Chee Peng Lim

Prof. Dr. Chee Peng Lim

Institute for Intelligent Systems Research and Innovation, Deakin University, Waurn Ponds, VIC 3216, Australia

Research Focus & Metrics

Academic Career Length

32Years (1995-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
18 July 2022 in IEEE Transactions on Pattern Analysis and Machine Intelligence
Verified
Journal Article
Open access
FPFarhad Pourpanah
Farhad Pourpanah
MAMoloud Abdar
Moloud Abdar
YLYuxuan Luo
Yuxuan Luo
XZXinlei Zhou
Xinlei Zhou
RWRan Wang
Ran Wang
JWJonathan Wu
Jonathan Wu
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. Firstly, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations.

6 June 2019 in IEEE Systems Journal
Verified
Journal Article
Closed access
Thanh Thi NguyenThanh Thi Nguyen
Thanh Thi Nguyen
Chee Peng LimChee Peng Lim
Chee Peng Lim
NNNgoc Duy Nguyen
Ngoc Duy Nguyen
LGLee Gordon-Brown
Lee Gordon-Brown
SNSaeid Nahavandi
Saeid Nahavandi
Situation awareness (SA) is an important constituent in human information processing and essential in pilots’ decision making processes. Acquiring and maintaining appropriate levels of SA is critical in aviation environments as it affects all decisions and actions taking place in flights and air traffic control. This paper provides an overview of recent measurement models and approaches to establishing and enhancing SA in aviation environments. Many aspects of SA are examined including the classification of SA techniques into six categories, and different theoretical SA models from individual, to shared or team, and to distributed or system levels. Quantitative and qualitative perspectives pertaining to SA methods and issues of SA for unmanned vehicles are also addressed. Furthermore, future research directions regarding SA assessment approaches are raised to deal with shortcomings of the existing state-of-the-art methods in the literature.

8 June 2021 in Annals of Operations Research
Verified
Journal Article
Closed access
PDF file
MSManjeevan Seera
Manjeevan Seera
Chee Peng LimChee Peng Lim
Chee Peng Lim
AKAjay Kumar
Ajay Kumar
LDLalitha Dhamotharan
Lalitha Dhamotharan
KTKim Hua Tan
Kim Hua Tan

21 June 2022 in Artificial Intelligence Review
Verified
Journal Article
Closed access
PDF file
FPFarhad Pourpanah
Farhad Pourpanah
RWRan Wang
Ran Wang
Chee Peng LimChee Peng Lim
Chee Peng Lim
XWXizhao Wang
Xizhao Wang
DYDanial Yazdani
Danial Yazdani

14 March 2020 in Mathematics
Verified
Journal Article
Open access
PDF file
GRGrienggrai Rajchakit
Grienggrai Rajchakit
PCPharunyou Chanthorn
Pharunyou Chanthorn
PKPramet Kaewmesri
Pramet Kaewmesri
RSRamalingam Sriraman
Ramalingam Sriraman
Chee Peng LimChee Peng Lim
Chee Peng Lim
This paper studies the global Mittag–Leffler stability and stabilization analysis of fractional-order quaternion-valued memristive neural networks (FOQVMNNs). The state feedback stabilizing control law is designed in order to stabilize the considered problem. Based on the non-commutativity of quaternion multiplication, the original fractional-order quaternion-valued systems is divided into four fractional-order real-valued systems. By using the method of Lyapunov fractional-order derivative, fractional-order differential inclusions, set-valued maps, several global Mittag–Leffler stability and stabilization conditions of considered FOQVMNNs are established. Two numerical examples are provided to illustrate the usefulness of our analytical results.

31 December 2013 in Neural Computing and Applications
Verified
Journal Article
Closed access
PDF file
LJLakhmi C. Jain
Lakhmi C. Jain
MSManjeevan Seera
Manjeevan Seera
Chee Peng LimChee Peng Lim
Chee Peng Lim
PBP. Balasubramaniam
P. Balasubramaniam