When Process Analysis Technology Meets Transfer Learning: A Model Transfer Strategy Between Different Spectrometers for Quantitative Analysis

Yu, Yan; Yao, Meibao; Huang, Jipeng; Xiao, Xueming

doi:10.1109/tim.2024.3353273

IEEE Trans. Instrum. Meas.

2024

DOI: 10.1109/tim.2024.3353273

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When Process Analysis Technology Meets Transfer Learning: A Model Transfer Strategy Between Different Spectrometers for Quantitative Analysis

Yan Yu,

Meibao Yao,

Jipeng Huang

et al.

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2024

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Cited by 4 publications

References 67 publications

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A Novel Model Based on Spatio-Temporal Dilated ConvLSTM Networks for Indian Ocean Dipole Forecasting Using Multi-Source Global Sea Surface Temperature and Heat Content Data

Janmaijaya,

Muhuri

2024

IEEE Access

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The Indian Ocean Dipole (IOD) is a critical coupled ocean-atmosphere oscillation system associated with significant weather anomalies in the global climate, particularly in the Indian Ocean rim countries. The paper presents a novel deep learning (DL) model, which we call the "spatio-temporal dilated ConvLSTM (STDNet) model", for forecasting the Dipole Mode Index (DMI) using global sea surface temperature (SST) and heat content (HC) data. The model combines the techniques of dilation and fine-tuning to learn efficiently from the training data. CMIP6 historical simulation data from 5 modelling centres for 1861-2014 is used to train the model. Furthermore, the model is fine-tuned on reanalysis data from 1871-1973. During the testing period , the dipole correlation coefficient (DCC) was the highest when compared with state-of-the-art dynamical North American Multi-Model Ensemble (NMME) models, a convolutional neural network (CNN) and a dilated CNN. On a lead of 12 months, the DCC is 0.40 for the CNN, 0.44 for the dilated CNN, and 0.51 for the STDNet, and all the NMME models have negative correlations. The results show that the STDNet efficiently forecasts the DMI at leads of up to 12 months. The STDNet shows results to overcome the winter predictability barrier.

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A Novel Model Based on Spatio-Temporal Dilated ConvLSTM Networks for Indian Ocean Dipole Forecasting Using Multi-Source Global Sea Surface Temperature and Heat Content Data

Janmaijaya,

Muhuri

2024

IEEE Access

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Application of an Improved Graph Neural Network for Drug Property Prediction

Ma,

Wang,

2024

IEEE Access

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The prediction of drug properties plays a vital role in drug research. However, the drug property prediction accuracy of traditional methods is limited due to their inability to fully capture molecular structure and function information. As a result, the use of graph neural networks has attracted significant attention as an effective drug property prediction approach. Nevertheless, traditional graph neural networks still exhibit certain drawbacks in this regard, including their disregard of the interaction information between nodes and edges, the loss of local information during global pooling operations, and the absence of feature fusion mechanisms. This study proposes an enhanced graph neural network (GNN) model that incorporates an attention mechanism, multiscale pooling, an adaptive weight generator, and an activation function to predict drug properties. A comparative analysis with the conventional graph neural network model reveals significant improvements in terms of predicting the side effects of drugs on the heart and liver, with increases of 1%, 7%, and 13%. Furthermore, the enhanced graph neural network model exhibits good performance across the remaining two datasets. Empirical findings underscore the efficacy of the model in drug property prediction tasks, and it is characterized by enhanced predictive precision and robust performance outcomes.

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3D Mesh Animation Optimization Algorithm Based on Integer Programming and Nonlinear Constraints

Jiang,

Wang

2024

IEEE Access

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When Process Analysis Technology Meets Transfer Learning: A Model Transfer Strategy Between Different Spectrometers for Quantitative Analysis

Cited by 4 publications

References 67 publications

A Novel Model Based on Spatio-Temporal Dilated ConvLSTM Networks for Indian Ocean Dipole Forecasting Using Multi-Source Global Sea Surface Temperature and Heat Content Data

A Novel Model Based on Spatio-Temporal Dilated ConvLSTM Networks for Indian Ocean Dipole Forecasting Using Multi-Source Global Sea Surface Temperature and Heat Content Data

Application of an Improved Graph Neural Network for Drug Property Prediction

3D Mesh Animation Optimization Algorithm Based on Integer Programming and Nonlinear Constraints

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