ORCID Profile
0000-0002-1305-5735
Current Organisation
City University of Hong Kong
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Neural, Evolutionary and Fuzzy Computation | Pattern Recognition and Data Mining | Decision Support and Group Support Systems | Artificial Intelligence and Image Processing
Expanding Knowledge in the Information and Computing Sciences | Application Software Packages (excl. Computer Games) | Information Processing Services (incl. Data Entry and Capture) |
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Elsevier BV
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/72.737496
Abstract: Semidefinite programming problem is an important optimization problem that has been extensively investigated. A real-time solution method for solving such a problem, however, is still not yet available. This paper proposes a novel recurrent neural network for this purpose. First, an auxiliary cost function is introduced to minimize the duality gap between the admissible points of the primal problem and the corresponding dual problem. Then a dynamical system is constructed to drive the duality gap to zero exponentially along any trajectory by modifying the gradient of the auxiliary cost function. Furthermore, a subsystem is developed to circumvent in the computation of matrix inverse, so that the resulting overall dynamical system can be realized using a recurrent neural network. The architecture of the resulting neural network is discussed. The operating characteristics and performance of the proposed approach are demonstrated by means of simulation results.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Elsevier BV
Date: 02-2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: MIT Press - Journals
Date: 02-2017
DOI: 10.1162/NECO_A_00922
Abstract: This letter studies the multistability analysis of delayed recurrent neural networks with Mexican hat activation function. Some sufficient conditions are obtained to ensure that an [Formula: see text]-dimensional recurrent neural network can have [Formula: see text] equilibrium points with [Formula: see text], and [Formula: see text] of them are locally exponentially stable. Furthermore, the attraction basins of these stable equilibrium points are estimated. We show that the attraction basins of these stable equilibrium points can be larger than their originally partitioned subsets. The results of this letter improve and extend the existing stability results in the literature. Finally, a numerical ex le containing different cases is given to illustrate the theoretical results.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Omnia Publisher SL
Date: 24-03-2022
DOI: 10.3926/JIEM.3678
Abstract: Purpose: In this study, we examine the food loss food waste (FLFW) risks in agricultural supply chain through combination the risk analysis approach and lean principles.Methodology: The methodology of this study includes the actor analysis, risk analysis approach, and lean principles. The actor analysis is conducted to identify the actor’s needs, problems, and characters. The risk analysis approach was combined with lean supply chain principles to identify risk points for FLFW in the agricultural supply chain for cayenne pepper in Indonesia. A risk–lean relationship matrix was developed to identify waste reduction efforts.Findings: In this study, the lean-risk matrix was created to discover the similarities and differences in waste reduction efforts in agricultural products compared with traditional manufacturing products, which can also apply to agricultural products in this case study.Research limitations/implications: This study focuses on combining the risk analysis and lean principles to determine the FLFW risk on the agricultural supply chain and find possible solutions to mitigate those risks. The case study for this research is the cayenne pepper supply chain.Originality/value: Lean principles in the FLFW problem are rarely found in studies. Lean principles are an approach that can be used to obtain solutions to reduce waste.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2022
Publisher: MDPI AG
Date: 10-09-2023
DOI: 10.3390/SU151813527
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1999
DOI: 10.1109/72.788651
Abstract: A recurrent neural network, called the Lagrangian network, is presented for the kinematic control of redundant robot manipulators. The optimal redundancy resolution is determined by the Lagrangian network through real-time solution to the inverse kinematics problem formulated as a quadratic optimization problem. While the signal for a desired velocity of the end-effector is fed into the inputs of the Lagrangian network, it generates the joint velocity vector of the manipulator in its outputs along with the associated Lagrange multipliers. The proposed Lagrangian network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2000
DOI: 10.1109/81.852947
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2007
Start Date: 06-2020
End Date: 12-2023
Amount: $480,000.00
Funder: Australian Research Council
View Funded Activity