المؤلفون: Senyuk, M., Rajab, K., Safaraliev, M., Kamalov, F.

المصدر: Mathematics

مصطلحات موضوعية: DIGITAL SIGNAL PROCESSING, PHASOR MEASUREMENT UNIT, POWER SYSTEM MODELING, SIGNAL ANALYSIS

وصف الملف: application/pdf

العلاقة: Senyuk, M, Rajab, K, Safaraliev, M & Kamalov, F 2023, 'Evaluation of the Fast Synchrophasors Estimation Algorithm Based on Physical Signals', Mathematics, Том. 11, № 2, 256. https://doi.org/10.3390/math11020256Test; Senyuk, M., Rajab, K., Safaraliev, M., & Kamalov, F. (2023). Evaluation of the Fast Synchrophasors Estimation Algorithm Based on Physical Signals. Mathematics, 11(2), [256]. https://doi.org/10.3390/math11020256Test; Final; All Open Access, Gold; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146761778&doi=10.3390%2fmath11020256&partnerID=40&md5=e1b592d5fafb46ae677bc58488792aabTest; https://www.mdpi.com/2227-7390/11/2/256/pdf?version=1672811131Test; http://elar.urfu.ru/handle/10995/130992Test; 85146761778; 000927256900001

الإتاحة: https://doi.org/10.3390/math11020256Test
http://elar.urfu.ru/handle/10995/130992Test
https://www.mdpi.com/2227-7390/11/2/256/pdf?version=1672811131Test

المؤلفون: Senyuk, M., Elnaggar, M. F., Safaraliev, M., Kamalov, F., Kamel, S.

المصدر: Mathematics

مصطلحات موضوعية: LOW-FREQUENCY OSCILLATION, PHASOR MEASUREMENT UNIT, POWER SYSTEM, SIGNAL PROCESSING, STATISTICAL ANALYSIS, SYNCHRONOUS GENERATOR

وصف الملف: application/pdf

العلاقة: Senyuk, M, Elnaggar, MF, Safaraliev, M, Kamalov, F & Kamel, S 2023, 'Statistical Method of Low Frequency Oscillations Analysis in Power Systems Based on Phasor Measurements', Mathematics, Том. 11, № 2, 393. https://doi.org/10.3390/math11020393Test; Senyuk, M., Elnaggar, M. F., Safaraliev, M., Kamalov, F., & Kamel, S. (2023). Statistical Method of Low Frequency Oscillations Analysis in Power Systems Based on Phasor Measurements. Mathematics, 11(2), [393]. https://doi.org/10.3390/math11020393Test; Final; All Open Access, Gold; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146738939&doi=10.3390%2fmath11020393&partnerID=40&md5=a9ce05d8e71704c5d895ec9580672837Test; https://www.mdpi.com/2227-7390/11/2/393/pdf?version=1673513515Test; http://elar.urfu.ru/handle/10995/130936Test; 85146738939; 000927579900001

الإتاحة: https://doi.org/10.3390/math11020393Test
http://elar.urfu.ru/handle/10995/130936Test
https://www.mdpi.com/2227-7390/11/2/393/pdf?version=1673513515Test

المؤلفون: Kamalov, F., Sulieman, H., Moussa, S., Reyes, J. A., Safaraliev, M.

المصدر: Heliyon

مصطلحات موضوعية: ENSEMBLE SELECTION, FEATURE SELECTION, FILTER METHOD, MACHINE LEARNING, RANDOM FOREST, SYNTHETIC DATA, WRAPPER METHOD

وصف الملف: application/pdf

العلاقة: Kamalov, F, Sulieman, H, Moussa, S, Reyes, JA & Safaraliev, M 2023, 'Nested ensemble selection: An effective hybrid feature selection method', Heliyon, Том. 9, № 9, стр. e19686. https://doi.org/10.1016/j.heliyon.2023.e19686Test; Kamalov, F., Sulieman, H., Moussa, S., Reyes, J. A., & Safaraliev, M. (2023). Nested ensemble selection: An effective hybrid feature selection method. Heliyon, 9(9), e19686. https://doi.org/10.1016/j.heliyon.2023.e19686Test; Final; All Open Access, Gold, Green; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171388549&doi=10.1016%2fj.heliyon.2023.e19686&partnerID=40&md5=069477ff41a059ac197571ab82781de5Test; http://www.cell.com/article/S2405844023068949/pdfTest; http://elar.urfu.ru/handle/10995/130780Test; 85171388549; 001140561100001

الإتاحة: https://doi.org/10.1016/j.heliyon.2023.e19686Test
http://elar.urfu.ru/handle/10995/130780Test
http://www.cell.com/article/S2405844023068949/pdfTest

المؤلفون: Senyuk, M., Safaraliev, M., Kamalov, F., Sulieman, H.

المصدر: Mathematics

مصطلحات موضوعية: ENSEMBLE MACHINE LEARNING, EXTREME GRADIENT BOOSTING, POWER SYSTEM MODELING, RANDOM FOREST, TRANSIENT STABILITY

وصف الملف: application/pdf

العلاقة: Senyuk, M, Safaraliev, M, Kamalov, F & Sulieman, H 2023, 'Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology', Mathematics, Том. 11, № 3, 525. https://doi.org/10.3390/math11030525Test; Senyuk, M., Safaraliev, M., Kamalov, F., & Sulieman, H. (2023). Power System Transient Stability Assessment Based on Machine Learning Algorithms and Grid Topology. Mathematics, 11(3), [525]. https://doi.org/10.3390/math11030525Test; Final; All Open Access, Gold; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147885714&doi=10.3390%2fmath11030525&partnerID=40&md5=98124c34d760a201277038db6d691ef3Test; https://www.mdpi.com/2227-7390/11/3/525/pdf?version=1674051917Test; http://elar.urfu.ru/handle/10995/130194Test; 85147885714; 000931020800001

الإتاحة: https://doi.org/10.3390/math11030525Test
http://elar.urfu.ru/handle/10995/130194Test
https://www.mdpi.com/2227-7390/11/3/525/pdf?version=1674051917Test

المؤلفون: Santhosh, N, Sivaraj, S, Prasad, VR, Beg, OA, Leung, H-H, Kamalov, F, Kuharat, S

وصف الملف: application/pdf

العلاقة: https://usir.salford.ac.uk/id/eprint/66164/1/WAVES%20IN%20RANDOM%20AND%20COMPLEX%20MEDIA%20magnetonanofluid%20fuel%20cell%20SLITS%20Joule%20HEATING%20accepted%20Jan%2012th%202023.pdfTest; Santhosh, N, Sivaraj, S, Prasad, VR, Beg, OA orcid:0000-0001-5925-6711 , Leung, H-H, Kamalov, F and Kuharat, S 2023, 'Computational study of MHD mixed convective flow of Cu/Al2O3-water nanofluid in a porous rectangular cavity with slits, viscous heating, Joule dissipation and heat source/sink effects' , Waves in Random and Complex Media .

الإتاحة: https://doi.org/10.1080/17455030.2023.2168786Test
http://usir.salford.ac.uk/id/eprint/66164Test/
https://usir.salford.ac.uk/id/eprint/66164/1/WAVES%20IN%20RANDOM%20AND%20COMPLEX%20MEDIA%20magnetonanofluid%20fuel%20cell%20SLITS%20Joule%20HEATING%20accepted%20Jan%2012th%202023.pdfTest

المؤلفون: Kamalov, F., Rajab, K., Cherukuri, A. K., Elnagar, A., Safaraliev, M.

المصدر: Neurocomputing

مصطلحات موضوعية: CNN, COVID-19, DEEP LEARNING, FORECASTING, GNN, LSTM, MLP, SURVEY, LEARNING SYSTEMS, LONG SHORT-TERM MEMORY, 'CURRENT, GOOGLE SCHOLAR, LEARNING METHODS, MACHINE LEARNING METHODS, STATE-OF-THE ART REVIEWS, ARTICLE, CONVOLUTIONAL NEURAL NETWORK, CORONAVIRUS DISEASE 2019, GRADIENT BOOSTING, GRAPH NEURAL NETWORK, HUMAN, K NEAREST NEIGHBOR, LONG SHORT TERM MEMORY NETWORK, MATHEMATICAL MODEL, MULTILAYER PERCEPTRON, RANDOM FOREST, RECURRENT NEURAL NETWORK, SUPPORT VECTOR MACHINE, SUSCEPTIBLE EXPOSED INFECTIOUS RECOVERED MODEL, TAXONOMY

وصف الملف: application/pdf

العلاقة: Kamalov, F, Rajab, K, Cherukuri, AK, Elnagar, A & Safaraliev, M 2022, 'Deep learning for Covid-19 forecasting: State-of-the-art review', Neurocomputing, Том. 511, стр. 142-154. https://doi.org/10.1016/j.neucom.2022.09.005Test; Kamalov, F., Rajab, K., Cherukuri, A. K., Elnagar, A., & Safaraliev, M. (2022). Deep learning for Covid-19 forecasting: State-of-the-art review. Neurocomputing, 511, 142-154. https://doi.org/10.1016/j.neucom.2022.09.005Test; Final; All Open Access; Green Open Access; https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454152Test; http://elar.urfu.ru/handle/10995/132485Test; 85138086201; 871948700012

الإتاحة: https://doi.org/10.1016/j.neucom.2022.09.005Test
http://elar.urfu.ru/handle/10995/132485Test
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454152Test

المؤلفون: Safaraliev, M., Kiryanova, N., Matrenin, P., Dmitriev, S., Kokin, S., Kamalov, F.

المصدر: Energy Reports

مصطلحات موضوعية: CLIMATE CHANGE, ENSEMBLE MODELS, GBAO, HYDROPOWER PLANT, ISOLATED POWER SYSTEM, MEDIUM-TERM FORECASTING OF POWER GENERATION, TEMPERATURE, ADAPTIVE BOOSTING, CLIMATE MODELS, DECISION TREES, ENERGY UTILIZATION, HYDROELECTRIC POWER, HYDROELECTRIC POWER PLANTS, LEARNING SYSTEMS, MACHINE LEARNING, NEAREST NEIGHBOR SEARCH, STOCHASTIC SYSTEMS, WIND, HYDROPOWER PLANTS, MACHINE LEARNING MODELS, MEDIUM TERM, POWER- GENERATIONS, RELIABLE OPERATION, STOCHASTICS, FORECASTING

وصف الملف: application/pdf

العلاقة: Safaraliev, M, Kiryanova, N, Matrenin, P, Dmitriev, S, Kokin, S & Kamalov, F 2022, 'Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change', Energy Reports, Том. 8, стр. 765-774. https://doi.org/10.1016/j.egyr.2022.09.164Test; Safaraliev, M., Kiryanova, N., Matrenin, P., Dmitriev, S., Kokin, S., & Kamalov, F. (2022). Medium-term forecasting of power generation by hydropower plants in isolated power systems under climate change. Energy Reports, 8, 765-774. https://doi.org/10.1016/j.egyr.2022.09.164Test; Final; All Open Access; Gold Open Access; https://doi.org/10.1016/j.egyr.2022.09.164Test; http://elar.urfu.ru/handle/10995/132319Test; 85140083937; 886228300015

الإتاحة: https://doi.org/10.1016/j.egyr.2022.09.164Test
http://elar.urfu.ru/handle/10995/132319Test

المؤلفون: Umavathi, J. C., Thameem Basha, H., Noor, N. F. M., Kamalov, F., Leung, H. H., Sivaraj, R.

العلاقة: INTERNATIONAL JOURNAL OF MODERN PHYSICS B, pp.2450396; https://scholarworks.unist.ac.kr/handle/201301/66203Test; 43977; 001085062700001

الإتاحة: https://doi.org/10.1142/S021797922450396XTest
https://scholarworks.unist.ac.kr/handle/201301/66203Test

المؤلفون: Pazderin, A., Kamalov, F., Gubin, P. Y., Safaraliev, M., Samoylenko, V., Mukhlynin, N., Odinaev, I., Zicmane, I.

المصدر: Energies

مصطلحات موضوعية: DISTRIBUTION NETWORKS, ELECTRICAL ENERGY ACCOUNTING, MACHINE LEARNING, NEURAL NETWORKS, NONTECHNICAL LOSSES OF ELECTRICAL ENERGY, THEFT OF ELECTRICAL ENERGY, ANOMALY DETECTION, ELECTRIC LOSSES, ELECTRIC NETWORK PARAMETERS, ENERGY UTILIZATION, ELECTRICAL ENERGY, ENERGY ACCOUNTING, MACHINE-LEARNING, NEURAL-NETWORKS, NON-TECHNICAL LOSS, NONTECHNICAL LOSS OF ELECTRICAL ENERGY, ELECTRIC POWER DISTRIBUTION

وصف الملف: application/pdf

العلاقة: Pazderin, A, Kamalov, F, Gubin, P, Safaraliev, M, Samoylenko, V, Mukhlynin, N, Odinaev, I & Zicmane, I 2023, 'Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review', Energies, Том. 16, № 21, 7460. https://doi.org/10.3390/en16217460Test; Pazderin, A., Kamalov, F., Gubin, P., Safaraliev, M., Samoylenko, V., Mukhlynin, N., Odinaev, I., & Zicmane, I. (2023). Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review. Energies, 16(21), [7460]. https://doi.org/10.3390/en16217460Test; Final; All Open Access, Gold; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176504516&doi=10.3390%2fen16217460&partnerID=40&md5=a1716f7a687cad2f0b8420afb11a0a3fTest; https://www.mdpi.com/1996-1073/16/21/7460/pdf?version=1699284909Test; http://elar.urfu.ru/handle/10995/130954Test; 85176504516; 001100284800001

الإتاحة: https://doi.org/10.3390/en16217460Test
http://elar.urfu.ru/handle/10995/130954Test
https://www.mdpi.com/1996-1073/16/21/7460/pdf?version=1699284909Test

المؤلفون: Pazderin, A., Zicmane, I., Senyuk, M., Gubin, P., Polyakov, I., Mukhlynin, N., Safaraliev, M., Kamalov, F.

المصدر: Energies

مصطلحات موضوعية: ADAPTIVE PROTECTION, CONTINUOUS POINT-ON-WAVE, DIGITAL COMMUNICATION CHANNEL, DIGITAL SUBSTATION, FAULT, HIGHLY DISCRETE MEASUREMENTS, INTELLIGENT ELECTRONIC DEVICE, PHASOR DATA CONCENTRATOR, PHASOR MEASUREMENT UNIT, RELAY PROTECTION, WIDE AREA PROTECTION SYSTEM, DIGITAL COMMUNICATION SYSTEMS, ELECTRIC POWER SYSTEM CONTROL, ELECTRIC POWER SYSTEM PROTECTION, ELECTRIC SUBSTATIONS, PHASE MEASUREMENT, REMOTE CONTROL, RENEWABLE ENERGY RESOURCES, RISK MANAGEMENT, WIDE AREA NETWORKS, DIGITAL COMMUNICATION CHANNELS, HIGHLY DISCRETE MEASUREMENT, INTELLIGENT ELECTRONICS DEVICES, PHASOR DATA CONCENTRATORS, POWER, WIDE AREA PROTECTION SYSTEMS, PHASOR MEASUREMENT UNITS

وصف الملف: application/pdf

العلاقة: Pazderin, A, Zicmane, I, Senyuk, M, Gubin, P, Polyakov, I, Mukhlynin, N, Safaraliev, M & Kamalov, F 2023, 'Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review', Energies, Том. 16, № 17, 6203. https://doi.org/10.3390/en16176203Test; Pazderin, A., Zicmane, I., Senyuk, M., Gubin, P., Polyakov, I., Mukhlynin, N., Safaraliev, M., & Kamalov, F. (2023). Directions of Application of Phasor Measurement Units for Control and Monitoring of Modern Power Systems: A State-of-the-Art Review. Energies, 16(17), [6203]. https://doi.org/10.3390/en16176203Test; Final; All Open Access, Gold; https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170538036&doi=10.3390%2fen16176203&partnerID=40&md5=c77725e3fdf6944993832f2a11122da7Test; https://www.mdpi.com/1996-1073/16/17/6203/pdf?version=1693198670Test; http://elar.urfu.ru/handle/10995/130778Test; 85170538036; 001061027800001

الإتاحة: https://doi.org/10.3390/en16176203Test
http://elar.urfu.ru/handle/10995/130778Test
https://www.mdpi.com/1996-1073/16/17/6203/pdf?version=1693198670Test