Relevant bibliographies by topics / DC algorithm

Academic literature on the topic 'DC algorithm'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "DC algorithm"

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Allam, Allam M., A. S. Ibrahim, and Essam Nabil. "Single loop PID controller design based on optimization algorithms for parallelly connected dc-dc converters." Journal of Physics: Conference Series 2128, no. 1 (December 1, 2021): 012027. http://dx.doi.org/10.1088/1742-6596/2128/1/012027.

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Abstract This paper addresses a viable single loop PID controller on the bases of optimization algorithms for parallelly connected DC-DC converters to improve current sharing, improve the systems dynamics and guarantee good steady-state performance simultaneously. Because of inconvenience and lack of accuracy of Ziegler-Nichols rule in tuning PID controller parameters, an optimized controller design strategy with the purpose of enhancing the system performance is introduced in this paper. The PID is tuned by the traditional Ziegler -Nichols technique along with three other different algorithms: Genetic algorithm, whale algorithm and grey wolf algorithm. A comparison has been established between these algorithms based on the objective function value, execution time, overshoot, settling time and current sharing. The simulation results were collected to authenticate effectiveness of the proposed techniques and to evaluate the advantages of these optimization algorithms over the traditional tuning method.
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Kapgate, Deepak. "Predictive Data Center Selection Scheme for Response Time Optimization in Cloud Computing." International Journal of Cloud Applications and Computing 11, no. 1 (January 2021): 93–111. http://dx.doi.org/10.4018/ijcac.2021010105.

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The quality of cloud computing services is evaluated based on various performance metrics out of which response time (RT) is most important. Nearly all cloud users demand its application's RT as minimum as possible, so to minimize overall system RT, the authors have proposed request response time prediction-based data center (DC) selection algorithm in this work. Proposed DC selection algorithm uses results of optimization function for DC selection formulated based on M/M/m queuing theory, as present cloud scenario roughly obeys M/M/m queuing model. In cloud environment, DC selection algorithms are assessed based on their performance in practice, rather than how they are supposed to be used. Hence, explained DC selection algorithm with various forecasting models is evaluated for minimum user application RT and RT prediction accuracy on various job arrival rates, real parallel workload types, and forecasting model training set length. Finally, performance of proposed DC selection algorithm with optimal forecasting model is compared with other DC selection algorithms on various cloud configurations.
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Hamed, Salah Beni, Mouna Ben Hamed, and Lassaad Sbita. "Robust Voltage Control of a Buck DC-DC Converter: A Sliding Mode Approach." Energies 15, no. 17 (August 24, 2022): 6128. http://dx.doi.org/10.3390/en15176128.

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This paper deals with voltage control in a buck DC-DC converter. In fact, dynamic mathematical equations describing the principle behavior of the above system have been derived. Due to the nonlinearity of the established model, a nonlinear control algorithm is adopted. It is based on the sliding mode control approach. To highlight the performance of the latter, a comparative study with four control algorithms is carried out. The validity of the model and the performance of the conceived algorithms are verified in simulation. Both the system and the algorithm controls are implemented in the Matlab/Simulink environment. Extensive results under different operational conditions are presented and discussed.
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Luo, Derong, Ting Wu, Ming Li, Benshun Yi, and Haibo Zuo. "Application of VMD and Hilbert Transform Algorithms on Detection of the Ripple Components of the DC Signal." Energies 13, no. 4 (February 19, 2020): 935. http://dx.doi.org/10.3390/en13040935.

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Accurate detection of ripple components of the direct-current (DC) signals is essential for evaluating DC power quality. In this study, the combination algorithm based on variational mode decomposition (VMD) and Hilbert transform (HT) is applied to detect and analyze the characteristics of the ripple components of the DC disturbance signals. Firstly, the optimal modal number of VMD algorithms is comprehensively determined by observing the center frequencies of the mode components and the Index of Orthogonality (IO) of mode components. Through utilizing the VMD algorithm, the DC disturbance signal is accurately decomposed into a series of amplitude modulation-frequency modulation (AM-FM) functions. Then, the HT algorithm is applied to each AM-FM function to obtain the corresponding instantaneous amplitude and frequency, and the characteristics of DC disturbance signal are determined. Some case studies are implemented to analyze the ripple components of the DC disturbance signal with the VMD-HT and empirical mode decomposition (EMD) algorithm. Finally, the experiment results of Gree Photovoltaic Cabin have verified the feasibility and effectiveness of the proposed combination VMD-HT algorithm by comparison with EMD and the window interpolation fast Fourier transform (WIFFT) algorithms.
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Joo, Dong-Myoung, Dong-Hee Kim, and Byoung-Kuk Lee. "Input Current Ripple Reduction Algorithm for Interleaved DC-DC Converter." Transactions of the Korean Institute of Power Electronics 19, no. 3 (June 20, 2014): 220–26. http://dx.doi.org/10.6113/tkpe.2014.19.3.220.

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Le, Hoai Minh, Hoai An Le Thi, Tao Pham Dinh, and Van Ngai Huynh. "Block Clustering Based on Difference of Convex Functions (DC) Programming and DC Algorithms." Neural Computation 25, no. 10 (October 2013): 2776–807. http://dx.doi.org/10.1162/neco_a_00490.

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We investigate difference of convex functions (DC) programming and the DC algorithm (DCA) to solve the block clustering problem in the continuous framework, which traditionally requires solving a hard combinatorial optimization problem. DC reformulation techniques and exact penalty in DC programming are developed to build an appropriate equivalent DC program of the block clustering problem. They lead to an elegant and explicit DCA scheme for the resulting DC program. Computational experiments show the robustness and efficiency of the proposed algorithm and its superiority over standard algorithms such as two-mode K-means, two-mode fuzzy clustering, and block classification EM.
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Hunsberger, Luke, and Roberto Posenato. "Faster Dynamic-Consistency Checking for Conditional Simple Temporal Networks." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 152–60. http://dx.doi.org/10.1609/icaps.v30i1.6656.

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A Conditional Simple Temporal Network (CSTN) is a structure for representing and reasoning about time in domains where temporal constraints may be conditioned on outcomes of observations made in real time. A CSTN is dynamically consistent (DC) if there is a strategy for executing its time-points such that all relevant constraints will necessarily be satisfied no matter which outcomes happen to be observed. The literature on CSTNs contains only one sound-and-complete DC-checking algorithm that has been implemented and empirically evaluated. It is a graph-based algorithm that propagates labeled constraints/edges. A second algorithm has been proposed, but not evaluated. It aims to speed up DC checking by more efficiently dealing with so-called negative q-loops.This paper presents a new two-phase approach to DC-checking for CSTNs. The first phase focuses on identifying negative q-loops and labeling key time-points within them. The second phase focuses on computing (labeled) distances from each time-point to a single sink node. The new algorithm, which is also sound and complete for DC-checking, is then empirically evaluated against both pre-existing algorithms and shown to be much faster across not only previously published benchmark problems, but also a new set of benchmark problems. The results show that, on DC instances, the new algorithm tends to be an order of magnitude faster than both existing algorithms. On all other benchmark cases, the new algorithm performs better than or equivalently to the existing algorithms.
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Hajighorbani, Shahrooz, Mohd Amran Mohd Radzi, Mohd Zainal Abidin Ab Kadir, and Suhaidi Bin Shafie. "DC-DC converter for photovoltaic powered battery charger." World Journal of Engineering 13, no. 6 (December 5, 2016): 516–23. http://dx.doi.org/10.1108/wje-09-2016-0085.

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Purpose The purpose of this study is to show in power-voltage curve, a unique maximum power point (MPP) is existed which has the maximum power. Design/methodology/approach This paper presents a MPP tracker algorithm for a standalone system includes DC-DC buck converter and battery storage. Findings By using this algorithm, the maximum available power is achieved and simultaneously, the battery is charged and also protected against overcharge and discharge. The operation of the proposed algorithm is evaluated in with Proteus software to be sure that it can be implemented in microcontroller in reality. Originality/value The simulations results show that the proposed algorithm is able to detect the MPP under different irradiations. Moreover, the battery is charged during the day by PV and protected against overcharge and discharge.
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Zumberge, Jon, Michael Boyd, and Raul Ordonez. "Validation of a DC-DC Boost Circuit Control Algorithm." SAE International Journal of Aerospace 9, no. 1 (September 20, 2016): 112–27. http://dx.doi.org/10.4271/2016-01-2030.

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Rabina, T. Alfa, P. Priyanka Darling Rosita, S. Ramesh, N. Ramani, and N. Ramani. "SYNCHRONOUS BUCK DC DC CONVERTER USING FIREFLY OPTIMIZATION ALGORITHM." INTERNATIONAL JOURNAL OF RECENT TRENDS IN ENGINEERING & RESEARCH 05, Special Issue 07 (March 4, 2019): 15–20. http://dx.doi.org/10.23883/ijrter.conf.20190304.003.fwkkr.

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Dissertations / Theses on the topic "DC algorithm"

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Zumberge, Jon T. "Validation of a DC-DC Boost Circuit Model and Control Algorithm." University of Dayton / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1435688617.

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Cai, Wei Kougianos Elias Mohanty Saraju. "FPGA prototyping of a watermarking algorithm for MPEG-4." [Denton, Tex.] : University of North Texas, 2007. http://digital.library.unt.edu/permalink/meta-dc-3695.

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Fernando, Pradeep R. "Genetic algorithm based two-dimensional and three-dimensional floorplanning for VLSI ASICs." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001549.

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Kaown, Dougsoo Liu Jianguo. "A new algorithm for finding the minimum distance between two convex hulls." [Denton, Tex.] : University of North Texas, 2009. http://digital.library.unt.edu/permalink/meta-dc-9845.

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Fernando, Pradeep Ruben. "Genetic algorithm based design and optimization of VLSI ASICs and reconfigurable hardware." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0003289.

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Jones, Omar. "DESIGN AND DEVELOPMENT OF AN EMBEDDED DC MOTOR CONTROLLER USING A PID ALGORITHM." Thesis, Linköping University, Electronics System, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-58947.

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This project was held at London South Bank University in the UK, with corporation with staff from Linköping University in Sweden as Bachelor thesis.

This report will guide you through the used techniques in order to achieve a successful cooler/Fan project with a minimum budget and good energy saving methods.

The steps of setting the used software and components are supported with figures and diagrams. You will find full explanation of the used components and mathematics, in additional to a complete working code.

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Sharma, Luetel Sadhana. "Performance analysis of a binary-tree-based algorithm for computing Spatial Distance Histograms." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0003136.

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Xu, Yangyang. "Comparison of routing and network coding in group communications." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0002938.

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Samala, Ravi K. "Efficient suspicious region segmentation algorithm for computer aided diagnosis of breast cancer based on tomosynthesis imaging." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001800.

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Bouallagui, Sarra. "Techniques d'optimisation déterministe et stochastique pour la résolution de problèmes difficiles en cryptologie." Phd thesis, INSA de Rouen, 2010. http://tel.archives-ouvertes.fr/tel-00557912.

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Cette thèse s'articule autour des fonctions booléennes liées à la cryptographie et la cryptanalyse de certains schémas d'identification. Les fonctions booléennes possèdent des propriétés algébriques fréquemment utilisées en cryptographie pour constituer des S-Boxes (tables de substitution).Nous nous intéressons, en particulier, à la construction de deux types de fonctions : les fonctions courbes et les fonctions équilibrées de haut degré de non-linéarité.Concernant la cryptanalyse, nous nous focalisons sur les techniques d'identification basées sur les problèmes de perceptron et de perceptron permuté. Nous réalisons une nouvelle attaque sur le schéma afin de décider de sa faisabilité.Nous développons ici des nouvelles méthodes combinant l'approche déterministe DCA (Difference of Convex functions Algorithm) et heuristique (recuit simulé, entropie croisée, algorithmes génétiques...). Cette approche hybride, utilisée dans toute cette thèse, est motivée par les résultats intéressants de la programmation DC.
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Books on the topic "DC algorithm"

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service), SpringerLink (Online, ed. Fast Software Encryption: 19th International Workshop, FSE 2012, Washington, DC, USA, March 19-21, 2012. Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Hanson, Ronald J. Implementing Closed-Loop Control Algorithms for DC-to-DC Converters and ARCP Inverters Using the Universal Controller. Ft. Belvoir: Defense Technical Information Center, 1997.

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Computational algebraic and analytic geometry: AMS special sessions on computational algebraic and analytic geometry for low-dimensional varieties, January 8, 2007, New Orleans, LA, January 6, 2009, Washington, DC, [and] January 6, 2011, New Orleans, LA. Providence, R.I: American Mathematical Society, 2012.

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Snoeyink, Jack, and Adam L. Buchsbaum. Algorithm Engineering and Experimentation: Third International Workshop, ALENEX 2001, Washington, DC, USA, January 5-6, 2001. Revised Papers. Springer London, Limited, 2003.

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Algorithm Engineering and Experimentation: Third International Workshop, ALENEX 2001, Washington, DC, USA, January 5-6, 2001. Revised Papers (Lecture Notes in Computer Science). Springer, 2001.

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Canteaut, Anne. Fast Software Encryption: 19th International Workshop, FSE 2012, Washington, DC, USA, March 19-21, 2012. Revised Selected Papers. Springer, 2012.

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Canteaut, Anne. Fast Software Encryption: 19th International Workshop, FSE 2012, Washington, DC, USA, March 19-21, 2012. Revised Selected Papers. Springer, 2012.

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E, Kascak Peter, and NASA Glenn Research Center, eds. DC bus regulation with a flywheel energy storage system. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2002.

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E, Kascak Peter, and NASA Glenn Research Center, eds. DC bus regulation with a flywheel energy storage system. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2003.

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Implementing Closed-Loop Control Algorithms for DC-to-DC Converters and ARCP Inverters Using the Universal Controller. Storming Media, 1997.

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Book chapters on the topic "DC algorithm"

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Chrobak, Marek. "Algorithm DC-TREE for k-Servers on Trees." In Encyclopedia of Algorithms, 1–4. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-3-642-27848-8_7-2.

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Chrobak, Marek. "Algorithm DC-Tree for k Servers on Trees." In Encyclopedia of Algorithms, 9–11. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_7.

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Mo, Hongwei, and Lifang Xu. "Biogeography Optimization Algorithm for DC Motor PID Control." In Advances in Swarm and Computational Intelligence, 385–94. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20466-6_41.

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Hooshmand, F., and S. A. MirHassani. "Efficient DC Algorithm for the Index-Tracking Problem." In Intelligent Computing & Optimization, 566–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93247-3_56.

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Chrobak, Marek. "Algorithm DC-Tree for k $$\boldsymbol{k}$$ -Servers on Trees." In Encyclopedia of Algorithms, 27–30. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_7.

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Taneja, Manas, and Dheeraj Joshi. "Genetic Algorithm Based Intelligent Control Strategy for Multi-input Multi-output DC–DC Converter." In Studies in Infrastructure and Control, 113–20. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-4663-8_10.

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Tian, Xiaopeng, and Ming Fu. "Flexible Power Flow Algorithm of AC–DC Power System." In Lecture Notes in Electrical Engineering, 439–46. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4981-2_48.

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Gan, Junyu, Wenping Cao, Wenjie Zhu, Cungang Hu, and Xi Chen. "Discrete Fuzzy Control Algorithm for Single-Phase-Shift Control of Isolated Full-Bridge DC-DC Converter." In Lecture Notes in Electrical Engineering, 193–205. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1922-0_16.

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Kitamura, Nobuyuki, Annette Werth, and Kenji Tanaka. "A Fully Renewable DC Microgrid with Autonomous Power Distribution Algorithm." In Sustainability Through Innovation in Product Life Cycle Design, 899–913. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0471-1_61.

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M. Zahir, A. A., S. S. N. Alhady, W. A. F. W. Othman, and M. F. Ahmad. "Genetic Algorithm Optimization of PID Controller for Brushed DC Motor." In Lecture Notes in Mechanical Engineering, 427–37. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8788-2_38.

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Conference papers on the topic "DC algorithm"

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Shchuchkin, Evgeniy Yu. "Parametric Optimization Algorithm for DC-DC Converters." In 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, 2020. http://dx.doi.org/10.1109/eiconrus49466.2020.9039184.

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Taher, Murad Ahmed Ali. "Design an algorithm to syntheses DC-DC converters." In 2013 IEEE XXXIII International Scientific Conference on Electronics and Nanotechnology (ELNANO 2013). IEEE, 2013. http://dx.doi.org/10.1109/elnano.2013.6552015.

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Skarolek, Pavel, and Jiri Lettl. "Tracking Deadtime Algorithm for GaN DC/DC Converter." In 2019 International Conference on Applied Electronics (AE). IEEE, 2019. http://dx.doi.org/10.23919/ae.2019.8866992.

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Xing Lei Chen, Zheng Zhao, Peng Zi, and Xiao Xin Zhou. "VSC based DC grid power flow algorithm and AC/DC power flow algorithm." In 2014 International Conference on Power System Technology (POWERCON). IEEE, 2014. http://dx.doi.org/10.1109/powercon.2014.6993554.

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Fengbin Peng, Qiang Liu, and Zhiqiang Li. "A novel Pseudo PID algorithm of DC-DC converter." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579293.

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Melnic, Vladimir, and Irina Cojuhari. "Synthesis the PID Control Algorithm for Speed Control of the DC Motor based on the Genetic Algorithm." In 11th International Conference on Electronics, Communications and Computing. Technical University of Moldova, 2022. http://dx.doi.org/10.52326/ic-ecco.2021/ce.03.

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In this paper, it is proposed to use the genetic algorithm for synthesis the PI and PID control algorithms for speed control of the DC motor, according to the imposed performance, namely settling time. The obtained results were compared with maximum stability degree method with iterations and to demonstrate the efficiency of proposed algorithm the computer simulation was done. The designed algorithm searches for the controller tuning parameters: kp, ki, and kd, so that performance for the closed-loop step response to be satisfied.
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Abidi, Houda, Afef Bennani Ben Abdelghani, and Daniel Montesinos-Miracle. "MPPT algorithm and photovoltaic array emulator using DC/DC converters." In MELECON 2012 - 2012 16th IEEE Mediterranean Electrotechnical Conference. IEEE, 2012. http://dx.doi.org/10.1109/melcon.2012.6196497.

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Garg, Man Mohan, Yogesh V. Hote, and M. K. Pathak. "Leverrier's algorithm based modeling of higher-order dc-dc converters." In 2012 IEEE 5th India International Conference on Power Electronics (IICPE). IEEE, 2012. http://dx.doi.org/10.1109/iicpe.2012.6450490.

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Garg, Man Mohan, and Yogesh V. Hote. "Leverrier algorithm based reduced order modeling of dc-dc converters." In 2014 IEEE 6th India International Conference on Power Electronics (IICPE). IEEE, 2014. http://dx.doi.org/10.1109/iicpe.2014.7115838.

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Sarkar, Prabhat Ranjan, Ahmad Faiz Minai, M. S. Bhaskar, Rupendra Kumar Pachauri, and Sashikant. "Examination of MPPT Algorithm on Three Step DC-DC Converter." In 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2022. http://dx.doi.org/10.1109/upcon56432.2022.9986394.

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Reports on the topic "DC algorithm"

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Arhin, Stephen, Babin Manandhar, Hamdiat Baba Adam, and Adam Gatiba. Predicting Bus Travel Times in Washington, DC Using Artificial Neural Networks (ANNs). Mineta Transportation Institute, April 2021. http://dx.doi.org/10.31979/mti.2021.1943.

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Washington, DC is ranked second among cities in terms of highest public transit commuters in the United States, with approximately 9% of the working population using the Washington Metropolitan Area Transit Authority (WMATA) Metrobuses to commute. Deducing accurate travel times of these metrobuses is an important task for transit authorities to provide reliable service to its patrons. This study, using Artificial Neural Networks (ANN), developed prediction models for transit buses to assist decision-makers to improve service quality and patronage. For this study, we used six months of Automatic Vehicle Location (AVL) and Automatic Passenger Counting (APC) data for six Washington Metropolitan Area Transit Authority (WMATA) bus routes operating in Washington, DC. We developed regression models and Artificial Neural Network (ANN) models for predicting travel times of buses for different peak periods (AM, Mid-Day and PM). Our analysis included variables such as number of served bus stops, length of route between bus stops, average number of passengers in the bus, average dwell time of buses, and number of intersections between bus stops. We obtained ANN models for travel times by using approximation technique incorporating two separate algorithms: Quasi-Newton and Levenberg-Marquardt. The training strategy for neural network models involved feed forward and errorback processes that minimized the generated errors. We also evaluated the models with a Comparison of the Normalized Squared Errors (NSE). From the results, we observed that the travel times of buses and the dwell times at bus stops generally increased over time of the day. We gathered travel time equations for buses for the AM, Mid-Day and PM Peaks. The lowest NSE for the AM, Mid-Day and PM Peak periods corresponded to training processes using Quasi-Newton algorithm, which had 3, 2 and 5 perceptron layers, respectively. These prediction models could be adapted by transit agencies to provide the patrons with accurate travel time information at bus stops or online.
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