May 2018 - Centre for IT-enabled Transformation (CITT) and IEEE NSW Computer Society Joint Research Seminar
Three topics will be presented:
Topic 1: Cloud Computing Adoption for E-Commerce in Developing Countries: Contributing Factors and Its Implication for Indonesia
Presenter: Mr. Fahrizal Budiono
Abstract: This study examines literature in cloud computing adoption for e-commerce in developing countries to understand its contributing factors, in particular their implications for Indonesia. Ten themes have been identified: business size and type, customer service improvement, security, economic value, infrastructure, business process improvement, cloud computing framework, regulatory framework, user acceptance, and stakeholders’ support. Among these ten themes, the infrastructure, security, stakeholders’ support, regulatory framework, user acceptance and business size/types themes are particularly relevant to Indonesia. The paper also presents efforts and projects that are currently in place, at the governmental level, that facilitate cloud computing adoption and e-commerce in Indonesia.
Topic 2: Large Scale Nonparametric Modelling for Dynamic Public Transit Accessibility
Presenter: Mr. Jianqing Wu
Abstract: Owing to the higher availability of different data sources, data fusion in intelligent transportation systems (ITS) has been promising yet very challenging, where machine learning based modelling and approaches are able to offer more comprehensive solutions. In this presentation, we will share an overview of the recent advances in Mobility as a Service (MaaS), including the basics of public transit accessibility, travel demand forecast and the related machine learning methods. The motivation behind the research is to improve the availability and accessibility of public or private (shared) transport to the community in demand. We also highlight the challenges in MaaS and discuss the large-scale nonparametric modelling for dynamic public transit accessibility, which might help the demand forecast and scheduling optimisation.
Topic 3: Machine Learning Based Product Quality and Cost Control
Presenter: Mr. Fucun Li
Abstract: Quality control is an important part of intelligent manufacturing system. It is also the most challenging part in the production of any enterprise. In the steel industry, the quality control of products is even more crucial, which is determined by the complex characteristics of the iron. The iron and steel industry needs to improve the production process, where each production line is continuously manufacturing, and the scale of each production line is relatively large. Therefore, when quality problems occur, the same batch of steel products will be reproduced. If all has similar quality problems it will incur fairly enormous economic losses. In this research, we explore how machine learning can be exploited to improve product quality in iron and steel manufacturing. With huge amount of data collected from real production line, extensive data analytics on complex parameters related to steel quality will be conducted in the three year PhD research.
Date and Time
- Date: 31 May 2018
- Time: 02:30 PM to 03:30 PM
- All times are Australia/Sydney
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