107學年度大專生科技部計畫 研究創作獎

恭喜本系李權明老師指導學生藍振瑋同學榮獲107年度大專生科技部計畫「研究創作獎」．科技部為了能夠落實研究基礎，提早基礎科學、應用科學、人文社會科學相關優秀人才，鼓勵二年級以上的大專生提早接受研究訓練，瞭解研究活動與學習研究方法，執行研究計畫，並且針對成果報告優良者，頒發「研究創作獎」．107學年度大專生科技部計畫共3159 件,其中僅選200件獲此殊榮．

作品: 「移動式違規停車自動偵測系統」

Congratulations to Prof. Cheng-Ming Li, student Zhen Wei Lan, received the 「MOST College Student Research Creativity Award」 from the Ministry of Science and Technology (MOST) in 2019.

In order to implement the research foundation and advance the cultivation of talents in basic science, applied science, and humanities and social sciences, the MOST encourages junior college students and above to receive research training, understand research activities and study methods, and implement research projects.

For the excellent report of the results, the 「MOST College Student Research Creativity Award」 was awarded. In the 107 academic year, there were 3,159 undergraduate student research programs, of which only 200 were selected.

Work: 「Mobile Illegal Parking Automatic Detection System」

IET FI COMPETITION, AI Track 2019 金牌 銅牌

競賽名稱: FRONTIER INNOVATION AWARD COMPETITION 2019 AI Track

作品簡介: 以深度遞歸神經網路準確預估台股與美金匯率走勢

Date & Location: KITAKYUSHU, JAPAN , JULY 9-12, 2019

Organizer: IET - Institution of Engineering and Technology

Gold Medal,  Title: Predicting International Exchange Rate Trends with Deep Neural Networks

Team: 陳煦茗 王姿 張宇涵 張勝豐 指導老師: 李明哲

Bronze Medal, Title: Predicting the Trend of Taiwan Stocks with Deep Learning

Team: 瞿邦泰 徐如君 蔡育維 指導老師: 李明哲

2019全國大專校院軟體創作競賽銅牌

本系葉生正老師指導團隊參加「2019全國大專校院軟體創作競賽」, 「智慧感知與互動多媒體」組, 在上百組團隊激烈競爭中勇奪銅牌!!!

指導教授: 葉生正老師

團隊成員: 黃偉愷、邱顯傑、林欣儀、陳示珮

作品: 即時情緒辨識之聊天機器人的研發

作品簡介: 使用七層深度卷積網路架構做出的情緒辨識系統．可依照使用者的臉部情緒做出相對的回應。另外系統具有身分辨識的功能，可以辨識出當下使用者的身分，讓使用者有親近且被陪伴的感覺。

Prof. Yeh led the team to participate in the "2019 National Colleges and Universities Software Creation Competition", "Smart Sensing and Interactive Multimedia" group, winning the bronze medal in the fierce competition of hundreds of teams!

Advisor : Sheng-Cheng Yeh

Team Members: 黃偉愷、邱顯傑、林欣儀、陳示珮

Work: Chatbot with Real Time Emotion Recognition

Introduction: This study uses a seven-layer convolutional neural network to implement an emotion recognition system. A relative response can be made according to the user's facial emotions. In addition, the system has the function of identity recognition, which can identify the user's identity in real time.