E. Z. Karimov, I. N. Myagkova, V. R. Shirokiy, O. G. Barinov, S. A. Dolenko
{"title":"The Significance of Input Features for Domain Adaptation of Spacecraft Data","authors":"E. Z. Karimov, I. N. Myagkova, V. R. Shirokiy, O. G. Barinov, S. A. Dolenko","doi":"10.1134/s0010952523700466","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The problem of improving the neural network forecast of geomagnetic index <i>Dst</i> under conditions in which the input data for such a forecast are measured by two spacecraft, one of which is close to the end of its life cycle, and the data history of the other is not yet enough to construct a neural network forecast of the required quality. For an efficient transition from the data of one spacecraft to the data of another, it is necessary to use methods of domain adaptation. This paper tests and compares several data translation methods. Also, for each translated attribute, an optimal set of parameters for its translation were found, which further reduces the difference between domains. The paper shows that the use of domain adaptation methods with the selection of significant features can improve the forecast compared to the results of using untranslated data.</p>","PeriodicalId":56319,"journal":{"name":"Cosmic Research","volume":"31 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cosmic Research","FirstCategoryId":"101","ListUrlMain":"https://doi.org/10.1134/s0010952523700466","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
The problem of improving the neural network forecast of geomagnetic index Dst under conditions in which the input data for such a forecast are measured by two spacecraft, one of which is close to the end of its life cycle, and the data history of the other is not yet enough to construct a neural network forecast of the required quality. For an efficient transition from the data of one spacecraft to the data of another, it is necessary to use methods of domain adaptation. This paper tests and compares several data translation methods. Also, for each translated attribute, an optimal set of parameters for its translation were found, which further reduces the difference between domains. The paper shows that the use of domain adaptation methods with the selection of significant features can improve the forecast compared to the results of using untranslated data.
期刊介绍：
Cosmic Research publishes scientific papers covering all subjects of space science and technology, including the following: ballistics, flight dynamics of the Earth’s artificial satellites and automatic interplanetary stations; problems of transatmospheric descent; design and structure of spacecraft and scientific research instrumentation; life support systems and radiation safety of manned spacecrafts; exploration of the Earth from Space; exploration of near space; exploration of the Sun, planets, secondary planets, and interplanetary medium; exploration of stars, nebulae, interstellar medium, galaxies, and quasars from spacecraft; and various astrophysical problems related to space exploration. A chronicle of scientific events and other notices concerning the main topics of the journal are also presented.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
