Collaborative Learning through Immersion Project (CLIP)

Synopsis

The aim of the proposed project is to establish a joint e-learning/MOOC platform to facilitate collaboration in teaching and learning for Hong Kong’s tertiary education sector and to adopt innovative pedagogies for enhancing the learning experience of students. The platform would also enable instructors to collect data on students’ learning patterns (in particular, students in Hong Kong) and perform learning analytics on how students learn. The platform will be set up using proven technology so that it would be operational in Summer 2015. Establishing such a platform would allow Hong Kong to position itself as a regional leader in the delivery of high-quality online/blended education.

Project Duration

1 Apr 2015 – 1 Sep 2018

Grant Type

TLF(UGC)


Syndromic Surveillance and Modeling for Infectious Disease(Completed)

Synopsis

The outbreaks of SARS and swine flu have exposed the need for early outbreak detection and effective disease-spread simulation analysis for health resource management under pandemic outbreaks. Current surveillance systems lack the ability to interrogate disparate data and diverse datasets and sources, and are inaccurate in predicting infectious disease outbreaks and spread trends. This research will develop a radically new "syndromic surveillance" approach to enable reliable data-oriented infectious disease forecasting, simulation, and risk analysis. We shall:

  • Develop advanced data-mining methods to understand and extract disease transmission dynamics and mechanisms based on multiple infectious disease data sources.
  • Develop syndromic surveillance methods for analyzing public health related data for early detection of infectious disease outbreaks.
  • Develop stochastic influenza simulation and health economics models for mimicking disease-spread and risk assessment.
  • Validate the proposed research models through simulated outbreaks, clinical experiments and field experiments, and medical data from previous pandemic periods.

Project Duration

1 Jun 2013 – 30 Nov 2018

Grant Type

CRF


Delivering 21st Century Healthcare in Hong Kong - Building a Quality-and-Efficiency Driven System - SEEM Team

Synopsis

Healthcare delivery worldwide has been fraught with high cost, low efficiency and poor quality of patient care service. Hong Kong is no exception, and the quality of service provided to patients has been far from exemplary. For instance, it is not uncommon that the waiting time for certain routine surgeries at public hospitals could be as long as 18 months. Further exacerbating the problem is the aging of the population: the number of people of age 65 and above is fast approaching 15% of the general population in Hong Kong, and this statistic is increasing by about one percentage point every year.

The objective of this project is to help develop a quality-and-efficiency driven healthcare delivery system in Hong Kong that is built upon data analytics and compatible to the Internet age. Our research plan calls for in-depth studies on problems in two topical areas:

  • hospital resource planning,
  • healthcare data analytics,

to address a common theme -- "better care at affordable cost." Novel features of the proposal include: addressing healthcare delivery in Hong Kong in the context of business services innovation, the focus on quality-and-efficiency driven strategies and systems-oriented solutions, and the emphasis on networked resources and human-centric characteristics in healthcare delivery.

Project Duration

1 Nov 2014 – 31 Oct 2019

Grant Type

TBRS


Safety, Reliability, and Disruption Management of High Speed Rail and Metro Systems

Synopsis

The project aims to extend the advantage of Hong Kong by establishing it as a center of expertise in the safety, reliability, and efficient management of complex networking systems.

In this project, the following deliverables are anticipated:

  • Create fundamental science and methodologies for rail safety, monitoring, control and management which will be documented in highly-regarded publications;
  • Guarantee safety through implementation of self-cognizant fault detection and PHM;
  • Achieve high quality HSR operation and metro engineering systems;
  • Develop credible and sustainable transport strategies and build up the global HSR image;
  • Establish consortium of international experts to provide training to industry and government.

Project Duration

1 Jan 2016 – 31 Dec 2020

Grant Type

TBRS


Integrated Modeling for Remaining Useful Life Prediction and System Health Management

Synopsis

Reliability assessment plays an important role in engineering design and maintenance management. Current methods for reliability assessment of complex systems have fundamental flaws, due to their inability to keep pace with new technologies, and to account for complex usage profiles. Failures of systems are common due to their complexity (e.g., airplane crashes caused by failed electronics, power grid shutdowns caused by failed sensors, and financial shut-downs due to failed servers). The impact of these failures to the society on safety, availability, and cost is staggering. Newly improved modeling techniques are needed for reliability and degradation assessment, fault diagnostics, and prognostics (the real-time prediction of reliability and the remaining useful life) of complex systems. The proposed research approach is a radically new approach, which focuses on effective and efficient reliability prognostics and system health management (PHM) for complex systems based on integration of failure time and degradation data, physics of failure knowledge, and the information on the actual field operational conditions of the systems. The proposed research methods can be applied to a wide range of complex systems, including electronic-rich systems, critical automotive components, and power systems. In particular, we will investigate its application to the rechargeable batteries, a widely used power sources in many electrical and electronic systems today. There is a strong industry demand both in Hong Kong and China to raise quality and reliability to a new international level.

The proposed PHM technologies and remaining useful life (RUL) estimation methods will meet this challenge. The main objective of this research is to develop an integrated modeling approach for accurately predicting system health that helps engineers and scientists understand and quantify the impact of risk and uncertainty in making reliability and maintenance decisions.

In particular, we will:

  • (i) develop new classes of integrated models that combine failure time and degradation data together with dynamic usage information to predict malfunctions and remaining useful life for complex systems, with a particular emphasis on battery systems;
  • (ii) develop model validation metrics and test cases for validating and assessing the proposed methods.
  • Project Duration

    1 Jan 2015 – 31 Dec 2017

    Grant Type

    GRF



Reliability and Degradation Modelling for Rechargeable Battery

Synopsis

The ubiquitous use of battery-powered electronic devices has created a strong demand of sophisticated battery management systems (BMSs) to maintain battery safety and reliability. Prognostic and health management (PHM), a framework offering comprehensive yet individualized solutions for managing system health, has been successfully applied in BMSs. Nevertheless, the increasingly complex battery systems pose significant barriers to existing PHM methods for battery status evaluation, due to the fact that these methods are often empirical and population based. As a consequence, the estimation and prediction of battery status might be highly biased and thus lead to safety hazard and other problems in practical operations.

Motivated by the new challenges encountered in existing BMSs, the proposed research develops a radically new approach for monitoring and evaluating battery health status by incorporating the advances in PHM modeling and analysis techniques. The proposed research approach focuses on effective and efficient estimation of battery health status based on integration of empirical knowledge and real-time data, and heterogeneous information on each individual, including the actual field operating conditions and ambient environment of a battery system. It is a worldwide trend in PHM research to shift the traditional paradigm from empirical to data fusion and from population based to individual based. The proposed research is promising across a wide range of battery-powered applications.

The health status of rechargeable battery is mainly characterized by two key parameters: state of health (SOH) and state of charge (SOC). SOH denotes the remaining performance of a battery over its whole life cycle, which is usually quantified by remaining useful life (RUL), while SOC quantifies the remaining usable energy at the present cycle. The main objective of this research is to develop innovative modeling methods for estimating SOH and SOC by incorporating additional information and PHM advances into the modeling process.

In particular, we will

  • Develop degradation models and estimation methods for reliable RUL prediction of rechargeable batteries;
  • Develop effective and robust methods that provide accurate estimation for SOC of rechargeable batteries, particularly under a dynamic ambient environment;
  • Evaluate and test the proposed methods using both experimental and field data, and compare their performance with the state of art methods.

Project Duration

1 Jan 2018 – 31 Dec 2020

Grant Type

GRF


Regulatory and Financial Technologies to Increase Incentives for Energy Efficiency Investments

Synopsis

Project Duration

1 Jun 2015 – 30 Nov 2016

Grant Type

ECF


Centre for System Informatics Engineering

Synopsis

The objective of the centre is to create a high quality and influential research hub for developing research methodologies and implementation tools in system informatics, knowledge mining, quality engineering, and prognostics and system health management to serve the industries and societies in Hong Kong, Greater China, and the Asia Pacific region.

Project Duration

1 Jul 2011 – 30 Jun 2020

Grant Type

ResCtrs