Integrated power information operation and maintenance system based on D3QN algorithm with experience replay
Due to the expanding scale of power system and the variety of equipment, the operation and maintenance tasks require more efficient scheduling algorithms to accommodate large-scale data. To solve the problems of delays and interruptions in scheduling, an ...
Digital twin-based fault detection for intelligent power production lines
Digital twin technology realises real-time capture of system operation status, real-time monitoring and prediction of potential risks. In view of this, a fault detection method based on digital twin of power production line is proposed, where the ...
Aerial remote sensing image registration based on dense residual network of asymmetric convolution
The existing image registration frameworks pay less attention to important local feature information and part of global feature information, resulting in low registration accuracy. However, asymmetric convolution and dense connection can pay more ...
Time series models for web service activity prediction
Web service providers have to be very vigilant in offering their services to their clients and ensure that there are no glitches. In this research, we propose a method for anticipating the load of user activities at various intervals of time that can ...
Adjustable rotation gate based quantum evolutionary algorithm for energy optimisation in cloud computing systems
The widespread adoption of cloud computing and a rapid rise in capacity and scale of data centres results in a significant increase in electricity usage, rising data centre ownership costs, and increased carbon footprints. One of the challenging research ...
Classification of hyperspectral images by utilising a lightweight cascaded deep convolutional network
In recent years, deep learning frameworks have been increasingly used for hyperspectral image classification (HIC) challenges, with outstanding results. Existing network models, on the other hand, are more sophisticated and require longer computing. ...
Convolutional neural network optimisation for discovering plant leaf diseases with particle swarm optimiser
The agriculture industry contributes most to expanding economies and populations, but plant diseases restrict the food production. Utilising an automatic detection method, early diagnosis of plant diseases can improve food production quality and reduce ...
Conjugate gradient with Armijo line search approach to investigate imprecisely defined unconstrained optimisation problem
The main focus of the study is to investigate nonlinear systems with uncertainties. Here the epistemic type of uncertainties is considered as fuzzy. As such, the present study analyses fuzzy nonlinear systems. In order to solve the fuzzy nonlinear ...
A deep learning based automated phenotyping for identification of overuse of synthetic fertilisers in Amaranthus crop
Amaranth (Amaranthus spp.) is a significant leafy vegetable and cereal crop with high nutrient benefits that is widely consumed worldwide. To maximise its yield, farmers massively rely upon synthetic fertilisers to enhance the quality of the crop. ...
A multi-agent intrusion detection model based on importance feature extraction
The swift evolution of the internet has delivered convenience to people while also introducing challenges concerning the security of information. Network intrusion detection, which recognises distinct attack behaviours in the network by gathering and ...
A content-adaptive video compression method based on transformer
Convolutional neural network architectures have been the primary choice for deep learning-based video compression algorithms in recent years, but common convolutional neural networks can only exploit local correlations, while compression is faced with a ...