Уважаемые коллеги, доброго времени суток! Представляем вам китайское научное издание International Journal of Automation and Computing. Журнал имеет второй квартиль, издаётся в Chinese Academy of Sciences, его SJR за 2021 г. равен 0.799, печатный ISSN - 2731-538X, электронный - 2731-5398, предметные области - Прикладная математика, Автоматизированные системы управления, Инжиниринг, Автоматизированные системы управления, Разработка систем управления, Прикладная наука о компьютерах. Вот так выглядит обложка:
Редактором является Тьениу Тан, контактные данные - tnt@nlpr.ia.ac.cn.
Дополнительные публикационные контакты - mir@ia.ac.cn, patricia.hofrichter@springer.com, journalpermissions@springernature.com, jojo.dong@springernature.com.
К публикации принимаются статьи об оригинальных теоретических и экспериментальных исследованиях и разработках в области автоматизации и вычислительной техники. Тематика журнала обширна. Темы включают, но не ограничиваются ими:
- Искусственный интеллект;
- Автоматическое управление;
- Биоинформатика;
- Информатика;
- Информационные технологии;
- Моделирование и имитации;
- Сети и коммуникации;
- Оптимизация и принятие решений;
- Распознавание образов;
- Робототехника;
- Обработка сигналов;
- Системная инженерия.
Адрес издания - https://www.springer.com/journal/11633
Пример статьи, название - EEG-based Emotion Recognition Using Multiple Kernel Learning. Заголовок (Abstract) - Emotion recognition based on electroencephalography (EEG) has a wide range of applications and has great potential value, so it has received increasing attention from academia and industry in recent years. Meanwhile, multiple kernel learning (MKL) has also been favored by researchers for its data-driven convenience and high accuracy. However, there is little research on MKL in EEG-based emotion recognition. Therefore, this paper is dedicated to exploring the application of MKL methods in the field of EEG emotion recognition and promoting the application of MKL methods in EEG emotion recognition. Thus, we proposed a support vector machine (SVM) classifier based on the MKL algorithm EasyMKL to investigate the feasibility of MKL algorithms in EEG-based emotion recognition problems. We designed two data partition methods, random division to verify the validity of the MKL method and sequential division to simulate practical applications. Then, tri-categorization experiments were performed for neutral, negative and positive emotions based on a commonly used dataset, the Shanghai Jiao Tong University emotional EEG dataset (SEED). The average classification accuracies for random division and sequential division were 92.25% and 74.37%, respectively, which shows better classification performance than the traditional single kernel SVM. The final results show that the MKL method is obviously effective, and the application of MKL in EEG emotion recognition is worthy of further study. Through the analysis of the experimental results, we discovered that the simple mathematical operations of the features on the symmetrical electrodes could not effectively integrate the spatial information of the EEG signals to obtain better performance. It is also confirmed that higher frequency band information is more correlated with emotional state and contributes more to emotion recognition. In summary, this paper explores research on MKL methods in the field of EEG emotion recognition and provides a new way of thinking for EEG-based emotion recognition research. Keywords: Emotion recognition; electroencephalography (EEG); multiple kernel learning; machine learning; brain computer interface