Prof. Dr. Cheng Siong Chin
Newcastle University in Singapore
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Academic Career Length
21Years (2006-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications6 December 2018 in IEEE Access
Journal Article
Water is a precious resource that should be managed carefully. However, due to leakages in Water Distributed Networks (WDNs), a large amount of water is lost each year that suggests the need for reliable and robust leak detection and localization system. This paper attempts to review the current technologies for leakage detection in WDN as well as several proposed intelligent methodologies (such as support vector machine, neural network and convolution neural network) over the past few years. The current methodologies and their limitations are discussed. Uncertainties involved in the implementation of WDN leakage detection are also discussed and several suggestions to overcome such uncertainties are provided for future implementations.
19 September 2019 in IEEE Transactions on Intelligent Transportation Systems
Journal Article
CKChan Teck Kai
Chan Teck KaiCheng Siong ChinHCHao Chen
Hao ChenXZXionghu Zhong
Xionghu Zhong
Human factors are the primary catalyst for traffic accidents. Among different factors, fatigue, distraction, drunkenness, and/or recklessness are the most common types of abnormal driving behavior that leads to an accident. With technological advances, modern smartphones have the capabilities for driving behavior analysis. There has not yet been a comprehensive review on methodologies utilizing only a smartphone for drowsiness detection and abnormal driver behavior detection. In this paper, different methodologies proposed by different authors are discussed. It includes the sensing schemes, detection algorithms, and their corresponding accuracy and limitations. Challenges and possible solutions such as integration of the smartphone behavior classification system with the concept of context-aware, mobile crowdsensing, and active steering control are analyzed. The issue of model training and updating on the smartphone and cloud environment is also included.
15 February 2018 in IEEE/ASME Transactions on Mechatronics
Journal Article
Cheng Siong ChinWLWei Peng Lin
Wei Peng Lin
The integration of inaccurate remotely operated vehicle (ROV) model data obtained by computational fluid dynamics for control is presented. Since the ROV is highly nonlinear and uncertain, a sliding-mode control (SMC) system using a direction-based genetic algorithm (GA) and fuzzy inference mechanism is proposed. The GA influences the right evolutionary step and direction of the SMC parameters subjected to uncertainties in the evolutionary process. The effectiveness of reducing the sensitivity of the proposed control scheme to model parameters and external disturbance is verified by simulations and sea trial. The results demonstrate that the proposed controller performed better with less chattering in position responses than SMC without GA-fuzzy optimization, fuzzy logic controller, and proportional-integral derivative.
2 June 2020 in IEEE Access
Journal Article
TCT. K. Chan
T. K. Chan
Cheng Siong ChinOne of the most amazing functions of the human auditory system is the ability to detect all kinds of sound events in the environment. With the technologies and hardware advances, polyphonic Sound Event Detection (SED) can be developed to mimic the ability of the human auditory system. However, the development of a SED system is no trivial task, and several different factors often hinder accuracy. Although there are several overview papers available, most of them only provide a theoretical overview of algorithms used with little discussion. Thus, to the best of the authors’ knowledge, there is no comprehensive review that covers this particular domain. Therefore, this paper aims to provide an in-depth discussion of different methodologies proposed by various authors that include the features used, detection algorithms, and their corresponding accuracy and limitations. Additional information on possible trends is also discussed that can be useful for future development works.
Journal Article
CSChunlai Shan
Chunlai ShanCheng Siong ChinVMVenkateshkumar Mohan
Venkateshkumar Mohan
CaiZhi ZhangBattery management systems (BMSs) play a critical role in electric vehicles (EVs), relying heavily on two essential factors: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains a challenge. To address this, many researchers have turned to machine learning (ML) techniques. This study provides a comprehensive overview of both BMSs and ML, reviewing the latest research on popular ML methods for estimating the SOC and SOH. Additionally, it highlights the challenges involved. Beyond traditional models like equivalent circuit models (ECMs) and electrochemical battery models, this review emphasizes the prevalence of a support vector machine (SVM), fuzzy logic (FL), k-nearest neighbors (KNN) algorithm, genetic algorithm (GA), and transfer learning in SOC and SOH estimation.
Journal Article
ZGZuchang Gao
Zuchang GaoCheng Siong Chin
Wai Lok WooZGZuchang Gao
Zuchang GaoA computational efficient battery pack model with thermal consideration is essential for simulation prototyping before real-time embedded implementation. The proposed model provides a coupled equivalent circuit and convective thermal model to determine the state-of-charge (SOC) and temperature of the LiFePO4 battery working in a real environment. A cell balancing strategy applied to the proposed temperature-dependent battery model balanced the SOC of each cell to increase the lifespan of the battery. The simulation outputs are validated by a set of independent experimental data at a different temperature to ensure the model validity and reliability. The results show a root mean square (RMS) error of 1.5609 × 10−5 for the terminal voltage and the comparison between the simulation and experiment at various temperatures (from 5 °C to 45 °C) shows a maximum RMS error of 7.2078 × 10−5.
About Me
Cheng Siong Chin is a Chair Professor in Intelligent Systems Modelling and Simulation at Newcastle University and one of the Editors-in-Chief for Cybernetics and Systems, Taylor Francis. He is also the Director of Newcastle University-Nvidia Joint Laboratory in Singapore. He received his Ph.D. in Applied Control Engineering at Research Robotics Centre at Nanyang Technological University (NTU) in 2008 and his M.Sc. (Distinction) in Advanced Control and Systems Engineering from The University of Manchester (formerly UMIST) in 2001. Professor Chin received a B.Eng. (Hons) degree in Mechanical and Production Engineering from Nanyang Technological University (NTU). Before moving into academia, he worked in the consumer electronics industry for a few years. Professor Chin obtained 7 Economic Development Board (EDB)-Industrial Postgraduate Programme (IPP) and 2 Singapore Maritime Institute (SMI) grants from the Singapore government in intelligent systems design, simulation, and predictive analytics as a PI and Co-PI. He has obtained over 170 publications, 5 authored books, 2 Singapore Patent Applications, and 3 US Patents. He published in AI-related conferences like EANN/EAAAI, IJCNN, AIAI, and DCASE. He also received the DCASE2019 Judges' Award in IEEE AASP Challenge on DCASE2019. He is an Associate Editor for IEEE Access Journal, Applied Artificial Intelligence, IEEE Transportation Electrification Community (TEC) eNewsletter, and Electronics.