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Shanghai, China
CONFERENCE PROGRAM
May 11-13, 2019
2019 The 5th International Conference on Data Processing and
Applications
(ICDPA 2019)
2019 The 2nd International Conference on Design, Manufacturing
and Automation
(ICDMA 2019)
Crowne Plaza Shanghai Fudan
上海复旦皇冠假日酒店
199 Handan Road, Shanghai | SH | 200433 | Mainland China | 86-21-55529999
上海市邯郸路 199 号（国权路口）, 上海 | 上海 | 200433 | 中国 | 86-21-55529999
www.crowneplaza.com/fudanshanghai
TECHNICAL SUPPORTED BY
CO-SPONSORED BY Media Partner
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Table of Contents
Welcome Address………………………………………………………………………………………… 2
Organizing Committee…………………………………………………………………………………… 3
Local Information………………………………………………………………………………………… 5
Instruction for Presentations………………………………………………………………………… 6
Program at a Glance………………………………………………………………………………………. 8
Keynote Speech……………………………………………………………………………………………… 10
Speech I--- Prof. Ying Tan……………………………………………………………..…….. 10
Speech II--- Prof. Jie Li……………….…….………………………………………………….. 12
Speech III--- Prof. Ming Wang Fu……………………………………………………………. 13
Speech IV--- Prof. John Mo …………………………………………………………………… 15
Plenary Speech………………………………………………………………………………………………. 16
Speech I--- Prof. Weigang Wu ………………………………….………………………… 16
Speech II --- Prof. Shih-Chieh Lin……………………………………………………………. 17
Technical Program (Oral Presentations)………………………………………………………….. 19
Session 1: Big data analytics ..........................................……………………………………. 19
Session 2: Big data analytics ..........................................……………………………………. 22
Session 3: Data mining ………………………………………………………………………………..…. 25
Session 4: Applications for big data……………………………………………………………….. 28
Poster Presentation………………………………………………………………………………………. 31
Listeners………………………………………………………………………………………………………. 32
Author Index………………………………………………………………………………………………… 32
Top 10 Viewponts in Shanghai………………………………………………………………………. 34
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Welcome Address
It is great honor to welcome you all to Shanghai for 2019 The 5th International Conference on
Data Processing and Applications (ICDPA 2019) , and 2019 The 2nd International Conference
on Design, Manufacturing and Automation (ICDMA 2019), to be held at Crowne Plaza Shanghai
Fudan, Shanghai, China from May 11-13, 2019.
After several rounds of review procedure, the program committee accepted those papers to be
published in conference proceedings. We wish to express our sincere appreciation to all the
individulas who have contributed to ICDPA 2019 and ICDMA 2019 conferences in various
ways. Special thanks are extended to our colleagues in the program committee for their
thorough review of all the submissions, which is vital to the success of the conference, and also
to the members in the organizing committee and the volunteers who had delicated their time
and efforts in planning, promoting, organizing and helping the conference.
This conference program is highlighted by four Keynote Speakers: Prof. Ying Tan, Peking
University, China; Prof. Jie Li University of Tsukuba, Japan; Prof. Ming Wang Fu, The Hong Kong
Polytechnic University, Hong Kong, Prof. John Mo, Royal Melbourne Institute of Technology,
Australia and two Plenary Speakers: Prof. Weigang Wu, Sun Yat-sen University, China; Prof.
Shih-Chieh Lin, National Tsing-Hua University, Taiwan.
One best presentation will be selected from each session, evaluated from: originality;
applicability; technical Merit; qualities of PPT; English. The best one will be announced at the
end of each Session, and awarded the certificate after the finish of sessions.
We wish you a wonderful conference and enjoyable visit in Shanghai!
Conference Organizing Committee
Shanghai, China
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Organizing Committee
Conference Chairs
Prof. Ying Tan, Peking University, China
Prof. Jie LI, Shanghai Jiaotong University, China
Prof. John Mo, Royal Melbourne Institute of Technology, Australia
Advisory Chair
Prof. Ming Wang Fu, The Hong Kong Polytechnic University, Hong Kong
Program Chair
Prof. Emanuel Grant, University of North Dakota, America
Prof. Weigang Wu, Sun Yat-sen University, China
Prof. Shih-Chieh Lin, National Tsing-Hua University, Taiwan
Prof. Yongjin (James) Kwon, Ajou University, South Korea
Technical Committees
Wei Wang, University of Cincinnati, USA
Ke-Lin Du, Concordia University, Canada
Simon K.S. Cheng, The Open University of Hong Kong, Hong Kong
Teh Ying Wah, University of Malaya, Malaysia
Ka Chun Wong, City University of Hong Kong, Hong Kong
Rishabh G. Upadhyay, Innopolis University, Russia
Norwati Mustapha, University Putra Malaysia, Malaysia
Souvik Pal, Elitte College of Engineering, India
Muhammad Shahbaz, University of Engineering & Technology, Pakistan
Turgay Kaya, Firat University, Turkey
HN. Venkatesan, Bharathiyar College of Engg & Tech, India
Ramacharan Sriramakavacham, G. Narayanamma Institute of Technology and Science, India
Tanvir Ahmad, Jamia Millia Islamia (A Central University), India
Ebru A. Sezer, Hacettepe University, Turkey
Mohammed Abdullah Al Nuem, King Saud University, Kingdom of Saudi Arabia
Shinq-Jen Wu, Da-Yeh University, Taiwan
C.Mala, National Institute of Technology, India
Yang Yongquan, Ocean University of China, China
Lan Yang, California State Polytechnic University, USA
Teodoro Macaraeg, University of Caloocan City, Philippines
Abhishek Kumar, Aryabhatt Engineering College and Research Centre, India
En-Chih Chang, I-Shou University,Taiwan
Akio Yamamoto, The University of Tokyo, Japan
Raj Das, RMIT University, Australia
Ilker Erkan, Suleyman Demirel University, Turkey
Wan Ahmad Yusmawiza Wan Yusoff, Hail University, Kingdom of Saudi Arabia
Helen Wu, University of Western Sydney, Australia
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Yasha Jyothi M Shirur, BNMIT, Bangalore, India
Mohan Vanarotti, Shaikh College of Engineering and Technology, India
Irfan Hilmy, MME Dept. IIUM, Malaysia
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Local Information
Conference Venue: Crowne Plaza Shanghai Fudan (上海复旦皇冠假日酒店)
199 Handan Road, Shanghai | SH | 200433 | Mainland China | 86-21-55529999
上海市邯郸路 199 号（国权路口）, 上海 | 上海 | 200433 | 中国 | 86-21-55529999
Time
UTC/GMT+8
Weather
The Weather Situation of Shanghai in China
Money
CNY（￥）
Most places in Shanghai deal with cash, wechat pay and alipay. Some foreign credit cards are
accepted in high-end establishments.
Banks or private money changers offer the best foreign-exchange rates. CNY is the only
accepted currency. Most banks charge a commission and duty for each travellers cheque
cashed. Current exchange rates are posted at exchange counters.
Getting Around
To get a taxi, simply follow signs to ground transportation. Rush hour traffic can add a
significant amount of time to your trip so plan accordingly.
Public transportation to and from Pudong is an affordable alternative to a taxi and a great way
to avoid traffic. Metro Line 2 is connected to Pudong International Airport by the Pudong
International Airport Station stop and is connected to all other metro lines.
By Subway: Operating from 5:30am to midnight daily, the subway currently has 12 lines, but
for tourists, Lines 1, 2, 4, 7, 9, and 10 are the most useful. You can use google map to search
your destination.
By Taxi: With more than 45,000 taxis in the streets, this is the most common means for visitors
to get around Shanghai. At the end of the trip, pay the indicated meter fare and no more. Tips
are not expected. It's a good idea to carry smaller bills (¥100 notes can sometimes be changed,
but don't count on it) to pay your fare.
*Part of the local information above comes from the network.
Average daily minimum temperature
20℃
Average daily highest temperature
27℃
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Instructions for Presentations
Oral presentations
Oral presentations have been allocated 15 minutes of effective presentation time, including Q/A time
between Session Chair and speakers.
Authors must prepare their oral presentations to be sure to convey their message in clear and sharp
manner, including giving outline of the key principles, facts and results. More detailed discussions can
continue during the breaks.
In order to ensure a smooth performance during your session, we kindly ask you to consider the following
instructions:
Be at the session room 15 minutes before session starts and introduce yourself to the session chairs.
A video projector and a PC will be available in all conference rooms. Speakers suggested not use their own
laptop computer, avoiding useless time breaks in between papers.
Bring your presentation on a USB memory stick in MS-PowerPoint or Adobe PDF formats, and upload it in
the Session Room computer no later than 10 minutes prior to your session start! You can also bring it
earlier, during the coffee/lunch breaks before your presentation. Please upload your presentation in a right
place in order to find it easily at the time of presentation.
Please wear formal clothes or national characteristics of clothing for participation.
In order to avoid any compatibility problems, read carefully the instructions below.
PowerPoint Instructions
For MS-PowerPoint presentations, please use the following versions only: PP 97-2003 (*.ppt) or 2007, 2010
to guarantee that it will be opened successfully on the on-site PC
We recommend to the PPT/PPTX format instead of PPS
All videos or animations in the presentation must run automatically!
Pictures/Videos
We cannot provide support for embedded videos in your presentation; please test your presentation with
the on-site PC several hours before your presentation.
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In case your video is not inserted in MS-PowerPoint, it is possible to have it in other formats – MPEG 2,4,
AVI (codecs: DivX, XviD, h264) or WMV. Suggested bitrate for all mpeg4 based codecs is about 1 Mbps with
SD PAL resolution (1024x576pix with square pixels, AR: 16/9).
Fonts
Only fonts that are included in the basic installation of MS-Windows will be available (English version of
Windows). Use of other fonts not included in Windows can cause wrong layout/style of your presentation.
Suggested fonts: Arial, Times New Roman.
If you insist on using different fonts, these must be embedded into your presentation by choosing the right
option when saving your presentation:
Click on “File”, then “Save As”
Check the “Tools” menu and select “Embed True Type Fonts”
Poster presentations
Suggested Poster with size of 60cm*80cm (width*height), with conference short name and paper ID on
right up corner.
Posters are required to be condensed and attractive. The characters should be large enough so that they are
visible from 1 meter apart.
During poster session, the author should stand by your poster, explaining and answering doubts or
questions.
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Program at a Glance
May 11
Saturday
Registration - Lobby
Registration
Note: *Collecting conference materials
*Certificate will be signed and issued after each session.
*Accommodation not provided, and it’s suggested to make an early
reservation.
10:00-17:00
May 12
Sunday
Keynote Speeches - Meeting Room 1+2
Opening Remarks:
Prof. Jie Li
University of Tsukuba, Japan 9:00-9:05
Keynote Speech I:
‘Progress in Swarm Intelligence, Fireworks Algorithm and Applications’
Prof. Ying Tan
Peking University, China
9:05-9:50
Keynote Speech II:
‘Big Data and AI Impacts on Information Networking, Security, and Privacy’
Prof. Jie Li
University of Tsukuba, Japan
9:50-10:35
Coffee Break & Group Photo 10:35-11:05
Keynote Speech III:
Prof. Ming Wang Fu
The Hong Kong Polytechnic University, Hong Kong
11:05-11:50
Keynote Speech IV:
‘In-process Optimization of Electric Discharge Machining of Polycrystalline
Diamond’
Prof. John Mo
Royal Melbourne Institute of Technology, Australia
11:50-12:35
Lunch 12:35-13:30
Plenary Speeches - Meeting Room 1
Plenary Speech I:
Prof. Weigang Wu
Sun Yat-sen University, China
13:30-14:00
Plenary Speech II:
‘Differential Interference Contrast – A potential Tool for the Measurement of
Transparent objects’
Prof. Shih-Chieh Lin
National Tsing-Hua University, Taiwan
14:00-14:30
Parallel Oral Sessions
Session 1: Big data analytics - Meeting Room 1
Session 2: Big data analytics- Meeting Room 2 14:40-16:10
Coffee Break 16:10-16:30
Session 3: Data mining - Meeting Room 1
Session 4： Applications for big data - Meeting Room 2 16:30-18:00
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Dinner 18:00-19:00
May 13
Monday Optional One Day Visit 9:00-17:00
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Keynote Speech
Prof. Ying Tan Peking University, China
Title: Progress in Swarm Intelligence, Fireworks Algorithm and Applications
Abstract: Inspired from the collective behaviors of many swarm-based creatures in nature or
social phenomena, swarm intelligence (SI) has been received attention and studied
extensively, gradually becomes a class of efficiently intelligent optimization methods. Inspired
by fireworks’ explosion in air, the so-called fireworks algorithm (FWA) was proposed in 2010.
Since then, many improvements and beyond were proposed to increase the efficiency of FWA
dramatically, furthermore, a variety of successful applications were reported to enrich the
studies of FWA considerably. In this talk, the novel swarm intelligence algorithm, i.e.,
fireworks algorithm, is briefly introduced and reviewed, then several effective improved
algorithms are highlighted, individually. In addition, the multi-objective fireworks algorithm
and the graphic processing unit (GPU) based FWA are also briefly presented, particularly the
GPU-based FWA is able to speed up the optimization process extremely. Extensive
experiments on benchmark functions demonstrate that the improved algorithms significantly
increase the accuracy of found solutions, yet decrease the running time sharply. Finally,
several typical applications of FWA, in particular, for big-data application, are presented in
detail.
Biography: Ying Tan is a full professor and PhD advisor at the School of Electronics
Engineering and Computer Science of Peking University, and director of Computational
Intelligence Laboratory at Peking University. He received his B.Eng from Electronic
Engineering Institute, M.S. from Xidian University, and PhD from Southeast University, in 1985,
1988, and 1997, respectively. Then a postdoctoral fellow and associate professor at University
of Science and Technology of China. He worked at Chinese University of Hong Kong in 1999
and 2004-2005. He was an electee of One Hundred Talent Program of China Academy of
Science (CAS) in 2005. He is the inventor of Fireworks Algorithm (FWA).
Ying Tan is a full professor and PhD advisor at the School of Electronics Engineering and
Computer Science of Peking University, and director of Computational Intelligence Laboratory
at Peking University. He received his B.Eng, M.S., and PhD from Southeast University, in 1985,
1988, and 1997, respectively. He was an electee of One-Hundred-Talent-Program of China
Academy of Science (CAS) in 2005 and the inventor of Fireworks Algorithm (FWA).
He serves as the Editor-in-Chief of International Journal of Computational Intelligence and
Pattern Recognition (IJCIPR), the Associate Editor of IEEE Transactions on Evolutionary
Computation (TEC), IEEE Transactions on Cybernetics (CYB), IEEE Transactions on Neural
Networks and Learning Systems (NNLS), International Journal of Swarm Intelligence Research
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(IJSIR), International Journal of Artificial Intelligence (IJAI), etc. He also served as an Editor of
Springer’s Lecture Notes on Computer Science (LNCS) for 20+ volumes, and Guest Editors of
several referred Journals, including IEEE/ACM Transactions on Computational Biology and
Bioinformatics, Information Science, Softcomputing, Neurocomputing, Natural Computation,
IJSIR, IJAI, etc. He is an IEEE senior member and a member of Emergent Technologies
Technical Committee (ETTC) of IEEE Computational Intelligence Society since 2010. He is the
founder general chair of the ICSI International Conference series since 2010. He won the
2nd-Class Natural Science Award of China in 2009.
His research interests include computational intelligence, swarm intelligence, swarm robotics,
data mining, pattern recognition, intelligent information processing for information security,
etc. He has published more than 280 papers in refereed journals and conferences in these
areas, and authored/co-authored 11 books and 12 chapters in book, and received 4 invention
patents.
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Prof. Jie Li University of Tsukuba, Japan
Title: Big Data and AI Impacts on Information Networking, Security, and Privacy
Abstract: Big data and AI (Artificial Intelligence) are essential for the cyber digital world.
They also bring a lot of impacts on our society. In this talk, we specifically address the impacts
of big data and AI on information networking, security, and privacy in the cyber digital world.
The challenge issues and possible solutions with big data and AI for the networking, security,
and privacy in cyber digital world will be discussed. Some examples will be provided and
explained.
Biography: Jie LI is a professor in computer science in Division of Information Engineering,
Faculty of Engineering, Information and Systems, University of Tsukuba, Japan. He has been a
visiting Professor in Yale University, USA, Inria Sophia Antipolis, France and Inria Grenoble-C
Rhone-Aples, France during the sabbatical year in September 2014 through August 2015. His
research interests are in Intelligent Mobile and Ubiquitous Computing, Networking, and
Security, Internet Computing and Networking, Big Data, Cloud, network security, OS, modeling
and performance evaluation of information systems. He is a senior member of IEEE and ACM,
and a member of IPSJ (The Information Processing Society of Japan). He is the founding chair
of the IEEE Technical sub-Committee on Big Data (TSCBD), ComSoc.
Dr. LI received the B.E. degree in computer science from Zhejiang University, Hangzhou, China,
the M.E. degree in electronic engineering and communication systems from China Academy of
Posts and Telecommunications, Beijing, China. He received the Dr. Eng. degree from the
University of Electro-Communications, Tokyo, Japan.
Dr. LI has served as a guest editor for many international journals such as IEEE JSAC and IEEE
Network recently. He has served as a secretary for Study Group on System Evaluation of IPSJ
and on Steering Committees of the SIG of System EVAluation (EVA) of IPSJ, the SIG of DataBase
System (DBS) of IPSJ, and the SIG of MoBiLe computing and ubiquitous communications of
IPSJ. He has served on several editorial boards for the IPSJ Journal and many international
professional journals. He has also served on the program committees for several international
professional conferences.
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Prof. Ming Wang Fu
The Hong Kong Polytechnic University, Hong Kong
Biography: M.W. FU joined the Department of Mechanical Engineering, The Hong Kong
Polytechnic University (HK PolyU) in Aug, 2006. Before he went to Singapore for his career
development in 1995, Prof Fu had worked in the Mainland China as a faculty member and also
been seconded to industry for technology development such that he has an in-depth
understanding and appreciation of industries. In this tenured period of his first academic
career, Dr Fu had conducted a number of R&D projects funded by governmental funding
agencies and industries. Some governmental ad-hoc projects led by him have been
successfully applied in industries. In 1991 and 1994, he received the honorary awards of
Outstanding Young Teacher and Outstanding Teacher from the Ministry of Aeronautic and
Astronautic Industries of P.R. China. In 1992 and 1995, he was promoted to associate and full
professor, respectively, under the fast-track promotion scheme for outstanding young faculty
members. Upon completion of his PhD study in the National University of Singapore in 1997,
he joined the Singapore Institute of Manufacturing Technology as a senior research engineer
in the same year. For his ten-year tenured career in this national R&D Institution of Singapore,
his research endeavors are more focused on development of state-of-the-art product design
and development and metal forming technologies. In addition, he initiated a few new research
directions in the Institute, which include micro-scaled product development via microforming,
CAE-enabled metal forming product and process design, etc.
After joined the HK PolyU in 2006, Dr Fu is quite active in exploring advanced materials
processing, numerical modeling and simulation, product design and development, and
micro-mechanics by using computer-aided design and finite element simulation technologies
to seek an epistemological understanding of the science behind materials processing and
micro-mechanics, advancing knowledge in these areas, and further successfully addressing a
plethora of challenges and bottleneck issues in the exploration arena. To support these
research activities, he has secured 8 research projects from the General Research Fund (GRF),
Innovation and Technology Fund from the Innovation and Technology Commission of Hong
Kong Government, and the National Science Foundation of China after 2007, and also various
internal funds from the HK PolyU.
In terms of research publication, Dr. Fu has about 165 published/accepted SCI journal papers
with the H-index of 32 in Dec 2016. He has published 4 monographs in English by Marcel
Dekker (New York), Springer-Verlag London Ltd, and CRC Press, Taylor & Francis Group. The
latest one will be published in Sept 2017. The Chinese version of "Computer-aided Injection
Mould Design and Manufacture" was published in 2010 by the Press of Chinese Mechanical
Engineering in the Mainland China. In addition, his paper entitled "Dimensional accuracy and
deformation behaviors in meso-scaled progressive forming of two-level flanged parts" was
awarded "Honorable Mention Paper Award" in the 4M/ICOMM2015 organized by the 4M
Association and the International Institution for micro-manufacturing in Milan, Italy, 31st
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March to 2nd April, 2015, an important conference in manufacturing arena. Another paper
entitled "Microheading Process: Deformation and Realization" was awarded the Outstanding
Paper in the 7th Inter. Conf. on Micro-manufacturing, Chicago, USA, 2012. His original
research contribution paper entitled "Ductile Fracture: Experiments and Computations"
published in the Int. J. of Plasticity in 2011 is listed as the Highly Cited Paper in Web of
ScienceTM. His another paper published in Int J of Adv Manuf Tech in 2013 on micro-forming
technology is also a Highly Cited Paper.
Dr. Fu is a Senior Member of Society of Manufacturing Engineers. He is also sitting in the
Editorial Board or as a regional editor in some longstanding journals, which include:
International Journal of Plasticity (SCI journal); Materials and Design (SCI); International
Journal of Damage Mechanics (SCI); International Journal of Advanced Manufacturing
Technology (SCI); The Chinese Journal of Mechanical Engineering-English (SCI); The Chinese
Journal of Mechanical Engineering-Chinese; Manufacturing Review (SCI); Advances in
manufacturing (SCI); Int Journal of light materials and manufacturing; Int. J. of Computer
Aided Engineering and Technology.
In addition, Dr Fu is also serving as an invited reviewer for many SCI journals, prestigious
project award, funding application, and PhD dissertation examination. He also actively serves
as session chair and a scientific/organizing committee member for a number of international
conferences in his research areas. In the past a few years, Dr Fu gave many keynote
presentations or invited talks for some prestigious international conferences in his research
areas.
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Prof. John Mo
Royal Melbourne Institute of Technology, Australia
Title: In-process Optimization of Electric Discharge Machining of Polycrystalline
Diamond
Abstract: Drilling is the most important manufacturing process in aerospace industry due to
extensive use of fibre reinforced plastics materials to reduce total mass carried by the aircraft.
Fibre reinforced plastics materials are prone to delaminate upon drilling. It is important to use
the hardest and sharpest drills at all times. Polycrystalline diamond drills are made from the
hardest tool material and can remain sharp 10 times longer than tungsten carbide drills.
However, machining of polycrystalline diamond is very difficult with mechanical means.
Electrical discharge machining (EDM) process is an important non-traditional approach to
fabricate cutting tools made in difficult-to-cut materials. EDM erodes material from a
conductive workpiece by applying a high frequency electrical pulse to it via a vibrating
electrode or a moving wire. A servo system controls movement of the electrode to approach
the workpiece but to keep a small gap, across which an insulating dielectric fluid flows. This
process is good for oblique objects where sharp edges are not required. For EDM of
polycrystalline diamond drills, a rotating electrode with complex servo axes system is
required. Furthermore, an optimized pulse control system to maximize the effect of electrical
erosion is most critical to successful machining of the polycrystalline diamond drills. This
paper describes research into the nature of the pulse control system and outlines the
mathematical algorithms that can be used to produce high quality polycrystalline diamond
drills.
Biography: John P. T. Mo is Professor of Manufacturing Engineering and former Head of
Manufacturing and Materials Engineering at RMIT University, Australia, since 2007. He has
been an active researcher in manufacturing and complex systems for over 35 years and
worked for educational and scientific institutions in Hong Kong and Australia. From 1996,
John was a Project Manager and Research Team Leader with Australia's Commonwealth
Scientific and Industrial Research Organisation (CSIRO) for 11 years leading a team of 15
research scientists. John has a broad research interest and has received numerous industrial
research grants. A few highlights of the projects include: signal diagnostics for plasma cutting
machines, ANZAC ship alliance engineering analysis, optimisation of titanium machining for
aerospace industry, critical infrastructure protection modelling and analysis, polycrystalline
diamond cutting tools on multi-axes CNC machine, system analysis for support of complex
engineering systems John obtained his doctorate from Loughborough University, UK and is a
Fellow of Institution of Mechanical Engineers (UK) and Institution of Engineers Australia.
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Plenary Speech
Prof. Weigang Wu Sun Yat-sen University, China
Biography: Prof. Weigang Wu received the B.Sc. degree in 1998 and the M.Sc. degree in 2003,
both from Xi’an Jiaotong University, China. He received the Ph.D. degree in computer science
in 2007 from Hong Kong Polytechnic University. He is currently a full professor at the school
of data and computer science, Sun Yat-sen University, China. His current research interests
include cloud computing, big data, and blockchain. He has published about 100 papers in
major conferences and journals. He has served as a member of editorial board of several SCI
journals. He is also an organizing/program committee member for many international
conferences. He is a member of the IEEE.
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Prof. Shih-Chieh Lin National Tsing-Hua University, Taiwan
Title: Differential Interference Contrast – A potential Tool for the Measurement of
Transparent objects
Abstract: Various transparent components are used in numerous optoelectronic devices. As
to ensure the product quality, there is an increasing demand for precision profile
measurement of these transparent objects. Therefore, developing a three-dimensional
topography measurement system with high speed and high precision for measuring
transparent specimens become important.
In this study, a phase shifting differential interference contrast (PS-DIC) topography
measurement system with quantitative phase restoration method is developed. First, the
feasibility of measuring step height specimen through the DIC technique is studied. A modified
Fourier phase integration (MFPI) method is proposed to improve the profile reconstruction
precision and reduce the effects of noise. Secondly, a PS-DIC measurement system is designed
and developed. The error compensation methods and calibration process are also presented.
Then a speed up two step phase shifting algorithm is proposed to accelerate the measuring
speed of the system for industrial real-time measurement purpose.
Biography: Shih-Chieh Lin is Full Professor of the Department of Power Mechanical
Engineering, and Director of the Scientific Instrument Center, National Tsing Hua University,
Taiwan. He received his Ph.D. in Mechanical Engineering from University of Illinois at
Urbana-Champaign, US, in Aug 1989.
Dr. Lin's research interests include Monitoring and Control of Manufacturing Process such as
Drilling, Face Milling, and Turing, Modeling and Optimization of Manufacturing Process, such
as Face Milling, Turning, Drilling, and Chemical Mechanical Polishing, Machine Vision,
Methodology of X-ray Computer Tomography, Inspection and Measurement of Transparent
objects, 3-D surface metrology, Analysis and Design of Hydrostatic Devices. Dr. Lin has
published more than 200 journal and conference papers and currently cooperated with
several companies.
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Detailed Program
Oral Presentation
Sunday Afternoon, 12 May 2019, 14:40-18:00
Note:
* Please control each presentation time within 15 mins, including Q & A.
* Best Presentation of each session is encouraged to award to student author prior.
* Winner of Best presentation will be announced at the end of each Session, and awarded the certificate at the
dinner.
* To show the respect to other authors, especially to encourage the student authors, we strongly suggest you attend
the whole session.
* The scheduled time for presentations might be changed due to unexpected situations, please arrive meeting room
at least 10 Mins before Session starts.
* Session photo will be taken at the end of the session and updated online.
Meeting Room 1
14:40-16:10 Session 1: Big data analytics
Meeting Room 2
14:40-16:10 Session 2: Big data analytics
Coffee Break
16:10-16:30
Meeting Room 1
16:30-18:00 Session 3: Data mining
Meeting Room 2
16:30-18:00 Session 4： Applications for big data
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Sunday, 12 May, 14:40-16:10
Session 1: Big data analytics Room: Meeting Room 1
Chair:
14:40-14:55
A File Prefetching Speculation Algorithm in MapReduce for Big Data Lan Yang California State Polytechnic University Pomona, United States MapReduce programming model [1] and Hadoop software framework [2] are key to big data processing on high performance computing (HPC) clusters. The Hadoop Distributed File System (HDFS) [2] is designed to stream large data sets at high bandwidth. An HDFS file splits into chunks, typically of 64-128MB in size. To benefit from Hadoop’s parallel ability an HDFS file must be large enough to be divided into multiple chunks. In big data applications, a data set could comprise multiple files of assorted sizes – here we refer as hetergeneous data set. For example, a data set could contain thousands of files ranging from 1KB to 100MB. Also data dependence may exist among files, for instance, after processing file 1, files 3, 5, and 6 very likely to be the next for processing. To efficiently perform hetergeneous big data tasks under Hadoop it is crucial to design a method that prepacks multiple, assorted files into chunks of a very large HDFS file. In this research, we first tested and analyzed accessing time of files ranging from 1K to 16MB on an HPC cluster, then devised a file prefetching speculation algorithm that uses the concept of compiler-directed instruction/data prefetching technique [3] to speculate and pre-load multiple/assorted files into K containers with size S (suggest S to be the HDFS chunk size and K to be the desired number of chunks with regard to requested compute nodes.) We developed the frequency-based, relationship-based, and distance-based speculation strategies. For example, when fileA is ready for processing we will prefetch files j1, j2, ... jm until filling up K containers. Under frequency-based, file ji will be chosen based on its frequency of usage (a usage counter will be kept for each file). For relationship-based, file ji will be seleted based on its relationship with fileA (relationships among files may be set initally but are adjusted dynamically pertaining to the result of speculation). In distance-basd, files physically closest in distance to fileA will be fetched first. Combination of speculation strategies are also under study. Currently we are in the process of implementing the speculation mechanisms and evaluating their performances.
14:55-15:10
Dropout in Testing Phase Makes Adversarial Samples Generation Difficult Yuan Wang, Zhiming Wang, Xucheng Yin, Chao Zhu University of Science and Technology Beijing, China Deep neural network (DNN) brings the rapid development of pattern recognition algorithm. However, experiments show the vulnerability of deep neural network. This paper studied the problem of generating adversarial samples when we adopt dropout in testing phase. Based on MNIST database, we test four adversarial generation algorithms, two types of adversarial samples, and dropout in different layers of DNN. Several conclusions are obtained: (1) Dropout in testing phase makes DNN more robust with tiny performance loss. (2) Dropout in full connect layer is the most efficient manner to improve the robustness of DNN. (3) Dropout has different impact on different adversarial samples generation algorithms.
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15:10-15:25
Research on Colorectal Cancer Prediction and Survival Analysis with Data Fusion Based on Deep Learning Shiqi Li, Jun Zheng, Shuxun Wei East China Normal University, China Colorectal cancer is a highly aggressive type of cancer. Accurate prognosis prediction of colorectal cancer can help patients and doctors choose the best treatment and avoid unnecessary costs. Nevertheless, most of previous work relied mostly on selected gene expression data to create a predictive model by traditional machine learning methods to reduce dimensions and predict cancer. Such methods cannot fully consider the correlation between samples to effectively extract feature information, and the cost of calculation is huge. In recent years, with the successful application of deep learning in various fields, it provides us with a new way of integrating multidimensional data to predict cancer and prognosis. In this paper, we propose a multi-modal neural network which integrates GCN and DNN to train multi-modal data for the prediction of colorectal cancer and its prognosis. The novelty of the method lies in the design of our method’s architecture and the fusion of multi-dimensional data, which can give full play to the performance of the neural network. The comprehensive performance evaluation results show that the proposed method achieves a better performance than the widely used prediction methods with single dimensional data and other existing approaches.
15:25-15:40
Naïve Bayes Sentiment Analysis with Fixed and Variable Length Classes Training Data Sets Saad Ibrahim Amaya, Yuxin Dong Harbin Engineering University, China The tremendous development in technology has led to the increasing number of people that join social networks to share information, opinion and so on. With these developments, the social networks are big targets and easy place to capture many people’s opinions about certain things. A lot of works have been done by many researchers on the extraction of sentiments from various data sources. Different works employed different techniques and approaches. This particular work focused on how to feed the training dataset into the training algorithm in order to get more accurate results. We categorized the training datasets into two (2) and termed them; the Variable Length/size (VS) training dataset and the Fixed Lengths/size (FS) training datasets. In the FS, we took the number of positive documents equals the number of negative documents. In the VL, we took the number of positive documents greater than the number of negative documents (VS positive) and vice versa (VS negative). Binary Naïve Bayes algorithm was used to test the accuracies of the FS, VS positive and VS negative training datasets on the test dataset. The results showed that, it is better to use the FL training dataset, and if the numbers of positive and negative texts are going to be unequal, the ratio number of one class to the other should be very small. We can conclusively say that, the wider the ratio the less accurate results, and the narrower the ratio, the more accurate the results.
15:40-15:55
Lost-Min Voting Strategies for Speeding up Multi-SVMs Shinq-Jen Wu, Van-Hung Pham Da-Yeh University, Taiwan Support vector machines (SVMs) possess good accuracy in big data classification. However, the computational cost in both training and testing stages is a critical issue.
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The authors recently proposed a two-phase sequential minimal optimization to largely reduce the training cost (tested with 3186–70,000-sample datasets). The authors now focus on speeding up the testing speed of SVMs for multi-class classification. A lost-min strategy is proposed to accelerate the voting algorithm used in multi-SVMs. The number of the used binary classifiers is reduced from an order of to (nearly to ). The proposed lost-min voting strategy was tested with DNA dataset (bioinformatics), Usps datasets (handwritten digits), Letter dataset (English alphabet) and Satimage dataset (satellite imagery of Earth). The time complexity for all of the datasets approaches to algorithm and the accuracy is remained at the same time.
15:55-16:10
Implementation and Improvement of Solar Power Data Monitoring and Sharing Platform based on IPv6 Guojing Zhang, Xiaoying Wang, Yuling Li Qinghai University, China As the application of photovoltaic power station system becomes more and more widely, research and implementation of the photovoltaic power station data display and sharing platform plays a significant role for related research. In this paper, we designed, implemented and improved the IPv6-based solar data monitoring and sharing platform. In this system, the function of real-time data updating was realized and improved. To improve the original system, we added the functions of data processing and statistical analysis, and modified the data display interface and user management functions. In order to establish the IPv6 photovoltaic power station data sharing and display platform, we used the B/S architecture to present and display the real-time data collected by the solar power station in the form of Web to realize data sharing. The system could provide online data sharing supporting IPv6 protocol through functional modules such as remote data synchronization and foreground asynchronous display.
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Sunday, 12 May, 14:40-16:10
Session 2: Big data analytics Room: Meeting Room 2
Chair: Prof. Vladimir Khryashchev,
People’s Friendship University of Russia (RUDN University), Russia
14:40-14:55
Hybrid Graph Convolutional Networks for Semi-Supervised Classification Dongyang Bao, Wei Zheng, Wenxin Hu East China Normal University, China In recent years, Graph Convolutional Network (GCN) have been successfully applied to many graph classification problems. It has the capability to learn many types data that Convolutional Neural Networks (CNN) cannot handle, such as irregular data. However, we found that GCN can not completely capture the graph structure information and especially for inference on data efficiently. In this paper, we analyze the advantages and disadvantages of several models and propose two different methods of combining models. Based on that, we propose a new model by using ensemble learning Based on GCN. This model has the ability to capture the advantages of multiple models. Finally, we conduct our experiment on several datasets, and the experimental results show that our approach is effective.
14:55-15:10
Deep Learning Approach for Identifying Emotions in Video Tests Lenin Kahanga, Wang Yan Harbin Engineering University, China This paper proposes a novel deep learning framework to identify student emotions or affective states in IELTS speaking video tests. This approach has one unique characteristic, it extracts features from a face image and sends it to a deep neural network model for emotion classification. The main objective of this study is to find the correlation between the test takers emotion status and their test grades. This framework is evaluated with extensive experiments. The achieved results show promising performance based on the size of the data and computing resources. The outcomes of this work would add value to OEP systems.
15:10-15:25
A Novel Object Detection Algorithm in Video Lu Shengyu, Liu Junhao, Liu Wenxi, Wang Beizhan Xiamen University, China Deep learning technology performs effect in feature extraction of images. Nowadays, with the development of video monitoring, the application of deep learning technology to surveillance video has profound implications. The effects of traditional video recognition are not satisfactory, but deep learning methods perform effect in many scenes of image classification. This paper proposed a novel object detection algorithm in video. It combined the traditional methods of extracting feature and deep learning algorithm to realize vehicle identification based on surveillance video. The method used the frame difference method and background subtraction to preprocess the image, and then trained a network model based on YOLO to perform object detection and obtain the categories and location information of the monitored vehicle. Compared with the existing object detection algorithms faster RNN, our method can achieve higher accuracy and can significantly shorten the time for
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detection, which can recognize the object of vehicle video quickly and efficiently. The method can meet the requirements of real-time detection.
15:25-15:40
Deep Video Object Contour Extraction Using Fully Convolutional Network Die Li, Murong Jiang, Guocai Du, Yinghua Li, Chunna Zhao Yunnan University, China Complex scene object contour extraction problem has become an important topic in computer vision. To solve the problem, a deep fully convolutional neural network model is established in this paper. The model tackles the task of semi-supervised video contour extract. In our model, the interactive segmentation method and Mask-RCNN algorithm are respectively applied to the first frame of a video to obtain the target semantic information and segmentation mask. Then the binary object mask is processed by the edge detection algorithm to generate a object contour mask. Next the video and the first frame contour mask are input to network of One-Shot Video Object Segmentation algorithm, and contour features and semantic information of the object are learned by the network. Finally the contour semantic information is automatically passed to subsequent frames, and the contours that particular objects in each frame of video are extracted. Experiments show that this model can detect and locate one or more objects quickly and accurately in a video sequence for various complex scene. Compared with the general edge detection operator, our algorithm does not need to extract redundant background edges and texture details and can be better applied to pose estimation and target recognition.
15:40-15:55
Aviation Surveillance Information Fusion Technology Based on Recurrent Neural Network Zhanchun Gao, Anyu Song Beijing University of Posts and Telecommunications, China Aviation surveillance information fusion is aimed at merging the detection data from multiple sources of the same target aircraft to obtain more accurate monitoring information, including aircraft position, heading, acceleration and other information. The traditional Kalman filter-based fusion technology has shortcomings, such as poor integration in the maneuvering state, and it takes a lot of manpower and material resources to repeat the adjustment. Therefore, this paper uses the recurrent neural network to conduct the experiment of aviation surveillance information fusion. Firstly, the recurrent neural network is used to identify the maneuver state of the aircraft, and the weighted least squares method is used to predict the position of the aircraft according to the maneuvering state, so as to obtain the monitoring information of each radar at the same time. After that, the recurrent neural network model is used to fuse the monitoring information of multiple radars. The experimental results show that the maneuvering state discriminant model based on recurrent neural network can effectively identify the maneuvering state. The least square method based on maneuvering state can accurately predict the position of the aircraft. The aeronautical surveillance information fusion model based on recurrent neural network can also obtain more accurate fusion results. The whole process includes four parts: preprocessing, maneuver status discrimination, position prediction and information fusion. The total time is about 500ms.
15:55-16:10
Application of Satellite Image Segmentation for Urban Planning Optimization Vladimir Khryashchev, Leonid Ivanovsky, Anna Ostrovskaya, Alexander Semenov
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People’s Friendship University of Russia (RUDN University), Russia This article presents research results of a convolution neural network for building detection on high-resolution aerial images of Planet database. Jaccard index was used for analysis of the quality of machine learning algorithm. This index of similarity compares results of algorithms with real masks. The masks were sliced on smaller parts together with images before training of developed model. The convolution neural network was launched on NVIDIA DGX-1 supercomputer, which was provided by AI-center of P.G Demidov Yaroslavl State University. The problem of building detection on satellite images can be put into practice for urban planning, building control, search of the best locations for outlets etc.
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Sunday, 12 May, 16:30-18:00
Session 3: Data mining Room: Meeting Room 1
Chair: Dr. Cheng Fang, Red Hat, Inc., USA
16:30-16:45
Effective Java Batch Processing with Jberet Cheng Fang Red Hat, Inc., United States Batch processing has played an important role in the history of enterprise computing. As we enter the era of big data and cloud computing, it is increasingly important to adapt batch processing to modern application architecture and development process. This session will cover rationale for Java batch processing, key concepts and standards used in designing batch applications. We will examine the specification for Java batch processing, JSR-352, including batch job specification language, batch application programming interface, and runtime behavior and lifecycle of batch application. It will focus on project JBeret as the leading open source framework for developing standard-based, portable and extensible batch applications, and for enabling job executions in a variety of runtime environment such as Java SE, WildFly Java EE application server and OpenShift cloud platform. At the end of the session, audience will gain a better understanding of Java batch processing programming model, and well equipped to develop programs to solve read-world batch processing needs.
16:45-17:00
Abnormal Detection of User Behavior in Online Banking Yuan Wang, Liming Wang, Wei An Chinese Academy of Sciences, China Abnormal detection is very important in online banking security. One of the most difficult issues in abnormal detection is how to calculate the distance between data samples. After the analysis of user behavior of online banking, we propose a mixed method based on Euclidean distance and cosine similarity, to measure the similarity among user behaviors. This paper develops an approach to catch similar behaviors to the abnormal behaviors in online banking transactions, by using the mixed similarity measurement. Experiment results show that our method can improve the performance of abnormal detection on the underground dataset, comparing to Euclidean distance and cosine similarity.
17:00-17:15
Lung Nodule Classification Algorithm Based on Fusion Features Lu Shengyu Xiamen University, China Lung nodules are the lesion areas of lung, and malignant lung nodules may led to lung cancer. Nowadays, computer-aided diagnosis based on CT images are important for lung cancer. However, the existing methods of feature extraction of CT image did not perform effect. This paper proposes the DSS algorithm for lung nodules classification based on fusion features. The DSS adopts the local-global model to extract the depth features of images, and jointly uses the shape descriptors based on medical knowledge. Besides, it combines the fusion features into the Support Vector Machine (SVM) for lung nodule classification. This paper has evaluated the DSS algorithm on LIDC-IDRI data set and the method performs effect for lung nodules classification.
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17:15-17:30
Deep Learning for Stock Market Prediction Using Social Media and Technical Information Di Wu, Jianhua Cao Dalian University of Technology, China In recent years, the stock market has played an increasingly important role and attracted more and more attention. However, the complexity of the stock market makes stock prediction facing a considerable challenge. Many studies found that investor sentiment and stock technical indicators have a secure connection with the stock market movement. Also, in recent studies, deep learning has been widely used in time series forecasting and natural language processing, making it possible to predict stock markets successfully. In this paper, we apply the two-layer bidirectional long short-term Memory networks(Bi-LSTM) model based on glove word embedding and attention mechanism to extract stock sentiment indicators from social media, and use decision tree (DT) and principal component analysis(PCA) integrated model to extract stock technical indicators; then, these indicators apply to the LSTM model to forecast the US stock market movement. The experimental results show that our proposed method can significantly improve the accuracy of the stock market forecast.
17:30-17:45
A Cloud-based Storage and Retrieval Solution for Rdf Data Sun Yuxiang, Yongju Lee Kyungpook National University, South Korea In recent years, RDF (Resource Description Framework) has been widely recognized as a standard data storage format. There is a real issue that how to store and retrieve RDF data efficiently, because it is the foundation of all semantic-based application development. In this paper, we propose a novel storage and retrieval solution based on cloud and R*-tree. Different to existing approaches, we use cloud-based storage approach to compress RDF data, meanwhile R*-tree is adapted to retrieve compressed data. The advantage is that it not only reduces the local storage pressure but also improves the retrieval performance compare to existing approaches. Because we separate the storage and retrieval and adapt dictionary-based compression approaches, in terms of security and flexibility, our solution is better than existing approaches.
17:45-18:00
Multi-Objective Optimization Recommendation Algorithm Based on Collaborative Filtering and Item Similarity Chaosheng Zhao Chongqing University, China The traditional collaborative filtering recommendation algorithm generates recommendations for users by calculating the similarity between users or items, without considering other factors and data sparsity problem. The appearance of personalized tags can reflect the characteristics of items. Some algorithms try to optimize the recommendation results by setting weight coefficients of various factors. However, it is unreasonable to set the same weight coefficients for different users. To solve these problems, we propose a multi-objective optimization recommendation algorithm based on collaborative filtering and item similarity. First. The algorithm calculates the similarity between users by improved user-based collaborative filtering recommendation algorithm and generates a candidate recommendation set. Then, the algorithm uses the coupled object similarity measure method to calculate the similarity between items. Finally, we use NNIA algorithm to optimize the former two goals and generate the final recommendation results. Experimental results on the Movielens
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dataset shows that the proposed algorithm has good recommendation effect.
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Sunday, 12 May, 16:30-18:00
Session 4: Applications for big data Room: Meeting Room 2
Chair:
16:30-16:45
Sequential Recommendation with Recurrent Convolutional Model Shiyu Peng, Jiaxing Song, Weidong Liu Tsinghua University, China Personalized sequential recommendation refers to making rec-ommendation based on users’ historical consumption behaviors. Most works based on RNN only model long-term patterns, which fail to capture skip behaviors. Contrarily, the CNN-based model whose target is to handle this problem can only leverage part of sequential behaviors and ignores global patterns, which limits its performance. In this paper, we propose a Recurrent Convolutional Recommendation Model (RCRM) to simultaneously catch global and local patterns. Specifically, we employ a recurrent layer to capture global patterns and a convolutional layer to extract local patterns. An attention mechanism is then introduced to generate the final attentive local pattern, which can further concatenate with global patterns to predict next item. We conduct extensive experiments on two benchmark datasets and the results demonstrate that RCRM outperforms state-of-the-art baselines by a large margin over a variety of common evaluation metrics.
16:45-17:00
Kidding Bot: a Chatbot Against Harassing Phone Calls Shihong Chen, Tianjiao Xu, Lu Chen Guangdong University of Foreign Studies, China Nowadays, the majority of mobile phone users suffer from harassment calls. However, traditional way to intercept the harassment calls cannot reduce this annoying behavior. In this paper, we designed and developed Kidding bot: a chatbot to counterwork the harassment calls. When the user received the harassment calls, he could connect our Kidding bot server by call forwarding, then Kidding bot starts to talk with the harassers. Kidding bot is combined with Turing chatbot and a dual encoder specially added in sequence to sequence model. The anti-harassment telephone test result shows that Kidding bot based our model performed better than the two baselines: a single Turing chatbot and a chatbot based on Sequence-to-Sequence model.
17:00-17:15
Semi-supervised Chinese Named Entity Recognition with ELMo Su Zhang East China Normal University, China Named entity recognition is a subtask of information extraction. In general, the task of named entity recognition is to identify three main categories, including entity, time and numeric class. In Chinese named entity recognition, ambiguity and out-of-vocabulary often occurs as tricky problems, but traditional character-based and word-based model do not fix it. In this paper, we propose a semi-supervised approach by taking pre-trained embeddings from language models (ELMo) as additional embedding of word embedding. Our method could catch deep contextualized word representation, which is capable to represent lexical ambiguity in different contexts and complexity of vocabulary usage, such as grammar and semantics, by this way we are capable to
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identify more precise content and label the word with limited labeling data. Experiments on MSRA show that our model outperforms both word-based and character-based LSTM baselines, achieving the best results.
17:15-17:30
Research on Network Public Opinion Detection Based on Improved TF-IDF Algorithm Lu Peng, Zongfeng Qin Wuhan University of Science and Technology, China TF-IDF algorithm is a widely used text feature weighting technology. The core idea of TF-IDF algorithm is as follows: In a corpus, if a participle appears frequently in a certain text and appears less in other texts, then it proves that the participle has a good feature of expression to this text. Although this idea is very simple, it also faces some problems in practical applications. Because it blindly increased the importance of uncommon words in the text and this blindness will also appear in the field of public opinion monitoring. In order to solve the mentioned problem, this thesis has done the following work: l Introduce the lexical weight coefficient of the characteristic word into TF-IDF; l Introduce the word position weight (span weight) coefficient into TF-IDF. The experiment proves that the improved TF-IDF method highlights the importance of text feature words and facilitates classification. Furthermore, the improved method is applied to the public opinion analysis system and got good results.
17:30-17:45
Flower Pollination Algorithm and Multilayer Perceptron Artificial Neural Network for Heart Disease Feature Selection and Classification Nasiru Muhammad Dankolo, Danlami Gabi, Nor Haizan Mohamed Radzi, Noorfa Haszlinna Mustaffa, Roselina Sallehudden Kebbi State Universty of Science & Technology, Nigeria Heart disease or scientifically known as cardiovascular disease (CVD) is a disease that involves the heart or blood vessels. There are different types of heat diseases and their causes, however the most common one is myocardial infection commonly refers to as heart attack. There are many reasons for heart attack that may be avoidable such as lack of physical fitness and obesity but the unavoidable one is genetic reason. To avoid the serious effect of heart attack and lower the danger of heart failure to patients, early detection of myocardial infection is necessary. Machine learning algorithms such as classification are used in early detection of dieses using historic medical data. Many algorithms are developed for early detection of heart disease, however, because myocardial infection data consists of many features which some of them may not be important to the analysis, there is need to try different alternatives and techniques to come up with the best detection algorithm. In this paper, we proposed flower pollination algorithm and Multilayer perceptron (MLP) Artificial Neural Network (ANN) for feature selection and prediction of myocardial infection. We called this algorithm FPA-ANN. The simulation results of this paper show that FPA-ANN is promising in correct prediction of myocardial infection with 84.2% accuracy.
17:45-18:00
Effect of Machining parameters and Tool shape on Entrance and Exit Hole Burr Characterise in Drilling of Ti-6Al-4V Quanli Han, Dongchen, Hongqiang Wan Xi'an Technological University, China Drilling is a key removal operation as it occupies about 50% of the total materials taken away in the aircraft frame industry. Drilling difficult-to-cut materials, like Ti-6AL-4V, is
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still a time-consuming challenge due to its physical and chemical characteristics. Besides the reason above, post-operation as de-burring becomes the indispensable process after drilling. However, the knowledge of burr formation characteristics is deficiency as well as tool manufacturing. To overcome such problems, detailed comparative experiments of drilling hole were conducted with new and traditional tools in various processing parameters, in the terms of following index such as bur height, bur width, and bur volume. The current study indicts, firstly, Hole burr characteristics at exit is bigger than that at entrance, in terms of burr height , width and volume; secondly, compared with traditional tools, new designed tool can decrease burr width and height, resulting into reduction of burr volume. and last but not least, The promising result of new designed tool application is to get the more reduction of the burr width than that of burr height, which decrease in the burr volume determining less working hours to de-burr hole edge. The above finding suggests new tool designed can be a promising future of de-burring in practical application.
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Research on Knowledge Management Technology of Aerospace Engineering Based on Big Data
Jun Liu Beijing Shenzhou Aerospace Software Technology Co., Ltd., China In the era of big data, mass production, analysis and application of data have become a new trend. In the long-term design, production, operation and testing process of aerospace enterprises, a large number of valuable data have been generated. Collection and analysis of these data can improve the management of aerospace enterprises and gain competitive advantages. With the increase of semi-structured and unstructured data produced by aerospace enterprises year by year, how to store and analyze data, how to mine and share knowledge has become a major problem. The existing knowledge management system can not meet the diversified needs of users only by traditional database technology. It also needs to combine distributed computing and storage technology to solve the problems of knowledge storage, knowledge sharing, knowledge mining, knowledge retrieval and recommendation in big data environment. Aerospace enterprises need to build a knowledge management system based on big data technology to support knowledge innovation and knowledge application. From the perspective of data operation and relying on Hadoop ecosystem related big data technology, this paper constructs a knowledge management framework model for aerospace enterprises based on Hadoop.
Digital NBC Protection System BIT Optimization Design Li Guangsheng, Ou Bo, Wang Jianyuan Academy of Army Armored Forces, China BIT technology is an important means of system and equipment fault detection, location and isolation. This paper takes the digital three defense system as the research object, and carries out research on the optimization design based on BIT technology. Based on the structural division of the digital three defense systems and the analysis of typical failure modes, combined with the weighted cost index function and the selection method of the improved special hierarchically optimized optimal test set, the calculation and comparison of the indices of the actual digitalized three defense systems was conducted. Determine the optimal test set, and finally design the BIT circuit for the selected optimal test set.
A Dynamic Integrated Classification Algorithm Based on Big Data Environment Dan Ma, Ji-Chun Jiang and Wei Wang Gui Zhou University, China With the developing of big data application, classification algorithm has been expanded to distributed datasets from the single dataset. So a dynamic integrated classification algorithm based on big data environment was proposed. This algorithm gain integrated classifiers of high classification accuracy for each local dataset, and dynamically generate the recognition model according to the distribution characteristics of local samples to be tested. In the application process, after numerous new sample data join the datasets, the classifier performance will drop gradually. By aiming at the above problem, this algorithm will retrain the classification model in the dynamic expansion process of datasets. According to the experimental results, the algorithm proposed in this paper has high classifier training performance and classification accuracy. At the same time, it also possesses high adaptive capacity when faced with dynamically changing distributed datasets.
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Listeners
IMI, Israel
Kanyakit Keerati-angkoon
Srinakharinwirot University, Thailand
Anna Ostrovskaya
People’s Friendship University of Russia (RUDN University), Russia
Marise Rosenfeld
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Name ID Session Page
A
Anyu Song DA1040 2 23
C
Chaosheng Zhao DA1030 3 26
Cheng Fang DA11001-A 3 25
D
Die Li DA1016 2 23
Dongyang Bao DA1053 2 22
G
Guojing Zhang DA1036 1 21
J
Jianhua Cao DA1015 3 26
L
Lan Yang DA1011-A 1 19
Lenin Kahanga DA1039 2 22
Lu Peng DA1057 4 29
Lu Shengyu DA1012 &
DA1013 2&3 22&25
N
Nasiru
Muhammad
Dankolo
DA1059 4 29
Quanli Han DA2011 4 29
S
Saad Ibrahim
Amaya DA1047 1 20
Shiqi Li DA1046 1 20
Shinq-Jen Wu DA1001 1 20
Shihong Chen DA1042 4 28
Shiyu Peng DA1006 4 28
Su Zhang DA1052 4 28
Sun Yuxiang DA1028 3 26
V
Vladimir
Khryashchev DA1056 2 23
Y
Yuan Wang DA1054 &
DA1007 1&3 19&25
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Attention:
This tour will charge 100USD fro each person. (Pay to join before May 11, 2019, or you could choose to
enjoy free time on May 13 to explore Shanghai yourself.
8:30 AM (May 13), pick up at gathering sport.
Please be there on time, or you will miss the tour without no refound.
Time: Destination
8:40-10:40 The Oriental Pearl Tower
东方明珠 2 号口（地铁 2 号线陆家嘴站下 2 号口出）集合
10:40-11:30 Huang Pu River (taking boat)
黄浦江游船
12:30-13:30 Shanghai Municipal History Museum
上海城市历史发展陈列馆
13:30-14:30 The Jade Buddha Temple
上海城隍庙
14:30-15:30 The Shanghai Museum
上海博物馆
Back to the hotel youself.
Service included:
Tour guide
Tips for tour guide and driver
Ticket s
Service excluded:
Personal expenses (not mentioned above)
Lunch
If you want to join in us, please contact the conference secretary.
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Top 10 View Points in
Shanghai
1. Shanghai's Promenade: The Bund
Best known by its Anglo-Indian name of Bund (Wàitan), the Zhongshan Lu is a lovely broad promenade
running along the west bank of the Huangpujiang River. It's particularly popular among tourists as the area
has retained a European feel (it was once the location of the city's International Settlement) that is
particularly noticeable in the many old English and French buildings now serving as restaurants, boutique
stores, galleries, and offices. Always bustling, it's a splendid place for a stroll day or night as you take in the
Bund's 52 unique buildings constructed in a variety of styles including Gothic, Romanesque, Baroque,
Neoclassical, and Renaissance influences, along with what amounts to one of the world's most impressive
collections of Art Deco architecture.
Moving from south to north, the dominant buildings are the former headquarters of the Hong Kong and
Shanghai Banking Corporation with its splendid cupola, the harbor customs office with its bell tower, the old
Peace Hotel, and the Bank of China. Huangpu Park, located at the north end of The Bund, opened in 1886
and is famous as the country's oldest public park. It's a pleasant place to visit, and has an interesting
museum dedicated to The Bund's history. The Bund is also a great place from which to embark upon a
sightseeing tour aboard a boat around the port and the confluence of the Huangpujiang and Yangtze rivers.
2. Yu Garden
To the northeast of the old town and laid out in 1559, the splendid Yu Garden (Yù Yuán), also known as the
Garden of Happiness, covers an area of more than 20,000 square meters and consists of an outer and an
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inner garden. The oldest section is the Outer Garden, with further changes being made in the 18th century
when Sansui Tang, the park's main hall, was added (the building is notable for its lovely roof ornaments,
figurative representations in bas-reliefs, and window openings, as well as its dragon-adorned walls).
The best-known building is the Hall of Spring where the Company of the Little Swords (Xiaodao Hui) had its
headquarters between 1853 and 1855 when it ruled Shanghai. Of great historical importance are the
artificial rocks in this part of the garden, the only work of the master garden designer Zhang Nanyang that
has been preserved. The newer and much smaller Inner Garden dates from 1709 and includes features
typical of a classical Chinese writer's garden: attractive little pavilions, decorative stones and miniature
mountain ranges, dividing walls and small ponds, and even a richly decorated theatrical stage.
3. The Jade Buddha Temple
In the Anyuan Lu district of Shanghai, the beautiful Jade Buddha Temple houses two Shakyamuni statues,
which the monk Huigen brought with him from Burma. The present building, erected in 1928 to replace the
original temple built in 1882, is divided into three halls and two courtyards and includes the splendid Hall
of the Kings of Heaven (Tian Wang Dian), notable for its statues of the four heavenly kings and two
Shakyamuni sculptures. Carved from white jade, one of these impressive statues stands nearly two meters
high in the Wentang Main hall, where a collection of Buddhist manuscripts is also kept (the smaller statue is
in the west courtyard). Also of interest is the charming Hall of the Great Hero (Daxiong Baodian) with its
Buddhas of the Three Ages, along with 18 Luohan figures. Another of Shanghai's many important Buddhist
sites is the stunning Jing'an Temple on Nanjing West Road.
4. The Shanghai Museum
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Founded in 1952, the Shanghai Museum remains China's most important museum of classical Chinese art.
In a modern building that's something of a work of art itself - its unique round top and square base
encompasses traditional Chinese concepts of the earth - the museum's four floors include impressive
displays of bronzes and ceramics from prehistoric cultures to the 19th century, ink drawings, calligraphy
and seals, as well as large collections of art from ethnic minorities. It's also home to large collections of jade,
coins, furnishings from the Ming and Qing periods (1368-1912), and a well-stocked gift shop.
5. Longhua Temple and Pagoda
In a pleasant park in the southwest area of Shanghai, the splendid Longhua Temple remains one of the
oldest religious sites in China. Built along with the nearby 40-meter-tall wood and brick pagoda around AD
242, this important place of worship was destroyed and rebuilt many times through the years, with the
present structure dating back to the 10th century. The site is still used for regular Buddhist ceremonies and
consists of five large halls, including the Maitreya Hall (Mile Dian), with its large Buddha statue; the
Heavenly King Hall (Tian Wang Dian), dedicated to the Four Heavenly Kings; and the Grand Hall of the Great
Sage (Daxiong Baodian), with its fine statues and a 16th-century bell. Other highlights include the Bell
Tower with an even older, two-meter-tall, five-ton bell from 1382, which is still used on special occasions;
the Library with its old manuscripts and ceremonial instruments; and the impressive sight of some 500
gold-painted Luohan Buddhas.
6. The Oriental Pearl Tower
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A must-visit while in Shanghai is the 468-meter-tall Oriental Pearl Radio and TV Tower (Dongfang
Míngzhuta) in Pudong-Park on the east bank of the Huangpu River. In addition to its excellent views over the
busy river and the new city, you'll be rewarded with superb views over the historic Bund promenade. Built
in 1991, the tower takes its name from its 11 linked spheres of various sizes, the highest of which - the
Space Module - contains an observation level at the 350 meter mark with a glass-floored outside deck. All
told, the tower boasts 15 viewing areas, including the Sightseeing Floor and Space City, as well as a revolving
restaurant with great views. Other highlights include a lower level shopping mall, the Space Hotel offering
rooms with spectacular views, and a fun virtual reality rollercoaster ride. Even if you can't make it up the
tower, you'll enjoy viewing it at night when the whole structure is lit up as part of a fascinating light show.
7. Shop 'til You Drop on Nanjing Road
Nanjing Road (Nánjing Lù), Shanghai's principal shopping street, was constructed in the second half of the
19th century and runs from the Zhongshan Lu for several miles towards the west. Along this largely
pedestrian-friendly street, you'll find every conceivable type of consumer good, from street vendors selling
Chinese-themed souvenirs to expensive boutiques selling traditional arts and crafts, as well as a number of
large shopping malls and department stores such as the iconic Yibai and Jiubai. It's also a busy
entertainment district, home to many restaurants and cinemas, as well as a hub for street performances (it's
especially fun to visit during major holidays such as Chinese New Year when the street becomes a focal
point for festivities and fireworks). Another dedicated shopping area to explore is Xintiandi, an affluent
pedestrian zone that retains some of the ambience of the old city.
8. People's Square
Built on what was once the city's racecourse, the People's Square (Rénmín Guangchang) has been
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transformed over the years into Shanghai's premier public space. Home to the new Shanghai City Hall,
the Shanghai Museum,and the state-of-the-art Grand Theatre, it's a perfect spot from which to begin touring
the city. Be sure to spend time visiting the excellent Shanghai Urban Planning Exhibition Center, where you'll
find superb displays and models - even a 360-degree movie theater - showing both existing and planned-for
buildings (be sure to view this massive scale-model from the upper galleries for a fascinating bird's-eye
perspective of this modern metropolis).
9. Xujiahui Cathedral and the Sheshan Basilica
Built in 1911 in Neo-Romanesque style, Xujiahui Cathedral - also known as St. Ignatius Cathedral - is
another splendid reminder of Shanghai's rich multi-national heritage. In the southern city district of
Xujiahui, it's the largest place of Roman Catholic worship in Shanghai, and in addition to its splendid
park-like setting is worth visiting for its twin 50-meter-high bell-towers and restored interior with fine
stained glass windows. Another important religious site is the Sheshan Basilica (the National Shrine and
Minor Basilica of Our Lady of Sheshan). This fine old Roman Catholic church stands on the western peak of
the hill after which it's named. Like so many other religious sites, it was heavily damaged during the Chinese
Cultural Revolution but in recent years has undergone extensive renovations and remains an important
pilgrimage site. A highlight of a visit is following the 14 Stations of the Cross, which zigzag up the hill to the
church, along with the many splendid views along the way.
10. Shanghai Disney Resort
Shanghai Disney Resort, China's second Disney venture after Hong Kong Disneyland Resort, opened to great
fanfare and massive crowds in 2016 and shows every sign of being a massive success. In addition to its two
themed hotels and the Disneytown entertainment and shopping district, this nearly 1,000-acre site in the
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city's Pudong district is home to the Shanghai Disneyland Park, the hub of all the action and the real reason
some 10 million people visit the resort each year. And it's every inch the kind of Disney experience fans and
families alike can't seem to get enough of. The fun starts on Mickey Avenue, with its character meet and
greets and merchandise-cum-souvenir shops, before guests head off to their favorite part of the park,
whether it be Gardens of Imagination, with its pleasant Chinese gardens and Dumbo carousel; Fantasyland,
in many ways the parks "heart and soul" as it's here you'll find the Enchanted Storybook Castle... and hordes
of mini wannabe princesses waiting to catch a glimpse of their favorite Disney royalty; or Treasure Cove,
home to a thrilling Pirates of the Caribbean-inspired ride.
Official Site: https://www.shanghaidisneyresort.com/en/
*Part of the local information and entrance fee above comes from the network.
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MEMO
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