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Sugar crystal growth is a non-linear process dependent on the complex interaction of several variables. This causes the development of first-principal models of the process to be exceedingly difficult and often inaccurate. Louisiana Sugar Refining has partnered with SysCAD to develop a machine learning model of crystal growth to incorporate into the digital twin infrastructure being developed at LSR. The collaboration has successfully developed a supervised training machine learning model based on Deep Feed Forward (DFF) and Convolutional Neural Network (ConvNet) models to simulate the crystal growth in vacuum pans. Initially, a proof-of-concept model showed promising results using both DFF and ConvNet architectures, which lead to the expansion of these models to include the entire white and recovery boiling schemes. In this paper we present the concept of using neural network dynamic models, their analysis, and results of predicted particle size distribution parameters compared to real field observations. These fully optimized models will be implemented as standard unit operation models in SysCAD to enhance the larger process simulation model used by LSR to monitor and predict process conditions in real-time. This machine learning technique will also be expanded to develop a concept model for other non-linear phenomena in the sugar industry including colour. [ABSTRACT FROM AUTHOR]

The 12-lead electrocardiogram (ECG) is a reliable diagnostic tool for detecting and treating severe cardiovascular conditions like arrhythmia and heart attack. Deep neural networks (DNNs) have achieved higher accuracy in recent years than traditional ECG signal classification task methods. Convolutional neural network (CNN) and Transformer are the two mainstream architectures of DNN, respectively good at extracting local and global features from input data. This paper proposes the multi-scale convolutional Transformer network (MCTnet), an efficient combination of Transformer encoder and CNN for ECG signal classification. MCTnet utilizes the advantages of CNN and self-attention mechanisms to capture potential features in ECG signal accurately. The dual-branch Transformer encoder extracts different-scale feature representations, enabling the capture of both local and global information. Additionally, an information bottleneck method eliminates redundant information and enhances task-relevant information in the learned representations. To evaluate the performance of MCTnet, comprehensive experiments are conducted on three commonly used ECG datasets. The results demonstrate that MCTnet outperforms current deep learning-based models, highlighting its effectiveness in ECG signal classification. It also shows that the performance of the model can be effectively improved by utilizing multi-scale representation learning and information bottleneck. [ABSTRACT FROM AUTHOR]

Turing dynamics mainly focuses on non-homogeneous self-adaptive spatial patterns of reaction diffusion systems in a continuous space. If defining the diffusion environment as network structure, then network patterns are available under Turing instability conditions. In allusion to parameter estimation for network Turing-instable systems, this paper proposes a new recognition method using artificial neural networks. When diffusion occurs on quadrilateral lattice networks, a deep convolutional neural network (CNN) is built to invert the unknown parameters. The results on training set and validation set indicate that the CNN model exhibits great robustness without overfitting and gradient explosion. Meanwhile, the prediction performance on two test sets is excellent, with average relative errors of the estimators ending up at 0.68% and 1.04%, respectively. When diffusion occurs on non-regular networks, a spatial domain graph convolutional network (GCN) is established, which is slightly less robust than the CNN. The average relative error of the estimators on both test sets ends up between 1.1% and 2.8%, which is still in the ideal range. • The parameter estimation method for network Turing systems is investigated. • A 14-layer deep neural network is designed for systems on lattice networks. • A 6-layer graph neural network is designed for systems on irregular networks. [ABSTRACT FROM AUTHOR]