Traditional Monitoring Methods Research Articles

Non‐extractive fish diversity assessment in Mediterranean rhodolith beds

Abstract Rhodolith beds, recognized as biodiversity hotspots with high ecological and conservation importance, face local anthropic pressures, including trawl fishing. However, monitoring the diversity associated with this sensitive biogenic habitat in the deep waters of the Mediterranean Sea is challenging. Traditional monitoring methods, such as experimental trawling and trammel nets, are extractive and have sampling limitations in providing comprehensive diversity inventories. In this work, we explore three alternative techniques (still video cameras, environmental DNA [eDNA] and bioacoustics) to monitor fish assemblages associated with rhodolith beds in the Menorca Channel at 45–80 m depth. Ten sites were surveyed with eDNA, and 66 fish taxa were identified, including endangered sharks and cryptic species. The molecular operational taxonomic units (MOTUs) richness varied significantly according to habitat heterogeneity, being higher in homogeneous bottoms, and to exposure to trawling, with higher richness in non‐exposed areas. Bioacoustics, though limited by a reduced library of fish sounds, detected differences in sound richness between habitat types, and the highest abundance of sounds was recorded in the areas with the greatest small‐scale habitat heterogeneity. Underwater video revealed limitations in species detection but proved effective for characterizing small‐scale habitat types and heterogeneity. In summary, each technique provides valuable information at different spatial scales and levels of detail. To assess the feasibility of these techniques for long‐term monitoring, we explored their capabilities and limitations in terms of costs, survey logistics and data output.

Object and person tracking systems to enable extremity dosimetry in nuclear medicine using computational methods

Nuclear medicine (NM) professionals are potentially exposed to high doses of ionising radiation, particularly in the skin of the hands. Ring dosimeters are used by the workers to ensure extremity doses are kept below the legal limits. However, ring dosimeters are often susceptible to large uncertainties, so it is difficult to ensure a correct measurement using the traditional occupational monitoring methods. An alternative solution is to calculate the absorbed dose by using Monte Carlo simulations. This method could reduce the uncertainty in dose calculation if the exact positions of the worker and the radiation source are represented in these simulations. In this study we present a set of computer vision and artificial intelligence algorithms that allow us to track the exact position of unshielded syringes and the hands of NM workers. We showcase a possible hardware configuration to acquire the necessary input data for the algorithms. And finally, we assess the tracking confidence of our software. The tracking accuracy achieved for the syringe detection was 57% and for the hand detection 98%.

The Wireless Health Monitoring for Neonatal Care

Abstract: The wireless health monitoring for neonatal care project addresses the critical need for advanced and non-intrusive health monitoring solutions for newborns in neonatal care settings. Approximately 10% of newborns require assistance at birth, and preterm infants often require specialized stabilization or resuscitation. Traditional monitoring methods, such as electrocardiography (ECG) and pulse oximetry (PO), face challenges in electrode application, delayed HR readings, and potential inaccuracies in assessing cardiac output. This project aims to develop a wireless health monitoring system that overcomes these limitations and offers real-time and accurate monitoring of vital parameters, with a focus on heart rate. The proposed system leverages wireless technology to enable continuous and non-intrusive monitoring, enhancing the efficiency of neonatal care in delivery rooms and neonatal intensive care units (NICUs). By incorporating state-of-the-art sensors and communication protocols, the system aims to provide timely and reliable data on neonatal vital signs. The wireless nature of the solution eliminates concerns related to electrode application difficulties, equipment availability, and the risk of hypothermia.

Enhancing traffic flow monitoring with machine learning integration on cloud data warehousing

With urbanization and rising vehicle ownership rates, global traffic congestion and accidents have become pressing issues. This paper delves into leveraging cloud data warehousing and machine learning to tackle traffic flow monitoring and prediction, along with their potential in intelligent transportation systems. Through comprehensive analysis and case studies, it highlights how modern technology can enhance urban traffic management and services. Initially, the paper underscores the importance of traffic monitoring and prediction, identifying shortcomings in traditional approaches and advocating for machine learning solutions. It then reviews traditional traffic monitoring methods and explores machine learning's role in traffic flow prediction, illustrating its widespread application and evolving trends. Subsequently, the paper delves into the pivotal role of data warehousing in machine learning, encompassing data integration, management, cleaning, and multidimensional analysis. Additionally, it discusses the significance of relation matrices in graph convolution and presents experimental designs and model results for traffic flow prediction. Finally, the paper summarizes research findings, emphasizing the significance and future prospects of machine learning model design and cloud data warehousing in intelligent transportation systems.

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Diabetes Mellitus (DM) as a non-communicable disease (NCD) continues to increase every year. Continuous glucose monitoring (CGM) is essential for effective DM management. However, existing disposable glucose monitoring methods still rely on invasive techniques, cause pain, and lack continuous monitoring capabilities. On the other hand, non-invasive techniques are not feasible for CGM due to the biometric data's complexity and the classification system's inadequate performance. This study aims to develop a non-invasive technology to improve the performance of a non-invasive blood glucose classification system using Artificial Intelligence (AI), specifically Convolutional Neural Network (CNN) and an oversampling technique. The oversampling technique could improve data quantity by balancing the amount of data for each class. This study recruited twenty-three participants in the age range of 20 to 22 years comprising seven females and fifteen males. During data recording sessions, blood glucose levels were simultaneously assessed using a gold-standard glucometer and a non-invasive CGM prototype. The proposed CNN model successfully improved the classification accuracy of non-invasive blood glucose monitoring significantly. With the implementation of oversampling for augmenting the data, the accuracy of the proposed model increased to more than 88%. This study concludes that non-invasive approaches combined with AI technology have the potential to provide a convenient and pain-free alternative to traditional monitoring methods, significantly improving diabetes management and enhancing the overall quality of life for those affected by this condition. These findings could revolutionize the field of diabetes management, offering a more comfortable and accurate monitoring solution that could potentially transform the lives of millions of diabetes patients.

ВИКОРИСТАННЯ ВІДДАЛЕНОГО МОНІТОРИНГУ ДЛЯ ПІДВИЩЕННЯ ЯКОСТІ ТА ЕФЕКТИВНОСТІ У ВИРОБНИЧОМУ СЕРЕДОВИЩІ

The article thoroughly examines the challenges associated with monitoring computer networks and servers in the context of rapid technological advancements and increasing complexity. The effective management and monitoring of networks and servers have become critically important due to the growing exchange of data and the need to ensure their continuous operation. The article proposes solutions based on innovative software tools such as Prometheus and Grafana, which provide real-time monitoring and data visualization capabilities to enhance understanding of the state of networks and servers. Traditional monitoring methods, their limitations, and the advantages of the new approach based on Prometheus and Grafana are discussed. Through these tools, network administrators can gain full control over the status and resource utilization of networks and servers, enabling them to promptly address any issues or deviations in their operation. The successful implementation of a monitoring system using these tools is demonstrated, along with an analysis of the prospects for further development of this approach. The methodology of the research is described in detail, including the main tasks of server and network monitoring, as well as the principles of operation of the Prometheus system. A comparison with traditional monitoring methods is conducted to highlight the advantages of the new approach and its importance in the modern information environment. The article also focuses on the possibilities of extending monitoring to other services and devices such as SSH, email, Jenkins, and Kubernetes. The architectural foundation of Prometheus and Grafana is presented, along with methods of their utilization for data visualization and system status notification. This article will serve as a valuable source of information for network administrators, system engineers, and anyone interested in the monitoring and management of information systems in industrial environments.

Monitoring high-carbon industry enterprise emission in carbon market: A multi-trusted approach using externally available big data

Carbon markets are widely recognized as effective strategies for regulating carbon emissions from the enterprises of the high-carbon industry. Accurate monitoring of emissions in these enterprises is crucial for ensuring fair and trusted carbon trading. However, traditional monitoring methods have encountered various challenges including poor accuracy, low frequency, significant hysteresis and weak credibility. Addressing these concerns, this paper introduces a novel, multi-trusted, high-frequency monitoring approach for the measurement of both direct and indirect carbon emissions of enterprises, utilizing externally available multi-source big data, vertical federated long short-term memory network with a self-attention mechanism (VF-LSTMSA) and additive homomorphic encryption. The efficacy of this model is thoroughly assessed via case studies on six hourly datasets from the power and steel sectors. Our principal findings include: (1) In the current scenario, the proposed VF-LSTMSA model demonstrates superior federated efficiency, improving with the inclusion of more federated participants. (2) The VF-LSTMSA model surpasses other prevalent AI models, achieving a Mean Absolute Percentage Error (MAPE) of less than 9% across power and steel datasets. (3) The advanced high-frequency carbon emission monitoring method exhibits errors of +0.28%, +1.29%, and −0.41% in the current scenario monitoring task, suggesting that the model effectively leverages multi-source externally available big data to precisely monitor the target enterprise's carbon emissions while safeguarding multi-party data security. These findings suggest that the proposed monitoring method can accurately track corporate hourly carbon emissions, significantly improving the carbon trading and verification processes for enterprises

Chlorophyll-a Estimation in 149 Tropical Semi-Arid Reservoirs Using Remote Sensing Data and Six Machine Learning Methods

It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R2 of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.

Localization of Dual Partial Discharge in Transformer Windings Using Fabry–Pérot Optical Fiber Sensor Array

The power transformer is one of the most critical core devices for energy exchange in power systems, and its safe and stable operation is directly related to the reliability of the power grid. Partial discharge is the main cause of insulation degradation and failure of high-voltage electrical equipment. Online monitoring and accurate localization of partial discharge can provide information on the aging of power equipment, which is of great value for improving the safe operation and maintenance of the power grid. Internal dual partial discharge in transformer windings is a more complex type of fault. Since it is located inside the windings, the signal is attenuated and distorted, making it difficult for traditional monitoring methods to capture such partial discharge signal The Fabry–Perot optical fiber sensor is an ultrasonic detection method that can be built into the transformer interior. This sensor has high sensitivity and a small size, enabling flexible placement at different locations inside the transformer for precise partial discharge detection. Especially for the narrow space inside the high and low voltage windings, F–P sensors can form an array, utilizing the array’s directivity to locate the fault points. In this study, an ultrasonic detection system based on the F–P optical fiber sensor array was developed. The system utilizes a directional cross-localization algorithm based on the multiple signal classification (MUSIC) algorithm to accurately locate dual partial discharge sources. This partial discharge detection system was applied to a 35 kV single-phase transformer, enabling the localization of dual partial discharge sources within the high and low voltage windings. Combined with experimental results, this method exhibits high localization accuracy and is particularly suitable for detecting partial discharge phenomena that occur within or between transformer windings.

Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging

Bakanae disease, caused by Fusarium fujikuroi, poses a significant threat to rice production and has been observed in most rice-growing regions. The disease symptoms caused by different pathogens may vary, including elongated and weak stems, slender and yellow leaves, and dwarfism, as example. Bakanae disease is likely to cause necrosis of diseased seedlings, and it may cause a large area of infection in the field through the transmission of conidia. Therefore, early disease surveillance plays a crucial role in securing rice production. Traditional monitoring methods are both time-consuming and labor-intensive and cannot be broadly applied. In this study, a combination of hyperspectral imaging technology and deep learning algorithms were used to achieve in situ detection of rice seedlings infected with bakanae disease. Phenotypic data were obtained on the 9th, 15th, and 21st day after rice infection to explore the physiological and biochemical performance, which helps to deepen the research on the disease mechanism. Hyperspectral data were obtained over these same periods of infection, and a deep learning model, named Rice Bakanae Disease-Visual Geometry Group (RBD-VGG), was established by leveraging hyperspectral imaging technology and deep learning algorithms. Based on this model, an average accuracy of 92.2% was achieved on the 21st day of infection. It also achieved an accuracy of 79.4% as early as the 9th day. Universal characteristic wavelengths were extracted to increase the feasibility of using portable spectral equipment for field surveillance. Collectively, the model offers an efficient and non-destructive surveillance methodology for monitoring bakanae disease, thereby providing an efficient avenue for disease prevention and control.

Artificial Intelligence Assisted Smart Self‐Powered Cable Monitoring System Driven by Time‐Varying Electric Field Using Triboelectricity Based Cable Deforming Detection

AbstractCable monitoring is essential for the prevention of machine malfunctions as machines are operated dynamically. Traditional methods of cable monitoring, conducted through portable or fixed devices, possess the inherent limitations in real‐time damage detection and precise location identification. Herein, a self‐powered, smart cable monitoring system is proposed, utilizing a triboelectric nanogenerator (TENG) as a sensor for the cable and an electric field energy harvester (EFEH) as a power source of the system. Also, the generated electrical outputs from the EFEH are theoretically and experimentally investigated according to the EFEH‐layer numbers, and the optimal number of EFEH‐layers is determined, generating an average electrical power of 2.04 mW. Through hybridization of TENG and EFEH, a synergistic effect is confirmed, resulting in a remarkable 155% enhancement in electrical energy. Consequently, the proposed system is endowed with self‐powered wireless communication capabilities. Additionally, employing a pre‐trained long short‐term memory‐based model, the system can predict the remaining lifespan of the cable with an accuracy rate of 93.7%. Considering these results, the proposed system demonstrates significant potential for industrial cable monitoring applications in the near future.

Farmland quality assessment using deep learning and UAVs

The transformation of agricultural landscapes due to the intensification of farming practices has placed a major threat on habitat and species diversity. Stopping the loss of biodiversity is addressed in various international treaties and adequate strategies are required to assess the initial status of biodiversity and to measure the success of policies. While traditional methods of field-based biodiversity monitoring are time consuming, costly, and highly surveyor-dependent, recent advances in remote sensing and machine learning enable a systematic, more general and cost-effective monitoring. Accordingly, this study investigated whether large-scale biodiversity monitoring campaigns such as the European Monitoring of Biodiversity in Agricultural Landscapes (EMBAL) could be supported by the use of Unmanned Aerial Vehicle (UAV) based remote sensing and Convolutional Neural Networks (CNNs). For this purpose, the Structural Nature Value (SNV) indicator was proposed, which allows an intuitive estimation of the species richness of landscape sections. Subsequently, a CNN was trained to directly predict the SNV. For the accuracy assessment, a survey was conducted in which participants were asked to assess the SNV for various landscape patches, both to obtain an independent test set, and to evaluate the intuitiveness of the concept. The CNN achieved a mean weighted F1-score of 0.81 with an overall accuracy of 81 %. It was shown that the CNN can provide promising results to not only aid large-scale biodiversity monitorings, but to enhance the quality of their results through the automated analysis of fine-resolution UAV imagery.

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages

Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the lag and limitations inherent in traditional drought monitoring methods, this paper proposes a multimodal deep learning-based drought stress monitoring S-DNet model for winter wheat during its critical growth periods. Drought stress images of winter wheat during the Rise-Jointing, Heading-Flowering and Flowering-Maturity stages were acquired to establish a dataset corresponding to soil moisture monitoring data. The DenseNet-121 model was selected as the base network to extract drought features. Combining the drought phenotypic characteristics of wheat in the field with meteorological factors and IoT technology, the study integrated the meteorological drought index SPEI, based on WSN sensors, and deep image learning data to build a multimodal deep learning-based S-DNet model for monitoring drought stress in winter wheat. The results show that, compared to the single-modal DenseNet-121 model, the multimodal S-DNet model has higher robustness and generalization capability, with an average drought recognition accuracy reaching 96.4%. This effectively achieves non-destructive, accurate, and rapid monitoring of drought stress in winter wheat.

Upstrapping to determine futility: predicting future outcomes nonparametrically from past data

BackgroundClinical trials often involve some form of interim monitoring to determine futility before planned trial completion. While many options for interim monitoring exist (e.g., alpha-spending, conditional power), nonparametric based interim monitoring methods are also needed to account for more complex trial designs and analyses. The upstrap is one recently proposed nonparametric method that may be applied for interim monitoring.MethodsUpstrapping is motivated by the case resampling bootstrap and involves repeatedly sampling with replacement from the interim data to simulate thousands of fully enrolled trials. The p-value is calculated for each upstrapped trial and the proportion of upstrapped trials for which the p-value criteria are met is compared with a pre-specified decision threshold. To evaluate the potential utility for upstrapping as a form of interim futility monitoring, we conducted a simulation study considering different sample sizes with several different proposed calibration strategies for the upstrap. We first compared trial rejection rates across a selection of threshold combinations to validate the upstrapping method. Then, we applied upstrapping methods to simulated clinical trial data, directly comparing their performance with more traditional alpha-spending and conditional power interim monitoring methods for futility.ResultsThe method validation demonstrated that upstrapping is much more likely to find evidence of futility in the null scenario than the alternative across a variety of simulations settings. Our three proposed approaches for calibration of the upstrap had different strengths depending on the stopping rules used. Compared to O’Brien-Fleming group sequential methods, upstrapped approaches had type I error rates that differed by at most 1.7% and expected sample size was 2–22% lower in the null scenario, while in the alternative scenario power fluctuated between 15.7% lower and 0.2% higher and expected sample size was 0–15% lower.ConclusionsIn this proof-of-concept simulation study, we evaluated the potential for upstrapping as a resampling-based method for futility monitoring in clinical trials. The trade-offs in expected sample size, power, and type I error rate control indicate that the upstrap can be calibrated to implement futility monitoring with varying degrees of aggressiveness and that performance similarities can be identified relative to considered alpha-spending and conditional power futility monitoring methods.

Applying environmental DNA approaches to inform marine biodiversity conservation: The case of the Ocean Twilight Zone

Effective governance of the global ocean under accelerating environmental and social changes depends on sufficient scientific knowledge that unravels the complexity of dynamic marine ecosystems. In the case of the vast and remote ocean twilight zone (OTZ), management is particularly challenged by the lack of data. Traditional biodiversity monitoring methods are unable to scale efficiently or effectively to address these knowledge gaps and new technologies are needed to inform policy. Environmental DNA (eDNA) has quickly gained traction in recent years as an enabling technology for marine conservation that will play a transformative role in OTZ biodiversity conservation and high seas governance in general. Through a comprehensive review of science and policy literature and an analysis of existing global marine biodiversity data, we discuss the importance of biodiversity conservation in the OTZ, review existing knowledge gaps, and summarize recent developments in eDNA and OTZ biodiversity research. We identify the key components of an eDNA framework that are needed for OTZ biodiversity monitoring and discuss policy implications relevant to the recent Biodiversity Beyond National Jurisdiction (BBNJ) Agreement. We show that an integrated science-policy approach based on transdisciplinary eDNA research is essential for achieving sustainability in the OTZ and the 30×30 target for conserving ocean biodiversity.

Dynamic identification of coupler force of heavy haul locomotive: An effective and long-term intelligent measurement method

Longitudinal impulse, as a common dynamic problem of heavy haul trains, could induce the lateral instability of the coupler and the separation of the locomotive and wagon connection and thus seriously hinder the train running safety. The coupler force undergoes complex changes during longitudinal impulse transmission and exhibits strong nonlinear characteristics, which poses significant challenges to the identification of the coupler force. To address these issues, this paper reports a novel quantitative method for the coupler force identification of heavy haul locomotives. Firstly, the moving average filtering method is used to preprocess the signal of longitudinal relative displacement between the coupler and carbody (LRDCC), and the approximate relative velocity (ARV) feature is also extracted from the preprocessed signal. Then, a Convolutional Neural Network (CNN) model is constructed to extract high-level features from LRDCC and ARV. The model with randomly initialized weight parameters does not require additional training and thus reduces the computational cost. Finally, an Extreme Learning Machine (ELM) is used as the regression layer to process the extracted high-level features and further improve the identification performance. Additionally, the dung beetle algorithm is used to obtain the optimal model parameters to improve the coupler force identification performance. Not only a dynamics simulation but a field test is carried out to show that the proposed method could accurately detect the coupler force of heavy haul locomotives. Furthermore, the proposed method shows promising prospects in replacing the traditional monitoring method under the actual long-term working conditions of trains.

MEMS Technology in Cardiology: Advancements and Applications in Heart Failure Management Focusing on the CardioMEMS Device.

Heart failure (HF) is a complex clinical syndrome associated with significant morbidity, mortality, and healthcare costs. It is characterized by various structural and/or functional abnormalities of the heart, resulting in elevated intracardiac pressure and/or inadequate cardiac output at rest and/or during exercise. These dysfunctions can originate from a variety of conditions, including coronary artery disease, hypertension, cardiomyopathies, heart valve disorders, arrhythmias, and other lifestyle or systemic factors. Identifying the underlying cause is crucial for detecting reversible or treatable forms of HF. Recent epidemiological studies indicate that there has not been an increase in the incidence of the disease. Instead, patients seem to experience a chronic trajectory marked by frequent hospitalizations and stagnant mortality rates. Managing these patients requires a multidisciplinary approach that focuses on preventing disease progression, controlling symptoms, and preventing acute decompensations. In the outpatient setting, patient self-care plays a vital role in achieving these goals. This involves implementing necessary lifestyle changes and promptly recognizing symptoms/signs such as dyspnea, lower limb edema, or unexpected weight gain over a few days, to alert the healthcare team for evaluation of medication adjustments. Traditional methods of HF monitoring, such as symptom assessment and periodic clinic visits, may not capture subtle changes in hemodynamics. Sensor-based technologies offer a promising solution for remote monitoring of HF patients, enabling early detection of fluid overload and optimization of medical therapy. In this review, we provide an overview of the CardioMEMS device, a novel sensor-based system for pulmonary artery pressure monitoring in HF patients. We discuss the technical aspects, clinical evidence, and future directions of CardioMEMS in HF management.

Assessment of in-situ monitoring and tracking the vertical migration of cyanobacterial blooms using LISST-HAB

Cyanobacterial harmful algal blooms (cyanoHABs) are becoming increasingly common in aquatic ecosystems worldwide. However, their heterogeneous distributions make it difficult to accurately estimate the total algae biomass and forecast the occurrence of surface cyanoHABs by using traditional monitoring methods. Although various optical instruments and remote sensing methods have been employed to monitor the dynamics of cyanoHABs at the water surface (i.e., bloom area, chlorophyll a), there is no effective in-situ methodology to monitor the dynamic change of cell density and integrated biovolume of algae throughout the water column. In this study, we propose a quantitative protocol for simultaneously measurements of multiple indicators (i.e., biovolume concentration, size distribution, cell density, and column-integrated biovolume) of cyanoHABs in water bodies by using the laser in-situ scattering and transmissometry (LISST) instrument. The accuracy of measurements of the biovolume and colony size of algae was evaluated and exceeded 95% when the water bloom was dominated by cyanobacteria. Furthermore, the cell density of cyanobacteria was well estimated based on total biovolume and mean cell volume measured by the instrument. Therefore, this methodology has the potential to be used for broader applications, not only to monitor the spatial and temporal distribution of algal biovolume concentration but also monitor the vertical distribution of cell density, biomass and their relationship with size distribution patterns. This provides new technical means for the monitoring and analysis of algae migration and early warning of the formation of cyanoHABs in lakes and reservoirs.

Control Moment Gyroscope Anomaly Detection Based on Sparse Autoencoder

The Control Moment Gyroscope (CMG) is an important executing part of spacecraft, and its anomaly detection has a key influence on the reliability of spacecraft. With the rapid growth of the number of spacecraft and telemetry data, data-driven spacecraft anomaly detection methods have received extensive attention, and such methods may become an important means of spacecraft anomaly detection. However, at present, the spacecraft is faced with the problem of unknown anomalies and lack of abnormal data during orbit. To solve this problem, a sparse autoencoder (SAE) based CMG anomaly detection method was proposed, which takes the telemetry signal of CMG as the object and lacks the sample of anomalous telemetry signal in training data. Firstly, data sample selection and processing were carried out to normalize the data of each telemetry value. Secondly, the SAE model was constructed, and the model was trained with the training set data. Then the sample reconstruction error of the test set was calculated to complete the anomaly detection of the CMG. Finally, it was compared with the traditional artificial threshold monitoring method. The results indicated that the CMG anomaly detection method based on SAE can effectively detect the anomaly samples of the CMG telemetry data, and has better anomaly detection performance than the traditional artificial threshold monitoring method.

A slope management method by monitoring soil volumetric water content and its validation through numerical analysis

Landslides induced by heavy rainfall have become increasingly common in recent years. As the groundwater level rises, the safety factor of slopes tends to decrease. However, traditional monitoring methods, such as employing water-level (pressure) gauges in boreholes, require significant effort. Thus, this study proposes a slope management approach based on monitoring the volumetric water content of the ground surface. The monitored slope, approximately 20 m in height and 30 m in length, consists of topsoil mixed with grass roots and sand with a slight silt admixture, as revealed by two borehole investigations. Soil moisture meters were strategically installed at three locations along the slope (upper, middle, and lower sections) to gauge the volumetric water content. Observations indicated a sensitive increase in ground water content following rainfall. Furthermore, a three-dimensional Finite Element Method (FEM) analysis was employed to explore the correlation between rainfall (intensity and duration) and groundwater content. The FEM analysis also examined the impact of rainfall-induced changes in slope water content on the overall slope safety factor, utilizing 3D point cloud data to model the monitored slope. Qualitative agreement was observed between the monitoring and analysis results, highlighting a decrease in slope safety factor as slope water content increased. These findings underscore the potential of conveniently assessing slope safety factors based on the water content of a slope’s ground surface.