Relevant bibliographies by topics / Learning and control

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

Academic literature on the topic 'Learning and control'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Learning and control"

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Hewing, Lukas, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC
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Chiuso, A., and G. Pillonetto. "System Identification: A Machine Learning Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 281–304. http://dx.doi.org/10.1146/annurev-control-053018-023744.

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Estimation of functions from sparse and noisy data is a central theme in machine learning. In the last few years, many algorithms have been developed that exploit Tikhonov regularization theory and reproducing kernel Hilbert spaces. These are the so-called kernel-based methods, which include powerful approaches like regularization networks, support vector machines, and Gaussian regression. Recently, these techniques have also gained popularity in the system identification community. In both linear and nonlinear settings, kernels that incorporate information on dynamic systems, such as the smoo
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Antsaklis, P. J. "Intelligent Learning Control." IEEE Control Systems 15, no. 3 (1995): 5–7. http://dx.doi.org/10.1109/mcs.1995.594467.

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Ali, S. Nageeb. "Learning Self-Control *." Quarterly Journal of Economics 126, no. 2 (2011): 857–93. http://dx.doi.org/10.1093/qje/qjr014.

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Barto, Andrew G. "Reinforcement learning control." Current Opinion in Neurobiology 4, no. 6 (1994): 888–93. http://dx.doi.org/10.1016/0959-4388(94)90138-4.

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Matsubara, Takamitsu. "Learning Control Policies by Reinforcement Learning." Journal of the Robotics Society of Japan 36, no. 9 (2018): 597–600. http://dx.doi.org/10.7210/jrsj.36.597.

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Dang, Ngoc Trung, and Phuong Nam Dao. "Data-Driven Reinforcement Learning Control for Quadrotor Systems." International Journal of Mechanical Engineering and Robotics Research 13, no. 5 (2024): 495–501. http://dx.doi.org/10.18178/ijmerr.13.5.495-501.

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This paper aims to solve the tracking problem and optimality effectiveness of an Unmanned Aerial Vehicle (UAV) by model-free data Reinforcement Learning (RL) algorithms in both sub-systems of attitude and position. First, a cascade UAV model structure is given to establish the control system diagram with two corresponding attitude and position control loops. Second, based on the computation of the time derivative of the Bellman function by two different methods, the combination of the Bellman function and the optimal control is adopted to maintain the control signal as time converges to infini
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Freeman, Chris, and Ying Tan. "Iterative learning control and repetitive control." International Journal of Control 84, no. 7 (2011): 1193–95. http://dx.doi.org/10.1080/00207179.2011.596574.

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Recht, Benjamin. "A Tour of Reinforcement Learning: The View from Continuous Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 253–79. http://dx.doi.org/10.1146/annurev-control-053018-023825.

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This article surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications. It reviews the general formulation, terminology, and typical experimental implementations of reinforcement learning as well as competing solution paradigms. In order to compare the relative merits of various techniques, it presents a case study of the linear quadratic regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. It also describes how merging techniques from learning theory and control can provi
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Ravichandar, Harish, Athanasios S. Polydoros, Sonia Chernova, and Aude Billard. "Recent Advances in Robot Learning from Demonstration." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 297–330. http://dx.doi.org/10.1146/annurev-control-100819-063206.

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In the context of robotics and automation, learning from demonstration (LfD) is the paradigm in which robots acquire new skills by learning to imitate an expert. The choice of LfD over other robot learning methods is compelling when ideal behavior can be neither easily scripted (as is done in traditional robot programming) nor easily defined as an optimization problem, but can be demonstrated. While there have been multiple surveys of this field in the past, there is a need for a new one given the considerable growth in the number of publications in recent years. This review aims to provide an
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Dissertations / Theses on the topic "Learning and control"

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Stendal, Ludvig. "Learning about process control." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Social Sciences and Technology Management, 2003. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-195.

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<p>The research site has been the Södra Cell Tofte pulp mill. The main focus in this thesis is how to learn about process control. The need for research on this theme is given implicitly in the foundation and construction of the INPRO programme. Norwegian engineering education is discipline oriented, and the INPRO programme aimed at integrating the three disciplines engineering cybernetics, chemical engineering, and organisation and work life science in a single PhD programme. One goal was to produce knowledge of modern production in chemical process plants based on socio-technical thinking.</
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Townley, Tracy Yvette. "Predictive iterative learning control." Thesis, University of Exeter, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.246383.

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Munde, Gurubachan. "Adaptive iterative learning control." Thesis, University of Exeter, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390139.

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Wallén, Johanna. "Estimation-based iterative learning control." Doctoral thesis, Linköpings universitet, Reglerteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-64017.

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In many  applications industrial robots perform the same motion  repeatedly. One way of compensating the repetitive part of the error  is by using iterative learning control (ILC). The ILC algorithm  makes use of the measured errors and iteratively calculates a  correction signal that is applied to the system. The main topic of the thesis is to apply an ILC algorithm to a  dynamic system where the controlled variable is not measured. A  remedy for handling this difficulty is to use additional sensors in  combination with signal processing algorithms to obtain estimates of  the controlled varia
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Gaskett, Chris, and cgaskett@it jcu edu au. "Q-Learning for Robot Control." The Australian National University. Research School of Information Sciences and Engineering, 2002. http://thesis.anu.edu.au./public/adt-ANU20041108.192425.

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Q-Learning is a method for solving reinforcement learning problems. Reinforcement learning problems require improvement of behaviour based on received rewards. Q-Learning has the potential to reduce robot programming effort and increase the range of robot abilities. However, most currentQ-learning systems are not suitable for robotics problems: they treat continuous variables, for example speeds or positions, as discretised values. Discretisation does not allow smooth control and does not fully exploit sensed information. A practical algorithm must also cope with real-time constraints, sensing
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Cleland, Benjamin George. "Reinforcement Learning for Racecar Control." The University of Waikato, 2006. http://hdl.handle.net/10289/2507.

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This thesis investigates the use of reinforcement learning to learn to drive a racecar in the simulated environment of the Robot Automobile Racing Simulator. Real-life race driving is known to be difficult for humans, and expert human drivers use complex sequences of actions. There are a large number of variables, some of which change stochastically and all of which may affect the outcome. This makes driving a promising domain for testing and developing Machine Learning techniques that have the potential to be robust enough to work in the real world. Therefore the principles of the algorithm
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Turnham, Edward James Anthony. "Meta-learning in sensorimotor control." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610592.

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Jackson, Carl Patrick Thomas. "Motor learning and predictive control." Thesis, University of Nottingham, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.519400.

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Layne, Jeffery Ray. "Fuzzy model reference learning control." Connect to resource, 1992. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1159541293.

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Liu, Bai S. M. Massachusetts Institute of Technology. "Reinforcement learning in network control." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122414.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2019<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 59-91).<br>With the rapid growth of information technology, network systems have become increasingly complex. In particular, designing network control policies requires knowledge of underlying network dynamics, which are often unknown, and need to be learned. Existing reinforcement learning methods such as Q-Learning, Actor-Critic, etc. are heuristic and do not offer performance guarantees. In contrast, mode
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Books on the topic "Learning and control"

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Ahn, Hyo-Sung, YangQuan Chen, and Kevin L. Moore. Iterative Learning Control. Springer London, 2007. http://dx.doi.org/10.1007/978-1-84628-859-3.

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Bien, Zeungnam, and Jian-Xin Xu, eds. Iterative Learning Control. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5629-9.

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Chen, Yangquan, and Changyun Wen, eds. Iterative learning control. Springer London, 1999. http://dx.doi.org/10.1007/bfb0110114.

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Owens, David H. Iterative Learning Control. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-6772-3.

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Tresilian, James. Sensorimotor Control and Learning. Macmillan Education UK, 2012. http://dx.doi.org/10.1007/978-1-137-00511-3.

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Minton, Steven. Learning Search Control Knowledge. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1703-6.

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Latash, Mark L., and Francis Lestienne, eds. Motor Control and Learning. Springer US, 2006. http://dx.doi.org/10.1007/0-387-28287-4.

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Shea, Charles H. Motor learning and control. Allyn and Bacon, 1993.

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Wayne, Shebilske, and Worchel Stephen, eds. Motor learning and control. Prentice Hall, 1993.

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Chu, Bing, and David H. Owens. Optimal Iterative Learning Control. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-80236-2.

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Book chapters on the topic "Learning and control"

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Westphal, L. C. "Learning control." In Sourcebook of Control Systems Engineering. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1805-1_32.

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Yang, Yi, and Furong Gao. "Learning Control." In Computer Modeling for Injection Molding. John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118444887.ch13.

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Calinon, Sylvain, and Dongheui Lee. "Learning Control." In Humanoid Robotics: A Reference. Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-007-7194-9_68-1.

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Calinon, Sylvain, and Dongheui Lee. "Learning Control." In Humanoid Robotics: A Reference. Springer Netherlands, 2018. http://dx.doi.org/10.1007/978-94-007-7194-9_68-2.

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Webb, Geoffrey I., Claude Sammut, Claudia Perlich, et al. "Learning Control." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_450.

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Calinon, Sylvain, and Dongheui Lee. "Learning Control." In Humanoid Robotics: A Reference. Springer Netherlands, 2018. http://dx.doi.org/10.1007/978-94-007-6046-2_68.

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Szepesvári, Csaba. "Control." In Algorithms for Reinforcement Learning. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01551-9_3.

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Paluszek, Michael, and Stephanie Thomas. "Adaptive Control." In MATLAB Machine Learning. Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2250-8_11.

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Rose, Sherri, and Mark J. van der Laan. "Independent Case-Control Studies." In Targeted Learning. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9782-1_13.

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Patan, Krzysztof. "Iterative Learning Control." In Studies in Systems, Decision and Control. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11869-3_6.

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Conference papers on the topic "Learning and control"

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Lee, Kyunghyun, Ukcheol Shin, and Byeong-Uk Lee. "Learning to Control Camera Exposure via Reinforcement Learning." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00287.

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Muthiah, Letchumanan, and Arun K. Tangirala. "Transfer Learning for Iterative Learning Control Using Gaussian Processes." In 2024 Tenth Indian Control Conference (ICC). IEEE, 2024. https://doi.org/10.1109/icc64753.2024.10883738.

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Xu, Lijun, Kang Li, Minrui Fei, and Dajun Du. "A new bandwidth scheduling method for networked learning control." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334594.

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Wang, Xuan, and Eric Rogers. "Noncausal finite time interval iterative learning control law design." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915113.

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Chu, Bing, Zhonglun Cai, David H. Owens, Eric Rogers, Chris T. Freeman, and Paul L. Lewin. "Experimental verification of constrained iterative learning control using successive projection." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334655.

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Yang, Zhile, Kang Li, and Lidong Zhang. "Binary teaching-learning based optimization for power system unit commitment." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737550.

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Mitchell, R. J. "Using MATLAB GUIs to improve the learning of frequency response methods." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334774.

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Mitchell, R. I. "A MATLAB GUI for learning controller design in the frequency domain." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915153.

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Postlethwaite, Bruce. "The development of PISim: Software for process control teaching and learning." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737576.

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Jewaratnam, J., J. Zhang, J. Morris, and A. Hussain. "Batch-to-batch iterative learning control using linearised models with adaptive model updating." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334641.

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Reports on the topic "Learning and control"

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Safonov, Michael G. Robust Control Feedback and Learning. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada399708.

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Kim, Jihie, and Paul S. Rosenbloom. Constraining Learning with Search Control. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada269517.

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Chen, Yan, Arnab Bhattacharya, Jing Li, and Draguna Vrabie. Optimal Control by Transfer-Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1988297.

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Feng, Zhili, Wei Zhang, Dali Wang, Jian Chen, and Keerti Kappagantula. Machine Learning for Joint Quality Control. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2448165.

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VanLehn, Kurt, and Randolph M. Jones. Learning Physics Via Explanation-Based Learning of Correctness and Analogical Search Control,. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada240775.

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Whitney, Paul. Learning from Text: A Cognitive Control Perspective. Defense Technical Information Center, 1992. http://dx.doi.org/10.21236/ada251842.

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Seo, Young-Woo, Drew Bagnell, and Katia Sycara. Cost-Sensitive Learning for Confidential Access Control. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada597125.

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Hu, Vincent C. Machine Learning for Access Control Policy Verification. National Institute of Standards and Technology, 2021. http://dx.doi.org/10.6028/nist.ir.8360.

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Jiang, Zhong-Ping. Cognitive Models for Learning to Control Dynamic Systems. Defense Technical Information Center, 2008. http://dx.doi.org/10.21236/ada487160.

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Ren, Liu, Gregory Shakhnarovich, Jessica K. Hodgins, Hanspeter Pfister, and Paul A. Viola. Learning Silhouette Features for Control of Human Motion. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada457871.

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