Yuanlin Gu

University of Stirling

Objective: Nonlinear modeling of cortical responses (EEG) to wrist perturbations allows for the quantification of cortical sensorimotor function in healthy and neurologically impaired individuals. A common model structure reflecting key characteristics shared across healthy individuals may provide a reference for future clinical studies investigat...

Severe geomagnetic storms caused by the solar wind disturbances have harmful influences on the operation of modern equipment and systems. The modeling and forecasting of AE index are extremely useful to understand the geomagnetic substorms. This study presents a novel cloud‐nonlinear autoregressive with exogenous input (NARX) model to predict AE in...

In model identification, the existence of uncertainty normally generates negative impact on the accuracy and performance of the identified models, especially when the size of data used is rather small. With a small data set, least squares estimates are biased, the resulting models may not be reliable for further analysis and future use. This study...

The modelling and analysis of appliance energy use (AEU) of residential buildings are important for energy consumption control, energy management and maintenance, building performance evaluation, and so on. Although some traditional machine learning methods have been applied to produce good prediction results, these models are usually not interpret...

Background Angiography-derived fractional flow reserve (angio-FFR) permits physiological lesion assessment without the need for an invasive pressure wire or induction of hyperaemia. However, accuracy is limited by assumptions made when defining the distal boundary, namely coronary microvascular resistance (CMVR). Aims We sought to determine whethe...

Air pollution prediction is a burning issue, as pollutants can harm human health. Traditional machine learning models usually aim to improve the overall prediction accuracy but neglect the accuracy for peak values. Moreover, these models are not interpretable. They fail to explain the interactions between various determining factors and their impac...

Background Angiography-derived computed (virtual) fractional flow reserve (vFFR) permits the assessment of coronary physiology without the need for a pressure wire or hyperaemia. However, accuracy is limited by assumptions made about coronary microvascular resistance (CMVR) [1]. We hypothesised that machine learning may allow us to “tune” our estim...

For the deployment and startup of microservice instances in different resource centres, we propose an optimization problem model based on the evolutionary multi-objective theory. The objective functions of the model consider the computation and storage resource utilization rate, load balancing rate, and actual microservice usage rate in resource se...

Solvent-based post combustion capture (PCC) is a well-developed technology for CO2 capture from power plants and industry. A reliable model that captures the dynamics of the solvent-based capture process is essential to implement suitable control design. Typically, first principle models are used, however they usually require comprehensive knowledg...

This paper is concerned with the model selection and model averaging problems in system identification and data-driven modelling for nonlinear systems. Given a set of data, the objective of model selection is to evaluate a series of candidate models and determine which one best presents the data. Three commonly used criteria, namely, Akaike informa...

This study aims to establish a quantitative relationship between lifestyle and happiness in the UK based on over 10,000 surveyed samples with 63 lifestyle variables from the UK Understanding Society Data. Transparent parametric models are built and a number of significant explanatory variables (lifestyle indicators) have been identified using a sys...

The severity of global magnetic disturbances in Near-Earth space can crucially affect human life. These geomagnetic disturbances are often indicated by a Kp index, which is derived from magnetic field data from ground stations, and is known to be correlated with solar wind observations. Forecasting of Kp index is important for understanding the dyn...