Relevant bibliographies by topics / Adaptive PID

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
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Academic literature on the topic 'Adaptive PID'

Author: Grafiati
Published: 23 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Adaptive PID"

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Taeib, Adel, and Abdelkader Chaari. "PID Controller Based Adaptive PSO." International Review of Automatic Control (IREACO) 7, no. 1 (January 31, 2014): 31. http://dx.doi.org/10.15866/ireaco.v7i1.1288.

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Bányász, Cs, and L. Keviczky. "A Simple Adaptive PID Tuner." IFAC Proceedings Volumes 31, no. 18 (July 1998): 471–77. http://dx.doi.org/10.1016/s1474-6670(17)42036-2.

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Amaral, W., J. C. Batista, G. Favier, L. Gimeno, A. Llamosas, and R. Machado. "PID Controller for Adaptive Applications." IFAC Proceedings Volumes 19, no. 13 (November 1986): 161–66. http://dx.doi.org/10.1016/s1474-6670(17)59534-8.

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Sun, Yan Xia, and Zeng Hui Wang. "Adaptive Optimal Digital PID Controller." Applied Mechanics and Materials 789-790 (September 2015): 1021–26. http://dx.doi.org/10.4028/www.scientific.net/amm.789-790.1021.

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It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.
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Hägglund, T., and K. J. Åström. "An Industrial Adaptive PID Controller." IFAC Proceedings Volumes 23, no. 1 (April 1990): 251–56. http://dx.doi.org/10.1016/s1474-6670(17)52729-9.

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Thompson, S., and E. G. McCluskey. "An Expert Adaptive PID Controller." IFAC Proceedings Volumes 23, no. 1 (April 1990): 257–62. http://dx.doi.org/10.1016/s1474-6670(17)52730-5.

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Keviczky, L., and C. S. Bányász. "A Completely Adaptive PID Regulator." IFAC Proceedings Volumes 21, no. 9 (August 1988): 89–95. http://dx.doi.org/10.1016/s1474-6670(17)54708-4.

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Bartlomiej, B., and Ł. Andrzej. "The Adaptive Robust PID Controller." IFAC Proceedings Volumes 21, no. 9 (August 1988): 227–32. http://dx.doi.org/10.1016/s1474-6670(17)54730-8.

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Burakov, M. V., V. F. Shishlakov, and A. S. Konovalov. "ADAPTIVE NEURAL NETWORK PID CONTROLLER." Issues of radio electronics, no. 10 (October 20, 2018): 86–92. http://dx.doi.org/10.21778/2218-5453-2018-10-86-92.

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The problem of constructing an adaptive PID controller based on the Hopfield neural network for a linear dynamic plant of the second order is considered. A description of the plant in the form of a discrete transfer function is used, the coefficients of which are determined with the help of a neural network that minimizes the discrepancy between the outputs of the plant and the model. The neural network processes the current and delayed input and output signals of the plant, forming an output for estimating the coefficients of the model. Another neural network determines the PID regulator coefficients at which the dynamics of the system approach the dynamics of the reference process. The calculation of weights and displacements of neurons in Hopfield networks used for identification and control is based on the construction of Lyapunov functions. The proposed methodology can be used to organize adaptive control of a wide class of linear dynamic systems with variable parameters. The results of the simulation in the article show the effectiveness of the proposed method.
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Benaskeur, A. R., and A. Desbiens. "Backstepping-based adaptive PID control." IEE Proceedings - Control Theory and Applications 149, no. 1 (January 1, 2002): 54–59. http://dx.doi.org/10.1049/ip-cta:20020100.

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Dissertations / Theses on the topic "Adaptive PID"

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Chan, Yat-fei. "Neurofuzzy network based adaptive nonlinear PID controllers." Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B43958357.

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Chan, Yat-fei, and 陳一飛. "Neurofuzzy network based adaptive nonlinear PID controllers." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43958357.

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Zickerman, Bexell Lilli. "Pressure and Level Control of a River Water Pumping Station." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176998.

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The river water pumping station at SSAB in Borlänge is a critical part of the factory since it supplies the whole factory with cooling water. The problem with the river water pumping station is that the pressure in the pipes is very dependent on the large water consumers in the factory. The large consumers causes large variations in pressure and water level when they suddenly turns on and off. The second problem in the river water pumping station is that it can not pump enough water to the consumers during the warmer periods of the year. The aim of this thesis is to improve the control system in the river water pumping station by first creating a model of the system which then can be used to test a new controller. The model is verified against measurements from the real process. The results show that the developed model captures the general behavior of the system. Further analysis of the system show that a smaller pump could be the cause of the problem in the control system. In the development of the new controller the smaller pump was then removed and replaced with a larger pump. The new and old controller perform similarly when it comes to pressure and flow rate however the new controller is slightly better when taking the control signal into account because it does not make any large sudden changes.
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Gonzalez, Juan. "Spacecraft Formation Control| Adaptive PID-Extended Memory Recurrent Neural Network Controller." Thesis, California State University, Long Beach, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10978237.

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In today’s space industry, satellite formation flying has become a cost-efficient alternative solution for science, on-orbit repair and military time-critical missions. While in orbit, the satellites are exposed to the space environment and unpredictable spacecraft on-board disturbances that negatively affect the attitude control system’s ability to reduce relative position and velocity error. Satellites utilizing a PID or adaptive controller are typically tune to reduce the error induced by space environment disturbances. However, in the case of an unforeseen spacecraft disturbance, such as a fault in an IMU, the PID based attitude control system effectiveness will deteriorate and will not be able to reduce the error to an acceptable magnitude.

In order to address the shortcomings a PID-Extended Memory RNN (EMRNN) adaptive controller is proposed. A PID-EMRNN with a short memory of multiple time steps is capable of producing a control input that improves the translational position and velocity error transient response compared to a PID. The results demonstrate the PID-EMRNN controller ability to generate a faster settling and rise time for control signal curves. The PID-EMRNN also produced similar results for an altitude range of 400 km to 1000 km and inclination range of 40 to 65 degrees angles of inclination. The proposed PID-EMRNN adaptive controller has demonstrated the capability of yielding a faster position error and control signal transient response in satellite formation flying scenario.

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Talebian, Kousha. "Automation in anesthesia : a look at L₁ adaptive and PID controllers." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/60260.

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Control of anesthesia is one of the many tasks performed by anesthesiologists during surgery. It involves adjusting drug dosage by monitoring patient’s vital and clinical signs. A control system can replace this tedious and routine task, and allow the anesthesiologists to concentrate on more life threatening procedures. Because of large intra- and inter-variability in patients Pharmacokinetics and Pharmacodynamics responses, an adaptive controller is desirable. This thesis thoroughly investigates the L₁ Adaptive Control by applying it on 44 simulation cases which cover a wide range of patient demographics. It is found that the controller approaches an implantable non-adaptive LTI controller as the adaptation gain increases, echoing the results found by other researches. This loss of adaptivity is shown through examples and mathematical derivations. It is concluded that the L₁ Adaptive Control in its current form is not applicable to closed-loop control of anesthesia. As an alternative to adaptive controller, partial adaptivity in a PID controller is investigated. iControl, a PID controller designed by us, can sometimes lead to oscillation in the control signal. It is desirable to automatically detect the oscillations and tune the controller in order to remove them. A real-time oscillation detection algorithm is discussed. It detects multiple oscillations in real-time and provides their frequency, amplitude, severity and regularity. A PID auto-tuning algorithm is developed that uses the dominant frequency metrics provided by the oscillation detection algorithm to retune the controller robustly and to guarantee stability. This technique is simulated and tested on 44 cases; the gain and the phase margin in all 44 cases are within < 7% of the optimal tuning parameters of iControl.
Applied Science, Faculty of
Graduate
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Kim, Sunghun. "Adaptive bug prediction by analyzing project history /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2006. http://uclibs.org/PID/11984.

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Covello, Paul Eugene. "Solving the eikonal equation on an adaptive mesh /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2003. http://uclibs.org/PID/11984.

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Rivera-Santos, Edmundo José. "Development of an adaptive PID controller for a Sawyer linear motor system." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10816.

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Viswanath, Kumar. "Adaptive integrated approach to group communications in multi-hop ad-hoc networks /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

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Ammons, Stephen Mark. "Towards advanced study of Active Galactic Nuclei with visible light adaptive optics /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2009. http://uclibs.org/PID/11984.

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Books on the topic "Adaptive PID"

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Saugen, John David. An expert system for optimal tuning of adaptive PID regulators. 1987.

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Bean, Ronnie A. Nonlinear control of high performance aircraft. 1994.

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Zavala, Edith. Towards adaptative monitoring for self-adaptative systems. OmniaScience, 2019. http://dx.doi.org/10.3926/phd-zavala.

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Conference, National Agricultural, Royal Agricultural Society of England., and Agricultural Development and Advisory Service., eds. Pig production - adapting to change: Tuesday 19th February 1991. [Stoneleigh]: [National Agricultural Centre], 1991.

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Li, Shengyang. Adaptive Multi-Reservoir-Based Flood Control and Management for the Yellow River: Towards a Next Generation Software System - UNESCO-IHE PhD Thesis. Taylor & Francis Group, 2013.

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Spiegel, Avi Max. Coevolution. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691159843.003.0003.

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This chapter seeks to understand how Islamist movements have evolved over time, and, in the process, provide important background on the political and religious contexts of the movements in question. In particular, it shows that Islamist movements coevolve. Focusing on the histories of Morocco's two main Islamist movements—the Justice and Spirituality Organization, or Al Adl wal Ihsan (Al Adl) and the Party of Justice and Development (PJD)—it suggests that their evolutions can only be fully appreciated if they are relayed in unison. These movements mirror one another depending on the competitive context, sometimes reflecting, sometimes refracting, sometimes borrowing, sometimes adapting or even reorganizing in order to keep up with the other.
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Book chapters on the topic "Adaptive PID"

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Verma, Shradhanand, and Rajani K. Mudi. "Genetic Algorithm-Based Adaptive PID Controller." In Advances in Intelligent Systems and Computing, 57–64. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2012-1_7.

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Wu, Zhizheng, Azhar Iqbal, and Foued Ben Amara. "Decentralized PID Controller Design." In Modeling and Control of Magnetic Fluid Deformable Mirrors for Adaptive Optics Systems, 189–225. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32229-7_7.

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Puangdownreong, Deacha, and Sarawut Sujitjorn. "Obtaining an Optimum PID Controller Via Adaptive Tabu Search." In Adaptive and Natural Computing Algorithms, 747–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71629-7_84.

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Singh, Rupam, and Bharat Bhushan. "Adaptive Neuro-Fuzzy-PID and Fuzzy-PID-Based Controller Design for Helicopter System." In Lecture Notes in Electrical Engineering, 281–93. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6772-4_25.

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Lebow, Lawrence G. "Microcomputer Based Expert Control and Adaptive PID Control." In NATO ASI Series, 437–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-83548-3_15.

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Ucak, Kemal, and Gulay Oke. "An Improved Adaptive PID Controller Based on Online LSSVR with Multi RBF Kernel Tuning." In Adaptive and Intelligent Systems, 40–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23857-4_8.

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Le, Ha Thi Thu, Nam Hoai Nguyen, and Phuoc Doan Nguyen. "PID Adaptive Tuning with the Principle of Receding Horizon." In Advances in Engineering Research and Application, 497–503. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04792-4_64.

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Lin, Hsien-I., and Vipul Dubey. "Design of an Adaptive Force Controlled Robotic Polishing System Using Adaptive Fuzzy-PID." In Intelligent Autonomous Systems 15, 825–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01370-7_64.

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Vladimir Vladimirovich, Ignatyev, Uranchimeg Tudevdagva, Andrey Vladimirovich Kovalev, Spiridonov Oleg Borisovich, Aleksandr Viktorovich Maksimov, and Ignatyeva Alexandra Sergeevna. "Model of Adaptive System of Neuro-Fuzzy Inference Based on PID- and PID-Fuzzy-Controllers." In Advances in Intelligent Systems and Computing, 519–33. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51971-1_43.

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Jing, Xuedong, and Kangkai Cheng. "Adaptive Control of DC Servo Based on PID Neural Network." In Lecture Notes in Electrical Engineering, 114–21. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9698-5_14.

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Conference papers on the topic "Adaptive PID"

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Chen, Jen-Yang, Chuan-Hsi Liu, Chang-Wei Fan, Haw-Yun Shin, and Mao-Hsu Yen. "An adaptive PID controller." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580596.

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Alexandrov, A. G. "Frequencial adaptive PID-controller." In 1999 European Control Conference (ECC). IEEE, 1999. http://dx.doi.org/10.23919/ecc.1999.7099723.

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Mohamed, Benrabah, Kamel Kara, AitSahed Oussama, and Laid Hadjili. "Adaptive Neural Network PID Controller." In 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2019. http://dx.doi.org/10.1109/eeeic.2019.8783803.

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Pohjola, Mikael. "Adaptive jitter margin PID controller." In 2008 IEEE International Conference on Automation Science and Engineering (CASE 2008). IEEE, 2008. http://dx.doi.org/10.1109/coase.2008.4626440.

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Wilkie, J. "Adaptive PID algorithms for industrial microcontrollers." In IEE Colloquium on Adaptive Controllers in Practice '97. IEE, 1997. http://dx.doi.org/10.1049/ic:19970956.

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Jia, Tian. "Follower Study Adaptive Fuzzy PID Controller." In 2014 International Symposium on Computer, Consumer and Control (IS3C). IEEE, 2014. http://dx.doi.org/10.1109/is3c.2014.112.

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Qianhua Xiao, Deqiong Zou, and Ping Wei. "Fuzzy Adaptive PID Control Tank Level." In 2010 International Conference on Multimedia Communications (Mediacom 2010). IEEE, 2010. http://dx.doi.org/10.1109/mediacom.2010.10.

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Wenqiang Yang and Minrui Fei. "An improved adaptive PID controller algorithm." In 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS). IEEE, 2011. http://dx.doi.org/10.1109/iccis.2011.6070318.

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Ranger, P., and A. Desbiens. "Improved backstepping-based adaptive PID control." In 4th International Conference on Control and Automation. Final Program and Book of Abstracts. IEEE, 2003. http://dx.doi.org/10.1109/icca.2003.1594997.

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Wang, Ya-Gang, and Xiao-Ming Xu. "Adaptive PID controllers with sensitivity specification." In 2009 Chinese Control and Decision Conference (CCDC). IEEE, 2009. http://dx.doi.org/10.1109/ccdc.2009.5191751.

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Reports on the topic "Adaptive PID"

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Martin, Robert S., and Jean-Luc Cambier. Moment Preserving Adaptive Particle Weights using Octree Velocity Distributions for PIC Simulations. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada593065.

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Johnson, R. L. Adaptive sampling strategy support for the unlined chromic acid pit, chemical waste landfill, Sandia National Laboratories, Albuquerque, New Mexico. Office of Scientific and Technical Information (OSTI), November 1993. http://dx.doi.org/10.2172/10110931.

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Friction Stir Welding Technology: Adapting NASA's Retractable Pin Tool. Purdue University, 2007. http://dx.doi.org/10.5703/1288284315855.

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‘Understanding developmental cognitive science from different cultural perspectives’ – In Conversation with Tochukwu Nweze. ACAMH, October 2020. http://dx.doi.org/10.13056/acamh.13666.

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Tochukwu Nweze, lecturer in the Department of Psychology, University of Nigeria, Nsukka and, PhD student in MRC Cognition and Brain Sciences Unit, University of Cambridge talks about his recent paper on parentally deprived Nigerian children having enhanced working memory ability, how important is it to study cultural differences in cognitive adaption during and following periods of adversity, and how can mental health professionals translate this understanding of difference into their work.
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