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590 results on '"Mladenov, Valeri"'

1. Advanced Memristor Modeling

Author

Mladenov, Valeri

Subjects
engineering, modelling, memristor, bic Book Industry Communication::T Technology, engineering, agriculture::TH Energy technology & engineering::THR Electrical engineering
Abstract

The investigation of new memory schemes, neural networks, computer systems and many other improved electronic devices is very important for future generations of electronic circuits and for their widespread application in all the areas of industry. Relatedly, the analysis of new efficient and advanced electronic elements and circuits is an essential field of highly developed electrical and electronic engineering. The resistance-switching phenomenon, observed in many amorphous oxides, has been investigated since 1970 and is promising for inclusion in technologies for constructing new electronic memories. It has been established that such oxide materials have the ability to change their conductance in accordance to the applied voltage and memorizing their state for a long time interval. Similar behavior was predicted for the memristor element by Leon Chua in 1971. The memristor was proposed in accordance with symmetry considerations and the relationships between the four basic electric quantities—electric current i, voltage v, charge q and flux linkage Ψ. The memristor is a passive one-port element, together with the capacitor, inductor and resistor. The Williams Hewlett Packard (HP) research group has made a link between resistive switching devices and the memristor proposed by Chua. In addition, a number of scientific papers related to memristors and memristor devices have been issued and several models for them have been proposed. The memristor is a highly nonlinear component. It relates the electric charge q and the flux linkage Ψ, expressed as a time integral of the voltage v. It has the important capability of remembering the electric charge passing through its cross-section, and its respective resistance, when the electrical signals are switched off. Due to its nano-scale dimensions, non-volatility and memorizing properties, the memristor is a sound potential candidate for applications in high-density computer memories, artificial neural networks, and many other electronic devices.

Published
2019
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2. Analysis and Evaluation of Distance Schooling and Learning with Respect to ICT Usage During COVID-19 Period in Bulgaria

Author

Nakov, Ognyan, Mladenov, Valeri, Mihaylova, Elizabet, Nakov, Plamen, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Rocha, Álvaro, editor, Adeli, Hojjat, editor, Dzemyda, Gintautas, editor, Moreira, Fernando, editor, and Ramalho Correia, Ana Maria, editor

Published
2021
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3. Experimental Benchmarking of Existing Offline Parameter Estimation Methods for Induction Motor Vector Control.

Author

Reddy, Butukuri Koti, Ayyagari, Krishna Sandeep, Kumar, Yemula Pradeep, Giri, Nimay Chandra, Rajgopal, Panganamamula Venkata, Fotis, Georgios, and Mladenov, Valeri

Subjects
INDUCTION motors, PARAMETER estimation, VECTOR control, RESEARCH personnel, INDUSTRIAL applications
Abstract

Induction motors dominate industrial applications due to their unwavering reliability. However, optimal vector control, critical for maximizing dynamic performance, hinges on accurate parameter estimation. This control strategy necessitates precise knowledge of the motor's parameters, obtainable through experimentation or calculation based on its design specifications. Numerous methods, ranging from traditional to computational, have been proposed by various researchers, often relying on specific assumptions that might compromise the performance of modern motor control techniques. This paper meticulously reviews the most frequently utilized methods and presents experimental results from a single motor. We rigorously compare these results against established benchmark methods, including IEEE Standard 112-2017, and subsequently identify the superior approach, boasting a maximum error of only 6.5% compared to 19.65% for competing methods. Our study investigates the parameter estimation of induction motor. The methodology primarily utilizes RMS values for measurement tasks. Moreover, the impact of harmonics, particularly when an induction motor is supplied by an inverter is briefly addressed. The pioneering contribution of this work lies in pinpointing a more accurate parameter estimation method for enhanced vector control performance. These findings pave the way for exceptional vector control, particularly at lower speeds, ultimately elevating both vector control and drive performance. [ABSTRACT FROM AUTHOR]

Published
2024
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5. Power System Studies in the Clean Energy Era: From Capacity to Flexibility Adequacy Through Research and Innovation

Author

Vita, Vasiliki, Zafiropoulos, Elias, Gonos, Ioannis F., Mladenov, Valeri, Chobanov, Veselin, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martin, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Németh, Bálint, editor, and Ekonomou, Lambros, editor

Published
2020
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8. Personalized and Intelligent Sleep Lifestyle Reasoner with Web Application for Improving Quality of Sleep Part of AAL Architecture

Author

Tonchev, Krasimir, Tsenov, Georgi, Mladenov, Valeri, Manolova, Agata, Poulkov, Vladimir, Akan, Ozgur, Series Editor, Bellavista, Paolo, Series Editor, Cao, Jiannong, Series Editor, Coulson, Geoffrey, Series Editor, Dressler, Falko, Series Editor, Ferrari, Domenico, Series Editor, Gerla, Mario, Series Editor, Kobayashi, Hisashi, Series Editor, Palazzo, Sergio, Series Editor, Sahni, Sartaj, Series Editor, Shen, Xuemin (Sherman), Series Editor, Stan, Mircea, Series Editor, Xiaohua, Jia, Series Editor, Zomaya, Albert Y., Series Editor, Oliver, Nuria, editor, Serino, Silvia, editor, Matic, Aleksandar, editor, Cipresso, Pietro, editor, Filipovic, Nenad, editor, and Gavrilovska, Liljana, editor

Published
2018
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10. A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors.

Author

Mladenov, Valeri and Kirilov, Stoyan

Subjects
TRANSFER functions, CMOS integrated circuits, COMPLEMENTARY metal oxide semiconductors, CIRCUIT complexity, TRANSISTORS, ARTIFICIAL intelligence, MEMRISTORS
Abstract

Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are potentially applicable in artificial and spiking neural networks, memory arrays, and many other devices and circuits for artificial intelligence. In this paper, a complete electronic realization of an analog circuit model of the modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in the LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an important step towards the forthcoming design of complex memristor-based neural networks for artificial intelligence, for implementation in very high-density integrated circuits and chips. [ABSTRACT FROM AUTHOR]

Published
2024
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18. Estimation of the Electric Field across Medium Voltage Surge Arresters Using Artificial Neural Networks

Author

Ekonomou, Lambros, Christodoulou, Christos A., Mladenov, Valeri, Junqueira Barbosa, Simone Diniz, Series editor, Chen, Phoebe, Series editor, Cuzzocrea, Alfredo, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Sivalingam, Krishna M., Series editor, Ślęzak, Dominik, Series editor, Washio, Takashi, Series editor, Yang, Xiaokang, Series editor, Mladenov, Valeri, editor, Jayne, Chrisina, editor, and Iliadis, Lazaros, editor

Published
2014
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19. A Design Procedure for Stable High Order, High Performance Sigma-Delta Modulator Loopfilters

Author

Tsenov, Georgi, Mladenov, Valeri, Junqueira Barbosa, Simone Diniz, editor, Chen, Phoebe, editor, Cuzzocrea, Alfredo, editor, Du, Xiaoyong, editor, Filipe, Joaquim, editor, Kara, Orhun, editor, Kotenko, Igor, editor, Sivalingam, Krishna M., editor, Ślęzak, Dominik, editor, Washio, Takashi, editor, Yang, Xiaokang, editor, Mladenov, Valeri M., editor, and Ivanov, Plamen Ch., editor

Published
2014
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21. Optimal Operation of Electric Power Production System without Transmission Losses Using Artificial Neural Networks Based on Augmented Lagrange Multiplier Method

Author

Tsekouras, George J., Kanellos, Fotis D., Mastorakis, Nikos E., Mladenov, Valeri, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mladenov, Valeri, editor, Koprinkova-Hristova, Petia, editor, Palm, Günther, editor, Villa, Alessandro E. P., editor, Appollini, Bruno, editor, and Kasabov, Nikola, editor

Published
2013
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