Hybrid-modelling of compact tension energy in high strength pipeline steel using a Gaussian Mixture Model based error compensation

In material science studies, it is often desired to know in advance the fracture toughness of a material, which is related to the released energy during its compact tension (CT) test to prevent catastrophic failure. In this paper, two frameworks are proposed for automatic model elicitation from experimental data to predict the fracture energy released during the CT test of X100 pipeline steel. The two models including an adaptive rule-based fuzzy modelling approach and a double-loop based neural network model, relate the load, crack mouth opening displacement (CMOD) and crack length to the released energies during this test. The relationship between how fracture is propagated and the fracture energy is further investigated in greater detail. To improve the performances of the models, a Gaussian Mixture Model (GMM)-based error compensation strategy which enables one monitor the error distributions of the predicted result is integrated in the model validation stage. This can help isolate the error distribution pattern and to establish the correlations with the predictions from the deterministic models. This is the first time a data-driven approach has been used in this fashion on an application that has conventionally been handled using finite element methods or physical models

Application of noninvasive magnetomyography in labour imminency prediction for term and preterm pregnancies and ethnicity specific labour prediction

This paper investigates the application of magnetomyography (MMG) signals from uterine contractions in pregnant patients towards the prediction of labour imminency within or above 48 h. The study utilised the MMG signals collected from a host of pregnant patients retrieved from a Physionet database, which also contained information regarding patients’ ethnicity and pregnancy. Utilising​ the information available in addition to the dataset, the study investigated the prospect of designing an ethnic specific labour imminency classifier to allow for an enhanced prediction, with an emphasis on Black and Caucasian ethnicities due to the nature of the data. Using an extended feature vector and a support vector machine (SVM) classifier, it was seen that the labour imminency was enhanced across the various classifier metrics considered in the ethnic specific classifier when compared with the generalised classifier. The results from the classification exercise, which considered the fusion of MMG signal information with the information on patients’ records, showed greater variability and a slightly lower classifier performance, thus suggesting that the MMG signals present a more reliable way of classifier training. Subsequent work in this area would now involve the application of optimisation algorithms to select an optimal number of electrodes that can be used for data acquisition, and thereby contributing towards the lowering of the cost associated with the implementation of the method using the MMG instrumentation