2022 5th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2022)

Keynote Speakers

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Prof. Defu Zhang, Xiamen University, China

张德富教授,厦门大学

Experience: He received his bachelor degree in computational mathematics in 1996, and master degree in computational mathematics in 1999, both from Xiangtan University, and his Ph.D. degree in computer software and its theory from the school of computer science in Huazhong University of Science & Technology. He was a senior researcher of Shanghai Jinxin financial engineering academe from Jun. 2002 to Apr. 2003. Now he works in the department of Computer Science at Xiamen University as a professor. He worked as a PostDoc at the Longtop for financial data mining group from 2006 to 2008. From June 6, 2008 to September 6, 2008 and from July 6, 2009 to September 6, 2009, he worked as a visiting scholar of Hong Kong City University. From December 28, 2009 to December 28, 2010, he works at University of Wisconsin-Madison as a visiting scholar. From July 13, 2011 to September 12, 2011, he worked as a visiting associate professor of Hong Kong City University. From Sept. 7, 2012 to September 20, 2012, he worked as a research fellow of Hong Kong City University. From July 12, 2013 to August 31, 2013, he worked as a research fellow of Hong Kong City University. From August 31, 2013 to October 31, 2013, he worked as a visiting fellow of Macau University.

Title: Identification of rice plant diseases using deep learning

Abstract: Rice is one of the most important crops in the world, and most people consume rice as a staple food, especially in Asian countries. Various rice plant diseases have a negative effect on crop yields. If proper detection is not taken, they can spread and lead to a significant decline in agricultural productions. In severe cases, they may even cause no grain harvest entirely, thus having a devastating impact on food security. This talk will introduce our research on deep learning models for rice plant diseases.  The computational results are reported.





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Prof. Zhikui Chen, Dalian University of Technology, China

陈志奎,大连理工大学

Experience: Dr. Chen Zhikui, professor and doctoral supervisor of Dalian University of technology since November 2007, director of big data Research Institute of Dalian University of technology. Research direction: big data and artificial intelligence. 1998-2007 engaged in scientific research on network and data computing in Hong Kong Baptist University, French Institute of automation, University of Stuttgart, Germany. National projects in recent five years:

National key R & D project, Key technology research for cross domain case filing and collaboration among professional courts (2018YFC083130020, 18.7.1-2021.6.30, with a total funding of 11.15 million yuan);

National key R & D program, Source apportionment and pollution characteristics of heavy metals in farmland and agricultural products (2016YFD08030202016.1.1-2020.12.31);

Key project of National Natural Science Foundation of China (NSFC) (technical director), research on management analysis and mining of multi-source heterogeneous big data for "three old" transformation (u13012532014.1-2017.12);

Two general projects of NSFC, research on adaptive depth computing model for land resource feature learning (61672123), Research on few-shots cross-modal learning model for interpretation of land utilization status data (62076047), 2021 / 01-2024 / 12.

Academic achievements and contributions in recent five years:

A series of big data computing theories and algorithms as well as artificial intelligence technology have been created, a big data algorithm library platform has been developed, and an artificial intelligence computing platform has been constructed, which has been applied in engineering practice. Published more than 100 academic papers and more than 50 SCI retrieved journals. 20 invention patents have been applied and 13 have been authorized. Four software copyrights were issued and three books were published. One special award, first award and second award of science and technology progress of Geographic Information Association in 2020,2018,2016; One third award of Huaxia Construction Science and technology in 2021.

Title: Research on ocean multi-modal data fusion

Abstract: With the widespread use of computing devices and multimedia technologies, multimodal data is becoming more and more common in daily life. For example, in social life, people use different modal data, such as images, texts, videos, to express their moods when posting updates on Weibo, WeChat and other social software. Therefore, how to analyze and process multimodal data and capture the complementary knowledge hidden in multimodal data becomes an urgent problem to be solved. For example, in the ocean data analysis task, in order to process the multi-domain, multi-source, optical-acoustic-electric-magnetic and other multi-modal data from the sky and the coast, the analysis algorithm based on the marine multi-modal data should be designed to perform effective analysis of marine multimodal data. For multimodal analysis in real scenarios, present works first collect multi-modal data with the help of multi-source heterogeneous sensor equipment, which provides the basis for multi-modal data analysis. Then, these heterogeneous data are mapped to a common space through algorithms to capture the common information of multimodal data. Finally, fusion techniques are used to fuse these features to obtain complementary knowledge of multimodal data. However, the whole multimodal analysis process still has the problems of data imbalance, data mismatch and multiple modalities, which requires further research. Aiming at the problem of data imbalance in data acquisition based on multi-source equipment, a multi-modal low-rank tensor data reconstruction algorithm for imbalanced data is designed to complete imbalanced multi-modal data; For the problem of multi-modal data scales non-uniformity, an anchored multi-modal alignment method for multi-scale data is designed to achieve mismatched data alignment; for the feature fusion problem of multi-modal data, deep semantic feature fusion method for multi-modal data is designed to achieve multi-modality data fusion.



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Prof. Deyu Qi, South China University of Technology

齐德昱教授,华南理工大学

Experience: Qi Deyu: B.sc., M.Tech., PhD.Tech, Professor, doctoral supervisor. He has served as professor of South China University of Technology(SCUT), doctoral supervisor of computer science and technology and software engineering of SCUT, director of Research Institute of Computer System at SCUT, director of Joint IoT Innovation Lab of The University of Texas at Arlington and South China University of Technology, chairman of Alliance of Guangdong Credibility Computing and Block Chain, vice chairman of Alliance of Guangzhou Smart Education, academic leader of Advanced Computing Architecture of SCUT, director of Research Institute of Guangzhou Future Information Technology, director of Research Institute of Intelligence Augmentation at South China Business College.

The main research directions include new generation computer system architecture, software development methods and software architecture, CASE and software development environment and tools, intelligence augmentation, intelligent control, computer system security and so on. The application directions include cloud computing, big data, Internet of things, artificial intelligence, information security, system software and high performance computing.

He has written and published many textbooks and monographs, published more than 200 academic papers in well-known journals, and obtained 11 authorized invention patents.

As the host, He have completed many research projects, including the projects of the National High Technology Program (863), the National Natural Science Foundation of China, the National Natural Science Foundation of Guangdong Province, the National Technology Innovation Fund project, the Guangdong key science and technology projects, breakthroughs for key areas of Guangdong and Hong Kong Joint and so on.

He has put forward a series of methods and techniques. It includes VLSI dynamic testability analysis method Fanalysis, object-oriented analysis abstract model LOODS, product conceptual design intelligent support architecture HCP, distributed application system interoperation model XIOM, multi-database middleware DoD, framed data collection and distribution environment and tool DataHub, computing model GridJack based on formal domain fusion, integrated line-speed network security inspection system, big data model and management system GriDoc, C mode software development method and framework EIO based on pumping service, unified software development platform OneP/USIP, unified application support environment OAA, pan-manufacturing platform UPP.