Current Projects

Dynamic Characteristics of Brunei Bridges for Remote Health Condition Monitoring

Aims and Objectives

This project aims to study the dynamic characteristics of bridges in Brunei for the purpose of condition monitoring and to anticipate future maintenance. These are the objectives of the project:

  1. Design and construction of a numerical model of the bridge
    1. This allows researchers to learn the dynamic properties of the bridge
    2. Observe and analyse the numerical data produced
 
  1. Data acquisition of the dynamic properties of a bridge
    1. Collection of onsite data measurements (triaxial movements/vibrations) considering several factors (effect of moving loads and sea waves)
    2. Observe and analyse collected data
    3. Using the collected data to develop the dynamical model of the bridge
 
  1. Development of real-time remote access bridge health monitoring system
    1. Data transmission from the local site to a remote server with local administrative access
    2. Analyse collected data to anticipate future maintenance
    3. Remote authorised user access to real-time condition of the bridge.

 

Figure 1: Different stages of the Remote Bridge Health Monitoring System

Figure 1 above shows the different stages of the project. Onsite data measurements would require several sensors to acquire good data measurements and collection. Therefore, the data collected will be large and its analysis will be periodic to ensure “real-time” response is achieved. 

 

Figure 2: Low Frequency Kistler Sensor

Figure 2 above shows one of the possible required sensors which have just recently been acquired. Data measurement trials would soon be done to ensure the right sensors have been chosen to measure the required data. The sensor shown in Figure 2 is a Kistler sensor has a sensitivity of 1984 – 2000 mV/g for a 100Hz and 0.5g rms (root mean square acceleration). The sensor has received an acceleration calibration certificate and has been calibrated on the 3rd June 2021 by Kistler recertified by NIST and has met or exceeded the requirements os ISO 9001:2015, ANSI/NSCL Z540-1-1994(R2002) and is accredited to ISO/IEC 17025:2017 as verified by ANSI-ASQ National Accreditation Board (ANAB). If this chosen sensor performs well in acquiring data measurements, more sensors would be required to effectively collect a huge amount of data to be processed.

Research Project Publications and Links

N/A

Project Members

  1. Principal Investigator: Dr Wahyu Caesarendra (FIT)
  2. Co-Principal Investigator: Dr Juliana Zaini (FIT)
  3. Project Member: Dr Haji Awg Abdul Ghani Naim (FOS)
  4. PhD Student in Systems Engineering: Pg Muhammad Nazri Bin Pg Hj Ahmad (Lecturer in UTB)
  5. External collaborators: Dr. Dina Shona Laila (Senior Assistant Professor in UTB)
  6. External collaborators: Dr. Irwanda Laory (Associate Professor in University of Warwick)
  7. External collaborators: Mr. Rafitra Bin Hj Abdul Razak (Assistant Director in Department of Roasds)

Start Date and Duration

1st July 2020, 24 months

Non-invasive ECG image classification and detection based on deep learning algorithm and Jetson microcontroller

Figure 3: A proposed software and hardware interface for online ECG image detection.

Aims and Objectives

In this project, we aim to investigate an appropriate one of deep learning methods for ECG image classification and detection. Once the selected algorithm determined we will develop the algorithm and embed it into the Jetson microcontroller for further training and testing process. This system capable of performing online ECG image detection that associated to one of cardiology diseases. A proposed framework of the online ECG image detection is presented in Figure 3. The following are the project objectives:

  1. Development of one of deep learning methods for finding a reliable algorithm.
    • To investigate an existing deep learning methods that will be suitable for online ECG image classification and detection.
    • To collect a substantial open source datasets of ECG images for training and testing purpose of the selected algorithm.
    • To develop and to refine the selected algorithm to be applicable for software and hardware connection and interface in term of time computation and complexity.
  1. Development of a hardware framework as an online ECG image detection of a certain cardiology diseases.
    • To investigate and determine an appropriate microcontroller for software and hardware connection and interface.
    • To build a hardware framework that capable of detecting one of cardiology disease based on the deep learning model obtain from deep learning process.
    • To evaluate of the performance of the hardware framework for online ECG image detection system.

Research Project Publications and Links

Caesarendra, W., Hishamuddin, T.A., Lai, D.T.C., Husaini, A., Emran, M.E., “ECG Signal Classification and Prediction based on Convolutional Neural Network (CNN) Method”, to be published in Proceedings of the 2021 2nd International Conference on Information System, Computer Science and Engineering (ICONISCSE), CEC 2021, Indonesia.

Project Members

Start Date and Duration

1st July 2020, 24 months

Application of Multi-objective Evolutionary Computation in Human Activity Discovery

 Introduction

Evolutionary Computation (EC) is a rapidly growing and exciting field of research which has recently gained popularity due to its achievement in outperforming deep learning (a prominent area of Artificial Intelligence research) in playing Atari games [1] in June 2018. Its origin dates back to the 1950s and is an important subtopic of AI [2]. EC has been applied to complex computational problems in a wide range of domains, particularly in producing human-competitive machine intelligence [3], which encompasses problem categories such as classification, clustering, regression, design optimisation, planning and automatic computer program generation.

While Deep Learning have been popularly used to solve problems in Computer Vision such as Human Activity Analysis (HAA), there is less work done in exploring EC techniques to solve such problems despite its widespread application in the problem categories stated above. Human Activity Analysis (HAA) can be seen as comprising of a few stages as shown in Fig.1, starting from collection of data through observation, discover the various activities within the observations, learning a model for each of the activities discovered and finally use these models to recognize human activities. Majority of the works in HAA are done in the learning and recognition, while there are less works done in the discovery stage. This work is to apply EC in the discovery stage.

Figure 4: A pipeline with different processes in a broader perspective of human activity analysis [4].

Aims and Objectives

In this project, we aim to develop an evolutionary algorithm which is capable of performing human activities discovery (HAC) for an autonomous robot. The following are the project objectives. 

  1. Development of multi-objective evolutionary algorithms (MOEA) for solving HAC problems.
    1. To build and investigate on an existing framework (unsupervised machine learning and/or deep learning) for HAC and test on available daily living activities data.
    2. To refine existing framework using MOEA methods on activities data.
  2. Development of an autonomous robot using the algorithms developed in 1.
    1. To investigate and determine an optimal set of features for clustering of human actions using machine learning technique, i.e. autonomous discovery of human actions from observations by a mobile robot.
    2. To collect a substantial dataset of Activities of Daily Living (ADL) for current and future research in human activity recognition.
    3. To evaluate the performance of the human action discovery on available datasets of activities of daily living (ADL), and the in-house dataset.

Research Project Publications and Links

  1. Lai, D. T. C., Sato, Y., “Hybrid Multiobjective Evolutionary Algorithms for Unsupervised QPSO, BBPSO and Fuzzy clustering”, to be published in Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2021, Poland.
  2. https://ailab.space/projects/unsupervised-human-activities-recognition/ [research webpage 1]
  3. https://www.researchgate.net/project/Application-of-Multi-objective-Evolutionary-Computation-in-Human-Activity-Discovery [research webpage 2].

Project Members

Start Date and Duration

1st January 2020, 24 months

References

[1] Wilson, D. G., Cussat-Blanc, S., Luga, H., & Miller, J. F. (2018). Evolving simple programs for playing Atari games. arXiv preprint arXiv:1806.05695. Available: https://arxiv.org/abs/1806.05695
[2] Bäck, T., Hammel, U., & Schwefel, H. P. (1997). Evolutionary computation: Comments on the history and current state. IEEE transactions on Evolutionary Computation, 1(1), 3-17. Available: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.391.802&rep=rep1&type=pdf
[3] Sipper, M., Olson, R. S., & Moore, J. H. (2017). Evolutionary computation: the next major transition of artificial intelligence?. Available: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534026/
[4] Ong, W., Palafox , L., Koseki, T., “Autonomous Learning and Recognition of Human Action based on An Incremental Approach of Clustering”, IEEJ Transactions on Electronics, Information and Systems, Vol 135, No.9, 2015 (pdf