Real-time pipeline leak detection and localization using an attention-based LSTM approach

Long short-term memory (LSTM) has been widely applied to real-time automated natural gas leak detection and localization. However, LSTM approach could not provide the interpretation that this leak position is localized instead of other positions. This study proposes a leakage detection and localization approach by integrating the attention...

Assessment of Vigilance Level during Work: Fitting a Hidden Markov Model to Heart Rate Variability

This study aimed to enhance the real-time performance and accuracy of vigilance assessment by developing a hidden Markov model (HMM). Electrocardiogram (ECG) signals were collected and processed to remove noise and baseline drift. A group of 20 volunteers participated in the study. Their heart rate variability (HRV) was measured to train...

Towards deep probabilistic graph neural network for natural gas leak detection and localization without labeled anomaly data

Deep learning has been widely applied to automated leakage detection and location of natural gas pipe networks. Prevalent deep learning approaches do not consider the spatial dependency of sensors, which limits leakage detection performance. Graph deep learning is a promising alternative to prevailing approaches as it can model spatial...

Erratum: Plane-wave orthogonal polynomial transform for amplitude-preserving noise attenuation (Geophysical Journal International (2018) 214 (ggy267) DOI: 10.1093/gji/ggy267)

The journal is publishing this Expression of Concern to alert readers while we investigate to determine whether further action is required.

Improving the Resilience of Postdisaster Water Distribution Systems Using Dynamic Optimization Framework

Improving the resilience of water distribution systems (WDSs) to handle natural disasters (e.g., earthquakes) is a critical step toward sustainable urban water management. This requires the water utility to be able to respond quickly to such disaster events, and in an organized manner, to prioritize the use of available resources to restore...

Fast and robust low-rank approximation for five-dimensional seismic data reconstruction

Five-dimensional (5D) seismic data reconstruction becomes more appealing in recent years because it takes advantage of five physical dimensions of the seismic data and can reconstruct data with large gap. The low-rank approximation approach is one of the most effective methods for reconstructing 5D dataset. However, the main disadvantage of...

Color Shift Modeling of Light-Emitting Diode Lamps in Step-Loaded Stress Testing

The color coordinate shift of light-emitting diode (LED) lamps is investigated by running three stress-loaded testing methods, namely step-up stress accelerated degradation testing, step-down stress accelerated degradation testing, and constant stress accelerated degradation testing. A power model is proposed as the statistical model of the...

Spectroscopic evidence for bulk-band inversion and three-dimensional massive Dirac fermions in ZrTe₅

Three-dimensional topological insulators (3D TIs) represent states of quantum matters in which surface states are protected by timereversal symmetry and an inversion occurs between bulk conduction and valence bands. However, the bulk-band inversion, which is intimately tied to the topologically nontrivial nature of 3D Tis, has rarely been...