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Tutorial 1

Hands-on Deep Learning for Industrial Informatics Applications

Brief description:

Deep learning is gradually becoming a mature artificial intelligence paradigm in both research and practice. Supported by a substantial evidence base, it demonstrates increasing potential for applications in the Industrial Informatics of automation, energy, manufacturing, transport, communication and human engagement. This workshop aims to develop essential knowledge of deep learning with hands-on tutorials in Python, using Google Collaboratory, Jupyter Notebooks and Visual Studio Code. The workshop will begin by exploring the structural elements of deep learning models, hyper-parameters, and comparison to standard machine learning algorithms, followed by the theory and application of deep neural networks (classification), convolutional neural networks (image processing), and recurrent neural networks (time-series prediction). Participants will conduct hands-on experiments of each technique using benchmark and real datasets, for training, testing and evaluation. Each technique will be demonstrated in the context of real-world projects in Industrial Informatics. The learning outcomes of this workshop are; the theoretical foundations of deep learning - when to use and in which settings, the design and development of deep learning models, rapid prototyping, evaluation and deployment using Python.

Participants will access Google Collaboratory using a Gmail account, a laptop and a stable Internet connection will be essential.


Daswin De Silva, Rashmika Nawaratne, Achini Adikari

Centre for Data Analytics and Cognition (CDAC)
La Trobe University, Australia

Daswin is Artificial Intelligence and Technology Platforms specialist in the Research Centre for Data Analytics and Cognition (CDAC) at La Trobe University, Australia. Daswin’s research interests are incremental machine learning, information fusion, deep learning, auto ML, with applications in energy, smart cities, and human emotions. He’s an associate editor of the IEEE Transactions of Industrial Informatics.
Rashmika and Achini are Technical Leads in the same Research Centre (CDAC). Rashmika leads the image, video analysis capability with applications in transport while Achini leads the human sentiment and emotions analysis with applications in digital health and social media. Besides academic pursuits, as part of CDAC strategic initiatives, all three presenters are actively involved in industry engagement, solving real-world AI problems and working with both analytics technology providers and consultants.

Tutorial 2

Machine Intelligence for Human-oriented Computing: From Deep Learning-based Video Processing to System Optimization

Brief description:

Recent years have witnessed unprecedented development of technologies in Artificial Intelligence (AI), Internet of Things (IoT), and cloud computing under the globally networking environment. Advanced video processing techniques play a central role in many real-world industrial systems, such as traffic surveillance, robot navigation, Unmanned Aerial Vehicles (UAV) patrols, automated electricity system inspection, etc. Efficient video sensing and computing on industrial applications can not only greatly reduce the consumption of labor and resources, but also guarantee the safety of operations in certain traditional industries. With high popularity of some video processing techniques such as object detection, localization, classification, clustering, scene understanding methods, many industrial engineering problems can now be solved effectively using data-driven machine learning approaches, especially the use of deep learning methods. But industrial systems have also become increasingly complex and there is an increasing interest and need from researchers and industrial engineers to solve more challenging and complex problems using more advanced machine learning concept. The proposed tutorial is aimed at introducing the most important machine learning techniques that are particular useful to industrial informatics engineers and researchers. The techniques that will be covered in the tutorial are the state-of–the-art and are still ongoing hot research topics. This tutorial is designed in a way to bridge the areas of industrial informatics and machine learning.


Haijun Zhang


Department of Computer Science, Harbin Institute of Technology, Shenzhen, P. R. China, Email:

Haijun Zhang received the B.Eng. and Master’s degrees from Northeastern University, Shenyang, China, and the Ph.D. degree from the Department of electronic Engineering, City University of Hong Kong, Hong Kong, in 2004, 2007, and 2010, respectively. He was a Post-Doctoral Research Fellow with the Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada, from 2010 to 2011. Since 2012, he has been with the Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China, where he is currently a Professor of Computer Science. His current research interests include multimedia data mining, neural networks, machine learning, system optimization, and computational advertising. He is an author and co-author of over 60 international technical Journal articles related to his research, including IEEE Trans. on Industrial Informatics, IEEE Trans. on Neural Networks, IEEE Trans. on Cybernetics, etc. Under the Google scholar, his works have received a citation of over 1,400 and an H index of 21. He is now an Associate Editor of the Neurocomputing, and Neural Computing & Applications, and Pattern Analysis and Applications.

Tommy W. S. Chow


Department of Electronic Engineering, City University of Hong Kong, Hong Kong, Email:

Tommy W. S. Chow is currently a Professor in the Department of Electronic Engineering at the City University of Hong Kong. He is an IEEE Fellow. His main research areas include Neural Network, machine learning, fault diagnosis and documents analysis. He is an author and co-author of over 180 international technical Journal articles related to his research, 5 book chapters, and 1 book. Under the Google scholar, his works have received a citation of over 5,900 and an H index of 39. He was the Guest Editor of Neural Computing & Applications on the 2010 Special Issue on “The Emerging Applications of Neural Networks.” He received the Best Paper Award in 2002 IEEE Industrial Electronics Society Annual meeting in Seville, Spain. He is now an Associate Editor of the IEEE Transactions on Industrial Informatics, IEEE Transactions on Neural Networks and Learning Systems, and Neural Processing Letters.

Tutorial 3

Energy-Efficient Train Operations and Its Applications: Boosting Energy Efficiency of Future Urban Electrified Transportation in Smart Cities

Brief Description:

This tutorial aims to offer a broad introduction on important methods for improving train operations efficiency using speed trajectory optimizations and other integrated energy management strategies. Based on Pontryagin’s Maximum Principle, in speed trajectory optimization problem can be solved by generating a series of optimal train operations, namely maximum acceleration, maximum braking, cruising and coasting. Different methods have been proposed to locate the series of operations to achieve the minimum traction energy as shown in Fig. 1. These methods include graphic-based search methods (Dynamic programming and A* search), optimal train control methods including both direct methods and indirect methods, mathematical programming and other heuristics and enhancement learning methods. This tutorial will provide a thorough introduction on these methods with an emphasis on the characteristics of each methods and the application of each methods on train energy efficient operations. Merging development of the study on train energy-efficient operation for emerging traction systems such as maglev train, fuel-cell train, train operation integrated with on-board energy storage, complex network applications for network-wide timetabling will be reviewed and discussed. This tutorial will touch on the topic of future development for electrified urban transportations including the fast-developing electric

vehicles and urban railway systems. A summary and conclusions will be drawn towards the end of the tutorial.


Shaofeng Lu


Associate Professor, Shien-ming Wu School of Intelligient Engineering, South China University of Technology

Shaofeng Lu is currently an associate professor with the Shien-Ming Wu School of Intelligent Engineering, South China University of Technology. He was an Associate Professor with Department of Electrical and Electronic Engineering, Xi'an Jiaotong- Liverpool University (XJTLU) from Sep. 2018 to Aug. 2019. From Sep. 2013 to Sep. 2018, he was a lecturer with the same department at XJTLU. From Sep. 2012 to Sep. 2013, he was a Research Fellow with the Energy Research Institute at the Nanyang Technological University (ERI@N). From Feb. 2011 to Aug. 2012, he was the Facilities Manager for Energy Integration System Laboratory, the University of Birmingham, UK.

Shaofeng Lu received the BEng degree with 1st Class Honours and PhD degree with the "School Best PhD Prize" from the University of Birmingham in 2007 and 2011 respectively. He also has a BEng degree from Huazhong University of Science and Technology (HUST), Wuhan, China. All are in Electrical and Electronic Engineering.

Shaofeng Lu was listed in ”2017 Suzhou Industry Park High-level Talents Salary Subsidy Programme, Suzhou Industry Park” and “2014 Jiangsu PhD Holder Plan – Oversea Prestigious University Innovation”. Since 2018, he has been an adjunct academic staff with Jiangxi University of Science and Technology and a fellow of the Higher Education Academy. In 2018, He received the Certificate in Professional Studies from the University of Liverpool with a grade of “Pass with Distinction”. In 2016, his research proposal “Study on Integrated Energy-efficiency Optimization Model for Trains with On- board Energy Storage Devices” was granted by China NSFC – Young Scientist Fund. His research interests include energy-efficient train operations, electric vehicles management in smart grids, energy storage application and complex network theory applications etc.

For Participants of Tutorials

All registered authors can attend the tutorials free of charge. If you are not a registered author, you can attend the tutorial by registering for the first day of the conference.

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