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Journal articles on the topic 'Error control'

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Published: 4 June 2021
Last updated: 9 February 2022

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1

Ralphs, J. D. "Error-control coding." Electronics & Communications Engineering Journal 3, no. 5 (1991): 204. http://dx.doi.org/10.1049/ecej:19910035.

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2

Kamali, B. "Error control coding." IEEE Potentials 14, no. 2 (1995): 15–19. http://dx.doi.org/10.1109/45.376638.

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3

Litwin, L., and K. Ramaswamy. "Error control coding." IEEE Potentials 20, no. 1 (2001): 26–28. http://dx.doi.org/10.1109/45.913208.

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4

Debelak, Kenneth A., and Mark L. Rutherford. "Partitioned Error Control." Industrial & Engineering Chemistry Research 38, no. 10 (October 1999): 4113–19. http://dx.doi.org/10.1021/ie990220p.

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5

Buck, James R., Steven M. Zellers, and Michael E. Opar. "Control error statistics." Ergonomics 43, no. 1 (January 2000): 1–16. http://dx.doi.org/10.1080/001401300184620.

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6

Zhang, Zhimin. "High-Speed Serial Data Transmission Error Control Based on Fuzzy Classification." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 7 (November 20, 2018): 1077–81. http://dx.doi.org/10.20965/jaciii.2018.p1077.

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At present, the error control method for high-speed serial data transmission obtains the errors by comparison and then controls them. If the data transmission channel is not denoised, the packet loss and error codes become serious, and energy consumption increases. The use of fuzzy classification is proposed to control data transmission errors. The method uses the combination of wavelet transform and transform domain difference to double denoise the channel, and it completes the clustering of data transmission errors by fuzzy classification. Considering packet loss, error codes, and energy consumption in data transmission error control, when the communication distance between two nodes is small, automatic repeat request is used to control data transmission errors. As the distance between nodes increases, forward error correction is used to control data transmission errors. When the communication distance gradually increases, data transmission errors are controlled by hybrid automatic repeat request. Experiments showed that the proposed method can reduce the data transmission error, control energy consumption, packet loss rate, and bit error rate, and enhance the denoising effect.
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7

KIM, PHILSU, and SUNYOUNG BU. "Error Control Strategy in Error Correction Methods." Kyungpook mathematical journal 55, no. 2 (June 23, 2015): 301–11. http://dx.doi.org/10.5666/kmj.2015.55.2.301.

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8

Tomizuka, Masayoshi, Jwu-Sheng Hu, Tsu-Chih Chiu, and Takuya Kamano. "Synchronization of Two Motion Control Axes Under Adaptive Feedforward Control." Journal of Dynamic Systems, Measurement, and Control 114, no. 2 (June 1, 1992): 196–203. http://dx.doi.org/10.1115/1.2896515.

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In this paper, motion synchronization of two d-c motors, or motion control axes, under adaptive feedforward control is considered. The adaptive feedforward control system for each axis consists of a proportional feedback controller, an adaptive disturbance compensator and an adaptive feedforward controller. If the two adaptive systems are left uncoupled, a disturbance input applied to one of the two axes will cause a motion error in the disturbed axis only, and the error becomes the synchronization error. To achieve a better synchronization, a coupling controller, which responds to the synchronization error, i.e., the difference between the two motion errors, is introduced. In this case, when a disturbance input is applied to one axis, the motion errors appear in the undisturbed axis as well as in the disturbed axis. The motion error in the undisturbed axis is introduced by the coupling controller and the adaptive feedforward controller. The adaptive synchronization problem is formulated and analyzed in the continuous time domain first, and then in the discrete time domain. Stability conditions are obtained. Effectiveness of the adaptive synchronization controller is demonstrated by simulation.
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9

Wittenberg, Sidney. "Control of refractive error." Current Opinion in Ophthalmology 1, no. 1 (February 1990): 69–71. http://dx.doi.org/10.1097/00055735-199002000-00015.

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10

Maskara, S. L., and S. Chakrabarti. "Understanding Error Control Coding." IETE Journal of Education 35, no. 1-2 (January 1994): 3–21. http://dx.doi.org/10.1080/09747338.1994.11436443.

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11

Yu, Chaoyu, and Peter D. Hoff. "Adaptive sign error control." Journal of Statistical Planning and Inference 201 (July 2019): 133–45. http://dx.doi.org/10.1016/j.jspi.2019.01.002.

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12

AZLAN, Norsinnira, and Hiroshi YAMAURA. "20204 Study of Feedback Error Learning Control for Underactuated Systems." Proceedings of Conference of Kanto Branch 2009.15 (2009): 149–50. http://dx.doi.org/10.1299/jsmekanto.2009.15.149.

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13

Kurfess, T. R., and D. E. Whitney. "Decoupled Control/Error Weighting for Predictive Control." Journal of Dynamic Systems, Measurement, and Control 115, no. 1 (March 1, 1993): 188–93. http://dx.doi.org/10.1115/1.2897396.

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This paper presents a basic review and explanation of the predictive control, discusses the inherent inadequacies with the control/error weighting scheme employed by the predictive control, and recommends some modifications to greatly improve its performance. Some simple examples are presented at the end of the paper to verify that the enhanced weighted scheme does, indeed, improve the predictive controller’s performance. Recommendations are also made on the implementation of the new controller.
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14

Kweon, S. H., W. Wang, and S. H. Yang. "An Input-Data-Type-Free Contour Error Controller with Weighted Contour Error Components(Precision positioning and control technology)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2005.3 (2005): 981–86. http://dx.doi.org/10.1299/jsmelem.2005.3.981.

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15

Tuan, Luu Trong. "Nursing governance and clinical error control." International Journal of Pharmaceutical and Healthcare Marketing 9, no. 2 (June 1, 2015): 136–57. http://dx.doi.org/10.1108/ijphm-02-2014-0014.

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Purpose – This study aims to fathom the role of nursing governance as a mechanism to activate the chain effect from corporate social responsibility (CSR) through psychological contract to knowledge sharing, which in turn reduces clinical errors in hospitals in the Vietnam context. Clinical errors not merely result from human factors but also from mechanisms which influence human factors. Design/methodology/approach – The clues for the research model were established through structural equation modeling-based analysis of cross-sectional data from 233 nurses of Vietnam-based hospitals. Findings – Research findings unveiled the positive correlation between nursing governance and ethical CSR as well as the negative correlations between nursing governance and legal CSR or economic CSR. Ethical CSR was found to have positive effect on psychological contract, whereas legal or economic CSR was found to have negative effect on psychological contract. The chain effects from psychological contract through knowledge sharing to clinical error control were also attested in this inquiry. Originality/value – Research results have contributed to literature in some ways, for example, expanding health-care quality and patient safety literature through the chain of antecedents (nursing governance, CSR, psychological contract and knowledge sharing) to clinical error control, underscoring the role of psychological contract in cultivating knowledge sharing and adding organizational outcomes such as knowledge sharing and clinical error control to the nursing governance literature.
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16

Konstantinidis, Stavros, Nelma Moreira, and Rogério Reis. "Randomized generation of error control codes with automata and transducers." RAIRO - Theoretical Informatics and Applications 52, no. 2-3-4 (April 2018): 169–84. http://dx.doi.org/10.1051/ita/2018015.

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We introduce the concept of an -maximal error-detecting block code, for some parameter in (0,1), in order to formalize the situation where a block code is close to maximal with respect to being error-detecting. Our motivation for this is that it is computationally hard to decide whether an error-detecting block code is maximal. We present an output-polynomial time randomized algorithm that takes as input two positive integers N, ℓ and a specification of the errors permitted in some application, and generates an error-detecting, or error-correcting, block code of length ℓ that is 99%-maximal, or contains N words with a high likelihood. We model error specifications as (nondeterministic) transducers, which allow one to represent any rational combination of substitution and synchronization errors. We also present some elements of our implementation of various error-detecting properties and their associated methods. Then, we show several tests of the implemented randomized algorithm on various error specifications. A methodological contribution is the presentation of how various desirable error combinations can be expressed formally and processed algorithmically.
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17

Cui, Jing Jun. "Analysis of Machining Error in Numerical Control Milling." Applied Mechanics and Materials 312 (February 2013): 710–13. http://dx.doi.org/10.4028/www.scientific.net/amm.312.710.

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Generally speaking, the error in machining is an important indicator measuring the accuracy of finished surface. The machining error often occurs in numerical control milling. Such error will be influenced by multiple factors, such as cutter wear, thermal deformation, machine tool deformation, vibration or positioning error. Nowadays, though our science and technology develops rapidly, machining error problem in numerical control milling occurs frequently. At present, several methods can be applied to forecast machining error problems in numerical control milling, including on the basis of machining theory, experimental study, design study and artificial intelligence. The analysis and forecast of machining error problems in numerical control milling can to some extent improve the degree of machining errors so as to promote the machining accuracy in milling. The author expresses the views on machining error problems according to current situations of numerical control milling.
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18

Amat, Sergio. "Nonseparable multiresolution with error control." Applied Mathematics and Computation 145, no. 1 (December 2003): 117–32. http://dx.doi.org/10.1016/s0096-3003(02)00473-3.

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19

Veres, S. M. "Error Control in Polytope Computations." Journal of Optimization Theory and Applications 113, no. 2 (May 2002): 325–55. http://dx.doi.org/10.1023/a:1014835026141.

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20

Costello, D. J., J. Hagenauer, H. Imai, and S. B. Wicker. "Applications of error-control coding." IEEE Transactions on Information Theory 44, no. 6 (1998): 2531–60. http://dx.doi.org/10.1109/18.720548.

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21

Pailing, Patricia E., Sidney J. Segalowitz, Jane Dywan, and Patricia L. Davies. "Error negativity and response control." Psychophysiology 39, no. 2 (March 2002): 198–206. http://dx.doi.org/10.1111/1469-8986.3920198.

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22

Aves, Mark A., David F. Griffiths, and Desmond J. Higham. "Does Error Control Suppress Spuriosity?" SIAM Journal on Numerical Analysis 34, no. 2 (April 1997): 756–78. http://dx.doi.org/10.1137/s0036142994276980.

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23

McCool, John I. "Control Charts for Radial Error." Quality Technology & Quantitative Management 3, no. 3 (January 2006): 283–93. http://dx.doi.org/10.1080/16843703.2006.11673115.

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24

Vaidya, N. H. "Unidirectional bit/byte error control." IEEE Transactions on Computers 44, no. 5 (May 1995): 710–14. http://dx.doi.org/10.1109/12.381959.

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25

Fujiwara, E., and D. K. Pradhan. "Error-control coding in computers." Computer 23, no. 7 (July 1990): 63–72. http://dx.doi.org/10.1109/2.56853.

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26

Robinson, John P., William D. Wade, Peter K. Leong, and Mary E. Mortara. "Generic error control coding modules." SIMULATION 48, no. 6 (June 1987): 229–35. http://dx.doi.org/10.1177/003754978704800604.

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27

Nørsett, Syvert P., and Per G. Thomsen. "Local error control inSDIRK-methods." BIT 26, no. 1 (March 1986): 100–113. http://dx.doi.org/10.1007/bf01939366.

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28

Sevilla-Escoboza, R., G. Huerta-Cuellar, R. Jaimes-Reátegui, J. H. García-López, C. I. Medel-Ruiz, C. E. Castañeda, D. López-Mancilla, and A. N. Pisarchik. "Error-feedback control of multistability." Journal of the Franklin Institute 354, no. 16 (November 2017): 7346–58. http://dx.doi.org/10.1016/j.jfranklin.2017.08.052.

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29

Sztandera, Les M. "Error propagation fuzzy control system." Information Sciences - Applications 3, no. 2 (March 1995): 75–89. http://dx.doi.org/10.1016/1069-0115(94)00045-4.

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30

Stewart, Mark G., and Robert E. Melchers. "Error control in member design." Structural Safety 6, no. 1 (July 1989): 11–24. http://dx.doi.org/10.1016/0167-4730(89)90004-0.

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31

Karon, Brad S., James C. Boyd, and George G. Klee. "Glucose Meter Performance Criteria for Tight Glycemic Control Estimated by Simulation Modeling." Clinical Chemistry 56, no. 7 (July 1, 2010): 1091–97. http://dx.doi.org/10.1373/clinchem.2010.145367.

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Abstract Background: Glucose meter analytical performance criteria required for safe and effective management of patients on tight glycemic control (TGC) are not currently defined. We used simulation modeling to relate glucose meter performance characteristics to insulin dosing errors during TGC. Methods: We used 29 920 glucose values from patients on TGC at 1 institution to represent the expected distribution of glucose values during TGC, and we used 2 different simulation models to relate glucose meter analytical performance to insulin dosing error using these 29 920 initial glucose values and assuming 10%, 15%, or 20% total allowable error (TEa) criteria. Results: One-category insulin dosing errors were common under all error conditions. Two-category insulin dosing errors occurred more frequently when either 20% or 15% TEa was assumed compared with 10% total error. Dosing errors of 3 or more categories, those most likely to result in hypoglycemia and thus patient harm, occurred infrequently under all error conditions with the exception of 20% TEa. Conclusions: Glucose meter technologies that operate within a 15% total allowable error tolerance are unlikely to produce large (≥3-category) insulin dosing errors during TGC. Increasing performance to 10% TEa should reduce the frequency of 2-category insulin dosing errors, although additional studies are necessary to determine the clinical impact of such errors during TGC. Current criteria that allow 20% total allowable error in glucose meters may not be optimal for patient management during TGC.
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32

Nishida, Yoshiharu, Takashi Harada, Nobuaki Imamura, and Nobuo Kimura. "Robust Impedance Control for Robot Manipulator." Journal of Robotics and Mechatronics 3, no. 6 (December 20, 1991): 470–74. http://dx.doi.org/10.20965/jrm.1991.p0470.

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In most robust impedance control methods, error factors such as disturbances and modeling errors in the joint space are dealt with. However, the dynamics for an end effector of the manipulator in the Cartesian space is more important than that of the manipulator in the joint space. In this paper, error factors are described in the Cartesian space, and the influence of these factors on the dynamics of the end-effector are considered. A robust controller is designed using either feedback of impedance error or a disturbance observer based on the Cartesian space, and its effectiveness is confirmed through experimental results.
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33

Takata, T., T. Fujiwara, T. Kasami, and S. Lin. "An error control system with multiple-stage forward error corrections." IEEE Transactions on Communications 38, no. 10 (1990): 1799–809. http://dx.doi.org/10.1109/26.61451.

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34

Weiss, Alexander, and Barbara I. Wohlmuth. "A posteriori error estimator and error control for contact problems." Mathematics of Computation 78, no. 267 (September 1, 2009): 1237–67. http://dx.doi.org/10.1090/s0025-5718-09-02235-2.

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35

Cluff, Tyler, Aspasia Manos, Timothy D. Lee, and Ramesh Balasubramaniam. "Multijoint error compensation mediates unstable object control." Journal of Neurophysiology 108, no. 4 (August 15, 2012): 1167–75. http://dx.doi.org/10.1152/jn.00691.2011.

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A key feature of skilled object control is the ability to correct performance errors. This process is not straightforward for unstable objects (e.g., inverted pendulum or “stick” balancing) because the mechanics of the object are sensitive to small control errors, which can lead to rapid performance changes. In this study, we have characterized joint recruitment and coordination processes in an unstable object control task. Our objective was to determine whether skill acquisition involves changes in the recruitment of individual joints or distributed error compensation. To address this problem, we monitored stick-balancing performance across four experimental sessions. We confirmed that subjects learned the task by showing an increase in the stability and length of balancing trials across training sessions. We demonstrated that motor learning led to the development of a multijoint error compensation strategy such that after training, subjects preferentially constrained joint angle variance that jeopardized task performance. The selective constraint of destabilizing joint angle variance was an important metric of motor learning. Finally, we performed a combined uncontrolled manifold-permutation analysis to ensure the variance structure was not confounded by differences in the variance of individual joint angles. We showed that reliance on multijoint error compensation increased, whereas individual joint variation (primarily at the wrist joint) decreased systematically with training. We propose a learning mechanism that is based on the accurate estimation of sensory states.
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36

Wang, Xiao Jun, and Xiao Guang Fu. "Geometric Error Compensation Methods of Numerical Control Machine Tool." Advanced Materials Research 426 (January 2012): 239–42. http://dx.doi.org/10.4028/www.scientific.net/amr.426.239.

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In this paper the characteristics of geometric errors is discussed in detail, error compensation methods used in productive practice and relevant examples are given. Finally, the application of error compensation in different situation is discussed according to the characteristics of machining center. The machine accuracy can be improved by error compensation. It has important practical reference value for reasonable use and maintaining of NC machine tool.
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37

Hattori, Shingo, Kentaro Kobayashi, Hiraku Okada, and Masaaki Katayama. "On– Off Error Control Coding Scheme for Minimizing Tracking Error in Wireless Feedback Control Systems." IEEE Transactions on Industrial Informatics 11, no. 6 (December 2015): 1411–21. http://dx.doi.org/10.1109/tii.2015.2489185.

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38

Gross, Georgia Himmelwright, Kenneth O. St. Louis, Dennis M. Ruscello, and Forrest M. Hull. "Language Abilities of Articulatory-Disordered School Children With Multiple or Residual Errors." Language, Speech, and Hearing Services in Schools 16, no. 3 (July 1985): 171–86. http://dx.doi.org/10.1044/0161-1461.1603.171.

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The primary question addressed by this study was whether or not there are two distinct articulatory subgroups (Residual Error and Multiple Error groups) that perform differently from each other and a control group with respect to language measures across a wide age range. A total of 144 subjects were selected from articulation test data on nearly 13,000 children for whom tape-recorded language samples were available. Twelve children in each of four grades (1, 3, 5, and 7) were chosen for each of three groups: Control (no articulation errors), Residual Error (mean of nearly five articulation errors, primarily on /r/, /1/, and /s/), and Multiple Error (mean of more than 15 errors). The articulation errors of the two articulatory defective groups were distributed differently with respect to type and position of errors. Mean language structural scores for completeness and complexity were substantially lower for the Multiple Error group than the Residual Error and Control groups. Scores for length of utterances were not significantly different. The Residual Error and Control groups were similar in mean scores for these language structure measures. The total number of language errors reduced progressively from Multiple Error to Residual Error to Control groups. Language structural and error scores improved from grades 1 through 7 but not in a consistently progressive developmenttal pattern.
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39

Yang, Hongtao, Mei Shen, Li Li, Yu Zhang, Qun Ma, and Mengyao Zhang. "New identification method for computer numerical control geometric errors." Measurement and Control 54, no. 5-6 (May 2021): 1055–67. http://dx.doi.org/10.1177/00202940211010835.

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To address the problems of the low accuracy of geometric error identification and incomplete identification results of the linear axis detection of computer numerical control (CNC) machine tools, a new 21-item geometric error identification method based on double ball-bar measurement was proposed. The model between the double ball-bar reading and the geometric error term in each plane was obtained according to the three-plane arc trajectory measurement. The mathematical model of geometric error components of CNC machine tools is established, and the error fitting coefficients are solved through the beetle antennae search particle swarm optimization (BAS–PSO) algorithm, in which 21 geometric errors, including roll angle errors, were identified. Experiments were performed to compare the optimization effect of the BAS–PSO and PSO and BAS and genetic particle swarm optimization (GA–PSO) algorithms. Experimental results show that the PSO algorithm is trapped in the local optimum, and the BAS–PSO is superior to the other three algorithms in terms of convergence speed and stability, has higher identification accuracy, has better optimization performance, and is suitable for identifying the geometric error coefficient of CNC machine tools. The accuracy and validity of the identification results are verified by the comparison with the results of the individual geometric errors detected through laser interferometer experiments. The identification accuracy of the double ball-bar is below 2.7 µm. The proposed identification method is inexpensive, has a short processing time, is easy to operate, and possesses a reference value for the identification and compensation of the linear axes of machine tools.
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40

Yousefi'zadeh, Homayoun, Hamid Jafarkhani, and Amir Habibi. "Layered Media Multicast Control (LMMC): Real-Time Error Control." IEEE Transactions on Multimedia 8, no. 6 (December 2006): 1219–27. http://dx.doi.org/10.1109/tmm.2006.884612.

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41

Hu, Lin Na, Yu Sun, and Yi Feng Jiang. "Research of H.264/AVC Error Control Approaches Base on FMO." Applied Mechanics and Materials 29-32 (August 2010): 2333–38. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2333.

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In order to reduce the channel errors influence on the decoding quality, the H.264/AVC uses a series of effective error- handling mechanism. This paper introduces error control technology in H.264/AVC, and focused on the FMO. On this basis, error pattern are used to simulate of the FMO, and the quantificational performance of the FMO will be given out.
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42

Prudhomme, Serge, Paul T. Bauman, and J. Tinsley Oden, Professor. "Error Control for Molecular Statics Problems." International Journal for Multiscale Computational Engineering 4, no. 5-6 (2006): 647–62. http://dx.doi.org/10.1615/intjmultcompeng.v4.i5-6.60.

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43

., Muhammad Sher. "Error-Control Coding in Satellite Communication." Journal of Applied Sciences 2, no. 1 (December 15, 2001): 10–16. http://dx.doi.org/10.3923/jas.2002.10.16.

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44

Yuan, Bao Ping, Wen Wei Yu, Jose Gonzalez, and David Gomez. "Feedback Error Learning for FES Control." Applied Mechanics and Materials 220-223 (November 2012): 1619–24. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1619.

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Walking assist using Functional Electrical Stimulation (FES) has been studied for a quite long time to help paraplegic persons overcome their walking impairment. One of the problems of these devices is how to cope with individual dependent, time-varying, and nonlinear user characteristics and external disturbances. In this study, Feedback Error Learning (FEL), a scheme that integrates feedback and feed-forward control, was applied to FES control. In this study, a FES experiment to human subjects was done to investigate the applicability of this system. The data obtained from the FES experiments were used to construct a new simulation model for further investigating the effect of FES in different condition. The results showed the usability of the FEL scheme for different FES control strategies.
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45

Ljung, Lennart. "Model Error Modeling and Control Design." IFAC Proceedings Volumes 33, no. 15 (June 2000): 31–36. http://dx.doi.org/10.1016/s1474-6670(17)39722-7.

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46

Nochetto, Ricardo H., Giuseppe Savaré, and Claudio Verdi. "Error control of nonlinear evolution equations." Comptes Rendus de l'Académie des Sciences - Series I - Mathematics 326, no. 12 (June 1998): 1437–42. http://dx.doi.org/10.1016/s0764-4442(98)80407-2.

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47

Sodha, Janak, and Adrian Als. "Shape nature of error-control codes." Signal Processing 83, no. 7 (July 2003): 1457–65. http://dx.doi.org/10.1016/s0165-1684(03)00062-8.

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48

Wenwen, Shi, Jiang Fuchuan, Zheng Qiang, and Cui Jingjing. "Analysis and Control of Human Error." Procedia Engineering 26 (2011): 2126–32. http://dx.doi.org/10.1016/j.proeng.2011.11.2415.

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49

Milburn, G. J., M. Sarovar, and C. Ahn. "Quantum control and quantum error correction." Australian Journal of Electrical and Electronics Engineering 2, no. 2 (January 2005): 151–57. http://dx.doi.org/10.1080/1448837x.2005.11464123.

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50

Romano, Joseph P., and Michael Wolf. "Balanced control of generalized error rates." Annals of Statistics 38, no. 1 (February 2010): 598–633. http://dx.doi.org/10.1214/09-aos734.

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