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Hydrogen is currently considered to be the best clean energy to replace fossil fuels, so hydrogen leak detection is crucial. The current research field of hydrogen sensors focuses on the discovery and optimization of sensitive materials. ZnFe 2 O 4 powder is prepared by the sol-gel method and sintered at different temperatures. The effects of sintering temperature on electrode morphology and hydrogen sensing performance are investigated. The sensitivity reaches a maximum value of −101.32 mV/decade at the sintering temperature of 1100 °C. The sensor has different sensing performance at different operating temperatures. It has the best response value and sensitivity at 450 °C, and the best response/recovery rate and response curve stability at 550 °C. The sensor responds and recovers in seconds. The material has good H 2 selectivity and is suitable for potentiometric hydrogen sensors. CO, NH 3 , and CH 4 have less effect on the response signal and exhibit good long-term stability. • Spinel ferrite is first used as an H 2 detection electrode for potentiometric sensors. • ZnFe 2 O 4 has high response value and sensitivity to hydrogen gas. • ZnFe 2 O 4 has a low detection limit and fast response/recovery rate for hydrogen gas. [ABSTRACT FROM AUTHOR]

This study presents a meticulous investigation facilitated by a bespoke high-pressure test rig. The rig stands as a cornerstone of the research endeavour, providing the capability to simulate diverse leakage scenarios under controlled conditions. With its capacity to replicate conditions akin to real-world hydraulic systems, including various levels of severity such as healthy operation, moderate leakage and severe leakage. The study delves into the intricate analysis of time-series discharge flow rate signals within this experimental framework. The study utilizes sophisticated signal processing techniques, particularly the Continuous Wavelet Transform (CWT), to observe frequency components and temporal occurrences. Despite prevalent challenges distinguishing between leakage severity levels, the CWT-based analysis provides crucial insights into the dominant low frequencies (2.3–3.3 hz) characterising all leakage conditions. An advanced computer vision-based methodology is devised to address the complexities inherent in leakage differentiation, integrating cutting-edge models such as You Only Look Once (YOLO-V7) and You Only Look Once-Neural Architecture Search (YOLO-NAS). These models are meticulously trained using annotated CWT images of flow rates corresponding to different leakage conditions. Notably, the superiority of YOLO-NAS in terms of both speed and accuracy underscores its efficacy in automated leakage detection. In summary, this comprehensive approach, underpinned by the innovative hydraulic test rig and advanced signal processing coupled with state-of-the-art computer vision techniques, presents a significant advancement in the realm of internal leakage detection in hydraulic systems. [ABSTRACT FROM AUTHOR]

Background: Cerebrospinal fluid (CSF) leaks can lead to serious complications if left untreated, making rapid and accurate diagnosis essential. Biomarkers such as β2-transferrin (B2TRF) and β-trace protein are used to detect CSF leaks, but their limitations warrant the exploration of alternative markers. This study investigates the potential of phosphorylated tau at threonine 181 (p-tau181) as a biomarker for CSF leaks. Methods: Samples from 56 subjects were analyzed for B2TRF and p-tau181 using immunoaffinity blotting and chemiluminescent enzyme immunoassay, respectively. Data analysis included Mann–Whitney test to assess the overall difference in median p-tau181 concentrations between B2TRF positive and negative patients and a receiver operating characteristic (ROC) curve analysis to determine optimal p-tau181 cutoff values for predicting B2TRF positivity. Results: p-tau181 levels were significantly higher in B2TRF positive samples compared to negative samples (p < 0.001). ROC analysis showed high diagnostic performance for p-tau181, with an optimal cutoff of 13.22 pg/mL providing 92.0% sensitivity and 93.1% specificity. Excluding hemolyzed samples improved further the diagnostic performances, maintaining high sensitivity (90.9%) and achieving perfect specificity (100.0%). Conclusions: This study highlights the potential of p-tau181 as a valuable biomarker for the detection of CSF leaks due to its high diagnostic accuracy and practical advantages over the current biomarkers. The characteristics of p-tau181 assay being both quantitative and rapid, with high diagnostic accuracy, suggest that it could be a valuable tool for the detection of CSF leaks. Further research are now needed to validate these findings. [ABSTRACT FROM AUTHOR]

As the main support part of the working platform of a high-rise building machine, the bearing pin support (BPS) plays a crucial role in the safety and stability of the platform, the conventional method has the problems of low detection efficiency, low accuracy, and high cost. To improve the accuracy and robustness of the detection algorithm under weak light, this paper proposes an intelligent detection algorithm for the BPS-piece states of the BS-YOLOV8, to improve the feature map utilization and reduce the model leakage detection error detection rate, Swin transformer is used to improve the YOLOV8 backbone network. In addition, the BiFormer attention mechanism is used to weigh the feature map to solve the problem of feature information loss in different feature layers and weak lighting conditions, and then the Scylla-IOU loss function is used instead of the original localization loss function to guide the model to learn to generate a predicted bounding box closer to the real target bounding box. Finally, the BS-YOLOV8 algorithm is used to compare with its classical algorithm on the self-constructed dataset of this study, The results show that the mAP0.5, mAP0.5:0.95, and FPS values of the BS-YOLOV8 algorithm reach 97.9%, 96.3% and 40 under normal lighting. The mAP0.5 value reaches 87.6% under low light conditions, which effectively solves the problems of low detection efficiency and poor detection under low light conditions, and is superior compared to other algorithms. [ABSTRACT FROM AUTHOR]

WO 3 -based optical hydrogen sensors, while advantageous due to their ignition-free operation and resistance to electromagnetic interference, face limitations due to slow response kinetics in hydrogen gasochromic processes. This study presents a novel post-treatment method using aqueous sulfuric acid, which significantly enhances the response speed of mesoporous Pt/WO 3 films. After treatment, the films exhibit an ultrashort response time of 2.4 s towards 4% H 2 /Ar, a marked improvement from 7.1 s prior to the treatment. The treated films also show excellent reversibility of 120 gasochromic cycles. The adsorbed acid layer is proved to be the cause of accelerated hydrogen gasochromic response. This exceptional performance is attributed to increased proton conductivity and the high porosity of the mesoporous structure. A modified model is proposed for the acceleration effect by acid post-treatment. Our post-treatment method represents a significant advancement in the reliable detection of hydrogen leaks at room temperature. • Post-treatment in acid significantly accelerate gasochromic response of Pt/WO 3 film. • A coloration response time of 2.4 s was obtained by treatment in 1 M sulfuric acid. • The adsorbed acid layer is the cause of accelerated hydrogen gasochromic response. • A modified model is proposed for the acceleration effect by acid post-treatment. [ABSTRACT FROM AUTHOR]

In order to reduce the probability of leakage and improve detection accuracy, multiple radar equipment is required to perform multiple detections on the same airspace. Due to the overlap of airspace, there are multiple local tracks reported by various radar equipment that belong to the same target. The local tracks reported by each radar require track fusion to form a system track, and track association is a prerequisite for track fusion. This paper proposes a multi-target and multi-sensor track association analysis method, which can distinguish whether local tracks come from the same target and associate local tracks with the same target, providing conditions for track fusion to form system tracks. This method divides track association into two steps: Coarse association and fine association, which greatly improves the accuracy of track association. [ABSTRACT FROM AUTHOR]

This paper investigates the application of Schlieren flow visualization for detecting leaks in pipelines carrying high-temperature fluids. Two experimental setups were constructed: one with a 25 mm PTFE tube featuring a 2 mm diameter perforation, and another with a 100 mm diameter pipe insulated with an aluminum jacket and featuring a 12 mm leak gap. A single-mirror-off-axis Schlieren system, employing a 150 mm diameter parabolic mirror, was used to visualize the leaks. The temperature of the leaking air varied between 20 and 100 °C, while the ambient temperature was maintained at 14 °C. To quantify the leaks, the coefficient of variation for pixel intensity within the leak region was calculated. Results showed that for the PTFE tube, leaks became detectable when the temperature difference exceeded 34 °C, with the coefficient of variation surpassing 0.1. However, in the insulated pipe, detecting clear leak patterns was challenging. This research demonstrates the potential of Schlieren visualization as a valuable tool in enhancing pipeline leak detection. [ABSTRACT FROM AUTHOR]

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of hydrogen gas detection in pipelines, focusing on the highly flammable nature and low ignition energy of hydrogen. It highlights the limitations of traditional point-based detection methods and emphasizes the need for advanced safety technologies. An integrated approach combining onsite monitoring with Internet of Things (IoT) technology is proposed to enhance systematic safety management and quick leak detection in hydrogen infrastructures. The study reviews various hydrogen detection methods, emphasizing distributed temperature sensing (DTS) techniques to identify leaks through temperature variations. Over nine months of monitoring, the results indicate that DTS temperature variations are more influenced by sensor location than chamber configuration, suggesting that external DTS chamber installation is more effective for leak detection. Additionally, the study integrates Gaussian Process Regression with Machine Learning to predict temperature distribution in pipelines, providing valuable insights for future hydrogen leak detection research. • Developed new DTS technology with enhanced resolution for sensitive hydrogen leak detection. • Confirmed DTS system's reliability against IEC 61757-2-2 standards through rigorous testing. • Integrated IoT and 3D mapping in software for efficient hydrogen leak monitoring. • Found DTS temperature variations more influenced by installation location than chamber design. • Employed GPR and Machine Learning for predicting hydrogen pipeline temperature distribution. [ABSTRACT FROM AUTHOR]

Aiming at the leakage problem of the fluid loop system for the thermal control system of spacecraft caused by the impact of micro-meteoroid and orbital debris, we investigate the dependence of the leak rate, pressure drop and flow characteristics before and after leaking on leak position, inlet pressure, and leaking aperture, calculated in a straight pipeline with single leakage, using stationary and transient three-dimensional computational fluid dynamics simulations with commercial software (FLUENT). The working fluid adopts single-phase water without gravity. It is found that the pressure increases obviously near the leak point, which is caused by the local peak of pressure. As the length between the leak point and the inlet becomes larger, the local peak of pressure and the dimensionless leak rate are smaller. When the inlet pressure increases, the leaked mass flow rate increases; however, the ratio of the leaked flow rate to inlet flow rate decreases. Furthermore, it is observed that the dependence of dimensionless leaking rate on dimensionless leaking aperture has a scaling relation with the scaling exponent approximating to 2, which may be related to the proportion of the vortex formed by the backward flow at the leak hole, the amplitude, and the affected length along the pipe of the local peak of pressure. This study is instructive and meaningful to the leak detection and plugging of fluid loop in space. [ABSTRACT FROM AUTHOR]

Porous graphene membranes (PGMs) have nanopores with single atomic thickness, which enables the precise and stable supply of ultralow flow rate gas below 10−14 Pa·m3·s−1. Different from a conventional channel, the surface diffusion (SD) process in PGM has become increasingly important and unique. However, the physical process and mathematical model of gas molecule transport in nanopores with single atomic thickness remain unclear. These inadequacies constrained the application of PGM in ultrasensitive leak detection. In this paper, the SD process in PGM was investigated using molecular dynamics simulation. A test rig was constructed to verify the simulation results. The nanopores in PGM were quantitatively characterized using a transmission electron microscope. Results show that a transfer region encircling the nanopores was identified, which plays a crucial role in the SD process. Furthermore, the physical model of SD process is described with a two-step model. Finally, a mathematical model of the SD process is established and validated. This paper provides nanoscale insights for an in-depth understanding of the SD process in PGM and promotes ultrasensitive leak detection technology. [ABSTRACT FROM AUTHOR]

Leakage in submarine oil and gas pipelines can result in financial losses and marine environmental pollution.Timely detecting, analyzing and locating leaks can minimize losses and strengthen pipeline quality control. In this paper, theleak detection system of submarine pipeline in offshore oil jetty projects was studied. By comparing and analyzing existing leakdetection technologies, a practical application solution for the submarine pipeline leak detection system in offshore oil jettyprojects was proposed. The combination of negative pressure wave method and flow balance method can improve the accuracy ofleak detection and reduce false alarm rates, providing a reliable and efficient solution for the project. [ABSTRACT FROM AUTHOR]

Anastomotic leak (AL) remains a severe complication following colorectal surgery, leading to increased morbidity and mortality, particularly in cases of delayed diagnosis. Existing diagnostic methods, including computed tomography (CT) scans, contrast enemas, endoscopic examinations, and reoperations can confirm AL but lack strong predictive value. Early detection is crucial for improving patient outcomes, yet a definitive and reliable predictive test, or "gold standard," is still lacking. A comprehensive PubMed review was focused on CT imaging, serum levels of C-reactive protein (CRP), and procalcitonin (PCT) to assess their predictive utility in detecting AL after colorectal resection. Three independent reviewers evaluated eligibility, extracted data, and assessed the methodological quality of the studies. Summarized in detailed tables, our analysis revealed the effectiveness of both CRP and PCT in the early detection of AL during the postoperative period. CT imaging, capable of identifying fluid collection, pneumoperitoneum, extraluminal contrast extravasation, abscess formation, and other early signs of leak, also proved valuable. Considering the variability in findings and statistics across these modalities, our study suggests a personalized, multimodal approach to predicting AL. Integrating CRP and PCT assessments with the diagnostic capabilities of CT imaging provides a nuanced, patient-specific strategy that significantly enhances early detection and management. By tailoring interventions based on individual clinical characteristics, surgeons can optimize patient outcomes, reduce morbidity, and mitigate the consequences associated with AL after colorectal surgery. This approach emphasizes the importance of personalized medicine in surgical care, paving the way for improved patient health outcomes. • Anastomotic leak (AL) after colorectal surgery demands urgent detection. • CRP and PCT show promise as biomarkers for early detection of AL. • Varied diagnostic methods at different postoperative stages enhance AL prediction. • Consider individual patient factors for personalized biomarker interpretation. [ABSTRACT FROM AUTHOR]

Leaks are a constant problem in water distribution systems, resulting in wasted resources, environmental impacts, and financial losses. Thus, it is crucial to develop effective and agile methods to detect network leaks. In this context, this study proposes a leak detection methodology using three different processes. The first consists of treating monitoring data through independent component analysis, and the other two detection processes use the interquartile range (IQR) and matrix profile (MP) techniques, respectively. The methodology is evaluated based on a set of benchmark data. The results indicate that the proposed approach is effective in detecting leaks, with some cases being detected in a few minutes after the beginning of the leak. It is worth mentioning that the IQR method presented better performance in detecting leaks with abrupt onset, whereas the MP method was more efficient in leaks with gradual increase in flow. In summary, the proposed methodology offers a robust and promising approach for fast and accurate leak detection in water distribution networks. [ABSTRACT FROM AUTHOR]

The problem of localizing two leaks using only flow rate and pressure measurements at the boundaries of a pipeline, and under steady-state flow conditions, is ill-posed due to the undetermined nature of the inverse problem, which involves two coupled equations with four unknowns associated with the presence of the leaks: two emitter coefficients and two locations. Therefore, attempting to solve this problem using any method without imposing additional constraints leads to an unbounded solution space, which contains solutions that may be physically meaningless. In this article, we propose a four-algorithm method that incorporates both spatial and hydraulic constraints to filter out physically infeasible solutions. The proposed method is based on Monte Carlo simulations, which use input data from hydraulic instruments installed at the boundaries of the pipeline, as well as random values with predefined probabilities that are bounded by the hydraulic-spatial constraints. The outputs of the method are probability distributions for the four unknowns. To demonstrate the feasibility of the method, results obtained through simulations and experimental testing on a test bed are presented. • A four-algorithm method based on Monte Carlo simulations to localize two leaks in a pipeline. • The method incorporates spatial and hydraulic constraints to eliminate physically infeasible localization estimations. • Tests results using both synthetic and experimental data from a test bed. [ABSTRACT FROM AUTHOR]

Especially if artificial intelligence (AI)-supported decisions affect the society, the fairness of such AI-based methodologies constitutes an important area of research. In this contribution, we investigate the applications of AI to the socioeconomically relevant infrastructure of water distribution systems (WDSs). We propose an appropriate definition of protected groups in WDSs and generalized definitions of group fairness, applicable even to multiple non-binary sensitive features, that provably coincide with existing definitions for a single binary sensitive feature. We demonstrate that typical methods for the detection of leakages in WDSs are unfair in this sense. Further, we thus propose a general fairness-enhancing framework as an extension of the specific leakage detection pipeline, but also for an arbitrary learning scheme, to increase the fairness of the AI-based algorithm. Finally, we evaluate and compare several specific instantiations of this framework on a toy and on a realistic WDS to show their utility. [ABSTRACT FROM AUTHOR]

A This paper proposes a modified real-time transient modelling (MRTTM) framework to address the critical challenge of leak detection and localization in pipeline transmission systems. Pipelines are essential infrastructure for transporting liquids and gases, but they are susceptible to leaks, with severe environmental and economic impacts. MRTTM tackles this challenge with a three-stage operational process. First, "Data Collection" gathers sensor data from designated observation points. Second, the "Detection" stage identifies leaks. Finally, "Decision-Making" utilizes MRTTM to pinpoint the exact leak magnitude and location. This paper introduces an innovative method designed to significantly enhance pipeline leak detection and localization through the application of artificial intelligence and advanced signal processing techniques. The improved MRTTM framework integrates AI for pattern recognition, state space modelling for leak segment identification, and an extended Kalman filter (EKF) for precise leak location estimation, addressing the limitations of traditional methods. This paper showcases the application of MRTTM through a case study using the K-nearest neighbors (KNN) method on a water transmission pipeline for leak detection. KNN aids in classifying leak patterns and identifying the most likely leak location. Additionally, MRTTM incorporates the EKF, enabling real-time updates during transient events for faster leak identification. Preprocessing sensor data before comparison with the leakage pattern bank (LPB) minimizes false alarms and enhances detection reliability. Overall, the AI-powered MRTTM framework offers a powerful solution for swift and precise leak detection and localization in pipeline systems. The functionality of the framework is examined, and the results effectively approve the effectiveness of this methodology. The experimental results validate the practical utility of the MRTTM framework in real-world applications, demonstrating up to 90% detection accuracy and an F1 score of 0.92. [ABSTRACT FROM AUTHOR]

Water scarcity is a significant global challenge, exacerbated by leakages in water distribution networks. This paper addresses the challenge of detecting leakages in rural and urban–rural water supply systems through hydraulic modelling and a sensitivity analysis. Two distinct real-world network models were studied to assess real and simulated leakage scenarios varying in location and magnitude. A distinct leakage detection approach utilizing outflow measurements from hydrants was tested. Additionally, the effectiveness of various statistical measures—such as correlation, angular closeness, Euclidean distance, Manhattan distance, Chebyshev distance, cosine similarity, and Spearman correlation—were evaluated to determine their efficacy in leakage detection. Different methods for identifying leak candidates were explored and compared, either by selecting a single leak candidate based on similarity measures or by identifying a group of candidates to mark leak hotspots. Density-based spatial clustering of applications with noise was used to assess the number of potential leak candidate groups. The study's findings contribute to the optimization of leak detection strategies in water supply networks, particularly in rural settings, where detection is challenging due to scarce measurement datasets, budget restrictions, and operational constraints. [ABSTRACT FROM AUTHOR]

This study introduces a novel deep learning framework for detecting leakage in water distribution systems (WDSs). The key innovation lies in a two-step process: First, the WDS is partitioned using a K-means clustering algorithm based on pressure sensitivity analysis. Then, an encoder–decoder neural network (EDNN) model is employed to extract and process the pressure and flow sensitivities. The core of the framework is the PP-LCNetV2 architecture that ensures the model's lightweight, which is optimized for CPU devices. This combination ensures rapid, accurate leakage detection. Three cases are employed to evaluate the method. By applying data augmentation techniques, including the demand and measurement noises, the framework demonstrates robustness across different noise levels. Compared with other methods, the results show this method can efficiently detect over 90% of leakage across different operating conditions while maintaining a higher recognition of the magnitude of leakages. This research offers a significant improvement in computational efficiency and detection accuracy over existing approaches. [ABSTRACT FROM AUTHOR]

This paper presents a pressure management technique for the reduction of leaks considering as a case study a branched water distribution system. The proposed technique is based on the detection and location of the leak using a genetic algorithm (GA) and pressure control using a Linear Quadratic Regulator (LQR). The validation of the proposed method uses measured pressure and flow data from a laboratory-scale water distribution system and its dynamic model. [ABSTRACT FROM AUTHOR]

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The quickly developing drone technology can be used efficiently in the field of pipeline leak detection. The aim of this article is to provide drone mission concepts for detecting releases from pipelines. It provides an overview of the current applications of natural gas pipeline surveys, it considers environmental conditions by plume modelling, it discusses suitable commercially available sensors, and develops concepts for routine monitoring of pipelines and short term missions for localising and identifying a known leakage. Suitable platforms depend on the particular mission and requirements concerning sensors and legislation. As an illustration, a feasibility study during a release experiment is introduced. The main challenge of this study was the variability of wind direction on a time scale of minutes, which produces considerable differences to the plume simulations. Nevertheless, the leakage rates derived from the observations are in the same order of magnitude as the emission rates. Finally the results from the modeling, the release experiment and possible drone scenarios are combined and requirements for future application derived. [ABSTRACT FROM AUTHOR]

Hydrogen (H2) is positioned as a key solution to the decarbonization challenge in both the energy and transportation sectors. While hydrogen is a clean and versatile energy carrier, it poses significant safety risks due to its wide flammability range and high detonation potential. Hydrogen leaks can occur throughout the hydrogen value chain, including production, storage, transportation, and utilization. Thus, effective leak detection systems are essential for the safe handling, storage, and transportation of hydrogen. This review aims to survey relevant codes and standards governing hydrogen-leak detection and evaluate various sensing technologies based on their working principles and effectiveness. Our analysis highlights the strengths and limitations of the current detection technologies, emphasizing the challenges in achieving sensitive and specific hydrogen detection. The results of this review provide critical insights into the existing technologies and regulatory frameworks, informing future advancements in hydrogen safety protocols. [ABSTRACT FROM AUTHOR]

Magnetic flux leakage (MFL) inspection employs leakage magnetic fields to effectively detect and locate pipeline defects. The spacing between magnetic poles significantly affects the leakage magnetic field strength. While most detectors typically opt for moderate pole spacing for routine detection, this study investigates the propagation characteristics of MFL signals at small pole spacings (under specimen oversaturated magnetization) and their impact on MFL detection. Through finite element simulation and experiments, it reveals a new signal phenomenon in the radial MFL signal By at small pole spacings, the double peak–valley (DPV) phenomenon, characterized by outer and inner peaks and valleys. Theoretical analysis based on the simulation results elucidates the mechanisms for this DPV phenomenon. Based on this, the impact of defect size, pipe wall thickness, and magnetic pole and rigid brush height on MFL signals under small magnetic pole spacings is examined. It is demonstrated that, under a smaller magnetic pole spacing, a potent background magnetic field manifests in the air above the defect. This DPV phenomenon is generated by the magnetic diffusion and compression interactions between the background and defect leakage magnetic fields. Notably, the intensity of the background magnetic field can be mitigated by reducing the height of the rigid brush. In contrast, the pipe wall thickness and magnetic pole height exhibit a negligible influence on the DPV phenomenon. The emergence of the DPV precipitates a reduction in the peak-to-valley difference within the MFL signal, constricting the depth range of detectable defects. However, the presence of DPV increases the identification of defects with smaller opening sizes. These findings reveal the characterization of the MFL signal under small pole spacing, offering a preliminary study on identifying specific defects using unconventional signals. This study provides valuable guidance for MFL detection. [ABSTRACT FROM AUTHOR]

In modern industries, pipelines play a crucial role, both as an essential element in energy transportation (water, gas and electricity) and also in the distribution of these resources. The large size of piping infrastructures, their age and unpredictable external factors are the main difficulties in monitoring the piping system. In this context, the detection and the localization of leaks are challenging but essential, as leaks lead to substantial economic losses. Current methods have many limitations, involving invasive procedures, working only with short pipes or requiring a system shutdown. This paper presents a non-intrusive method based on acoustic signal processing. Leak detection is performed using matched filters, while localization is performed based on the phase diagram representation method and diagram-based entropy computation. Our continuous monitoring system was used for two months and a full comparison with the video inspection-based technique was conducted. The results indicate that this method has a high accuracy, regardless of the length of the pipe. [ABSTRACT FROM AUTHOR]

The detection of gas leaks using acoustic signals is often compromised by environmental noise, which significantly impacts the accuracy of subsequent leak identification. Current noise reduction algorithms based on non-negative matrix factorization (NMF) typically utilize the Euclidean distance as their objective function, which can exacerbate noise anomalies. Moreover, these algorithms predominantly rely on simple techniques like Wiener filtering to estimate the amplitude spectrum of pure signals. This approach, however, falls short in accurately estimating the amplitude spectrum of non-stationary signals. Consequently, this paper proposes an improved non-negative matrix factorization (INMF) noise reduction algorithm that enhances the traditional NMF by refining both the objective function and the amplitude spectrum estimation process for reconstructed signals. The improved algorithm replaces the conventional Euclidean distance with the Kullback–Leibler (KL) divergence and incorporates noise and sparse constraint terms into the objective function to mitigate the adverse effects of signal amplification. Unlike traditional methods such as Wiener filtering, the proposed algorithm employs an adaptive Minimum Mean-Square Error-Log Spectral Amplitude (MMSE-LSA) method to estimate the amplitude spectrum of non-stationary signals adaptively across varying signal-to-noise ratios. Comparative experiments demonstrate that the INMF algorithm significantly outperforms existing methods in denoising leakage acoustic signals. [ABSTRACT FROM AUTHOR]

Fuel leakage detection is crucial for solid oxide fuel cells (SOFCs) as fuel leakage significantly reduces the reliability and lifetime of SOFC devices. In this study, fuel leakage detection using voltage signals is thoroughly investigated. Rectangular SOFCs with four segmented cathodes are prepared and tested with fuel leakage along the seals. Voltammetric tests show that the ring-shaped area on the current-voltage curve is primarily caused by a delay of gas diffusion. Such a ring-shaped area is exacerbated by fuel leakage but is not sufficient as an indicator for detecting fuel leakage. Additionally, the open-circuit voltages measured at all segmented cathodes are well below the theoretical potential of the fuel gas. This is because a localized steam electrolysis circuit is formed when fuel leakage occurs. The measured open-circuit voltage is the potential difference between the two interconnectors in the closed electrolysis circuit. This work contributes to the accurate detection of fuel leakage and the commercialization of SOFCs. [Display omitted] • OCVs tested with fuel leakage are well below equilibrium potential. • Fuel leakage causes localized steam electrolysis. • The ring-shaped area on I–V curves results from a delay in gas diffusion. • Ring-shaped areas are not sufficient as indicators of fuel leakage. [ABSTRACT FROM AUTHOR]

Prospects for the application of orbital lidars for the detection of gas leaks from spacecrafts are investigated. The optical characteristics of the main light-scattering components – molecules and atoms of gases, are estimated at altitudes of 100–600 km from the Earth surface. It is shown that an orbital lidar with modern technical parameters can reliably detect signals from dispersed gas leak components at distances from several tens to one hundred meters from the spacecraft. [ABSTRACT FROM AUTHOR]

Creating an effective deep learning technique for accurately diagnosing leak signals across diverse environments is crucial for integrating artificial intelligence (AI) into the power plant industry. We propose an automatic weight redistribution ensemble model based on transfer learning (TL) for detecting leaks in diverse power plant environments, overcoming the challenges of site-specific AI methods. This innovative model processes time series acoustic data collected from multiple homogeneous sensors located at different positions into three-dimensional root-mean-square (RMS) and frequency volume features, enabling accurate leak detection. Utilizing a TL-driven, two-stage learning process, we first train residual-network-based models for each domain using these preprocessed features. Subsequently, these models are retrained in an ensemble for comprehensive leak detection across domains, with control weight ratios finely adjusted through a softmax score-based approach. The experiment results demonstrate that the proposed method effectively distinguishes low-level leaks and noise compared to existing techniques, even when the data available for model training are very limited. [ABSTRACT FROM AUTHOR]

Anomaly detection systems based on artificial intelligence (AI) have demonstrated high performance and efficiency in a wide range of applications such as power plants and smart factories. However, due to the inherent reliance of AI systems on the quality of training data, they still demonstrate poor performance in certain environments. Especially in hazardous facilities with constrained data collection, deploying these systems remains a challenge. In this paper, we propose Generative Anomaly Detection using Prototypical Networks (GAD-PN) designed to detect anomalies using only a limited number of normal samples. GAD-PN is a structure that integrates CycleGAN with Prototypical Networks (PNs), learning from metadata similar to the target environment. This approach enables the collection of data that are difficult to gather in real-world environments by using simulation or demonstration models, thus providing opportunities to learn a variety of environmental parameters under ideal and normal conditions. During the inference phase, PNs can classify normal and leak samples using only a small number of normal data from the target environment by prototypes that represent normal and abnormal features. We also complement the challenge of collecting anomaly data by generating anomaly data from normal data using CycleGAN trained on anomaly features. It can also be adapted to various environments that have similar anomalous scenarios, regardless of differences in environmental parameters. To validate the proposed structure, data were collected specifically targeting pipe leakage scenarios, which are significant problems in environments such as power plants. In addition, acoustic ultrasound signals were collected from the pipe nozzles in three different environments. As a result, the proposed model achieved a leak detection accuracy of over 90% in all environments, even with only a small number of normal data. This performance shows an average improvement of approximately 30% compared with traditional unsupervised learning models trained with a limited dataset. [ABSTRACT FROM AUTHOR]

This study proposes a YOLOv5 inspection model based on polariser filtering (PF) to improve the recognition accuracy of the machine vision inspection model for tea leaf shoots when operating under intense outdoor light. To study the influence of the polariser parameters on the quality of the tea shoot image datasets, we improved the YOLOv5 algorithm module, inputted the results obtained from the spatial pyramid pooling structure in the backbone module into the neck module, set the up-sampling link of the neck module as a low-level feature alignment (LFA) structure, and used a bounding box similarity comparison metric based on the minimum point distance (mpdiou) to improve the accuracy of the YOLOv5 detection model. The mpdiou loss function is used to replace the original loss function. Experimental results show that the proposed method can effectively address the impact of intense outdoor light on tea identification, effectively solving the problem of poor detection accuracy of tea buds in the top view state. In the same identification environment, the model mAP50 value increased by 3.3% compared to that of the existing best mainstream detection model, and the mAP50-90 increased by 3.1%. Under an environment of light intensity greater than 5 × 10 4   L u x , the proposed YOLOv5s+LFA+mpdiou+PF model reduced the leakage detection rate by 35% and false detection rate by 10% compared to that with YOLOv5s alone. [ABSTRACT FROM AUTHOR]

Deep Learning-based Side-Channel Analysis (DL-SCA) has emerged as a powerful method in the field of side-channel analysis. Current works on DL-SCA primarily rely on publicly available datasets, which typically consist of well-organized and well-aligned training and attack sets. However, this disregards the challenges faced in real-world attacks, where the attack traces are not well-aligned with the training traces as attackers have different levels of control over profiling and attack devices. A network that is capable of identifying areas of leakage and subsequently predicting the leaked values can bypass such difficulty. Therefore, we proposed Arbitrary Trace Attacks, which are placed under the flexible scenario that provides training traces and attack traces with arbitrary sizes. To implement such attacks, we present the Arbitrary Convolutional Neural Network (ACNN), which scans the input trace of arbitrary sizes for leakage area identification and leakage value prediction using a sliding window. Experimental evaluation is conducted on two datasets DPAv4.2 and ASCAD to verify the effectiveness of our approach on unprotected and masked implementation respectively. As a result, the target leakage areas are detected with a significant frequency and the key recovery performance is on par with state-of-the-art. Moreover, the trained model shows the potential for detecting leakage in a general context, that is, detecting leakage of key bytes other than the target one. [ABSTRACT FROM AUTHOR]

Maximizing the net economic benefits of leak management programs provides the most value to water utilities and their customers. Leak management programs with alternative objectives (e.g., to "break even" or to maximize leak reduction) are not necessarily the most efficient use of resources. Combining hydraulic modeling with principles of economics and using a real distribution system to provide a case study, we present a methodology for maximizing the net economic benefits of a leak management program consisting of leak detection and repair activities and pressure management interventions. The optimization results include recommended leak detection and repair intervals for individual district metered areas, optimal siting of both pressure reducing valves and pumps-as-turbines (PATs), and modifications to the distribution system's connectivity to isolate high-pressure areas. The case study results indicate that 10% to 11% of the system's current energy use can be recovered by PATs and that the avoided leakage over the study period is equivalent to 13% to 23% of the system's current customer demand. While leak detection and repair and pressure management are more commonly considered independently, this analysis demonstrates the feasibility of including their interdependencies when optimizing a leak management program, resulting in more useful recommendations. [ABSTRACT FROM AUTHOR]

The use of temperature field to detect leakage in earth-rock dams has a low cost and high efficiency, which is crucial to the safety of dams. This paper briefly introduces the principle of temperature field leak detection, reviews the research results of temperature leak detection, and categorises the research on the detection of leakage through the temperature field of earth-rock dam into three levels: 1) technical research based on temperature field detection; 2) study on the theoretical model of temperature detection; 3) research on numerical calculations of multi-field coupling such as temperature field and seepage field. The main achievements and characteristics of various research levels are reviewed, revealing the development trends of the field of temperature field leakage detection in earth-rock dams. Consequently, UAVs mounted with infrared thermal imaging leak detection technology are integrated, and the technical scheme for patrol leak detection is noted. Finally, future research directions for the temperature field detection of earth-rock dam leakage are explored for the timely and accurate detection of the location of leakage channels and to establish an intuitive and effective judgement and identification method for dam leakage. [ABSTRACT FROM AUTHOR]

Side channels are unintended pathways within target systems that leak internal target information. Side-channel sensing (SCS) is the process of exploiting side channels to extract embedded target information. SCS is well established within the cybersecurity (CYB) domain, and has recently been proposed for medical diagnostics and monitoring (MDM). Remaining unrecognised is its applicability to human–computer interaction (HCI), among other domains (Misc). This article analyses literature demonstrating SCS examples across the MDM, HCI, Misc, and CYB domains. Despite their diversity, established fields of advanced sensing and signal processing underlie each example, enabling the unification of these currently otherwise isolated domains. Identified themes are collating under a proposed domain-agnostic SCS framework. This SCS framework enables a formalised and systematic approach to studying, detecting, and exploiting of side channels both within and between domains. Opportunities exist for modelling SCS as data structures, allowing for computation irrespective of domain. Future methodologies can take such data structures to enable cross- and intra-domain transferability of extraction techniques, perform side-channel leakage detection, and discover new side channels within target systems. [ABSTRACT FROM AUTHOR]

This paper introduces a novel methodology that integrates low‐ and high‐frequency pressure monitoring in the parts of the urban water distribution network characterized by the largest number of repairing operations. This integrated approach aims to pinpoint the source and characterize potentially dangerous pressure variations. The actual behavior of a real operational water distribution network is considered as an example to verify the effectiveness of the proposed methodology. The analysis of the acquired pressure signals allows for mapping frequent pressure surges and identifying their source in the functioning conditions of a big user. Notwithstanding these pressure variations are not overly large, according to the literature, their near‐continuous nature poses a risk of inducing pipe breaks, and then leakage, due to fatigue. The proposed method integrating routine low‐frequency measurements with targeted high‐frequency surveys provides a cost‐effective, proactive management strategy. Key Points: A method is proposed for detecting transient source in Urban Water Distribution NetworksLow‐frequency pressure monitoring initially identifies the most pressure‐stressed section in a high‐maintenance areaHigh‐frequency pressure monitoring allows localizing the transient source [ABSTRACT FROM AUTHOR]

In this subsection, a simulation model of underground pipeline leaks was created. Modeling the flow fields of overhead pipelines and underground pipelines with different soil porosity was conducted. The influence of the underground environment and soil porosity on the pipeline leakage field was analyzed. The influence of changes in the underground environment and soil porosity on the loss of kinetic energy at gas leakage was defined. Thus, the law of change in the characteristics of the sound signal of a leak on the pipe wall was obtained. Based on the results of study, it was determined the area of damage to a gas pipeline which is depended on proportionality of the diameter of the gas jet. As the gas is released from a crack in the gas pipeline and with different porosity of the pipe material, the gas diffusion occurred. As a result, the explosion zone increased which ultimately the release of gases into the environment under the influence of pressure increased. [ABSTRACT FROM AUTHOR]

Poor sealing of gas extraction boreholes is one key to restrict gas extraction efficiency. In this paper, a novel borehole sealed quality detection device for the gas extraction of a coal mine is developed based on the theory of air leakage. By comparing the amount of gas extracted at different test points, it is possible to determine whether there is air leakage around the borehole, and the specific leakage position and leakage amount. Moreover, this device has the advantages of simple operation and a short test period. Based on the above analysis, a corresponding air leakage disposal method was proposed to handle the leaky boreholes. Field tests showed that the air–gas mixture flow in the test borehole was reduced by a factor of approximately 1.55 and the concentration of pure seam gas was increased approximately six times after the disposal of the air leaks. The combination of the leakage disposal method and the leakage detection device can accurately seal the borehole at the position of the leak, thus effectively ensuring the effectiveness of gas extraction from the borehole. The findings have important implications for improving the efficiency of gas extraction in coal mining operations. [ABSTRACT FROM AUTHOR]

Copyright of Wool Textile Journal is the property of National Wool Textile Science & Technology Information Center and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Natural gas is widely used in many homes for cooking, but a lack of gas leak detection has led to large fires and accidents. This article presents the design and implementation of a natural gas detection and extraction system for domestic kitchens in Lima, Peru. The ESP32 microcontroller allowed remote circuit control, resulting in a more convenient setup than the Arduino UNO microcontroller. After calibration of the sensors and their corresponding programming, three actions were established in response to different gas levels: alarm activation, space ventilation and gas extraction, and thermal shutdown. Strategic sensor placement and improved physical presentation of the system were performed to ensure accurate readings and effective deployment. The results demonstrate the proper functioning of the circuit and its ability to prevent accidents related to gas leaks. The designed system offers the advantage of remote monitoring, providing access to the user from any location. In conclusion, this project offers a comprehensive solution to prevent accidents caused by gas leaks in home kitchens. With satisfactory results in terms of operation and rapid response, the project demonstrates its effectiveness in accident prevention. This design offers a practical and accessible solution, improving security and bringing peace of mind to homes. [ABSTRACT FROM AUTHOR]

Wood surface defect detection is a challenging task due to the complexity and variability of defect types. To address these challenges, this paper introduces a novel deep learning approach named SiM-YOLO, which is built upon the YOLOv8 object detection framework. A fine-grained convolutional structure, SPD-Conv, is introduced with the aim of preserving detailed defect information during the feature extraction process, thus enabling the model to capture the subtle variations and complex details of wood surface defects. In the feature fusion stage, a SiAFF-PANet-based wood defect feature fusion module is designed to improve the model's ability to focus on local contextual information and enhance defect localization. For classification and regression tasks, the multi-attention detection head (MADH) is employed to capture cross-channel information and the accurate spatial localization of defects. In addition, MPDIoU is employed to optimize the loss function of the model to reduce the leakage of detection due to defect overlap. The experimental results show that SiM-YOLO achieves superior performance compared to the state-of-the-art YOLO algorithm, with a 9.3% improvement in mAP over YOLOX and a 4.3% improvement in mAP over YOLOv8. The Grad-CAM visualization further illustrates that SiM-YOLO provides more accurate defect localization and effectively reduces misdetection and omission issues. This study highlights the effectiveness of SiM-YOLO for wood surface defect detection and offers valuable insights for future research and practical applications in quality control. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pipeline leakage seriously threatens the efficient and safe gas drainage in coal mines. To achieve the accurate detection and localization of gas drainage pipeline leakages, this study proposes a gas drainage pipeline leakage detection and localization approach based on the pressure gradient method. Firstly, the basic law of gas flow in the drainage pipeline was analyzed, and a pipeline network resistance correction formula was deduced based on the pressure gradient method. Then, a drainage pipeline model was established based on the realizable k - ε turbulence model, and the pressure and flow velocity distribution during pipeline leakage under different leakage degrees, leakage locations, and pipeline negative pressures were simulated and analyzed, thus verifying the feasibility of the pipeline leakage detection and localization method. It is concluded that the positioning errors of pipeline leakage points under different leakage degrees, different leakage positions, and different pipeline negative pressures were 0.88~1.08%, 0.88~1.49%, and 0.68~0.88%, respectively. Finally, field tests were conducted in the highly located drainage roadway 8421 of the Fifth Mine of Yangquan Coal Industry Group to verify the accuracy of the proposed pipeline leakage detection and localization method, and the relative error was about 8.2%. The results show that with increased pipeline leakage hole diameters, elevated pipeline negative pressures, and closer leakage positions to the pipeline center, the relative localization error was smaller, the localization accuracy was higher, and the stability was greater. The research results could lay the foundation for the fault diagnosis and localization of coal mine gas drainage pipeline networks and provide technical support for safe and efficient coal mine gas drainage. [ABSTRACT FROM AUTHOR]

Pipeline networks have a wide range of applications, from the transportation of energy sources such as oil and natural gas to the conveyance and distribution of water resources. However, leaks and ruptures in pipelines can cause significant harm to the environment. Therefore, it is crucial to accurately detect pipeline faults in order to avoid economic losses and protect the environment. In this study, pipeline networks carrying water fluid are represented using graph structures. The graph convolutional network (GCN) algorithm is employed for the detection of leaks and blockages in pipeline networks. Experimental methods are employed to collect the necessary data (pressure data) for the GCN algorithm, creating two datasets by considering five different scenarios. The fault detection performance of the GCN algorithm is compared with other graph machine learning algorithms, namely, RGCN, HinSAGE, and GraphSAGE. The results of this study indicate that the performance of the GCN model surpasses that of the other algorithms. Reviewing the literature, accuracy rates for fault diagnosis in pipeline networks using machine learning algorithms range from 78.51% to 99%. In this study, it is found that the GCN, GraphSAGE, HinSAGE, and RGCN algorithms achieve fault detection accuracies of 91%, 90%, 87%, and 89%, respectively, in pipeline networks. Classical machine learning SVM model was used to compare the performance of graph-based algorithms. It is seen that the performances of the algorithms face the literature and the results are above the literature average. [ABSTRACT FROM AUTHOR]

Due to the significant advantages of methane sensitivity and area-type leakage detection, tunable diode laser absorption spectroscopy (TDLAS) gas detection has been promoted as an effective method for microleakage monitoring in oil and gas stations. However, the output of the TDLAS detector is the integral concentration (IC). Based on the relevant research, the alarm threshold and risk are assessed via the gas concentration rather than the IC. How to evaluate the concentration of leaked gas clouds based on IC from TDLAS detectors is still a challenge. To address this problem, the characteristics of IC and the influence of the laser path, wind speed and leakage rate were studied via computational fluid dynamics (CFD). A neural network classification model (NNCM) was proposed to obtain the probability distribution of the maximal concentration along the laser path (Cmax). The results indicated that the IC is strongly correlated with the Cmax. Considering the accuracy and operability, the NNCM with input features of IC, wind speed and angle of laser path was selected. Field tests showed that the developed model achieved the concentration evaluation of leaked gas clouds. Additionally, the NNCM can also quantify the uncertainty of the results, which avoids misjudgments caused by deviations. [ABSTRACT FROM AUTHOR]

The recent transition to H2-based energy storage demands reliable H2 sensors that allow for easy, fast, and reliable detection of leaks. Conventional H2 detectors are based on the changes of physical properties of H2 probes induced by subsurface H-atoms to a material such as electrical conductivity. Herein, we report on highly reactive gasochromic H2 detectors based on the adsorption of H2 on the material surface. We prepared supraparticles (SPs) containing different types of noble metal nanoparticles (NPs), silica NPs, and the dye resazurin by spray-drying and tested their performance for H2 detection. The material undergoes a distinct color change due to the hydrogenation of the purple resazurin to pink resorufin and, finally, colorless hydroresorufin. The stepwise transition is fast and visible to the naked eye. To further improve the performance of the sensor, we tested the reactivity of SPs with different catalytically active NPs by means of in situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS). We show that the choice of the NP catalyst has a pronounced effect on the response of the H2 indicator. In addition, we demonstrate that the performance depends on the size of the NPs. These effects are attributed to the availability of reactive H-atoms on the NP surface. Among the materials studied, Pt-containing SPs gave the best results for H2 detection. [ABSTRACT FROM AUTHOR]

AbstractThis study proposes an efficient and effective method to estimate the water temperature inside buried gas pipelines during hydrostatic tests. The goal is to develop appropriate correlations that can easily calculate the pipeline’s water temperature, taking into account the adjacent soil temperature variations, pipeline size, thickness, and coating properties. The study first presents a mathematical model of the problem, introducing relevant non-dimensional parameters. Then, the unsteady problem is numerically analyzed, considering all significant variables, and next, the effects of each non-dimensional parameter on the water temperature are carefully examined and evaluated. The numerical simulations show that the difference between the pipeline water temperature and the adjacent soil temperature can be up to 80%, particularly for large-diameter pipelines. The proposed correlations are able to estimate the pipeline water temperature during the hydrostatic test with around 1% deviation from the direct numerical solutions. Additionally, the results indicate that for typical test cases, the pipeline size is the main influential parameter, while the pipeline thickness and material coating with conventional properties do not significantly affect the water temperature variations. [ABSTRACT FROM AUTHOR]

In large public places such as railway stations and airports, dense pedestrian detection is important for safety and security. Deep learning methods provide relatively effective solutions but still face problems such as feature extraction difficulties, image multi-scale variations, and high leakage detection rates, which bring great challenges to the research in this field. In this paper, we propose an improved dense pedestrian detection algorithm GR-yolo based on Yolov8. GR-yolo introduces the repc3 module to optimize the backbone network, which enhances the ability of feature extraction, adopts the aggregation–distribution mechanism to reconstruct the yolov8 neck structure, fuses multi-level information, achieves a more efficient exchange of information, and enhances the detection ability of the model. Meanwhile, the Giou loss calculation is used to help GR-yolo converge better, improve the detection accuracy of the target position, and reduce missed detection. Experiments show that GR-yolo has improved detection performance over yolov8, with a 3.1% improvement in detection means accuracy on the wider people dataset, 7.2% on the crowd human dataset, and 11.7% on the people detection images dataset. Therefore, the proposed GR-yolo algorithm is suitable for dense, multi-scale, and scene-variable pedestrian detection, and the improvement also provides a new idea to solve dense pedestrian detection in real scenes. [ABSTRACT FROM AUTHOR]

Aiming at the complex characteristics of negative pressure waves in low-pressure pipelines inside of buildings, we proposed an estimation method of pressure fluctuation trends based on the robust Kalman filter and the improved VMD, which can be used for leakage detection. The reconstructed baseline signal can accurately describe the fluctuation trend of the negative pressure wave after the pressure drop, and quantitatively express the characteristic difference between the leakage condition and the gas usage condition. The robust Kalman filter was used to estimate the pressure fluctuations. The parameters of VMD were adaptively calculated based on the WAA and discrete scale space. The trend components contained in the IMFs were separated by a reconstruction based on the Fourier series. Based on the simulation signal, the method can accurately restore the trend component contained in the complex pressure signal. Based on the actual signals, the accuracy of small leakage detection is 96.7% and the accuracy of large leakage detection is 73.3%. [ABSTRACT FROM AUTHOR]