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Relevant bibliographies by topics / Engineering - Statistical methods / Journal articles

Journal articles on the topic 'Engineering - Statistical methods'

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Author: Grafiati

Published: 4 June 2021

Last updated: 28 July 2024

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1

Marco-Almagro, Lluís, and Xavier Tort-Martorell. "Statistical Methods in Kansei Engineering: a Case of Statistical Engineering." Quality and Reliability Engineering International 28, no. 5 (July 2012): 563–73. http://dx.doi.org/10.1002/qre.1434.

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2

Abate, Marcey. "Statistical Methods in Software Engineering." Technometrics 43, no. 1 (February 2001): 108. http://dx.doi.org/10.1198/tech.2001.s563.

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3

Chernick, Michael R., and John E. Brown. "Statistical Methods in Engineering and Manufacturing." Technometrics 33, no. 3 (August 1991): 356. http://dx.doi.org/10.2307/1268787.

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4

Chernick, Michael R. "Statistical Methods in Engineering and Manufacturing." Technometrics 33, no. 3 (August 1991): 355–56. http://dx.doi.org/10.1080/00401706.1991.10484841.

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5

Nelson, Lloyd S. "Handbook of Statistical Methods in Engineering." Journal of Quality Technology 25, no. 1 (January 1993): 64–65. http://dx.doi.org/10.1080/00224065.1993.11979420.

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6

Nelson, Lloyd S. "Statistical Methods in Engineering and Manufacturing." Journal of Quality Technology 26, no. 1 (January 1994): 74–76. http://dx.doi.org/10.1080/00224065.1994.11979504.

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7

Brugger, Richard M., and Peter W. M. John. "Statistical Methods in Engineering and Quality Assurance." Technometrics 35, no. 1 (February 1993): 91. http://dx.doi.org/10.2307/1269300.

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8

Wardrop, Daniel M., and Peter W. M. John. "Statistical Methods in Engineering and Quality Assurance." American Statistician 47, no. 3 (August 1993): 234. http://dx.doi.org/10.2307/2684985.

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9

Bugger, Richard M. "Statistical Methods iu Engineering and Quality Assurance." Technometrics 35, no. 1 (February 1993): 91–92. http://dx.doi.org/10.1080/00401706.1993.10485004.

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10

Park, Sung H. "Statistical methods in engineering and quality assurance." Computational Statistics & Data Analysis 13, no. 1 (January 1992): 108. http://dx.doi.org/10.1016/0167-9473(92)90160-h.

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11

Voelkel, Joseph G. "Statistical Methods in Engineering and Quality Assurance." Journal of Quality Technology 24, no. 3 (July 1992): 165–66. http://dx.doi.org/10.1080/00224065.1992.11979389.

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12

Wolstenholme, Linda, and P. W. M. John. "Statistical Methods in Engineering and Quality Assurance." Applied Statistics 41, no. 2 (1992): 432. http://dx.doi.org/10.2307/2347576.

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13

Kitchenham, Barbara, Lech Madeyski, David Budgen, Jacky Keung, Pearl Brereton, Stuart Charters, Shirley Gibbs, and Amnart Pohthong. "Robust Statistical Methods for Empirical Software Engineering." Empirical Software Engineering 22, no. 2 (June 16, 2016): 579–630. http://dx.doi.org/10.1007/s10664-016-9437-5.

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14

Saleem, Iram, Muhammad Aslam, and Muhammad Azam. "The use of Statistical Methods in Mechanical Engineering." Research Journal of Applied Sciences, Engineering and Technology 5, no. 7 (March 11, 2013): 2327–31. http://dx.doi.org/10.19026/rjaset.5.4660.

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15

P.E., James T. P. Yao,. "Probability, Reliability and Statistical Methods in Engineering Design." Journal of Structural Engineering 127, no. 1 (January 2001): 101. http://dx.doi.org/10.1061/(asce)0733-9445(2001)127:1(101).

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16

Murzewski, Janusz. "Probability, Reliability and Statistical Methods in Engineering Design." Structural Safety 23, no. 1 (January 2001): 93–102. http://dx.doi.org/10.1016/s0167-4730(00)00025-4.

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17

Rippin, D. W. T. "Statistical methods for experimental planning in chemical engineering." Computers & Chemical Engineering 12, no. 2-3 (February 1988): 109–16. http://dx.doi.org/10.1016/0098-1354(88)85012-9.

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18

Farnum, Nicholas R. "Statistical Methods." Journal of Quality Technology 22, no. 3 (July 1990): 252–53. http://dx.doi.org/10.1080/00224065.1990.11979254.

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19

Kalev, Krasimir. "Application of statistical methods in forecasting for spare elements demand." Journal scientific and applied research 2, no. 1 (March 3, 2012): 34–38. http://dx.doi.org/10.46687/jsar.v2i1.40.

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The problem of predicting the demand for spare elements is extremely important for continuous operation of machines. It is necessary to know when and how much to order. To calculate and ensure the availability of spare elements, appropriate mathematical models should be applied. The statistical method for predicting spare elements demand is considered in this paper. The study shows how to use a demand forecasting technique for determining the expected number of spare elements. Some of the results are given by engineering software.

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20

G.W.A.D. "Statistical methods." Microelectronics Reliability 33, no. 1 (January 1993): 106. http://dx.doi.org/10.1016/0026-2714(93)90050-9.

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21

Hipel, K. W. "Stochastic and statistical methods in hydrology and environmental engineering." Stochastic Hydrology and Hydraulics 9, no. 1 (March 1995): 1–11. http://dx.doi.org/10.1007/bf01581755.

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22

Pessanha, J. F. M., F. L. C. Oliveira, and R. C. Souza. "Teaching Statistical Methods in Engineering Courses Through Wind Power Data." Revista de Ensino de Engenharia 34, no. 1 (June 30, 2015): 85–92. http://dx.doi.org/10.15552/2236-0158/abenge.v34n1p85-92.

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23

Neidert, Sharon, Irwin Miller, and Marylees Miller. "Statistical Methods for Quality: With Application to Engineering and Management." American Statistician 50, no. 2 (May 1996): 195. http://dx.doi.org/10.2307/2684443.

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24

Brouk, Moran, Yuval Nov, and Ayelet Fishman. "Improving Biocatalyst Performance by Integrating Statistical Methods into Protein Engineering." Applied and Environmental Microbiology 76, no. 19 (August 13, 2010): 6397–403. http://dx.doi.org/10.1128/aem.00878-10.

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ABSTRACT Directed evolution and rational design were used to generate active variants of toluene-4-monooxygenase (T4MO) on 2-phenylethanol (PEA), with the aim of producing hydroxytyrosol, a potent antioxidant. Due to the complexity of the enzymatic system—four proteins encoded by six genes—mutagenesis is labor-intensive and time-consuming. Therefore, the statistical model of Nov and Wein (J. Comput. Biol. 12:247-282) was used to reduce the number of variants produced and evaluated in a lab. From an initial data set of 24 variants, with mutations at nine positions, seven double or triple mutants were identified through statistical analysis. The average activity of these mutants was 4.6-fold higher than the average activity of the initial data set. In an attempt to further improve the enzyme activity to obtain PEA hydroxylation, a second round of statistical analysis was performed. Nine variants were considered, with 3, 4, and 5 point mutations. The average activity of the variants obtained in the second statistical round was 1.6-fold higher than in the first round and 7.3-fold higher than that of the initial data set. The best variant discovered, TmoA I100A E214G D285Q, exhibited an initial oxidation rate of 4.4 ± 0.3 nmol/min/mg protein, which is 190-fold higher than the rate obtained by the wild type. This rate was also 2.6-fold higher than the activity of the wild type on the natural substrate toluene. By considering only 16 preselected mutants (out of ∼13,000 possible combinations), a highly active variant was discovered with minimum time and effort.

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25

Wyckoff, James. "Statistical Methods for Quality: With Applications to Engineering and Management." Technometrics 38, no. 2 (May 1996): 183. http://dx.doi.org/10.1080/00401706.1996.10484466.

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26

Ansell, J. I., and M. J. Phillips. "Reliability and statistical methods." Reliability Engineering & System Safety 34, no. 1 (January 1991): 1–3. http://dx.doi.org/10.1016/0951-8320(91)90094-n.

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27

Bonato, Jasminka, Biserka Draščić Ban, and Antun Kraš. "Statistical Methods in Theory of Technical Systems." Journal of Maritime & Transportation Science 3, no. 3 (June 2020): 373–79. http://dx.doi.org/10.18048/2020.00.28.

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Today’s society is served by modern technology, accompanied by effects of insecurity and uncertainty, which are important and inevitable. Engineering analysis must include methods of assessing these impacts on the design, implementation and operation of technical systems. Mathematical statistics and probability theory provide the mathematical basis for modeling unreliability and analysis as well as their impact on already built technical systems. The aim of the paper is to introduce quantitative methods that enable systematic development of planned and constructed criteria, problem modeling and evaluation of economically technical optimum.

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28

Amster, Sigmund J., and Jeffrey H. Hooper. "Report: Statistical Methods for Reliability Improvement." AT&T Technical Journal 65, no. 2 (March 4, 1986): 69–76. http://dx.doi.org/10.1002/j.1538-7305.1986.tb00294.x.

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29

Mujumdar, Dr A. S. "Statistical Methods for Physical Science." Drying Technology 13, no. 8-9 (January 1995): 2253–54. http://dx.doi.org/10.1080/07373939508917086.

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30

Horna, Мaryna, and Yaroslava Ishchuk. "Using statistical methods to analyze education." Scientific notes, no. 27 (October 21, 2022): 112–22. http://dx.doi.org/10.33111/vz_kneu.27.22.02.11.075.081.

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31

Blum, Bruce. "Introduction to Statistical Methods (Vol. 2)." Journal of Clinical Engineering 11, no. 3 (May 1986): 212. http://dx.doi.org/10.1097/00004669-198605000-00008.

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32

Klebanov, Lev, Xing Qiu, Stephen Welle, and Andrei Yakovlev. "Statistical methods and microarray data." Nature Biotechnology 25, no. 1 (January 1, 2007): 25–26. http://dx.doi.org/10.1038/nbt0107-25.

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33

FIGIEL, KERRY, and MICHAEL FORBES. "The evolution of reel statistical methods." June 2019 18, no. 6 (July 1, 2019): 365–77. http://dx.doi.org/10.32964/tj18.6.365.

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Multiple statistical methods for calculating the variance partition analysis (VPA) of reel data have existed for decades. In the paper industry, VPA is also commonly known as reel statistics. VPA commonly consists of total variance (TOT) that is then divided into three components: cross direction (CD), machine direction (MD), and residual (RES). A common mathematical procedure is referred to as ANOVA (analysis of variance). TAPPI Standard Test Method T 545 “Cross-machine grammage profile measurement (gravimetric method)” addresses paper testing and includes the ANOVA equations that have also been used to analyze scanning data. In the 1990s, TAPPI published TIP 1101-01 “Calculation and partitioning of variance using paper machine scanning sensor measurements,” which contained simple formulas that were easy to implement and could be used by a novice to generate statistics on a spreadsheet. All involved quality control system (QCS) suppliers agreed to support this common method in their QCS. TIP 1101 was recently revised, and this paper concerns the analysis of data collected from a scanning sensor in a QCS and the creation of a common method for the calculation of reel statistics by TAPPI’s Process Control Division.

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34

Zaslonov, V. V., N. A. Kravchenko, and Y. Yu Metreveli. "NUMERICAL METHODS OF DEVELOPMENT OF MARINE ENGINEERING SYSTEMS." World of Transport and Transportation 16, no. 2 (April 28, 2018): 6–13. http://dx.doi.org/10.30932/1992-3252-2018-16-2-1.

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For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version). ABSTRACT Active development of the World Ocean dictates the need to intensively develop marine engineering systems, designed to solve a wide variety of scientific research, geological, prospecting, search, and other tasks. Basing on the analysis of scientific works and research in this field, the authors aim to find the optimal mathematical model for increasing the efficiency of such complexes using the example of an underwater unmanned vehicle (UV). As a result, the conclusion is made that the application of mathematical-statistical methods allows correctly and evenly distribute resources (finance, time, labor, etc.), determine the level of innovation, and also more fully and rationally assess the conditions for formation, maintenance and operation of UV. At the same time, the authors especially noted the importance of having qualityrelated solutions within the requirements (standards) of a single system synchronously working at the stages of design, maintenance and operation of marine engineering systems. Keywords: marine engineering systems, unmanned vehicles, mathematical model, physical principles, quality management system, statistical data, scientific research, algebraic solutions.

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35

Turkmen, Ahmet. "Numerical and Statistical Methods for Bioengineering." Biomedical Instrumentation & Technology 46, no. 2 (March 1, 2012): 149. http://dx.doi.org/10.2345/0899-8205-46.2.149.

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36

Mitrović, Marko, Dragan Ignjatović, Lidija Ignjatović, and Saša Stepanović. "Possibility of application the statistical methods in defining the engineering geological complexes." Mining and Metallurgy Engineering Bor, no. 1-2 (2018): 27–36. http://dx.doi.org/10.5937/mmeb1802027m.

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37

Wang, Lining. "Research Methods for Reliability Analysis of Engineering Structures Based on Statistical Theory." Highlights in Science, Engineering and Technology 28 (December 31, 2022): 94–100. http://dx.doi.org/10.54097/hset.v28i.4066.

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With the increasing need for construction engineering projects, the reliability problem of engineering structures has been the focus of public concern.Currently, there is a lack of a systematic summary of structural reliability theory. Therefore, this paper attempts to summarize some basic principles of structural reliability theory. This paper discusses various factors affecting reliability analysis and three typical analysis methods. It is pointed out that multiple factors affecting reliability analysis, such as the applying loads and resistance parameters such as material properties and dimension parameters, can be regarded as random variables and obey specific statistical distribution. First Order Reliability Method presents the most excellent convenience in calculating failure probability, especially for the structure with a relatively small, and the accuracy of this method is acceptable when the nonlinear degree of structural performance function is not significant.The Second Order Reliability Method could provide a relatively accurate solution with a more complicated calculation process. Monte Carlo Simulation is general enough to be applied to all the cases despite the considerable number of simulations resulting from a minuscule failure probability. Researchers are expected to implement improvement measures to simplify analysis methods by combining them with mathematics and computer science. The results of this paper can provide a theoretical reference for the researchers and designers of structural reliability theory.

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38

McCall, Chester H. "Statistical Methods: The Geometric Approach." Journal of Quality Technology 25, no. 1 (January 1993): 74–75. http://dx.doi.org/10.1080/00224065.1993.11979428.

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39

Yang, Kai. "Multivariate statistical methods and Six-Sigma." International Journal of Six Sigma and Competitive Advantage 1, no. 1 (2004): 76. http://dx.doi.org/10.1504/ijssca.2004.005279.

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40

Sood, Arun K., Carl B. Friedlander, and Ali Amin Fahs. "Engine Fault Analysis: Part I-Statistical Methods." IEEE Transactions on Industrial Electronics IE-32, no. 4 (November 1985): 294–300. http://dx.doi.org/10.1109/tie.1985.350100.

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41

Dar, Fazilat H., Judith R. Meakin, and Richard M. Aspden. "Statistical methods in finite element analysis." Journal of Biomechanics 35, no. 9 (September 2002): 1155–61. http://dx.doi.org/10.1016/s0021-9290(02)00085-4.

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42

Krafczyk, Manfred, Anthony J. C. Ladd, and Li-Shi Luo. "Preface: Mesoscopic Methods in Engineering and Science." Journal of Statistical Physics 121, no. 1-2 (October 2005): 1–2. http://dx.doi.org/10.1007/s10955-005-9107-2.

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43

Hagan, Paul, and Dymphna Fellowes. "Multivariate statistical methods in battery research." Journal of Power Sources 122, no. 1 (July 2003): 77–84. http://dx.doi.org/10.1016/s0378-7753(03)00344-6.

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44

Lee, Elisa T. "Statistical Methods for Survival Data Analysis." IEEE Transactions on Reliability 35, no. 1 (1986): 123. http://dx.doi.org/10.1109/tr.1986.4335370.

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45

Lee, I. W. C., and A. O. Fapojuwo. "Statistical methods for computer network traffic analysis." IEE Proceedings - Communications 153, no. 6 (2006): 939. http://dx.doi.org/10.1049/ip-com:20060070.

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46

Yin, Shen, Xiangping Zhu, and Hamid Reza Karimi. "Quality Evaluation Based on Multivariate Statistical Methods." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/639652.

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Quality prediction models are constructed based on multivariate statistical methods, including ordinary least squares regression (OLSR), principal component regression (PCR), partial least squares regression (PLSR), and modified partial least squares regression (MPLSR). The prediction model constructed by MPLSR achieves superior results, compared with the other three methods from both aspects of fitting efficiency and prediction ability. Based on it, further research is dedicated to selecting key variables to directly predict the product quality with satisfactory performance. The prediction models presented are more efficient than tradition ones and can be useful to support human experts in the evaluation and classification of the product quality. The effectiveness of the quality prediction models is finally illustrated and verified based on the practical data set of the red wine.

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47

Kubečková, Darja, and Stanislav Smugala. "Statistical methods applied to construction process management." Asian Journal of Civil Engineering 21, no. 3 (February 5, 2020): 479–94. http://dx.doi.org/10.1007/s42107-020-00221-7.

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48

Shumakov, A. V., and V. P. Kasatkin. "Comparison of statistical methods of radiation monitoring." Atomic Energy 107, no. 3 (September 2009): 209–17. http://dx.doi.org/10.1007/s10512-010-9218-y.

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49

Rosec, O., J. Boucher, B. Nsiri, and T. Chonavel. "Blind marine seismic deconvolution using statistical MCMC methods." IEEE Journal of Oceanic Engineering 28, no. 3 (July 2003): 502–12. http://dx.doi.org/10.1109/joe.2003.816683.

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50

Sipos, Tibor. "Evaluation of Stated Preference Surveys with Statistical Methods." Promet - Traffic&Transportation 35, no. 5 (October 30, 2023): 655–61. http://dx.doi.org/10.7307/ptt.v35i5.259.

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In this paper, the author investigated the stated preference survey in transport modelling. The research was conducted to ensure that the best fractional orthogonal design of stated preference paired comparison survey would not increase the error or uncertainty in transport-related decision modelling. The research was conducted based on artificial Monte Carlo simulated respondents, and the results were assessed with standard mathematical-statistical tools. Although the assessment should have resulted in 0% errors, according to our 2,000 sample, a minor 5% of errors occurred. The problem to be investigated in this paper is that the best-designed survey could have some errors.

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