A5034832522- Metaheuristic Optimization Algorithms Research
- Maritime Ports and Logistics
- Evolutionary Algorithms and Applications
- Neural Networks and Applications
- Vehicle Routing Optimization Methods
- Genetic and phenotypic traits in livestock
- Urban and Freight Transport Logistics
- Obstructive Sleep Apnea Research
- Optical measurement and interference techniques
- Animal Behavior and Welfare Studies
- Distributed and Parallel Computing Systems
- Robot Manipulation and Learning
- Cloud Computing and Resource Management
- Robotics and Sensor-Based Localization
- Sleep and Wakefulness Research
- Wood Treatment and Properties
- Modular Robots and Swarm Intelligence
- Structural Health Monitoring Techniques
- Effects of Environmental Stressors on Livestock
- Advanced Multi-Objective Optimization Algorithms
- Infrastructure Maintenance and Monitoring
- Advanced Manufacturing and Logistics Optimization
- 3D Surveying and Cultural Heritage
- Robotic Path Planning Algorithms
- Optimization and Packing Problems
Woolcock Institute of Medical Research
2023
Macquarie University
2023
University of Technology Sydney
2004-2017
This paper presents a machine-learning-based approach for the structural health monitoring (SHM) of in-situ timber utility poles based on guided wave (GW) propagation. The proposed non-destructive testing method combines new multi-sensor system with advanced statistical signal processing techniques and state-of-the-art machine learning algorithms condition assessment poles. Currently used pole inspection have critical limitations including inability to assess underground section. GW methods,...
To compare overnight declarative memory consolidation and non-rapid eye movement (NREM) sleep electroencephalogram (EEG) oscillations in older adults with obstructive apnea (OSA) to a control group assess slow-wave activity (SWA) spindles as correlates of consolidation. Forty-six (24 without OSA 22 OSA) completed word-pair associate's task before after polysomnography. Recall recognition were expressed percentage the morning relative evening scores. Power spectral analysis was performed on...
The objective of this study was to develop a proof concept for using off-the-shelf Red Green Blue-Depth (RGB-D) Microsoft Kinect cameras objectively assess P8 rump fat (P8 fat; mm) and muscle score (MS) traits in Angus cows steers. Data from low high muscled cattle (156 79 steers) were collected at multiple locations time points. following steps required the 3-dimensional (3D) image data subsequent machine learning techniques learn traits: 1) reduce dimensionality point cloud by extracting...
This paper investigates the efficacy of genetic-based learning classifier system XCS, for classification noisy, artefact-inclusive human electroencephalogram (EEG) signals represented using large condition strings (108 bits). EEG from three participants were recorded while they performed four mental tasks designed to elicit hemispheric responses. Autoregressive (AR) models and fast Fourier transform (FFT) methods used form feature vectors with which can be discriminated. XCS achieved a...
The main contribution of this paper is a mathematical model describing performance metrics for coordinating multiple mobile robots in seaport container terminal. scenario described here requires dealing with many difficult practical challenges such as the presence levels stacking and sequencing, variable orientations, vehicular dynamics that require finite acceleration deceleration times. Furthermore, contrast to automatically guided vehicle planning problem manufacturing environment,...
This paper presents an investigation into combining migration strategies inspired by multi-deme parallel genetic algorithms with the XCS learning classifier system to provide and distributed migration. Migrations occur between sub-populations using classifiers ranked according numerosity, fitness or randomly selected. The influence of degree-of-connectivity introduced fully-connected, bi-directional ring uni-directional topologies is examined. Results indicate that effective method for...
This paper investigates the hybridisation of two very different optimisation methods, namely Parallel Genetic Algorithm (PGA) and Sequential Quadratic Programming (SQP) Algorithm. The characteristics genetic-based traditional quadratic programming-based methods are discussed to what extent hybrid method can benefit solving problems with nonlinear complex objective constraint functions. Experiments show effectively combines robust global search property Algorithms high convergence velocity...