelihood function are in general too narrow to have the desired coverage probabilities. From the point view of practice, a conservative approach, if is at least as precise as any existing method, is preferred sinceit gives the correct results with a probability higher than claimed. Under this rationale, we propose the so-called modified likelihood approach in this paper. Simulation study shows that, the proposed method in general has better performance than currently used methods.

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Ollikainen, Kati. "PARAMETER ESTIMATION IN LINEAR REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4138.

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Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist with confidence intervals not including the true parameter (even though the variable was selected). The Elder/Pregibon rule was used for variable selection. A comparison of the bootstrap Percentile and BCa confidence interval was made as well as an investigation of adjustments to the usual 95% confidence intervals based on the Bonferroni and Scheffe multiple comparison principles. The results show that linear regression has problems in capturing the true parameters in the confidence intervals for the sample sizes considered, the bootstrap intervals perform no better than linear regression, and the Scheffe method is too wide for any application considered. The Bonferroni adjustment is recommended for larger sample sizes and when the t-value for a selected variable is about 3.35 or higher. For smaller sample sizes all methods show problems with type II errors resulting from confidence intervals being too wide.
Ph.D.
Department of Industrial Engineering and Management Systems
Engineering and Computer Science
Industrial Engineering and Management Systems

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Kim, Jong Phil. "Efficient confidence interval methodologies for the noncentrality parameters of noncentral T-distributions." Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-04062007-124123/.

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Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007.
Lewis VanBrackle, Committee Member ; Brani Vidakovic, Committee Member ; Anthony J. Hayter, Committee Chair ; Nicholeta Serban, Committee Member ; Alexander Shapiro, Committee Member.

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Sabel, Till Verfasser], Axel [Akademischer Betreuer] Munk, and Lutz [Akademischer Betreuer] [Dümbgen. "Simultaneous Confidence Statements about the Diffusion Coefficient of an Itô-Process with Application to Spot Volatility Estimation / Till Sabel. Gutachter: Axel Munk ; Lutz Dümbgen. Betreuer: Axel Munk." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2014. http://d-nb.info/1054542279/34.

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Sabel, Till Verfasser], Axel [Akademischer Betreuer] [Munk, and Lutz [Akademischer Betreuer] Dümbgen. "Simultaneous Confidence Statements about the Diffusion Coefficient of an Itô-Process with Application to Spot Volatility Estimation / Till Sabel. Gutachter: Axel Munk ; Lutz Dümbgen. Betreuer: Axel Munk." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2014. http://d-nb.info/1054542279/34.

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Jin, Shaobo. "Computing Exact Confidence Coefficients of Simultaneous Confidence Intervals for Multinomial Proportions and their Functions." Uppsala universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-200550.

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Колпакова, Тетяна Олексіївна. "Методи, моделі та інформаційна технологія підтримки процесу вибору конкуруючих агентів". Thesis, Запорізький національний технічний університет, 2016. http://repository.kpi.kharkov.ua/handle/KhPI-Press/27267.

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Дисертація на здобуття наукового ступеня кандидата технічних наук за спеціальністю 05.13.06 – інформаційні технології. – Національний технічний університет "Харківський політехнічний інститут", Харків, 2017. Мета дисертації – підвищення ефективності процесу експертного оцінювання на основі методів, моделей та інформаційної технології підтримки процесу вибору конкуруючих агентів. Об'єкт дослідження – процес вибору конкуруючих агентів. Предмет дослідження – моделі, методи та інформаційна технологія вибору конкуруючих агентів. Розроблено нові моделі, методи та інформаційну технологію, які забез-печують підтримку впродовж всього процесу вибору конкуруючих агентів з метою підвищення якості рішення, що приймається. Запропоновано модель процесу вибору конкуруючих агентів, в якій склад-ний процес вибору декомпозується на чітко визначені етапи. Удосконалено метод визначення групових оцінок агентів на основі отримання, формалізації та агрегації індивідуальних експертних оцінок за кожним рішенням, що включає етапи абсолютного і відносного оцінювання та враховує коефіцієнти довіри до оцінки кожного учасника. Удосконалено метод оцінювання конкуруючих агентів за набором критеріїв, що дозволяє складати рейтинг агентів. Удосконалено метод оптимізації розподілу обсягу замовлення між конкуруючими агентами, який враховує відносні коефіцієнти важливості критеріїв. Створено інформаційну технологію, що дозволяє автоматизувати процес вибору конкуруючих агентів. Виконано експериментальні дослідження з вирішення практичних задач вибору конкуруючих агентів. Розроблену інформаційну систему впроваджено на підприємствах i в організаціях.
The thesis for a candidate degree in technical sciences on the specialty 05.13.06 – Information Technologies. – National Technical University "Kharkiv Polytechnic Institute", Kharkiv, 2017. The purpose of the thesis – increasing of selection process effectiveness basing on methods, models and information technology of supporting the process of competing agents selecting. The object of research – the process of competing agents selecting. The subject of research – models, methods and information technology of competing agents selecting. New models, methods and information technology which provide support of the entire process of competing agents selecting and allow to improve the quality of decisions are developed. The model of the process of competing agents selecting, which represents decomposition of the process into a set of defined stages is proposed. The method of determining group ratings based on individual expert ratings for each decision which includes obtaining of individual ratings of experts comprising absolute and relative evaluation and takes into account coefficients of confidence in the evaluations provided by each participant is improved. The method of competing agents evaluating by a set of criteria that allows to rank the agents is improved. A method of optimizing the distribution of the order among competing agents which takes into account the relative importance of criteria is improved. An information technology which allows to automate the process competing agents selecting is developed. Experimental research on solving practical problems of competing agents selection was performed. The information system is implemented in enterprises and organizations.

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Книги з теми "Coefficient of confidence"

Verrill, S. P. Confidence bounds and hypothesis tests for normal distribution coefficients of variation. Madison, WI: USDA, Forest Service, Forest Products Laboratory, 2007.

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Forest Products Laboratory (U.S.), ed. Confidence bounds for normal and lognormal distribution coefficients of variation. [Madison, Wis.]: U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory, 2003.

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Частини книг з теми "Coefficient of confidence"

Slivnyak, I. M. "A Lower Confidence Limit for the Multiple Correlation Coefficient." In New Results in Operator Theory and Its Applications, 225–29. Basel: Birkhäuser Basel, 1997. http://dx.doi.org/10.1007/978-3-0348-8910-0_17.

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Kaewprasert, Theerapong, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Interval for Coefficient of Variation of Inverse Gamma Distributions." In Lecture Notes in Computer Science, 407–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62509-2_34.

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Yosboonruang, Noppadon, Suparat Niwitpong, and Sa-Aat Niwitpong. "Confidence Intervals for Coefficient of Variation of Three Parameters Delta-Lognormal Distribution." In Structural Changes and their Econometric Modeling, 352–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04263-9_27.

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Yosboonruang, Noppadon, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Intervals for the Coefficient of Variation of the Delta-Lognormal Distribution." In Econometrics for Financial Applications, 327–37. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-73150-6_26.

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Sodanin, Sukritta, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Intervals for Common Mean of Normal Distributions with Known Coefficient of Variation." In Lecture Notes in Computer Science, 574–85. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49046-5_49.

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Panichkitkosolkul, Wararit. "Approximate Confidence Interval for the Ratio of Normal Means with a Known Coefficient of Variation." In Lecture Notes in Computer Science, 183–92. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25135-6_18.

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Niwitpong, Suparat, and Sa-Aat Niwitpong. "Confidence Intervals for the Difference Between Normal Means with Known Coefficients." In Lecture Notes in Computer Science, 171–82. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25135-6_17.

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Thangjai, Warisa, Sa-Aat Niwitpong, and Suparat Niwitpong. "Bayesian Confidence Intervals for Means of Normal Distributions with Unknown Coefficients of Variation." In Lecture Notes in Computer Science, 361–71. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62509-2_30.

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Sangnawakij, Patarawan, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Intervals for the Ratio of Coefficients of Variation of the Gamma Distributions." In Lecture Notes in Computer Science, 193–203. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-25135-6_19.

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Chankham, Wasana, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Intervals for the Difference Between the Coefficients of Variation of Inverse Gaussian Distributions." In Lecture Notes in Computer Science, 372–83. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62509-2_31.

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Тези доповідей конференцій з теми "Coefficient of confidence"

EL Boujnouni, Mohamed, Mohamed Jedra, and Noureddine Zahid. "Support Vector Domain Description with a new confidence coefficient." In 2014 9th International Conference on Intelligent Systems: Theories and Applications (SITA). IEEE, 2014. http://dx.doi.org/10.1109/sita.2014.6847276.

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La-ongkaew, Manussaya, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Intervals for Single Coefficient of Variation of Weibull Distribution." In ICVISP 2019: 3rd International Conference on Vision, Image and Signal Processing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3387168.3387253.

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Chankham, Wasana, Sa-Aat Niwitpong, and Suparat Niwitpong. "Confidence Intervals for Coefficient of Variation of Inverse Gaussian Distribution." In ICVISP 2019: 3rd International Conference on Vision, Image and Signal Processing. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3387168.3387254.

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Sodanin, Sukritta, Sa-Aat Niwitpong, and Suparat Niwitpong. "Generalized confidence intervals for the normal mean with unknown coefficient of variation." In INTERNATIONAL CONFERENCE ON MATHEMATICS, ENGINEERING AND INDUSTRIAL APPLICATIONS 2016 (ICoMEIA2016): Proceedings of the 2nd International Conference on Mathematics, Engineering and Industrial Applications 2016. Author(s), 2016. http://dx.doi.org/10.1063/1.4965163.

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Hongmei, Ren, Jing Huang, Tang Chuanzi, and Bo Li. "The method of micro-motion cycle feature extraction based on confidence coefficient evaluation criteria." In LIDAR Imaging Detection and Target Recognition 2017, edited by Yueguang Lv, Jianzhong Su, Wei Gong, Jian Yang, Weimin Bao, Weibiao Chen, Zelin Shi, Jindong Fei, Shensheng Han, and Weiqi Jin. SPIE, 2017. http://dx.doi.org/10.1117/12.2293474.

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Hareyama, Soichi, Ken-ichi Manabe, Takayuki Shimodaira, and Takashi Naganawa. "Experimental Study to Verify Elliptical Confidence Limit Method for Bolted Joint Tightening." In ASME 2016 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/imece2016-66336.

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The calibrated wrench method is often used for tightening. When tightening bolted joints, it is important to apply high axial tension. However, since the axial tension is indirectly applied in this method, it varies and has a distribution in the case of tightening carried out in the production line of a factory, for example. However, the calibrated wrench method is still widely used because of the simple tool and easy standardization. In our previous papers, we analyzed and discussed the main points of this research by a theoretical approach as discussed below. Conventionally, this type of distribution has been considered to lie within a rhombus (more precisely, within a rectangular area). However, when considering the tightening torque and axial tension as independent random variables, the distribution becomes elliptical. The same idea applies to the relation between the tightening torque and the equivalent stress for a bolt axis based on shear strain energy theory. On the other hand, regarding the variation in the tightening torque (tightening work coefficient a) actually applied to a bolt, it was reported by Bickford, Kawasaki, and others that it can vary by 15% or more from the target (indicated) tightening torque. However, the torques for wrenches used at actual assembly sites or under lubricated conditions were not reported. Therefore, it is necessary to experimentally verify that the relation between the tightening torque and the axial tension (axial stress) and equivalent stress of a bolt axis is distributed in an ellipse. Furthermore, the screw-thread characteristics (torque coefficient, equivalent stress coefficient, coefficient of friction, etc.) during the tightening process should be clarified by an experimental approach and observation. Thus, in this study, in experiments under dry (as-obtained) and lubricated (Loctite 263) conditions, the tool (preset-type and dial-type torque wrenches) and bolt strength classification (8.8 and 10.9) were changed, and the screw-thread characteristics were observed during actual bolt tightening and the characteristics under different conditions were analyzed. It was clearly shown that the tightening torque and the axial tension (axial stress) of a bolt axis and the equivalent stress vary with an elliptical distribution rather than a rhombic distribution. Finally, the validity of the tightening theory based on the elliptical confidence limit method was also verified experimentally.

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Armfield, Mary V., Heather M. Langford, Donald E. Beasley, and Michael E. Conner. "Average Heat Transfer Coefficient Measurements in a Fuel Bundle: Method Development." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1424.

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Abstract Measurement of single-phase heat transfer rates in a laboratory-scale rod bundle can aid in the design of a full-scale nuclear fuel bundle. It is desirable to obtain heat transfer coefficient measurements as a function of axial position with a high degree of measurement confidence. To achieve this goal, a heated calorimeter is designed and instrumented to measure power input and temperature. Temperature measurement is accomplished using thermocouples embedded in the calorimeter material, and a cartridge heater inserted concentrically in the calorimeter material creates a constant heat flux. The present study examines the contributions of the design parameters and uncertainties in the measurements to the overall uncertainty in the heat transfer coefficient. A design stage uncertainty analysis allows prediction of the required uncertainty in temperature difference measurement to achieve a desired uncertainty in the heat transfer coefficient. The experimental design for differential temperature calibration and representative uncertainty results are presented. All significant contributions to the total uncertainty in heat transfer coefficient are described and quantified.

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Ruiz-Guerrero, Juan Ignacio, Manuel Dario Hernandez-Ripalda, Salvador Echeverria-Villagomez, and Moises Tapia-Esquivias. "Study of Intraclass Correlation Coefficient Method in a Measurement System." In NCSL International Workshop & Symposium. NCSL International, 2013. http://dx.doi.org/10.51843/wsproceedings.2013.54.

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There is the uncertainty of knowing with certainty the quality and appropriateness of the data obtained by a measurement; variation, bias, and calibration are some of the factors affecting the measurement systems, so the purpose of analyzing these systems is to ensure the validity of the results. This project involves measurement systems that have a need to get real results, i.e. that these systems try to determine the variation of the measurement, based on the equipment, piece, or the change of person and different measurements generated by the same person. The data are a representation of the process. The reliability of the collection of data must be ensured before doing any capability study, statistical process control (SPC). Without a validated measurement system, you can reach erroneous conclusions and act on the process the wrong way. It is therefore necessary to ensure that the variation is not of the measurement system used. Regarding the repeatability and reproducibility studies, one of the main actors is the automotive organization, AIAG (Automotive Industry Action Group), backed by their acceptance of the measurement system in a number of distinct categories. This is contradicted by Donald J. Wheeler who proposes a methodology using the intraclass correlation coefficient for correct measurement study. He references errors and inconsistencies in the AIAG method, with which measurement studies have been performed for many years. On this basis, the methods comparison study is performed. The Comparison of methods developed through simulations to study measurement from similar data, and using estimates, confidence intervals are created in order to compare the different results and determine if any of the methodologies are more likely to generate a correct measurement study.

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Hareyama, Soichi, and Ken-ichi Manabe. "Advantage of Elliptical Confidence Limit Method for Bolted Joint Tightening Reliability." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50729.

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Анотація:

The calibrated wrench method is often used for tightening. When tightening bolted joints, it is important to apply high initial axial tension. However, since the axial tension is indirectly applied in this method, it varies and is widely distributed in the case of tightening carried out in the production line of a factory, for example. However, the calibrated wrench method is still widely used because of the simple tool used and easy standardization. Conventionally, this type of distribution has been considered to lie within a rhombus. In our previous paper, we analyzed and discussed the case when the distribution of the tightening torque and the equivalent stress of the bolted joint are considered to be independent random variables; in this case, the distribution becomes elliptical. Using this feature, a higher target tightening torque can be set than before. Finally, we established a procedure for the analysis and calculation of the optimum tightening torque for bolted joints. To ensure sufficient long-term tightening reliability to prevent breakage and loosening, a high initial axial tension and high equivalent stress can be realized using this proposed method. In this study, we analyze and discuss the case of differences in the tightening work condition (process control capability) and the tightening design condition. The tightening work coefficient a depends on the management state, the tightening working posture, and the process control capability of a tool or shop floor at a production site. According to the results of our trial calculation in Appendix A, the improvement ratio of the proposed target tightening torque is approximately 8.3% compared with the conventional method for dry friction and approximately 7.5% in the case of oily friction. Furthermore, in bolted joint tightening design, the tightening conditions under which the design conditions are satisfied are derived analytically. For the tightening design conditions of (1) a minimum axial stress of at least 50% at the yield point, and (2) an equivalent stress of 70% to 90% at the yield point, both the conventional and proposed areas of the confidence limit are obtained by precise analysis. Although the permitted limit of the tightening design condition cannot be realized by the conventional method, it can be realized by the proposed elliptical confidence limit method. Finally, we establish a method for maintaining the tightening reliability that involves applying high axial tension by increasing the target design tightening torque using the elliptical confidence limit.

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Dimond, Timothy W., Amir A. Younan, and Paul Allaire. "A System Identification Method for Excitation-Frequency Dependent Rotordynamic Coefficients." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-69311.

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Experimental identification of rotordynamic systems presents unique challenges. Gyroscopics, generally damped systems, and non-self-adjoint systems due to fluid structure interaction forces mean that symmetry cannot be used to reduce the number of parameters to be identified. Rotordynamic system experimental measurements are often noisy, which complicates comparisons with theory. When linearized, the resulting dynamic coefficients are also often a function of excitation frequency, as distinct from operating speed. In this paper, a frequency domain system identification technique is presented that addresses these issues for rigid-rotor test rigs. The method employs power spectral density functions and forward and backward whirl orbits to obtain the excitation frequency dependent effective stiffness and damping. The method is highly suited for use with experiments that employ active magnetic exciters that can perturb the rotor in the forward and backward whirl directions. Simulation examples are provided for excitation-frequency reduced tilting pad bearing dynamic coefficients. In the simulations, 20 and 50 percent Gaussian output noise was considered. Based on ensemble averages of the coefficient estimates, the 95 percent confidence intervals due to noise effects were within 1.2% of the identified value. The method is suitable for identification of linear dynamic coefficients for rotordynamic system components referenced to shaft motion. The method can be used to reduce the effect of noise on measurement uncertainty. The statistical framework can also be used to make decisions about experimental run times and acceptable levels of measurement uncertainty. The data obtained from such an experimental design can be used to verify component models and give rotordynamicists greater confidence in the design of turbomachinery.

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Звіти організацій з теми "Coefficient of confidence"

Verrill, Steve. Confidence bounds for normal and lognormal distribution coefficients of variation. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2003. http://dx.doi.org/10.2737/fpl-rp-609.

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Verrill, Steve P., and Richard A. Johnson. Confidence bounds and hypothesis tests for normal distribution coefficients of variation. Madison, WI: U.S. Department of Agriculture, Forest Service, Forest Products Laboratory, 2007. http://dx.doi.org/10.2737/fpl-rp-638.

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Chernozhukov, Victor, Alexandre Belloni, and Abhishek Kaul. Confidence bands for coefficients in high dimensional linear models with error-in-variables. The IFS, May 2017. http://dx.doi.org/10.1920/wp.cem.2017.2217.

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Nantung, Tommy E., Jusang Lee, John E. Haddock, M. Reza Pouranian, Dario Batioja Alvarez, Jongmyung Jeon, Boonam Shin, and Peter J. Becker. Structural Evaluation of Full-Depth Flexible Pavement Using APT. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317319.

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The fundamentals of rutting behavior for thin full-depth flexible pavements (i.e., asphalt thickness less than 12 inches) are investigated in this study. The scope incorporates an experimental study using full-scale Accelerated Pavement Tests (APTs) to monitor the evolution of each pavement structural layer's transverse profiles. The findings were then employed to verify the local rutting model coefficients used in the current pavement design method, the Mechanistic-Empirical Pavement Design Guide (MEPDG). Four APT sections were constructed using two thin typical pavement structures (seven-and ten-inches thick) and two types of surface course material (dense-graded and SMA). A mid-depth rut monitoring and automated laser profile systems were designed to reconstruct the transverse profiles at each pavement layer interface throughout the process of accelerated pavement deterioration that is produced during the APT. The contributions of each pavement structural layer to rutting and the evolution of layer deformation were derived. This study found that the permanent deformation within full-depth asphalt concrete significantly depends upon the pavement thickness. However, once the pavement reaches sufficient thickness (more than 12.5 inches), increasing the thickness does not significantly affect the permanent deformation. Additionally, for thin full-depth asphalt pavements with a dense-graded Hot Mix Asphalt (HMA) surface course, most pavement rutting is caused by the deformation of the asphalt concrete, with about half the rutting amount observed within the top four inches of the pavement layers. However, for thin full-depth asphalt pavements with an SMA surface course, most pavement rutting comes from the closet sublayer to the surface, i.e., the intermediate layer. The accuracy of the MEPDG’s prediction models for thin full-depth asphalt pavement was evaluated using some statistical parameters, including bias, the sum of squared error, and the standard error of estimates between the predicted and actual measurements. Based on the statistical analysis (at the 95% confidence level), no significant difference was found between the version 2.3-predicted and measured rutting of total asphalt concrete layer and subgrade for thick and thin pavements.

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