Monitoring of the Efficiency of the IRT-T Reactor Heat Exchanger System by Machine Learning Method; Physics of Particles and Nuclei Letters; Vol. 21, iss. 4
| Parent link: | Physics of Particles and Nuclei Letters=Письма в журнал «Физика элементарных частиц и атомного ядра». Письма в ЭЧАЯ.— .— New York: Springer Science+Business Media LLC. Vol. 21, iss. 4.— 2024.— P. 808-810 |
|---|---|
| Päätekijä: | |
| Yhteisötekijä: | |
| Muut tekijät: | , |
| Yhteenveto: | Title screen This paper presents a study aimed at studying and evaluating the possibility of using machine learning in methods of predictive analysis of the operation of the cooling system of the IRT-T reactor. Machine learning is a subspecies of artificial intelligence used in large-volume data analytics. The currently existing methods of processing data on technological parameters are imperfect and do not allow predicting the development of operational events. The proposed approach will allow not only to centrally collect data on technological parameters, but also to output an analysis of possible outcomes and recommendations for changing operating modes. Текстовый файл AM_Agreement |
| Kieli: | englanti |
| Julkaistu: |
2024
|
| Aiheet: | |
| Linkit: | https://doi.org/10.1134/S1547477124701413 Статья на русском языке |
| Aineistotyyppi: | Elektroninen Kirjan osa |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=675047 |
MARC
| LEADER | 00000naa0a2200000 4500 | ||
|---|---|---|---|
| 001 | 675047 | ||
| 005 | 20260722161406.0 | ||
| 090 | |a 675047 | ||
| 100 | |a 20240930d2024 k||y0rusy50 ba | ||
| 101 | 0 | |a eng | |
| 135 | |a drcn ---uucaa | ||
| 181 | 0 | |a i |b e | |
| 182 | 0 | |a b | |
| 183 | 0 | |a cr |2 RDAcarrier | |
| 200 | 1 | |a Monitoring of the Efficiency of the IRT-T Reactor Heat Exchanger System by Machine Learning Method |f M. Kublinskiy, N. Smolinkov, A. Naimushin |d Мониторинг эффективности системы теплообменников реактора ИРТ-Т методом машинного обучения |z rus | |
| 283 | |a online_resource |2 RDAcarrier | ||
| 300 | |a Title screen | ||
| 320 | |a References: 6 tit. | ||
| 330 | |a This paper presents a study aimed at studying and evaluating the possibility of using machine learning in methods of predictive analysis of the operation of the cooling system of the IRT-T reactor. Machine learning is a subspecies of artificial intelligence used in large-volume data analytics. The currently existing methods of processing data on technological parameters are imperfect and do not allow predicting the development of operational events. The proposed approach will allow not only to centrally collect data on technological parameters, but also to output an analysis of possible outcomes and recommendations for changing operating modes. | ||
| 336 | |a Текстовый файл | ||
| 371 | 0 | |a AM_Agreement | |
| 461 | 1 | |t Physics of Particles and Nuclei Letters |c New York |l Письма в журнал «Физика элементарных частиц и атомного ядра». Письма в ЭЧАЯ |n Springer Science+Business Media LLC. | |
| 463 | 1 | |t Vol. 21, iss. 4 |v P. 808-810 |d 2024 | |
| 610 | 1 | |a research reactor | |
| 610 | 1 | |a cooling system | |
| 610 | 1 | |a heat exchangers | |
| 610 | 1 | |a data analytics | |
| 610 | 1 | |a artificial intelligence | |
| 610 | 1 | |a machine learning | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 700 | 1 | |a Kublinsky |b M. K. |c Specialist in the field of nuclear technologies |c Engineer of Tomsk Polytechnic University |f 1999- |g Maksym Konstantinovich |9 22984 | |
| 701 | 1 | |a Smolnikov |b N. V. |c Specialist in the field of nuclear technologies |c Engineer-physicist of Tomsk Polytechnic University |f 1998- |g Nikita Viktorovich |9 22654 | |
| 701 | 1 | |a Naymushin |b A. G. |c specialist in the field of nuclear physics |c Associate Professor of Tomsk Polytechnic University, Candidate of physical and mathematical sciences |f 1986- |g Artem Georgievich |9 17783 | |
| 712 | 0 | 2 | |a Томский политехнический университет |c 1991- |9 26305 |
| 801 | 0 | |a RU |b 63413507 |c 20240930 |g RCR | |
| 856 | 4 | 0 | |u https://doi.org/10.1134/S1547477124701413 |z https://doi.org/10.1134/S1547477124701413 |
| 856 | 4 | 0 | |u http://www1.jinr.ru/Pepan_letters/panl_2024_4/69_Kublinskiy_ann.pdf |z Статья на русском языке |
| 942 | |c CR | ||