In the past decade, deep learning has been widely applied across various fields. However,
its applicability in open-world scenarios is often limited due to the lack of quantifying …

In the machining industry, tool wear has a great influence on machining efficiency, product
quality, and production costs. To achieve accurate tool wear estimation, a novel CNN …

The need to recover and recycle material toward building a circular economy is increasingly
a global imperative. Nonferrous metals in particular are highly recyclable and can be …

This article investigates the use of hybrid machine learning (HML) for the detection of
anomalous multivariate time-series (MVTS). Focusing on a specific industrial use-case from …

Like any science and engineering field, Instrumentation and Measurement (I&M) is currently
experiencing the impact of the recent rise of Applied AI and in particular Machine Learning …

Seismic inversion is aimed at building a map** from low-resolution seismic data to high-
resolution impedance data. Most of the traditional methods have satisfactory interpretability …

State-of-charge (SOC) estimation is crucial for improving the safety, reliability, and
performance of the battery. Neural networks-based methods for battery SOC estimation have …

Neural network-based computer vision is widely used in industrial image detection due to
the outstanding performance of fast and accurate defect recognition, which can be applied to …

Defect detection is one of the most essential processes for industrial quality inspection.
However, in continuous defect detection (CDD), where defect categories and samples …

Maritime ship classification is essential for effectively monitoring oceanic activities but faces
challenges when using heterogeneous remote sensing data. This research presents a novel …