Xin Li

I live in Yokohama. I sincerely thanks for your coming. The following is my brief resume.

Educational experience

Yokohama National University, Japan

Tokyo International University, Japan

Shanxi University, China

Research and software works

Publications

  1. Xin Li, Hiroshi Arai, and Tomoki Hamagami. Wave-making resistance estimation through deep learning considering the distribution of ship figure. IEEJ Transactions on Electronics, Information and Systems, 140(3):391–397, 2020. ( in Japanese )

    Available onDOIJ-STAGECiNiiNDL Online
  2. Xin Li and Tomoki Hamagami. An improved auto-encoder based on 2-level prioritized experience replay for high dimension skewed data. Proceedings in Adaptation, Learning and Optimization, 12:93–105, 2020.

    Available onDOISpringer Link
  3. Xin Li and Tomoki Hamagami. Prioritized sampling method for autoencoder to reduce loss rate for skewed data. The papers of technical meeting on systems, IEE Japan, 2018(39):199–203, 2018.

    Available onCiNiiNDL Online
  4. Jinwei Wang and Xin Li. Multi-translation parallel corpus construction of don quixote. In Spanish Paper Series 2017, pages 4250–4423. Foreign Language Teaching and Researching Press, Beijing, 11 2018. ( in Simplified Chinese )

    Available onAmazon Kindle Store
  5. Jinwei Wang and Xin Li. An empirical study of oral communication strategies for foreign language students - taking spanish as an example. In Proceedings of the 11th Cross-Strait Foreign Language Teaching Symposium, pages 1–11. Fu Jen Catholic University Press, Taibei, 9 2016. ( in Traditional Chinese )

    Available onGoogle Books

Academic conference

  1. Xin Li and Tomoki Hamagami. An improved auto-encoder based on 2-level prioritized experience replay for high dimension skewed data. In Proceedings of the 23rd Asia Pacific Symposium on Intelligent and Evolutionary Systems, 2019.

  2. Xin Li and Tomoki Hamagami. Prioritized sampling method for autoencoder to reduce loss rate for skewed data. In The 28th Fuzzy, Artificial Intelligence, Neural Networks and Computational Intelligence, 2018.

  3. Xin Li, Masaya Nakata, Hamatsu Fumiya, and Tomoki Hamagami. A research about anomaly detection method for multidimensional time series data. In The International Conference on Electrical Engineering, 2017.

Software works

Computer software copyright registration certificate from China National Copyright Administration,

  1. November 2014, Eye exercises software based on face recognition and speech recognition, Registration No.2014SR177728, Certificate No.0846964.

  2. September 2014, Image retrieval and classification system based on the bag-of-words model, Registration No.2014SR136257, Certificate No.0805497.

  3. May 2014, HD image generation system based on Android, Registration No.2014SR052862,Certificate No.0722106.

Work experience and internships

Yokohama, Kanagawa, Japan

  • April 2020 - Now

    In Faculty of Engineering, Yokohama National University, as a part-time teaching staff and postdoctoral researcher.

  • April 2019 - March 2020

    In College of Engineering Science, Yokohama National University, as a part-time lecturer for Information System Basic Experiment Course I & II.

  • April 2017 - July 2017

    In Graduate School of Engineering, Yokohama National University, as a teaching assistant for C-programming Experiment Course I.

Kawagoe, Saitama, Japan

  • June 2014 - February 2015

    In Seria Corporation Tobu Kasumigaseki Store, as a part-time working staff.

Beijing, China

  • July 2012 - September 2012

    In ChinaSoft International Co., Ltd., for internship.

Taiyuan, Shanxi, China

  • April 2013 - March 2014

    In Taiyuan ChuangChengHuanXin Technology Co., Ltd., as a part-time software and business manager.

  • June 2010 - August 2010

    In Taiyuan Union Technology Co., Ltd., for internship.

Xin Li
Xin Li
Lecturer

Research in areas such as machine learning, deep learning, and artificial intelligence applications, specialize in priority sampling and learning algorithm for non-uniform data and offset data.Learn more