Search

The modelling and prediction of extreme temperature changes in enclosed compartments is a domain with applications ranging from residential fire alarms, industrial temperature sensors to search and rescue personnel safety systems. The spread of fire in enclosed compartments is a highly uncertain and nonlinear process. Hence, in safety-critical cases, any false negatives pose a serious threat to the safety of individuals such as firefighters that are engaged in rescue activities. This work aims to model the nonlinear fire spread behaviour as a temporal, deep learning-based regressive methodology. The objective is to efficiently identify abrupt and extreme temperature changes that often result in increases of 300+ °C. A major challenge in such time-series models is that of learning from historic time-series samples which are known to suffer from high noise levels, outliers and data imbalance. This work contributes on the development of a convolutional neural network (CNN)-long short-term memory (LSTM) methodology to handle temperature data originating from body-mounted and fixed sensors and develop a temperature increase warning mechanism. The main contribution exploits the contextualisation ability of CNN-LSTM to predict temperature changes in windows of 5–120 s. The model identifies the spatial temperature change patterns via a CNN encoder, which are then fed into an LSTM network. This regression mechanism is trained and validated against a set of unique fire spread conditions and involved live tests ranging from containers to residential and industrial units. The model's performance was evaluated with MAPE sensitivity analysis against data originating from body-mounted sensors and third-party NIST datasets. The outcome showed an error ranging from 0.89 to 2.05% to 5.46% and 6.23%, respectively. The model efficacy was also evaluated against a range of input–output temperature ranges from 5, 30 to 120-s windows and showed a FN rate of 2.15% for pre-alarm-to-normal and 3.11% for alarm-to-pre-alarm cases in body-mounted sensors and a higher FN rate of 5.14% reported for pre-alarm-to-normal case for the raised platfor sensor tests. [ABSTRACT FROM AUTHOR]

Brain connectivity-based methods are efficient and reliable for assessing the mental workload during high task demands as the human brain is functionally interconnected during any psychological task. On the other hand, the graph theory approach is a mathematical study that draws the pairwise relationships between objects. This paper covers the deployment of graph theory concepts on the brain connectivity methods to find the complex underlying behaviors of the brain in the simplest way. Furthermore, in this work, mental workload assessments on multimedia animations were performed using a brain connectivity approach based on partial directed coherence (PDC) with graph theory analysis. Electroencephalography (EEG) data were collected from 34 adult participants at baseline and during multimedia learning tasks. The results revealed that the EEG-based connectivity approach with graph theory offers more promising results than the traditional feature extraction techniques. The connectivity approach achieved an accuracy of 85.77% in comparison with the 78.50% accuracy achieved by the existing feature extraction techniques. It is concluded that the proposed PDC method with graph theory network analysis is a better solution for cognitive load assessment during any cognitive task. [ABSTRACT FROM AUTHOR]