The vision-based pipeline leakage detection is an intelligent leak detection method based on the industrial Internet of Things monitoring platform. It has the advantages of high safety factor and detection visualization. However, in the actual complex industrial environment, there are some problems, such as environmental interference and transmission quality of sensing networks. These low-quality sensing image data bring noise to the vision-based pipeline leakage detection, resulting in the risk of missed detection and false detection. In view of the above problems, we propose a highly reliable pipeline leakage detection method based on machine vision in the complex industrial environment. First, we propose a key frame selection method based on a lightweight image quality assessment, which can adapt to the complex detection environment. The key frame containing available feature information is selected to eliminate the interference of low-quality sensing image data on subsequent feature extraction and realize pipeline leakage video denoising. Then, the C3D network is used to extract space–time features at the same time to detect the leakage of pipeline leakage video. The experimental results show that the effect of our proposed method is better than other existing methods in the complex industrial environment. When the noisy data ratio of the detected image is 15%, the accuracy can be improved by 2.6 percentage points, up to 97.8%, which ensures the reliability of the pipeline leakage detection in the complex industrial environment.
We study numerically a sequence of eddies in two-dimensional electrohydrodynamics (EHD) flows of a dielectric liquid, driven by an electric potential difference between a hyperbolic blade electrode and a flat plate electrode (or the blade–plate configuration). The electrically driven flow impinges on the plate to generate vortices, which resemble Moffatt eddies (Moffatt, J. Fluid Mech. , vol. 18, 1964, pp. 1–18). Such a phenomenon in EHD was first reported in the experimental work of Perri et al. ( J. Fluid Mech. , vol. 900, 2020, A12). We conduct direct numerical simulations of the EHD flow with three Moffatt-type eddies in a large computational domain at moderate electric Rayleigh numbers ( $T$ , quantifying the strength of the electric field). The ratios of size and intensity of the adjacent eddies are examined, and they can be compared favourably to the theoretical prediction of Moffatt; interestingly, the quantitative comparison is remarkably accurate for the two eddies in the far field. Our investigation also shows that a larger $T$ strengthens the vortex intensity, and a stronger charge diffusion effect enlarges the vortex size. A sufficiently large $T$ can further result in an oscillating flow, consistent with the experimental observation. In addition, a global stability analysis of the steady blade–plate EHD flow is conducted. The global mode is characterised in detail at different values of $T$ . When $T$ is large, the confinement effect of the geometry in the centre region may lead to an increased oscillation frequency. This work contributes to the quantitative characterisation of the Moffatt-type eddies in EHD flows.
This study was designed to investigate the effects of hemodynamic environment and design factors on the hydraulic performance and hemocompatibility of interventional blood pumps using computational fluid dynamics methods combined with specialized mathematical models. These analyses assessed how different hemodynamic environments (such as support mode and artery size) and blood pump configurations (including entrance/exit blade angles, rotor diameter, blade number, and diffuser presence) affect hydraulic performance indicators (rotational speed, flow rate, pressure head, and efficiency) and hemocompatibility indicators (bleeding, hemolysis, and thrombosis). Our findings indicate that higher perfused flow rates necessitate greater rotational speeds, which, in turn, reduce both efficiency and hemocompatibility. As the artery size increases, the hydraulic performance of the pump improves but at the cost of worsening hemocompatibility. Among the design parameters, optimal configurations exist that balance both hydraulic performance and hemocompatibility. Notably, a configuration without a diffuser demonstrated better hydraulic performance and hemocompatibility compared to one with a diffuser. Further analysis revealed that flow losses primarily contribute to the degradation of hydraulic performance and deterioration of hemocompatibility. Shear stress was identified as the major cause of blood damage in interventional blood pumps, with residence time having a limited impact. This study comprehensively explored the effects of operating environment and design parameters on catheter pump performance using a multi-faceted blood damage model, providing insights into related complications from a biomechanical perspective. These findings offer valuable guidance for engineering design and clinical treatment.
This work studies nonlinear spatiotemporal stability of two-dimensional electroconvection between two flat plates subjected to a through-flow, using numerical simulations and weakly nonlinear analyses. We found that the traveling speeds of the leading and trailing edges of the wave packet in the nonlinear regime are consistent with the linear ones. We derived for the first time the Ginzburg-Landau equation (GLE) using an amplitude expansion method extending earlier work of Pham and Suslov. This GLE can predict the absolute growth rate even when the parameters are away from the linear critical conditions, outperforming the GLE derived using a multiple-scale expansion method.
In a social network, the strength of relationships between users can significantly affect the stability of the network. In this paper, we use the k-truss model to measure the stability of a social network. To identify critical connections, we propose a novel problem, named k-truss minimization. Given a social network G and a budget b, it aims to find b edges for deletion which can lead to the maximum number of edge breaks in the k-truss of G. We show that the problem is NP-hard. To accelerate the computation, novel pruning rules are developed to reduce the candidate size. In addition, we propose an upper bound based strategy to further reduce the searching space. Comprehensive experiments are conducted over real social networks to demonstrate the efficiency and effectiveness of the proposed techniques.
The use of green intelligent sensing systems which are based on triboelectric nanogenerators have sparked a surge of research in recent years. The development has made significant contributions to the field of promoting human health. However, the integration of an intelligent sensing system with multi-directional triboelectric nanogenerators (TENGs) remains challenges in the field of motion monitoring. To solve this research issue, this study designed a self-powered multifunctional fitness blanket (SF-MFB) which incorporates four TENGs, features multi-sensors and wireless motion monitoring capabilities. It presents a self-powered integrated sensing system which utilizes four TENG sensing units to monitor human motion. Each TENG sensing unit collects the mechanical energy generated during motion. The system is composed of SF-MFB, Bluetooth transmission terminal, and upper computer analysis terminal. Its main purpose is to wirelessly monitor and diagnose human sports skills and enables real-time human-computer interaction. The TENG integrated self-powered sensing system demonstrates practicality in sports skills monitoring, diagnosis, human-computer interaction and entertainment. This research introduces a novel approach for the application of TENG self-powered intelligent integrated sensing system in health promotion.
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