TL;DR
A lightweight recursive attention-based framework for efficient multi-channel data recovery that integrates the windowing operation and recursive attention mechanism to reduce the complexity of attention calculations while preserving temporal dependencies across sensor measurements.
Reliable sensor data are fundamental to the effectiveness of long-term structural health monitoring (SHM) systems, in which measurement anomalies can compromise condition assessment, damage detection, and maintenance decision-making. This study presents a data separability-driven framework for automated anomaly classif...
This paper proposes an anomaly detection framework that combines the sequential retraining of CAE networks with an exclusion logic strategy, and demonstrates good generalizability, seamless adaptability to varying monitoring scenarios, and robustness across both training and validation datasets.
The dense networked deployment of structural health monitoring (SHM) sensors is driving engineering structures toward comprehensive digital monitoring, enabling real-time health assessment through multipoint data acquisition. However, their extensive distribution exposes sensor networks to complex security risks. Facto...
This paper investigates the relationship between data quality and deep learning model performance for predicting pedestrian flows in smart cities, using data from the UK’s Newcastle Urban Observatory. We evaluate eight forecasting models (including multivariate LSTMs and Transformers) across 110 pedestrian sensors. Cru...
A reconstruction-based anomaly diagnosis framework is developed to characterize condition-consistent normal structural states and improve abnormal state discrimination and the potential of diffusion models for intelligent infrastructure monitoring is indicated.
An intelligent SHM framework that integrates one-dimensional Convolutional Neural Networks (1D-CNN) with Long Short-Term Memory (LSTM) networks for automated damage detection from vibration sensor data acquired through Internet of Things (IoT) sensor networks is proposed.
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