1,950 research outputs found
Classification of damage in structural systems using time series analysis and supervised and unsupervised pattern recognition techniques
Peer reviewedPostprin
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
Explainable machine learning for labquake prediction using catalog-driven features
Recently, Machine learning (ML) has been widely utilized for laboratory earthquake (labquake) prediction using various types of data. This study pioneers in time to failure (TTF) prediction based on ML using acoustic emission (AE) records from three laboratory stick-slip experiments performed on Westerly granite samples with naturally fractured rough faults, more similar to the heterogeneous fault structures in the nature. 47 catalog-driven seismo-mechanical and statistical features are extracted introducing some new features based on focal mechanism. A regression voting ensemble of Long-Short Term Memory (LSTM) networks predicts TTF with a coefficient of determination (R2) of 70% on the test dataset. Feature importance analysis revealed that AE rate, correlation integral, event proximity, and focal mechanism-based features are the most important features for TTF prediction. Results reveal that the network uses all information among the features for prediction, including general trends in high correlated features as well as fine details about local variations and fault evolution involved in low correlated features. Therefore, some highly correlated and physically meaningful features may be considered less important for TTF prediction due to their correlation with other important features. Our study provides a ground for applying catalog-driven to constrain TTF of complex heterogeneous rough faults, which is capable to be developed for real application
Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flow
In this paper, we introduce the novel state-of-the-art Dual-attention
Transformer and Discriminative Flow (DADF) framework for visual anomaly
detection. Based on only normal knowledge, visual anomaly detection has wide
applications in industrial scenarios and has attracted significant attention.
However, most existing methods fail to meet the requirements. In contrast, the
proposed DTDF presents a new paradigm: it firstly leverages a pre-trained
network to acquire multi-scale prior embeddings, followed by the development of
a vision Transformer with dual attention mechanisms, namely self-attention and
memorial-attention, to achieve two-level reconstruction for prior embeddings
with the sequential and normality association. Additionally, we propose using
normalizing flow to establish discriminative likelihood for the joint
distribution of prior and reconstructions at each scale. The DADF achieves
98.3/98.4 of image/pixel AUROC on Mvtec AD; 83.7 of image AUROC and 67.4 of
pixel sPRO on Mvtec LOCO AD benchmarks, demonstrating the effectiveness of our
proposed approach.Comment: Submission to IEEE Transactions On Industrial Informatic
Vibration Monitoring: Gearbox identification and faults detection
L'abstract è presente nell'allegato / the abstract is in the attachmen
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