In this work, different types of artificial neural networks are investigated for the estimation of the time of arrival (ToA) in acoustic emission (AE) signals. In particular, convolutional neural network (CNN) models and a novel capsule neural network are proposed in place of standard statistical strategies which cannot handle, with enough robustness, very noisy scenarios and, thus, cannot be sufficiently reliable when the signal statistics are perturbed by local drifts or outliers. This concept was validated with two experiments: the pure ToA identification capability was firstly assessed on synthetic signals for which a ground truth is available, showing a 10× gain in accuracy when compared to the classical Akaike information criterion (AIC). Then, the same models were tested via experimental data acquired in the framework of a localization problem to identify targets with known coordinates on a square aluminum plate, demonstrating an overreaching precision under significant noise levels.
The paper aims to examine the proliferation of bone marrow cell pool in Djungarian hamsters and the subsequent restoration of their genetic stability after the action of thiotepa (TT). The study involved 36 animals, of which 16 were in the control group (injected with 0.25 ml of physiological solution), and 20 in the experimental group (0.25 ml of thiotepa at a dose of 1.5 mg per 1 kg of body weight). The maximum number of cells with CA amounting to 30.0% was observed 13 hours after TT injection (p≤0.05 between the control and experimental groups) and rapidly declined to 5.7% over subsequent periods by the 37th hour of the experiment (p≤0.05). The results suggest that the restoration of cell pool genetic stability is largely associated with the cell selection mechanisms, which confers an advantage over cell proliferation without chromosome anomalies.
In oil depots and fuel storage facilities, undetected storage tanks damages can lead to the leakage of the oil stored in the soil leading to pollution and economical losses. Leaks are generally due to the perforation of the storage tank floor due to corrosion. The detection of corrosion and leaks is a complicated task, especially for operative tanks with inaccessible floor for detailed inspections and is generally attempted by mean of acoustic emission systems operating from the outer skin of the tank. In this paper, we present a compact sensor node (SN) designed for long-term and real-time acoustic emission monitoring. The SN exploits up to three inexpensive low-frequency sensors based on piezoelectric diaphragms, and it is capable by means of built-in Digital Signal Processing functionalities to process the acquired time waveforms extracting the AE features usually required by testing protocols. An experimental validation on a floating-roof aboveground storage tank 17 m high and 18 m in diameter, filled with water to a level of about 6.2 m, is proposed. Leaks were induced by opening and closing a drainage valve existing at the bottom skirt of the storage tank while acoustic emission signals were recorded at three sensors and processed in real time. Designed nozzles of different diameter, from 1 mm to 9 mm, where used to simulate leakages of different entities.
The results confirm the possibility of detecting and monitoring leaks of various diameters in the low-frequency region 1–2 kHz not traditionally considered by state-of-art acoustic-emission monitoring systems.
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