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Journal articles on the topic 'Control variables'

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Published: 4 June 2021
Last updated: 20 February 2023

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1

Arnold, Paul N. "Control of Study Variables." Journal of Cataract & Refractive Surgery 20, no. 6 (November 1994): 676. http://dx.doi.org/10.1016/s0886-3350(13)80674-8.

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2

Masket, Samuel. "Control of Study Variables." Journal of Cataract & Refractive Surgery 20, no. 6 (November 1994): 676. http://dx.doi.org/10.1016/s0886-3350(13)80675-x.

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3

Hatsopoulos, Nicholas G., and William H. Warren. "Do control variables exist?" Behavioral and Brain Sciences 18, no. 4 (December 1995): 762. http://dx.doi.org/10.1017/s0140525x00040851.

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AbstractWe argue that the concept of a control variable (CV) as described by Feldman and Levin needs to be revised because it does not account for the influence of sensory feedback from the periphery. We provide evidence from the realm of rhythmic movements that sensory feedback can permanently alter the frequency and phase of a centrally generated rhythm.
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4

Albertos, P., F. Morant, J. Picó, and J. Simó. "Adaptive Control Under Partially Measured Control Variables." IFAC Proceedings Volumes 25, no. 8 (June 1992): 502–8. http://dx.doi.org/10.1016/s1474-6670(17)54103-8.

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5

Shore, Haim. "General control charts for variables." International Journal of Production Research 38, no. 8 (May 2000): 1875–97. http://dx.doi.org/10.1080/002075400188645.

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6

Shore, H. "General control charts for variables." International Journal of Production Research 39, no. 9 (January 2001): 2063–64. http://dx.doi.org/10.1080/00207540110028137.

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7

Knudsen, Finn B. "Fermentation Variables and Their Control." Journal of the American Society of Brewing Chemists 43, no. 2 (April 1985): 91–95. http://dx.doi.org/10.1094/asbcj-43-0091.

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8

Klarmann, Martin, and Sven Feurer. "Control Variables in Marketing Research." Marketing ZFP 40, no. 2 (2018): 26–40. http://dx.doi.org/10.15358/0344-1369-2018-2-26.

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9

García-Bustos, Sandra, Mónica Mite, and Francisco Vera. "Control Charts with Variable Dimension for Linear Combination of Poisson Variables." Quality and Reliability Engineering International 32, no. 5 (November 25, 2015): 1741–55. http://dx.doi.org/10.1002/qre.1910.

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10

Cembrero, J., M. Perales, M. Mollar, and B. Marí. "Obtención de columnas de ZnO. Variables a controlar (I)." Boletín de la Sociedad Española de Cerámica y Vidrio 42, no. 6 (December 30, 2003): 379–87. http://dx.doi.org/10.3989/cyv.2003.v42.i6.626.

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11

KHAN, MUHAMMAD ALTAF, SAEED ISLAM, JOSE C. VALVERDE, and SHER AFZAL KHAN. "CONTROL STRATEGIES of HEPATITIS B WITH THREE CONTROL VARIABLES." Journal of Biological Systems 26, no. 01 (March 2018): 1–21. http://dx.doi.org/10.1142/s0218339018500018.

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In this paper, we present a compartmental mathematical model of hepatitis B virus with optimal control strategies. First, we formulate the model applying the optimal control techniques which use control variables in the form of isolation, educational campaign and vaccination. We derive the conditions under which it is optimal to eradicate the disease and examine the impact of possible vaccination treatment strategies on disease transmission. When such an elimination is impossible, we use the techniques of Pontryagin’s Maximum Principle to derive the necessary conditions for the optimal control problem. The numerical results show that some effective vaccination and control can reduce the disease spread in the community.
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12

CASTILLO, E. DEL. "Relations between control chart design variables and production control." International Journal of Production Research 33, no. 10 (October 1995): 2709–21. http://dx.doi.org/10.1080/00207549508904840.

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13

Herrera Acosta, Roberto José, Paola Milena Rojas Manga, and Karla Patricia Jiménez Moreno. "Cartas de control con variables convolucionadas." I+D Revista de Investigaciones 13, no. 1 (January 1, 2019): 82–87. http://dx.doi.org/10.33304/revinv.v13n1-2019008.

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14

Kasac, Josip, Josko Deur, Branko Novakovic, Matthew Hancock, and Francis Assadian. "Optimization of Global Chassis Control Variables." IFAC Proceedings Volumes 41, no. 2 (2008): 2081–86. http://dx.doi.org/10.3182/20080706-5-kr-1001.00353.

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15

Utkin, Anton V., Svetlana A. Krasnova, and Victor A. Utkin. "Output Variables Control in Mechanical Systems." IFAC Proceedings Volumes 42, no. 16 (2009): 335–40. http://dx.doi.org/10.3182/20090909-4-jp-2010.00058.

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16

Domański, Paweł D. "Multifractal Properties of Process Control Variables." International Journal of Bifurcation and Chaos 27, no. 06 (June 15, 2017): 1750094. http://dx.doi.org/10.1142/s0218127417500948.

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Control system is an inevitable element of any industrial installation. Its quality affects overall process performance significantly. The assessment, whether control system needs any improvement or not, requires relevant and constructive measures. There are various methods, like time domain based, Minimum Variance, Gaussian and non-Gaussian statistical factors, fractal and entropy indexes. Majority of approaches use time series of control variables. They are able to cover many phenomena. But process complexities and human interventions cause effects that are hardly visible for standard measures. It is shown that the signals originating from industrial installations have multifractal properties and such an analysis may extend standard approach to further observations. The work is based on industrial and simulation data. The analysis delivers additional insight into the properties of control system and the process. It helps to discover internal dependencies and human factors, which are hardly detectable.
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17

Katona, P. G. "Closed Loop Control of Physiological Variables." IFAC Proceedings Volumes 21, no. 1 (April 1988): 13–19. http://dx.doi.org/10.1016/s1474-6670(17)57530-8.

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18

Bánkovi, G., J. Veliczky, and M. Ziermann. "Dynamic Factor Models with Control Variables." IFAC Proceedings Volumes 19, no. 10 (June 1986): 359–62. http://dx.doi.org/10.1016/s1474-6670(17)59692-5.

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19

Chao, M. T., and Smiley W. Cheng. "Semicircle Control Chart for Variables Data." Quality Engineering 8, no. 3 (March 1996): 441–46. http://dx.doi.org/10.1080/08982119608904646.

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20

Campbell, John. "II-Control Variables and Mental Causation." Proceedings of the Aristotelian Society (Hardback) 110, no. 1pt1 (April 23, 2010): 15–30. http://dx.doi.org/10.1111/j.1467-9264.2010.00277.x.

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21

Cheng, Smiley W., and Keoagile Thaga. "Single Variables Control Charts: an Overview." Quality and Reliability Engineering International 22, no. 7 (2006): 811–20. http://dx.doi.org/10.1002/qre.730.

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22

Amman, Hans M., and David A. Kendrick. "Forward-looking variables in deterministic control." Annals of Operations Research 68, no. 1 (March 1996): 141–59. http://dx.doi.org/10.1007/bf02205452.

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23

Lee Ho, Linda, and Airlane Pereira Alencar. "Control Charts for Binary Correlated Variables." Quality and Reliability Engineering International 29, no. 6 (June 27, 2012): 855–67. http://dx.doi.org/10.1002/qre.1441.

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24

Marí, B., J. Cembrero, M. Mollar, M. Pascual, and M. Perales. "Obtención de columnas de ZnO. Variables a controlar (y II)." Boletín de la Sociedad Española de Cerámica y Vidrio 45, no. 4 (August 30, 2006): 278–82. http://dx.doi.org/10.3989/cyv.2006.v45.i4.285.

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25

Blahous, Leopold, and Thomas Marx. "Control of Flotation and Acquisition of the Key Control Variables." IFAC Proceedings Volumes 42, no. 23 (2009): 73–78. http://dx.doi.org/10.3182/20091014-3-cl-4011.00014.

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26

Agrawal, Om P., Ozlem Defterli, and Dumitru Baleanu. "Fractional Optimal Control Problems with Several State and Control Variables." Journal of Vibration and Control 16, no. 13 (May 18, 2010): 1967–76. http://dx.doi.org/10.1177/1077546309353361.

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27

Pan, Yunlong, Qingkai Yang, Bo Zhou, and Hao Fang. "Stress-matrix-based formation transformation control under mixed control variables." Advanced Control for Applications: Engineering and Industrial Systems 2, no. 2 (June 2020): e36. http://dx.doi.org/10.1002/adc2.36.

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28

Nguyen, Huu Du, Kim Phuc Tran, and Thong Ngee Goh. "Variable Sampling Interval Control Charts for Monitoring the Ratio of Two Normal Variables." Journal of Testing and Evaluation 48, no. 3 (December 27, 2019): 20190327. http://dx.doi.org/10.1520/jte20190327.

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29

Hahn, Jinyong, and Geert Ridder. "Instrumental variable estimation of nonlinear models with nonclassical measurement error using control variables." Journal of Econometrics 200, no. 2 (October 2017): 238–50. http://dx.doi.org/10.1016/j.jeconom.2017.06.008.

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30

Kang, Chul-Goo. "Variable structure fuzzy control using three input variables for reducing motion tracking errors." Journal of Mechanical Science and Technology 23, no. 5 (May 2009): 1354–64. http://dx.doi.org/10.1007/s12206-009-0350-3.

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31

Kahina, Louadj, and Aidene Mohamed. "Adaptive Method for Solving Optimal Control Problem with State and Control Variables." Mathematical Problems in Engineering 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/209329.

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The problem of optimal control with state and control variables is studied. The variables are: a scalar vectorxand the controlu(t); these variables are bonded, that is, the right-hand side of the ordinary differential equation contains both state and control variables in a mixed form. For solution of this problem, we used adaptive method and technology of linear programming.
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32

Kuhn, Deanna. "Reasoning about multiple variables: Control of variables is not the only challenge." Science Education 91, no. 5 (September 2007): 710–26. http://dx.doi.org/10.1002/sce.20214.

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33

Fitria, Irma, Talitha B. Atlanta, Nadia Azahra, Choiriyah Agustina, Subchan Subchan, and S. Cahyaningtias. "OPTIMAL CONTROL ON CHOLERA DISEASE SPREADING MODEL WITH THREE VARIABLES CONTROL VARIATION." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 2 (June 1, 2022): 463–70. http://dx.doi.org/10.30598/barekengvol16iss2pp463-470.

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Cholera is an infection of the small intestine by some strains of the bacterium Vibrio Cholerae. This disease is a deadly disease that necessitates efficient prevention and control measures. In this research, the optimal control of the cholera spread model with variations of three control variables is discussed. There are four controls to minimize the spread of diseases such as sanitation, treatment consisting of quarantine, increased education, and chlorination. The dynamic system is formed with three controls variation. Then it is compared and analyzed for the most effective result. The optimal control solution is derived using the Pontryagin Minimum Principle and solved using the Runge-Kutta method.
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34

Restrepo-Tamayo, Luz Marcela, and Juan Carlos Correa-Morales. "Cartas de control para monitorear variables multinomiales." Respuestas 19, no. 2 (July 1, 2014): 93–100. http://dx.doi.org/10.22463/0122820x.441.

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Antecedentes: La carta de control como herramienta de monitoreo de la calidad de un producto, permite estudiar la estabilidad de los procesos en el tiempo, contrastando dos hipótesis, una que expresa que el proceso se encuentra en estado estable y otra que lo niega. Su utilización ha sido masiva para variables continuas más no para variables categóricas, motivo por el cual es imperante el diseño de tales herramientas para ese tipo de variables. Objetivo: Proponer dos (2) cartas de control para procesos con variables multinomiales basadas en el valor-p resultado de la prueba de homogeneidad de proporciones, empleando la transformación chicuadrado para variables uniformes y la aproximación Wilson - Hilferty para variables chi cuadrado. Métodos: El desempeño de las cartas propuestas es estimado vía simulación considerando un proceso en Fase II y considerando incrementos en la primera categoría de 2%, 4% y 6% en la etapa de control. Resultados: La carta de control multinomial usando aproximación Wilson- Hilferty para variables chi cuadrado, provenientes de la transformación del valor-p, presenta un desempeño deficiente comparado con las cartas de control usando valor-p y usando transformación chi cuadrado al valor-p, pues tienen menor habilidad para detectar cambios pequeños. Conclusión: Proponemos dos cartas de control para monitorear variables multinomiales y, una vez estudiadas vía simulación, con base en la Longitud de corrida promedio (ARL) y la probabilidad de rechazar la hipótesis nula de igualdad de proporciones, se recomienda el uso de la carta de control usando valor-p,o equivalentemente, de la carta de control usando transformación chi cuadrado del valor-p.Palabras clave: carta de control, distribución multinomial, prueba de homogeneidad, valor-p. Abstract Background: The control as a tool for monitoring the quality of a product, allows to study the stability of processes over time, contrasting two hypothesis, which states that the process is in stable condition and the other denies it. Its use has been massive for continuous variables but not for categorical variables, why it is imperative to design such tools for such variables. Objective: To propose two (2) control charts for variables multinomial processes based on the p-value test result for homogeneity of proportions using the chi square test for uniform processing variables and approximation Wilson - Hilferty for variables chi square. Methods: The performance of proposed charts via simulation is estimated considering a Phase II process and considering the first category increments of 2%, 4% and 6% in the control stage. Results: The multinomial control chart using Wilson-Hilferty approximation for variables chi square, from the transformation of value-p, has poor performance compared to the control charts using p-value processing and using chi-square p-value, as they have less ability to detect small changes. Conclusion: We propose two control charts to monitor multinomial variables and once studied via simulation, based on the average run length (ARL) and the probability of rejecting the null hypothesis of equal proportions, we recommend the control chart using value-p, or equivalently, the control chart processing using chi square p-value.Keywords: control chart, multinomial distribución, homogeneity test, p-value.
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35

Thaga, Keoagile, and Ramasamy Sivasamy. "Single Variables Control Charts: A Further Overview." Indian Journal of Science and Technology 8, no. 6 (March 1, 2015): 518. http://dx.doi.org/10.17485/ijst/2015/v8i6/61064.

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36

Haverhals, Luke M., Hadley M. Sulpizio, Zane A. Fayos, Matthew A. Trulove, William M. Reichert, Matthew P. Foley, H. C. De Long, and P. C. Trulove. "Process Variables that Control Natural Fiber Welding." ECS Transactions 33, no. 7 (December 17, 2019): 79–90. http://dx.doi.org/10.1149/1.3484764.

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37

SONG, Jinli, Huimin XIAO, and Zhiqiang LI. "Partial variables controllability of Boolean control networks." SCIENTIA SINICA Informationis 46, no. 3 (January 27, 2016): 338–49. http://dx.doi.org/10.1360/n112015-00023.

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38

Mustafa, Altyeb Mohammed, Zengtai Gong, and Mawia Osman. "Fuzzy Optimal Control Problem of Several Variables." Advances in Mathematical Physics 2019 (December 29, 2019): 1–12. http://dx.doi.org/10.1155/2019/2182640.

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The purpose of this paper is to establish the necessary conditions for a fuzzy optimal control problem of several variables. Also, we define fuzzy optimal control problems involving isoperimetric constraints and higher order differential equations. Then, we convert these problems to fuzzy optimal control problems of several variables in order to solve these problems using the same solution method. The main results of this paper are illustrated throughout three examples, more specifically, a discussion on the strong solutions (fuzzy solutions) of our problems.
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39

Prazdny, Kvetoslav. "What Variables Control (Long-Range) Apparent Motion?" Perception 15, no. 1 (February 1986): 37–40. http://dx.doi.org/10.1068/p150037.

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40

Aradillas-Lopez, Andres, Bo E. Honoré, and James L. Powell. "PAIRWISE DIFFERENCE ESTIMATION WITH NONPARAMETRIC CONTROL VARIABLES*." International Economic Review 48, no. 4 (December 11, 2007): 1119–58. http://dx.doi.org/10.1111/j.1468-2354.2007.00457.x.

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41

PARR, C., and G. WEATHERHOGG. "Process Control in terms of Process Variables." Journal of the Society of Dyers and Colourists 95, no. 3 (October 22, 2008): 94–97. http://dx.doi.org/10.1111/j.1478-4408.1979.tb03458.x.

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42

Saniga, Erwin, Thomas McWilliams, Darwin Davis, and James Lucas. "Economic control chart policies for monitoring variables." International Journal of Productivity and Quality Management 1, no. 1/2 (2006): 116. http://dx.doi.org/10.1504/ijpqm.2006.008377.

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43

Franceschini, Fiorenzo, Maurizio Galetto, and Marco Varetto. "Ordered Samples Control Charts for Ordinal Variables." Quality and Reliability Engineering International 21, no. 2 (2005): 177–95. http://dx.doi.org/10.1002/qre.614.

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44

Robins, James. "The control of confounding by intermediate variables." Statistics in Medicine 8, no. 6 (June 1989): 679–701. http://dx.doi.org/10.1002/sim.4780080608.

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45

Jaskula, Marek, and Piotr Lesniewski. "Constraining State Variables and Control Signal via Sliding Mode Control Approach." IEEE Access 8 (2020): 111475–81. http://dx.doi.org/10.1109/access.2020.3002569.

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46

Thomas, Simon, and David A. Fell. "Metabolic Control Analysis: Sensitivity of Control Coefficients to Experimentally Determined Variables." Journal of Theoretical Biology 167, no. 2 (March 1994): 175–200. http://dx.doi.org/10.1006/jtbi.1994.1063.

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47

Tsai, Yao-Wen, and Cong-Trang Nguyen. "Variable structure control for mismatched uncertain systems using output variables with finite-time convergence." Journal of the Chinese Institute of Engineers 43, no. 5 (May 12, 2020): 467–76. http://dx.doi.org/10.1080/02533839.2020.1751720.

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48

Cho, Tae Yeon, Connie M. Borror, and Douglas C. Montgomery. "Mixture-process variable experiments including control and noise variables within a split-plot structure." International Journal of Quality Engineering and Technology 2, no. 1 (2011): 1. http://dx.doi.org/10.1504/ijqet.2011.038719.

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49

Mervis, Carolyn B., and Bonita P. Klein-Tasman. "Methodological Issues in Group-Matching Designs: Levels for Control Variable Comparisons and Measurement Characteristics of Control and Target Variables." Journal of Autism and Developmental Disorders 34, no. 1 (February 2004): 7–17. http://dx.doi.org/10.1023/b:jadd.0000018069.69562.b8.

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50

Anderson, Brian D. O., and Manfred Deistler. "Dynamic errors-in-variables systems with three variables." Automatica 23, no. 5 (September 1987): 611–16. http://dx.doi.org/10.1016/0005-1098(87)90056-2.

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