Frontiers

Graph-Guided Neural Networks: Revolutionizing Anomaly Detection in Chemical Engineering

The increasing complexity of modern chemical engineering processes presents significant challenges for timely and accurate anomaly detection. Traditional ...
Scientific Research Publishing

KAN-Transformer Fusion with Mixture of Experts for Temporal Imputation of Spatiotemporal Air Pollution Data ()

The Kolmogorov-Arnold Network (abbr. KAN) is a novel neural network architecture inspired by the Kolmogorov-Arnold ...
EDN

Self-correcting codes conquer noise, part one: Viterbi codecs

FEC (forward-error-correction) techniques correct errors at the receiver end of digital communications systems. In contrast with error-detection and retransmission ...
IEEE

CRGCN: A Collaborative Graph Recurrent Convolution Network with Evolving Adjacency Matrices for Residential Short-Term Load Forecasting

Abstract: Accurate short-term load forecasting (STLF) requires capturing complex spatio-temporal dependencies, a task where standard Graph Neural Networks (GNNs) struggle due to static graph ...
IEEE

A Parallel Fault Diagnosis Framework Based on KNN-Graph Convolution and BiGRU with Global Attention

Abstract: The importance of fault diagnosis for safety and efficiency was enhanced by the intelligent transformation of industrial systems. To overcome the limitations of manual feature extraction in ...
GitHub

convolutional_autoencoder_architecture.py

x = Conv2D(128, (3, 3), activation="relu", padding="same")(input_img) x = Conv2D(128, (3, 3), activation="relu", padding="same")(x) x = MaxPooling2D((2, 2), padding ...