報告人：李可
報告時間：2017年12月17日8:00開始
報告地點：伟德bvA302
主辦單位：中國礦業大學伟德bv，徐州市工業與應用數學學會
報告摘要：
The spacecraft electrical signal characteristic data exist a large amount of data, high dimension features, computational complexity degree and low rate of identification problems. This report proposes the feature extraction method of deep neural networks (DNN) and transfer learning (TL) algorithm applying to classification and recognition of spacecraft experiment data. It is divided into two parts. The first one is the spacecraft electrical signal data classification, and in this part, the semi-supervised deep neural network is used for data feature extraction and classification. The second part introduces the classification of hyperspectral data, in this part transfer learning was used to solve the problem of insufficient data samples, and deep learning algorithm was used to build feature extraction classification model. The experimental results show that compared with other algorithms, the proposed method shows excellent performance in accuracy, computational efficiency, stability in dealing with spacecraft electrical signal data.
報告人簡介：
北京航空航天大學人機工效與環境控制國防重點學科實驗室副教授、博士，發表學術論文40餘篇，獲批國家發明專利五項。主持國家自然科學基金面上項目、航空科學基金、航天科技基金、凡舟基金及航空、航天院所等多項課題研究。獲得國防科學技術進步一等獎一項。獲得北京市德育先進工作者稱号。現任中國人工智能學會認知系統與信息處理專業委員會委員。