Two UM FST professors won Best Presentation Awards in international symposium
澳大科技學院兩教授於國際會議上榮膺最佳演示獎

04 Jan 2013

Prof. Pak Kin WONG, Head of Department of Electromechanical Engineering, and Dr. Chi Man VONG from Department of Computer and Information Science, have attended the International Symposium of Extreme Learning Machines 2012 (ELM2012) in Singapore for presentation of two research papers. Among 200 attending papers and 230 attending scholars, the two professors were both conferred “Best Presentation Awards”. Their papers are entitled “Diesel Engine Modelling using Extreme Learning Machine under Scarce and Exponential Data Sets”, and “Predicting Minority Class for Suspended Particulate Matters Level by Extreme Learning Machine”.

This year, the symposium attendees are from the academic institutions of 15 different countries or districts including USA, China, Britain, Germany, France, Spain, Finland, Australia, Brazil, Korea, Singapore, India, Taiwan, Hong Kong, and Macau. There is only “Best Presentation Award” in ELM2012, so the award is the highest honour in this symposium. Ten papers were fairly and justly selected for the award by the experts of program committee of ELM2012. Two papers from UM won these awards.

Extreme Learning Machines (ELM) is an emerging machine learning technique, which can be applied to pattern classification and mathematical modeling. Currently ELM has been applied to different practical applications. ELM2012 provides a platform for academics, researchers and engineers to share and exchange R&D experience in both theoretical studies and practical applications of the ELM technique.

澳門大學科技學院機電工程系系主任王百鍵教授及電腦及資訊科學系黃志文博士，日前出席於新加坡舉行的2012極速學習機器國際會議(ELM2012)並發表兩篇研究論文，分別為《於稀少及指數資料下使用極速學習機器為柴油引擎建模》及《使用極速學習機器預測懸浮粒子的少數類》。從參加會議的接近200篇論文及230位學者中，兩人均奪得「最佳演示獎」。

今屆會議的參加者來自全世界15個國家及地區的學術機構，包括美國、中國、英國、德國、法國、西班牙、芬蘭、澳洲、巴西、韓國、新加坡、印度、台灣、香港及澳門。ELM2012只設「最佳演示獎」，所以，此獎項是這次大會的最高榮譽。經過大會委員會的專家小組從論文內容及演示內容中，公平公正地選出十篇獲獎論文。而澳門大學所發表的兩篇論文均獲得該項殊榮。

極速學習機器為一門新興的機器學習技術，可用作模式分辨及數學建模，現時已廣泛被應用到不同領域。極速學習機器國際會議為世界各地科學家和工程師提供一個平台，以分享及交流極速學習機器的理論、 實際應用，以至科研成果和經驗。