| bibtype |
J -
Journal Article
|
| ARLID |
0538109 |
| utime |
20240103225215.3 |
| mtime |
20210120235959.9 |
| SCOPUS |
85095722107 |
| WOS |
000586815000001 |
| DOI |
10.1080/15361055.2020.1820805 |
| title
(primary) (eng) |
Detection of Alfvén Eigenmodes on COMPASS with Generative Neural Networks |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0257867 |
| ISSN |
1536-1055 |
| title
|
Fusion Science and Technology |
| volume_id |
76 |
| volume |
8 (2020) |
| page_num |
962-971 |
| publisher |
|
|
| keyword |
Alfvén eigenmodes |
| keyword |
generative models |
| keyword |
neural networks |
| keyword |
Tokamak |
| author
(primary) |
| ARLID |
cav_un_auth*0398466 |
| name1 |
Škvára |
| name2 |
Vít |
| institution |
UFP-V |
| full_dept (cz) |
Tokamak |
| full_dept (eng) |
Tokamak |
| department (cz) |
TOK |
| department (eng) |
TOK |
| country |
CZ |
| fullinstit |
Ústav fyziky plazmatu AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101207 |
| name1 |
Šmídl |
| name2 |
Václav |
| institution |
UTIA-B |
| full_dept (cz) |
Adaptivní systémy |
| full_dept |
Department of Adaptive Systems |
| department (cz) |
AS |
| department |
AS |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0307300 |
| name1 |
Pevný |
| name2 |
T. |
| country |
CZ |
| mrcb701-q |
Ceské vysoké ucení technické v Praze |
|
| author
|
| ARLID |
cav_un_auth*0257948 |
| name1 |
Seidl |
| name2 |
Jakub |
| institution |
UFP-V |
| full_dept (cz) |
Tokamak |
| full_dept |
Tokamak |
| department (cz) |
TOK |
| department |
TOK |
| country |
CZ |
| fullinstit |
Ústav fyziky plazmatu AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0325242 |
| name1 |
Havránek |
| name2 |
Aleš |
| institution |
UFP-V |
| full_dept (cz) |
Tokamak |
| full_dept |
Tokamak |
| department (cz) |
TOK |
| department |
TOK |
| country |
CZ |
| fullinstit |
Ústav fyziky plazmatu AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0395810 |
| name1 |
Tskhakaya |
| name2 |
David |
| institution |
UFP-V |
| full_dept (cz) |
Tokamak |
| full_dept |
Tokamak |
| department (cz) |
TOK |
| department |
TOK |
| country |
AT |
| fullinstit |
Ústav fyziky plazmatu AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
GA18-21409S |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0374053 |
|
| project |
| project_id |
EF16_019/0000768 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0372154 |
|
| project |
| project_id |
633053 |
| agency |
EC |
| country |
XE |
| ARLID |
cav_un_auth*0318270 |
|
| abstract
(eng) |
Chirping Alfvén eigenmodes were observed at the COMPASS tokamak. They are believed to be driven by runaway electrons (REs), and as such, they provide a unique opportunity to study the physics of nonlinear interaction between REs and electromagnetic instabilities, including important topics of RE mitigation and losses. On COMPASS, they can be detected from spectrograms of certain magnetic probes. So far, their detection has required much manual effort since they occur rarely. We strive to automate this process using machine learning techniques based on generative neural networks. We present two different models that are trained using a smaller, manually labeled database and a larger unlabeled database from COMPASS experiments. In a number of experiments, we demonstrate that our approach is a viable option for automated detection of rare instabilities in tokamak plasma. |
| result_subspec |
WOS |
| RIV |
BC |
| FORD0 |
10000 |
| FORD1 |
10200 |
| FORD2 |
10201 |
| reportyear |
2021 |
| num_of_auth |
6 |
| mrcbC47 |
UTIA-B 10000 10100 10102 |
| mrcbC55 |
UTIA-B BC |
| inst_support |
RVO:61389021 |
| inst_support |
RVO:67985556 |
| permalink |
http://hdl.handle.net/11104/0315921 |
| cooperation |
| ARLID |
cav_un_auth*0369599 |
| name |
Ceské vysoké ucení technické v Praze |
|
| mrcbC86 |
1 Article Nuclear Science Technology |
| mrcbC91 |
A |
| mrcbT16-e |
NUCLEARSCIENCETECHNOLOGY |
| mrcbT16-i |
0.00286 |
| mrcbT16-j |
0.364 |
| mrcbT16-s |
0.749 |
| mrcbT16-B |
29.374 |
| mrcbT16-D |
Q3 |
| mrcbT16-E |
Q3 |
| arlyear |
2020 |
| mrcbU01 |
Fusion Science and Technology 15361055 19437641 2020-01-01 76 8 |
| mrcbU14 |
85095722107 SCOPUS |
| mrcbU34 |
000586815000001 WOS |
| mrcbU63 |
cav_un_epca*0257867 Fusion Science and Technology 1536-1055 1943-7641 Roč. 76 č. 8 2020 962 971 Taylor & Francis |
|