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Anomaly detection in pipe networks is critical for targeted maintenance within water distribution systems. A paired impulse response function (termed paired-IRF) technique to detect anomalies in pipe networks has been previously proposed by the authors, and its high sensitivity and accuracy have been proved through laboratory experiments. In this paper, numerical experiments were conducted to individually investigate the features of the response induced by different anomalies (including leaks, blockages, and wall-deteriorated sections) in the paired-IRF trace. With these features characterized from these case studies, an anomaly characterization framework was developed to automatically determine the type, location, and size of anomalies by recognizing the features in the paired-IRF trace. The robustness of the framework was tested on a numerical pipe network where leaks, junctions, a short section of blockage, and a wall deteriorated section existed. These different types of anomalies were successfully detected and classified using the characterization and recognition process. [ABSTRACT FROM AUTHOR]

With the advancement of Fuel Cell Vehicles (FCVs), detecting hydrogen leaks is critically important in facilities such as hydrogen refilling stations. Despite its significance, the dynamic response performance of hydrogen sensors in confined spaces, particularly under ceilings, has not been comprehensively assessed. This study utilizes a catalytic combustion hydrogen sensor to monitor hydrogen leaks in a confined area. It examines the effects of leak size and placement height on the distribution of hydrogen concentrations beneath the ceiling. Results indicate that hydrogen concentration rapidly decreases within a 0.5–1.0 m range below the ceiling and declines more gradually from 1.0 to 2.0 m. The study further explores the attenuation pattern of hydrogen concentration radially from the hydrogen jet under the ceiling. By normalizing the radius and concentration, it was determined that the distribution conforms to a Gaussian model, akin to that observed in open space jet flows. Utilizing this Gaussian assumption, the model is refined by incorporating an impact reflux term, thereby enhancing the accuracy of the predictive formula. [ABSTRACT FROM AUTHOR]

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]

Recent years have seen increased interest in object detection‐based applications for fire detection in digital images and videos from edge devices. The environment's complexity and variability often lead to interference from factors such as fire and smoke characteristics, background noise, and camera settings like angle, sharpness, and exposure, which hampers the effectiveness of fire detection applications. Limited picture data for fire and smoke scenes further challenges model accuracy and robustness, resulting in high false detection and leakage rates. To address the need for efficient detection and adaptability to various environments, this paper focuses on (1) proposing a cloud‐edge collaborative architecture for real‐time fire and smoke detection, incorporating an iterative transfer learning strategy based on user feedback to enhance adaptability; (2) improving the detection capabilities of the base model YOLOv8 by enhancing the data augmentation method and introducing the coordinate attention mechanism to improve global feature extraction. The improved algorithm shows a 2‐point accuracy increase. After three iterations of transfer learning in the production environment, accuracy improves from 93.3% to 96.4%, and mAP0.5:0.95 increases by nearly 5 points. This program effectively addresses false detection issues in fire and smoke detection systems, demonstrating practical applicability. [ABSTRACT FROM AUTHOR]

Leak detection in nuclear reactor coolant systems is crucial for maintaining the safety and operational integrity of nuclear power plants. Traditional leak detection methods, such as acoustic emission sensors and spectroscopy, face challenges in sensitivity, response time, and accurate leak localization, particularly in complex piping systems. In this study, we propose a novel leak detection approach that incorporates a rigid guide tube into the insulation layer surrounding reactor coolant pipes and combines this with an advanced detection criterion based on Frequency Center of Gravity shifts and Signal-to-Noise Ratio analysis. This dual-method strategy significantly improves the sensitivity and accuracy of leak detection by providing a stable transmission path for ultrasonic signals and enabling robust signal analysis. The rigid guide tube-based system, along with the integrated criteria, addresses several limitations of existing technologies, including the detection of minor leaks and the complexity of installation and maintenance. By enhancing the early detection of leaks and enabling precise localization, this approach contributes to increased reactor safety, reduced downtime, and lower operational costs. Experimental evaluations demonstrate the system's effectiveness, focusing on its potential as a valuable addition to the current array of nuclear power plant maintenance technologies. Future research will focus on optimizing key parameters, such as the threshold frequency shift (Δf) and the number of randomly selected frequencies (N), using machine learning techniques to further enhance the system's accuracy and reliability in various reactor environments. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review of the methods and techniques developed for detecting leaks in water distribution systems, with a focus on highlighting their strengths, weaknesses, and areas for future research. Given the substantial economic, social, and environmental impacts of undetected leaks, timely detection and precise location of leaks are critical concerns for water authorities. This review categorizes existing methods into traditional approaches, such as manual sounding, and modern techniques involving smart water management and sensor technologies. A multidimensional bibliometric analysis was employed to systematically identify, select, and evaluate 600 scholarly articles on water leak detection, sourced from the Scopus database over a 23-year period (2000–2023). The paper evaluates each method based on leak sensitivity, burst detection, continuous monitoring, alarm accuracy, and implementation costs. Novel insights include an analysis of emerging smart water technologies and their integration into real-world water distribution networks, offering improved efficiency in leak detection. The paper also identifies key gaps in current research and suggests future directions for advancing the accuracy and cost-effectiveness of these technologies. [ABSTRACT FROM AUTHOR]

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks. [ABSTRACT FROM AUTHOR]

In the Internet, ASs are interconnected using BGP. However, due to a lack of security considerations in the design of BGP, a series of security issues arise during the propagation of routing information, such as prefix hijacking, route leakage, and AS path tampering. Therefore, this paper conducts research on the detection of route leakage. By analyzing BGP routing information, we abstract the routing propagation relationship between ASs into a network topology graph, and extract graph features from the graph abstracted from routing data at certain time intervals. Based on the structural robustness features and centrality measurement features of the graph, we determine whether a route leakage has occurred during the current time period. To this end, we use machine learning methods and propose a weighted voting model. This model trains multiple single models and assigns weights to them, and through the weighted analysis of the results of multiple models, it can determine whether a route leakage has occurred. In addition, to determine the corresponding weights, we use genetic algorithms for identifying route leaks. The experimental results show that the method used in this paper has a high accuracy rate, and compared with a single model, it performs better on multiple datasets. [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]

The parameters of the magnetic excitation circuit can significantly impact the Magnetic Flux Leakage (MFL) signal of rail inspection. This research proposes a unique approach for optimizing the parameter of the magnetic excitation circuit in MFL detection based on finite element simulation. The magnetic circuit equation was established firstly to find the key parameter that affected the MFL signal most significantly. Then, the size parameters, including the thickness of the magnets, the cross-sectional width, and the thickness of the steel brush, were compared and analyzed in the finite simulation model. Meanwhile, the detection parameter of the liftoff value was compared and analyzed by finite element simulation. Finally, the experiments were designed to verify the optimization method. The finite element situation and experiment results showed that the MFL signal did not change linearly as the parameters were changed. This method demonstrates that the parameters of the MFL detection equipment can be optimized by the simulation results. [ABSTRACT FROM AUTHOR]

This research paper underscores the critical importance of advancing submarine technology by proposing a fully hydrogen‐powered submarine integrated with an air‐independent propulsion (AIP) system and artificial intelligence (AI)‐driven safety measures. Such an innovative approach not only addresses the limitations of traditional diesel‐electric submarines but also promises to revolutionize naval operations. The adoption of hydrogen as the primary power source significantly extends the submarine's operational range, reducing its dependence on fossil fuels and minimizing environmental impact. The incorporation of AIP technology enables sustained underwater endurance, reducing vulnerability and enhancing mission effectiveness. Concurrently, AI‐driven safety measures provide robust leak detection and safe hydrogen handling, ensuring the well‐being of the crew while optimizing operational efficiency. This integrated solution represents a transformative step toward achieving a more sustainable, capable, and stealthy submarine fleet for modern warfare scenarios. [ABSTRACT FROM AUTHOR]

The efficient detection of leakages in water distribution networks (WDNs) is crucial to ensuring municipal water supply safety and improving urban operations. Traditionally, machine learning methods such as Convolutional Neural Networks (CNNs) and Autoencoders (AEs) have been used for leakage detection. However, these methods heavily rely on local pressure information and often fail to capture long-term dependencies in pressure series. In this paper, we propose a transformer-based model for detecting leakages in WDNs. The transformer incorporates an attention mechanism to learn data distributions and account for correlations between historical pressure data and data from the same time on different days, thereby emphasizing long-term dependencies in pressure series. Additionally, we apply pressure data normalization across each leakage scenario and concatenate position embeddings with pressure data in the transformer model to avoid feature misleading. The performance of the proposed method is evaluated by using detection accuracy and F1-score. The experimental studies conducted on simulated pressure datasets from three different WDNs demonstrate that the transformer-based model significantly outperforms traditional CNN methods. [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]

Boiler tube leaks significantly reduce the operational availability of power units, yet their early detection and prediction have not been fully realised in the industry. This paper introduces a novel approach employing deep feedforward neural networks for early detection of boiler tube leaks in pulverised coal-fired boilers. Early detection enhances repair planning, minimising downtime and production losses. It also improves monitoring and control of boiler tube failures, thereby optimising power plant operations and revenue. Diverse deep neural network models were developed and rigorously tested by leveraging 9 years of operational data (2012-2020). Exhaustive hyper-parameter optimisation, involving seven parameters, substantially improved predictive accuracy. By achieving training and testing accuracies of 82.8% to 99.3%, the study assessed their ability to detect boiler tube leaks over the same 9-year span, providing insights into leak detection capabilities. The models notably predicted all 12 identified tube leak events, averaging a 14-day lead time before boiler shutdown. In addition to leak prediction, a leak detection matrix was devised to analyse residual behaviour and reduce the likelihood of false alarms. However, the models' predictive performance was observed to be limited to the following year, with satisfactory results for 2021 only. Incorporating the 2021 data into retraining significantly improved the predictions for 2022. The study concludes that while the models demonstrate robust short-term prediction capabilities, they require continuous retraining to maintain accuracy and relevance. This ongoing refinement is essential for keeping the models up-to-date and reliable in predicting future boiler tube leaks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China University of Petroleum is the property of China University of Petroleum 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.)

The introduction of remote sensing technologies, AI and big data analytics in the utility sector is warranted by the need to provide critical services with the least disruption to customers, but also to enable preventive maintenance, extend the life cycle of infrastructure components and reduce grid loss—or overall, to exhibit 'durability' and 'resilience' when faced with the certainty of breakage and decay. In this paper, we first explore the concept of 'resilience' and the nature of practice from a performativist perspective in order to set the scene for discussing the impact of 'datafication' on maintenance practices and infrastructure durability. We then describe an instance of introducing remote sensing technologies in district heating network surveillance and leak detection: drone-operated thermographic cameras and underground wire sensors. Based on insights from this case study, we discuss the specificity of data-driven infrastructure maintenance practices, and what it means to exhibit practical resilience in relation to how such practices unfold, interrelate and evolve over time. We reflect on how the use of remote sensing technologies and data analytics (1) potentially changes district heating workers' epistemic worlds (i.e., how knowledge emerges, is negotiated and ordered in practice) and (2) provides opportunities for 'messy' pipe repair work to tacitly adopt proactive and preventive logics to meet continuously evolving organizational and societal needs. [ABSTRACT FROM AUTHOR]

The saline aquifer CCS is a crucial site for carbon storage. Safety monitoring is a key technology for saline aquifer CCS. Current CO2 leakage detection methods include microseismic, electromagnetic, and well-logging techniques. However, these methods face challenges, such as difficulties in determining CO2 migration fronts and predicting potential leakage events; as a result, the formulation of test timing and methods for these safety monitoring techniques are somewhat arbitrary. This study establishes a gas–water two-phase seepage model and solves it using a semi-analytical method to obtain the injection pressure and the derivative curve characteristics of the injection well. The pressure derivative curve can reflect the physical properties of the reservoir through which CO2 flows underground, and it can also be used to determine whether CO2 leakage has occurred, as well as the timing and amount of leakage, based on boundary responses. This study conducted sensitivity analyses on eight parameters to determine the impact of each parameter on the bottom-hole pressure and its derivatives, thereby obtaining the influence of its parameters on different flow stages. The research indicates that, when a steady-state flow characteristic appears at the outer boundary, CO2 leakage will occur. Additionally, the leakage location can be determined by calculating the distance from the injection well. This can guide the placement and measurement of safety monitoring methods for saline aquifer CCS. The method proposed in this paper can effectively monitor the timing, location, and amount of leakage, providing a technical safeguard for promoting CCS technology. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays 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.)

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]

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 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]

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]

For the modern stage of science and technology development, the problem of information protection from unauthorized access is becoming relevant. The object of research is the process of monitoring information protection objects for timely detection and securing of leakage channels. The subject of research is ensuring automatic control of monitoring means for information protection objects. The article presents the results of the development of a method for automatic control of information protection object monitoring means by improving the control process, taking into account the peculiarities of the potential threats impact. The advantage of this study is the involvement of artificial intelligence in monitoring information protection objects in order to timely detect new threats to leakage channels. The essence of the method is to use a cybernetic approach to the development of adaptive control systems for monitoring information protection objects. The structure of the modeling method is considered, the procedure for assessing the adequacy and accuracy of determining the parameters of monitoring information protection objects. Proposals for implementing a method for controlling information protection object monitoring means based on associative control devices are substantiated. Schemes for implementing an associative control device for determining the parameters of an information protection object are presented, and the results of the practical implementation of the proposed method are also presented. A feature of the study are the developed associative control devices that provide the accumulation of knowledge in the process of learning about the threats of information leakage to the object of protection. The results of the study allow to improve the quality of detecting threats of information leakage to the object of protection and take into account possible changes in the characteristics of promising information leakage channels [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]

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]

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]

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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]

Pipeline infrastructure plays a critical role in the transportation of vital resources, including oil, gas, and water. However, pipeline failures and leaks can have devastating consequences, resulting in environmental damage, economic losses, and risk to human life. Traditional methods of leak detection, such as visual inspection and pressure testing, are often time-consuming, labor-intensive, and unreliable. With the advent of wireless sensor networks (WSNs), there is an opportunity to revolutionize pipeline monitoring and leak detection. In this paper, we present a system that can monitor and detect leakage early, to enable engineers carry out prompt maintenance. This is made possible by the use of a network of nodes in a WSN, placed along a pipeline, each of which is capable of measuring and reporting varying flow rates, indicative of possible leakages. The system design consists of three major layers namely, the nodes layer, the cloud layer (for data logging), and the reporting layer. Tests were conducted under various conditions. The results show that with no leakages, the average flow rates for nodes 1, 2, 3, and 4 were 16.89747978, 16.89935602, 16.90978163, and 16.93380634 respectively. Furthermore, percentage flow rate differences of -0.02550353, 29.959675, and 30.3944134 were recorded for nodes 2, 3, and 4 respectively, after leakages occurred. The high values of the percentage difference for nodes 3 and 4 indicate a significant discrepancy in flow rate, worthy of physical inspection. The system is capable of detecting faults and leakages, even in the event of sensor failure, or network disruption. [ABSTRACT FROM AUTHOR]

The volume of water loss because of leakage in the conveyance pipe has been alarming. Old and poorly constructed pipelines, inadequate corrosion protection, poorly maintained valves, and mechanical damage contribute to leakage. Water-carrying pipes were buried underground, so tracing leak points manually could be tasking, if not impossible. This work was to report on the effectiveness of a developed Automated Leak and Water Quality Detection (ALWQD) system. This device can detect leaks in the piped water system automatically and can also report any deterioration in the quality of water that flows through affected pipes. The ALWQD consisted of several drainpipe connections, pipe accessories, electronic components, and sensors to monitor water quality impairment. The control signal was the solenoid valves that interfaced with the ESP-32 microcontroller boards placed on the pipe manifold at intervals, along with water quality monitoring sensors of turbidity, Total Dissolved Solids (TDS), and pH. The fabrication and testing of the device followed standard procedures. Testing of ALWQD was done at 0, 5, and 10 minutes under load and no-load conditions, with average variation in reading recorded after three trials. The findings indicated that the efficiency of ALWQD was between 70% and 80%, which could be improved upon. The trend in the results of the monitored parameters was not different from that of similar previous work. Leaks caused pressure drops and disallowed the full flow of water found at pipe joints, which could be a pathway for the intrusion of contaminants into the water conveyance system. [ABSTRACT FROM AUTHOR]

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]