Monitoring Method Research Articles

Comprehensive Assessment of Carbon Disulfide Exposure in Laboratories—Air and Biological Monitoring Approaches

Carbon disulfide (CS2) is a highly toxic and flammable solvent extensively used in chemical processes and laboratory analyses. This review examines both air and biological monitoring methods for assessing the exposure of laboratory workers to CS2. Emphasis is placed on the measurement of airborne CS2 concentrations and the monitoring of 2-thiothiazolidine-4-carboxylic acid (TTCA) in urine, a key biomarker of exposure. By analysing case studies and practical applications, the paper outlines the effectiveness and limitations of current monitoring techniques. Additionally, the review addresses key challenges such as CS2 volatility, dietary influence on biomarker levels, and the sufficiency of protective measures, including nitrile gloves. It also proposes best practices to mitigate exposure, such as improved ventilation, the use of polyvinyl alcohol gloves, and the substitution of CS2 with less hazardous solvents. This comprehensive review underscores the need for continuous vigilance in managing chemical exposure and offers insights into how laboratories can better protect their workers by integrating air and biological monitoring strategies.

Electric trucks pantograph-catenary interaction condition monitoring method based on semantic segmentation network and linear fitting

Abstract The pantograph-catenary system (PCS) in dual-source electric trucks is crucial for maintaining a stable connection to the power grid, which directly impacts power quality. Ensuring reliable contact between the pantograph and catenary requires accurate detection of the contact point (CPT). However, existing CPT detection methods designed for trains are not well-suited for electric trucks due to differences in structural design. To address this challenge, this paper proposes a novel CPT detection method that integrates semantic segmentation and linear fitting techniques. Firstly, we introduce a lightweight Pantograph and Contact Line Segmentation Network (PCSN), which accurately extracts the regions of the pantograph and contact line. Secondly, a position correction algorithm combined with a least-squares linear fitting technique is employed to detect the CPT of electric trucks. Experimental results demonstrate that the proposed method achieves a detection error within ±5 pixels. In terms of processing speed, it reaches 76.9 FPS on an RTX 3080 GPU, 47.13 FPS on an Intel I9-12900 CPU, and 11.63 FPS on an embedded Jetson TX2 device.

Analysis of the vibration behaviour of a gas turbine during operation

The vibratory behaviour of the gas turbine during operation is analysed in this article. The modern method consists of studying the state of a machine during its operation, to intervene only when the parameters allow it. However, experience has shown that vibration analysis is the most widely used technique to make a reliable diagnosis and to detect the appearance and evolution of most mechanical and hydraulic defects. In our study, we applied the method of vibration analysis of the gas turbine line using monitoring methods to diagnose mechanical defects in shaft bearings. In this sense, the experimental was carried out by measuring and monitoring the level of vibration produced by the turbine, an indicator on its condition was obtained. Should the rise in vibration levels of the machine facilitate the identification of a fault, the subsequent analysis of the machine's vibration characteristics will enable the source of the fault to be determined. It is then possible to determine with accuracy the time before the situation becomes critical.

Capnodynamic assessment of mixed venous oxygen saturation in a porcine experimental endotoxemic model.

Sepsis continues to be a major cause of death and illness globally, posing significant challenges for healthcare professionals. In the pursuit of more accurate and timely monitoring tools, the concept of capnodynamically derived mixed venous oxygen saturation (Capno-SvO2) has emerged as a promising method. Capno-SvO2 provides a non-invasive way to assess and track SvO2 and could serve as an additional tool alongside more invasive methods like the pulmonary artery catheter. This could potentially be of great value in the care of critically ill patients with sepsis, where alternative minimal invasive monitoring methods may vary in reliability. The aim of the current study was to compare capno-SvO2 against values obtained through pulmonary artery blood sample CO-oximetry and continuous fiberoptic SvO2 monitoring, using a well-established porcine experimental sepsis model. Anesthetized pigs were exposed to a standardized endotoxin infusion sepsis protocol, followed by a series of maneuvers typically applied in sepsis care. Simultaneous recordings were done throughout the experiment for all three monitoring methods. Bland-Altman analysis corrected for repeated measurements was used to assess the agreement of absolute values between the paired recording of CO-oximetry and Capno-SvO2 as well as between CO-oximetry and fiberoptic SvO2. The ability of Capno-SvO2 and fiberoptic SvO2 to track changes was assessed by concordance rate. A total of 10 animals and 275 paired datapoints were included in the study. The majority of the animals displayed pronounced hemodynamical instability in response to endotoxin exposure and subsequent treatment interventions. Analysis of all paired data points showed a bias between Capno-SvO2 and CO-oximetry SvO2 of + 1% with 95% limits of agreement of -14% to + 17%. The corresponding numbers for fiberoptic SvO2 and CO-oximetry SvO2 were -4% and -15% to + 8%. The concordance rate as compared to CO-oximetry, were 97% and 93% for Capno-SvO2 and fiberoptic SvO2, respectively. In this experimental sepsis model, continuous, non-invasive Capno-SvO2 generates average absolute values comparable to the gold standard CO-oximetry albeit with relatively wide limits of agreement. Capno-SvO2 displayed a concordance rate of 97% against CO-oximetry and exhibits better trending ability compared to invasive fiberoptic SvO2.

Evaluating ATP testing for distribution system monitoring: comparison to HPC, impact of chlorine quenching, and hold time dependency.

Adenosine triphosphate (ATP) assays have a faster turnaround time and higher sensitivity than traditional cultivation methods for microbial monitoring. Challenges implementing ATP testing include incompatibility with chlorine quenching agents and hold time sensitivity, which are not well-studied. Chlorinated distribution system samples were collected from two Canadian utilities, Metro Vancouver (n = 40 samples) and Halifax Water (n = 283). No significant correlations were observed between heterotrophic cell count (HPC) and cellular ATP, suggesting these do not correlate well in waters with low biological activity (median HPC < 2CFU/mL). However, interpretation of HPC and cATP results (based on the HPC guideline of 100CFU/mL and cATP of 10pg/mL) yielded the same conclusion for 95% of samples, suggesting a potential decision-making framework to replace HPC with cATP. Moreover, cATP correlates better with free chlorine (p < 0.04) compared with HPC for one of the studied systems. Importantly, adding chlorine quench (10% sodium thiosulfate) did not produce significantly different cATP results, nor did analyzing at various hold times of 4-, 6-, and 24-h. This study supports the integration of ATP testing into existing sampling procedures for water utilities, as a sensitive, fast, and reliable monitoring method.

Strengthening Of Students’ Class Attendance Using Biometric Authentication System To Enhance Delivery Of Competence-Based Quality Education

Delivery of competence-based quality education for students' academic achievements strongly correlates with effective student class attendance. In Tanzania, no student registered by the NACTVET is eligible for examination unless he/she has earned 75% of class attendance. In this regard, most education institutions use paper-based class attendance to manage students' class attendance. The system requires students to sign to his/her name as the class session is in progress. Despite this effort made by educational institutions still, the manual process of monitoring and tracking students’ class attendance remains challenging for module facilitators. This study examined the problem associated with the paper-based methods of monitoring and tracking students’ class attendance by collecting primary data using questionnaires from students of higher learning institutions in the Arusha Region. After the data analysis findings revealed that paper-based students’ class attendance has many drawbacks which include time-consuming and students’ signature impersonation. The paper-based class attendance facilitates dishonest students to forge the signatures of the absent students. To address these challenges, the study recommends education institutions implement cost-effective biometric class attendance such as fingerprints or facial recognition scanners, which can automatically monitor and keep students' attendance records on the database without interfering with the class session.

Triggering mechanics and early warning for snowmelt-rainfall-induced loess landslide.

Loess landslides represent a significant natural hazard, especially in arid and semi-arid regions where slopes are exposed to prolonged snowmelt and rainfall infiltration. To analyze landslide stability and conduct early warning evaluations effectively, a robust monitoring system and appropriate equipment are crucial. This study utilizes historical data from the Kalahaisu landslide in Xinyuan County, Ili region, which was monitored using various techniques. The research assesses the time resolution, spatial resolution, and data accuracy of these monitoring methods. Additionally, the study explores the landslide disaster patterns and early warning indicators for the Kalahaisu landslide. It suggested that the deformation of the rainfall- and snowmelt- landslide is not only affected by the seasonal climates but also closely related to the internal structures of the loess. The other observation is that the respective time and spatial resolution of monitoring instrument would significantly affect the the interpretation time of the landslide deformation. Instruments with higher spatial resolution can more effectively identify unstable areas within a landslide, while instruments with higher temporal resolution can provide detailed time-series deformation data. Therefore, real-time and comprehensive monitoring of landslides using a suitable combination of monitoring equipment can provide strong data support for landslide stability evaluation and deformation trend prediction. Full-area monitoring techniques, such as Synthetic Aperture Radar (SAR) and equipment that measures changes in internal properties like deep water content, deep displacement, and pore pressure, offer a more comprehensive and effective approach to monitoring and interpreting landslide deformation. The insights gained from this research may enhance the prediction and prevention of loess landslides in the Ili River valley.

Integrating dynamic high-throughput phenotyping and genetic analysis to monitor growth variation in foxtail millet

BackgroundFoxtail millet [Setaria italica (L.) Beauv] is a C4 graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation.ResultsThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH.ConclusionThe HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.

Unsupervised Fault Detection in a Controlled Conical Tank

Current trends in the Industrial Internet of Things (IIoT) have increased the sensorization of systems, thus increasing data availability to apply data-driven fault detection and diagnosis techniques to monitor these systems. In this work, we show the capabilities of an information-driven method for detecting and quantifying faults in a subsystem common among a broad range of industries: the conical tank. Our main experiment consists of using a simple black-box model (multi-layer perceptron -- MLP) to capture the dynamics of a PID-controlled conical tank built in Simulink and then induce pump failures of different severities; the derived data-driven indicators that we developed increase with the severity of the fault validating its usefulness in this controlled setting. A complementary experiment is carried out to enrich our analysis; this consists of simulating an open-loop discrete-time version of the conical tank to explore a range of fault severity and analyze the distribution of the indicators across this range. All our results show the applicability of the data-driven fault monitoring method in conical tanks subjected to either open- or closed-loop operation.

Intelligent Pressure Monitoring Method of BP Neural Network Optimized by Genetic Algorithm: A Case Study of X Well Area in Yinggehai Basin

While drilling formation pressure monitoring is an important basis for ensuring drilling safety and oil and gas discovery, the calculation of existing pressure monitoring methods is complicated and the accuracy is difficult to improve. Taking the actual well data of well area X in Yinggehai Basin as the object, correlation analysis was first carried out to select and standardize the data features, and relevant effective parameters were extracted. Two kinds of neural networks, back-propagation network BP and back-propagation network GA-BP optimized by genetic algorithm, were used to establish artificial intelligence monitoring models of formation pressure based on 10 kinds of measuring and logging data, respectively. The application effect of the model was evaluated based on the results of monitoring the pressure while drilling. The results show that the monitoring accuracy of the BP neural network model is 91.25%, and that of the GA-BP neural network model is 92.89%. The latter has a better monitoring effect on formation pore pressure. In formation pressure monitoring in areas with a high degree of well control, the introduction of artificial intelligence technology has the advantages of simplicity, speed and high precision, and can provide a reference for other areas of pressure monitoring while drilling.

Utilizing Multiple Triboelectric Nanogenerator Sensors and Signal Processing Technology for Monitoring Periodic Leg Movements of Sleep

High-quality sleep is essential for both physiological and cognitive functions. However, periodic leg movements of sleep (PLMS), an involuntary phenomenon during sleep, affects millions of people worldwide, contributing to sleep fragmentation and functional impairments. The accurate monitoring of PLMS is important for identifying and addressing these issues. Traditional methods, such as polysomnography (PSG), which monitor the bare tibialis muscle movements in clinical environments, may not adequately reflect the natural sleep patterns at home. They are costly and unsuitable for long-term studies. In recent years, there has been growing interest in using flexible sensors for sleep monitoring. Previous studies have applied triboelectric nanogenerators (TENGs) as flexible sensors to detect muscle movements during sleep. However, distinguishing true PLMS from false signals caused by external factors, such as blankets, remains a challenge. This study proposes a method using three TENG sensors placed on the dorsum, ankle, and tibialis, respectively, along with signal processing techniques to enhance the accuracy of PLMS detection. This study provides a cost-effective, comfortable method for PLMS monitoring, with the potential for widespread use in home-based sleep studies and long-term care in the future.

Роль банків у процесах тінізації економіки України

Introduction. The relevance of the article is due to the growing role of banking institutions in the process of shadowing the Ukrainian economy, which negatively affects the financial stability of the state. Problem Statement. The shadow economy has been identified as one of the main threats to the economic stability and security of the state. The study of the role of banking institutions in illegal transactions by entities whose purpose is money laundering, using various schemes and methods of financial fraud, is considered from the standpoint of substantiating directions and effective tools for countering shadowing of the economy. The implementation of such measures at the national and corporate level in accordance with international norms will make it possible to strengthen control over banking transactions, increase stability of the banking and financial systems and, ultimately, improve the level of economic security of the country. Purpose. Analysis of the role of banking institutions in the shadow economy, justification of directions and tools for counteracting the participation of banks in the shadow economy. Methods. The methodological basis of the study is the fundamental provisions of economic theory, theories of management and state regulation of the economy, economic and mathematical modeling, the theory of money, finance and credit, scientific achievements on the shadow economy and measures to counteract its manifestations. Results. The study identifies the role of banking institutions and proposes a typology of forms of their participation in the shadow economy, including: legalization of proceeds of crime; use of correspondent accounts; use of fictitious companies and front persons; offshore accounts; lending and deposit operations of banks; securities manipulation; and other financial fraud. The impact of these mechanisms on the economic stability of the state and the banking system is analyzed. A comparative analysis of international experience in combating money laundering is carried out, and effective tools for preventing shadow operations are identified. The trends and factors of shadowing the Ukrainian economy are analyzed. To assess the impact of banking institutions on the spread of the shadow economy, it is proposed to use the Simpson distribution for shadow cash flows and discount rates with the participation of banking institutions. The instruments of counteracting the participation of banks in the processes of shadowing the Ukrainian economy are substantiated. Conclusions. Further research is planned to focus on the impact of modern digital technologies, such as blockchain and cryptocurrencies, on money laundering through banking institutions. A promising area is the development of innovative methods of financial monitoring and identification of suspicious transactions, in particular with the use of artificial intelligence and machine learning. It is also important to study the effectiveness of international cooperation and develop recommendations for improving the legislative framework for combating money laundering through banking institutions.

Detecting muscle fatigue among community-dwelling senior adults with shape features of the probability density function of sEMG

BackgroundPhysical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features.MethodssEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK.ResultsStatistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy.ConclusionThis study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG’s PDF features in detecting muscle fatigue among the elderly.

Development and Validation of Concept of Innovative Method of Computer-Aided Monitoring and Diagnostics of Machine Components

The monitoring and diagnostic system has been suggested as a non-destructive diagnostic method. The structure and operation of the suggested system can be described by the concept of digital shadow (DS). One of the main DS subsystems is a set of sensors properly placed on the monitored object and coupled with a discrete data processing model created in Matlab/Simulink. The discrete model, as another important DS subsystem of the monitored facility, transfers information about its technical condition to the operator based on data recorded by the sensor system. The digital monitoring model processes the recorded data in the form of the object’s response to actions caused by its operating conditions. This work formalized a mathematical model determining the coupling of the digital model with the sensors placed on the monitored object. The formulated method using DS, due to its sensitivity, enables the detection of the damage in the object at an early stage. The tests allowed for detecting the regularities enabling the determination of the area of damage to the shaft and its size.

Retrieve water quality parameters of urban rivers from hyperspectral images through feature interaction based two-stage method fused with spectral unmixing and spatial relatedness

Monitoring water quality through efficient quantification of water quality parameters (WQPs) is of paramount importance to environmental management of urban rivers. Unmanned aerial vehicle (UAV) remote sensing techniques posed a great opportunity for visualizing spatial distributions of WQPs concentrations with higher flexibility and monitoring frequency compared to satellite remote sensing techniques, assisting to trace potential contamination sources and prevent water quality from degradation. However, current methods of water quality monitoring usually involved large masses of water samples as training data to keep calculation accuracy every time their study area changed, increasing financial cost and incurring time delay in monitoring and evaluating water quality. This study proposed a UAV-based two-stage multidirectional fusion with probabilistic matrix factorization (TSMF-PMF) method in unified framework, to effectively quantify WQPs including chemical oxygen demand (COD), biochemical oxygen demand (BOD), and turbidity from UAV hyperspectral images. TSMF-PMF established feature interaction and outlier feedback module, ensuring relatively high calculation stability with less training samples. The experimental results showed that TSMF-PMF achieved good performance on predicting concentrations of WQPs with coefficient of determination (R2) and mean absolute percentage error (MAPE) ranging from 0.82 to 0.91 and from 5.35 % to 10.23 %. This study established theoretical and technical foundation to optimize environmental management scheme of urban rivers and provided an application demonstration.

Nonlinear Dynamic Process Monitoring Based on Discriminative Denoising Autoencoder and Canonical Variate Analysis

Modern industrial processes are characterized by increasing complexity, often exhibiting pronounced dynamic behaviors and significant nonlinearity. Addressing these dynamic and nonlinear characteristics is essential for effective process monitoring. However, many existing methods for process monitoring and fault diagnosis are insufficient in handling these challenges. In this article, we present a novel process monitoring approach, CVA-DisDAE, which integrates an improved Denoising Autoencoder (DAE) with Canonical Variate Analysis (CVA) to address the challenges posed by dynamic behaviors and nonlinear relationships in industrial processes. First, CVA is employed to reduce data dimensionality and minimize information redundancy by maximizing correlations between past and future observations, thereby effectively capturing process dynamics. Following this, we introduce a discriminative DAE model (DisDAE) designed to serve as a semi-supervised denoising autoencoder for precise feature extraction. This is achieved by incorporating both between-class separability and within-class variability into the traditional DAE framework. The key distinction between the proposed DisDAE and the conventional DAE lies in the integration of a linear discriminant analysis (LDA) penalty into the DAE’s loss function, resulting in extracted features that are more conducive to fault classification. Finally, we validate the effectiveness of the proposed semi-supervised dynamic process monitoring approach through its application to the Tennessee Eastman benchmark process, demonstrating its superior performance.

Screening and validating the optimal panel of housekeeping genes for 4T1 breast carcinoma and metastasis studies in mice

The 4T1 model is extensively employed in murine studies to elucidate the mechanisms underlying the carcinogenesis of triple-negative breast cancer. Molecular biology serves as a cornerstone in these investigations. However, accurate gene expression analyses necessitate data normalization employing housekeeping genes (HKGs) to avert spurious results. Here, we initially delve into the characteristics of the tumor evolution induced by 4T1 in mice, underscoring the imperative for additional tools for tumor monitoring and assessment methods for tracking the animals, thereby facilitating prospective studies employing this methodology. Subsequently, leveraging various software platforms, we assessed ten distinct HKGs (GAPDH, 18 S, ACTB, HPRT1, B2M, GUSB, PGK1, CCSER2, SYMPK, ANKRD17) not hitherto evaluated in the 4T1 breast cancer model, across tumors and diverse tissues afflicted by metastasis. Our principal findings underscore GAPDH as the optimal HKG for gene expression analyses in tumors, while HPRT1 emerged as the most stable in the liver and CCSER2 in the lung. These genes demonstrated consistent expression and minimal variation among experimental groups. Furthermore, employing these HKGs for normalization, we assessed TNF-α and VEGF expression in tissues and discerned significant disparities among groups. We posit that this constitutes the inaugural delineation of an ideal HKG for experiments utilizing the 4T1 model, particularly in vivo settings.

Development of a method for estimating asari clam distribution by combining three-dimensional acoustic coring system and deep neural network

Developing non-contact, non-destructive monitoring methods for marine life is crucial for sustainable resource management. Recent monitoring technologies and machine learning analysis advancements have enhanced underwater image and acoustic data acquisition. Systems to obtain 3D acoustic data from beneath the seafloor are being developed; however, manual analysis of large 3D datasets is challenging. Therefore, an automatic method for analyzing benthic resource distribution is needed. This study developed a system to estimate benthic resource distribution non-destructively by combining high-precision habitat data acquisition using high-frequency ultrasonic waves and prediction models based on a 3D convolutional neural network (3D-CNN). The system estimated the distribution of asari clams (Ruditapes philippinarum) in Lake Hamana, Japan. Clam presence and count were successfully estimated in a voxel with an ROC-AUC of 0.9 and a macro-average ROC-AUC of 0.8, respectively. This system visualized clam distribution and estimated numbers, demonstrating its effectiveness for quantifying marine resources beneath the seafloor.

An intelligent monitoring approach for urban natural gas pipeline leak using semi-supervised learning generative adversarial networks

Traditional gas pipeline leak monitoring methods are subjected to the long response times and high false alarm rates. Deep learning can enhance the accuracy and real-time performance of pipeline leak monitoring. This paper develops an intelligent monitoring approach for urban gas pipeline leaks based on a semi-supervised learning Generative Adversarial Network (SGAN). First, the Isolation Forest algorithm is used to classify anomalies in the collected process parameter data of urban natural gas pipelines. One-Hot Encoding is used to label a small amount of sample data of pipeline leak. Second, both the labeled and unlabeled data are input into SGAN model for semi-supervised learning and classification to monitor the state of urban gas pipeline leak. The methodology addresses the imbalance between pipeline leak status data and normal data. The comparison with GAN and MLP shows that the methodology reaches the highest values in all evaluation metrics (precision = 94.1%, accuracy = 95.63%, recall = 93.93%, F1 score = 94.32%). The superior performance and accuracy make it more effective for urban natural gas pipeline leak monitoring.

Deformation monitoring and data denoising without clean data for sandbars protective flexible mattress in the Yangtze River

The stability of the shoreline, such as sandbars, is vital for navigation and flood control in the Yangtze River, and the flexible mattresses are widely used to protect shorelines and submerged structures from erosion. To determine the long-term stability of the sandbars, it can be achieved by analyzing the monitoring data of the protective flexible mattress deformation. These mattresses are deployed in challenging environments, where traditional monitoring methods fall short in capturing their subtle, short-term deformations. To address this gap, this study introduces a novel deformation monitoring method based on Distributed Fiber Optic Sensing (DFOS) tailored for flexible mattresses and proposes a BiLSTM-CNN model for denoising the measured data, which circumvents the need for clean reference data. Key findings include: (1) For the monitoring of flexible mattresses, armored optical sensing cables should be installed from the perimeter inward, arranged in a U-shape post-submersion on the surface of the flexible mattress, and installed simultaneously with construction. The strain is measured by the single-end BOTDR. (2) The proposed BiLSTM-CNN model which is established without clean data, outperformed traditional denoising techniques like wavelet transform and Kalman filtering, effectively preserving essential strain responses while suppressing noise, enabling accurate depiction of the deformation of the flexible mattress, and demonstrating its applicability in complex monitoring scenarios. (3) The flexible mattress monitoring method was successfully deployed in the Yudaizhou shoreline protection engineering project. The BiLSTM-CNN denoising model, combined with a post-adjust method to process the different periods’ data, resulted in analyzable strain data. Comprehensive analysis of long-term strain monitoring data indicates that the flexible mattress in the monitoring area remains stable. This study’s significant contribution lies in advancing the methodology for monitoring and denoising strain data from underwater flexible mattresses, thereby enhancing the understanding and management of erosion protection systems in challenging environments.