32 citations · 76 across the 25 of their papers we have counts for
26 papers
Foundation Models for Clean Energy Forecasting: A Comprehensive Review
Md Meftahul Ferdaus, Tanmoy Dam, Md Rasel Sarkar +2
As global energy systems transit to clean energy, accurate renewable generation and renewable demand forecasting is imperative for effective grid management. Foundation Models (FMs…
Active Sensing Strategy: Multi-Modal, Multi-Robot Source Localization and Mapping in Real-World Settings with Fixed One-Way Switching
Vu Phi Tran, Asanka G. Perera, Matthew A. Garratt +2
This paper introduces a state-machine model for a multi-modal, multi-robot environmental sensing algorithm tailored to dynamic real-world settings. The algorithm uniquely combines…
Dynamic Long-Term Time-Series Forecasting via Meta Transformer Networks
Muhammad Anwar Ma'sum, MD Rasel Sarkar, Mahardhika Pratama +5
A reliable long-term time-series forecaster is highly demanded in practice but comes across many challenges such as low computational and memory footprints as well as robustness ag…
Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting
Md Rasel Sarkar, Sreenatha G. Anavatti, Tanmoy Dam +2
The main objective of this study is to propose an enhanced wind power forecasting (EWPF) transformer model for handling power grid operations and boosting power market competition.…
Lightweight Monocular Depth Estimation with an Edge Guided Network
Xingshuai Dong, Matthew A. Garratt, Sreenatha G. Anavatti +2
Monocular depth estimation is an important task that can be applied to many robotic applications. Existing methods focus on improving depth estimation accuracy via training increas…
Improving Self-supervised Learning for Out-of-distribution Task via Auxiliary Classifier
Harshita Boonlia, Tanmoy Dam, Md Meftahul Ferdaus +2
In real world scenarios, out-of-distribution (OOD) datasets may have a large distributional shift from training datasets. This phenomena generally occurs when a trained classifier…
Scalable Adversarial Online Continual Learning
Tanmoy Dam, Mahardhika Pratama, MD Meftahul Ferdaus +2
Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susce…
Latent Preserving Generative Adversarial Network for Imbalance classification
Tanmoy Dam, Md Meftahul Ferdaus, Mahardhika Pratama +3
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem. Classic classification algorithms tend…
Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems
Vu Phi Tran, M. A Mabrok, Sreenatha G. Anavatti +2
Quadrotors are one of the popular unmanned aerial vehicles (UAVs) due to their versatility and simple design. However, the tuning of gains for quadrotor flight controllers can be l…
MobileXNet: An Efficient Convolutional Neural Network for Monocular Depth Estimation
Xingshuai Dong, Matthew A. Garratt, Sreenatha G. Anavatti +1
Depth is a vital piece of information for autonomous vehicles to perceive obstacles. Due to the relatively low price and small size of monocular cameras, depth estimation from a si…
Towards Real-Time Monocular Depth Estimation for Robotics: A Survey
Xingshuai Dong, Matthew A. Garratt, Sreenatha G. Anavatti +1
As an essential component for many autonomous driving and robotic activities such as ego-motion estimation, obstacle avoidance and scene understanding, monocular depth estimation (…
Frontier-led Swarming: Robust Multi-Robot Coverage of Unknown Environments
Vu Phi Tran, Matthew A. Garratt, Kathryn Kasmarik +1
This paper proposes a novel swarm-based control algorithm for exploration and coverage of unknown environments, while maintaining a formation that permits short-range communication…
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification
Tanmoy Dam, Nidhi Swami, Sreenatha G. Anavatti +1
This paper presents a novel multi-fake evolutionary generative adversarial network(MFEGAN) for handling imbalance hyperspectral image classification. It is an end-to-end approach i…
Does Adversarial Oversampling Help us?
Tanmoy Dam, Md Meftahul Ferdaus, Sreenatha G. Anavatti +2
Traditional oversampling methods are generally employed to handle class imbalance in datasets. This oversampling approach is independent of the classifier; thus, it does not offer…
Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces
Tanmoy Dam, Sreenatha G. Anavatti, Hussein A. Abbass
Since their introduction in the last few years, conditional generative models have seen remarkable achievements. However, they often need the use of large amounts of labelled infor…
Continuous Deep Hierarchical Reinforcement Learning for Ground-Air Swarm Shepherding
Hung The Nguyen, Tung Duy Nguyen, Vu Phi Tran +5
The control and guidance of multi-robots (swarm) is a non-trivial problem due to the complexity inherent in the coupled interaction among the group. Whether the swarm is cooperativ…
Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers
Huanneng Qiu, Matthew Garratt, David Howard +1
A critical issue in evolutionary robotics is the transfer of controllers learned in simulation to reality. This is especially the case for small Unmanned Aerial Vehicles (UAVs), as…
Evolving Spiking Neural Networks for Nonlinear Control Problems
Huanneng Qiu, Matthew Garratt, David Howard +1
Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons.…
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti +2
There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In t…
Apprenticeship Bootstrapping Via Deep Learning with a Safety Net for UAV-UGV Interaction
Hung Nguyen, Vu Tran, Tung Nguyen +5
In apprenticeship learning (AL), agents learn by watching or acquiring human demonstrations on some tasks of interest. However, the lack of human demonstrations in novel tasks wher…
Development of a Sliding Mode Control Based Adaptive Fuzzy Controller for a Flapping Flight
Md Meftahul Ferdaus, Sreenatha G. Anavatti, Matthew A. Garratt +1
Controlling of a flapping flight is one of the recent research topics related to the field of Flapping Wing Micro Air Vehicle (FW MAV). In this work, an adaptive control system for…
Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems
Ahmad Jobran Al-Mahasneh, Sreenatha G. Anavatti, Matthew A. Garratt
This paper depicts a brief revision of Generalized Regression Neural Networks (GRNN) applications in system identification and control of dynamic systems. In addition, a comparison…
A Generic Self-Evolving Neuro-Fuzzy Controller based High-performance Hexacopter Altitude Control System
Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti +1
Nowadays, the application of fully autonomous system like rotary wing unmanned air vehicles (UAVs) is increasing sharply. Due to the complex nonlinear dynamics, a huge research int…
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti +1
Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative lea…