Search Results (665)

Leakage in water distribution systems (WDSs) causes a waste of water resources and increased carbon emissions. Rapid and accurate leakage localization to reduce the waste of water resources caused by leakages is an important way to overcome the problem. Using spatiotemporal correlation in monitoring data forms the basis of a leakage localization method proposed in a previous study. It is crucial to acknowledge that the chosen parameter settings significantly influence the localization performance of this method. This paper primarily seeks to optimize three essential parameters of this method: localization metrics weight (LMW), score threshold (ST), and the indicator of detection priority (IDP). LMW evaluates the similarity between simulated and measured pressure residuals. ST determines the size of the datasets involved in the spatial clustering, and IDP quantifies the likelihood of a true leakage within the candidate region. The leakage localization method is tested on a realistic full-scale distribution network to assess leakage flow rates and sensor noise. The results show that the optimized parameter settings could improve the efficiency and accuracy of leakage localization. Further, the findings indicate that the optimized parameter settings can enhance the effectiveness and precision of leakage localization. Full article

With shifts in lifestyle and dietary patterns, obesity has become an increasing health issue among younger demographics, particularly affecting young adults. This trend is strongly associated with a heightened risk of developing chronic diseases, especially cardiovascular conditions. However, conventional health monitoring systems are often limited to basic parameters such as heart rate, pulse pressure (PP), and systolic blood pressure (SBP), which may not provide a comprehensive assessment of cardiac health. This study introduces an intelligent heart health monitoring system that leverages the Internet of Things (IoT) and advanced sensor technologies. By incorporating IoT-based sensors, this system aims to improve the early detection and continuous monitoring of cardiac function in young obese women. The research employed a TERUMO ES-P2000 to measure blood pressure and a PhysioFlow device to assess noninvasive cardiac hemodynamic parameters. Through precise sensor data collection, the study identified key indicators for monitoring cardiovascular health. Machine learning models and big data analysis were utilized to predict cardiac index (CI) values based on the sensor-derived inputs. The findings indicated that young obese women showed significant deviations in blood pressure (SBP and PP) and cardiac hemodynamic metrics (SVI, EDV, and ESV) at an early stage. The implementation of signal processing techniques and IoT sensors enhanced the CI prediction accuracy from 33% (using basic parameters like heart rate, PP, and SBP) to 66%. Moreover, the integration of extra sensor-based parameters, such as Stroke Volume Index (SVI) and Cardiac Output (CO), along with the use of color space transformations, successfully improved the prediction accuracy of the original data by 36.68%, increasing from 53.33% to 90.01%. This represents a significant improvement of 30.01% compared to the existing technology’s accuracy of 60%. These results underscore the importance of utilizing sensor-derived parameters as critical early indicators of cardiac function in young obese women. This research advances smart healthcare through early cardiovascular risk assessment using AI and noninvasive sensors. Full article

This work presents a data-driven approach for diagnosing sensor faults and leaks in hydraulic pipelines using neuro-fuzzy Zonotopic Kalman Filters (ZKF). The approach involves two key steps: first, identifying the nonlinear pipeline system using an adaptive neuro-fuzzy inference system (ANFIS), resulting in a set of Takagi–Sugeno fuzzy models derived from pressure and flow data, and second, implementing a neuro-fuzzy ZKF bench to detect pipeline leaks and sensor faults with adaptive thresholds. The learning phase of the neuro-fuzzy systems considers only fault-free data. Fault isolation is achieved by comparing zonotopic sets and evaluating a fault signature matrix. The method accounts for parametric uncertainty and measurement noise, ensuring robustness. Experimental validation on a hydraulic pipeline demonstrated high precision (up to 99.24%), recall (up to 99.20%), and low false positive rates (as low as 0.76%) across various fault scenarios and operational points. Full article

River ecosystems are integral to sustainable environmental development, playing a crucial role in understanding suspended solid matter (SSM) transport dynamics and soil conservation. Accurate monitoring of SSM concentrations in watersheds is foundational for these studies. This research introduces and evaluates a novel HHSW·NUG-1 photoelectric sand meter, specifically designed for SSM measurement. Its reliability was validated at three hydrological stations, including Xiaolangdi. The instrument, based on light scattering principles, is optimized for environments with high SSM loads and rapid flow rates. Laboratory tests indicate a measuring range of 0 to 730 kg/m³, and field trials show effective operation within 0 to 375 kg/m³, meeting the monitoring needs of hydrological stations. Through comparative analysis of measurement data, we established conversion relationships for various SSM concentration ranges, confirming that the instrument’s system error is less than 1%. The photoelectric sand meter adheres to standards outlined in the “Guidelines for SSM Test in Rivers”, demonstrating stability in reliability, calibration methods, observation accuracy, real-time monitoring, data storage, and continuous operation. For optimal use, adherence to relevant hydrological instrument standards is recommended, particularly in stations requiring SSM analysis. Standard sampling and calibration of conversion coefficients should be conducted, and proper sensor installation is crucial to avoid interference from flow conditions. In conclusion, the HHSW·NUG-1 optoelectronic sand meter exhibits stable and reliable performance in practical applications, with broad potential for rapid deployment in other river hydrological stations. Full article

Background/Objective: Conventional techniques for evaluating hydration status include the analysis of blood, urine, and body weight. Recently, advancements in dentistry have introduced capacitance sensor-based oral epithelial moisture meters as promising avenues for assessment. This study aimed to examine the correlation between oral mucosal moisture content, as determined using a capacitance sensor, and exercise-induced dehydration. Methods: A total of 21 participants engaged in a 120 min slow distance exercise session. A series of measurements were taken before and after the exercise session, including body weight, sweat rate, secretory immunoglobulin A (s-IgA) concentration in saliva samples, saliva flow rate, and oral mucosal moisture content, which were assessed using a capacitance sensor. The relationship between physical dehydration and oral mucosal moisture content was investigated using statistical analysis. Receiver operating characteristic curves were constructed to ascertain whether variations in oral mucosal moisture content could discern body mass losses (BMLs) of 1.5% and 2%. Results: A significant correlation was observed between the sweat rate during exercise and the change in oral mucosal moisture content before and after exercise (Spearman’s rank correlation coefficient: ρ = −0.58, p < 0.001). The salivary flow and s-IgA secretion rates were lower after the exercise period than before, whereas the s-IgA concentration was higher. Oral mucosal moisture decreased during the exercise period. Receiver operating characteristic curve analysis revealed that differences in oral mucosal moisture content exhibited discriminative capabilities, with area under the curve values of 0.79 at 1.5% BML and 0.72 at 2% BML. Conclusions: The measurement of oral mucosal moisture using capacitance sensors represents a promising noninvasive approach for the assessment of exercise-induced dehydration. Full article

This research focuses on addressing the limitations of conventional physical sensors and developing a virtual water flow rate prediction technology. With HVAC systems being increasingly adopted, research on optimizing control settings based on load variations is critical. Existing systems often operate based on peak load conditions, leading to energy overconsumption in partial load scenarios. Physical sensors used for water flow measurement face challenges such as installation difficulties in constrained spaces and increased costs in large buildings. Virtual water flow rate prediction technology offers a cost-effective solution by leveraging in situ measurement data instead of extensive physical sensors. To achieve this, a test bed with a pump, valve, and heat pump was used, controlled via a BAS. Water flow rate was measured using an ultrasonic flow meter, and differential pressure was recorded using pressure gauges. Equations were developed to replace differential pressure values with valve opening rates and pump speeds by deriving performance curves and differential pressure ratio equations. Measurement uncertainty was calculated to assess the reliability of the experimental setup. Various test numbers were created to evaluate the virtual water flow rate model under controlled conditions. The results showed that relative errors ranged from 0.32% to 10.54%, with RMSE, MBE, and CvRMSE meeting all threshold criteria. The virtual water flow rate model demonstrated strong predictive accuracy and reliability, supported by an R² value close to 1. This research confirms the effectiveness of the proposed model for reducing the dependence on physical sensors while enabling accurate water flow rate predictions in HVAC systems. Full article

Most pier scour monitoring methods cannot be carried out during floods, and data cannot be recorded in real-time. Since scour holes are often refilled by sediment after floods, the maximum scour depth may not be accurately recorded, making it difficult to derive the equilibrium scour depth. This study proposes a novel approach using 16 proximity sensors (VCNL4200), which are low-cost (less than USD 3 each) and low-power (380 µA in standby current mode), to monitor and record the pier scour depth at eight different positions in a flume as it varies with water flow rate. Based on the regression relationship between PS data and distance, the scour trend related to the equilibrium scour depth can be derived. Through the results of 13 local live-bed sediment scour experiments, this PS module was able to record not only the scour depth, but also the development and geometry of the scour under different water flows. Additionally, based on PS data readings, changes in the topography of the scour hole throughout the entire scouring process can be observed and recorded. Since the maximum scour depth can be accurately recorded and the scour trend can be used to estimate the equilibrium scour depth, observations from the experimental results suggest that the critical velocity derived by Melville and Coleman (2000) may have been underestimated. The experimental results have verified that, beyond achieving centimeter-level accuracy, this method also leverages the Internet of Things (IoT) for the long-term real-time observation, measurement, and recording of the formation, changes, and size of scour pits. In addition to further exploring scouring behavior in laboratory studies, this method is feasible and highly promising for future applications in on-site scour monitoring due to its simplicity and low cost. In future on-site applications, it is believed that the safety of bridge piers can be assessed more economically, precisely, and effectively. Full article

The accurate estimation of flow angles is crucial for enhancing flight performance and aircraft safety. Flow angles of fixed-wing small unmanned aerial vehicles (SUAVs) are more vulnerable due to their low airspeed. Current flow angle measurement devices have not been widely implemented in SUAVs due to their substantial cost and size constraints. Moreover, there are no general estimation methods suitable for SUAVs based on their rudimentary sensor suite. This study presents a generalized optimization-assisted filter estimation (OAFE) method for estimating the relative velocity and flow angles of fixed-wing SUAVs based on a standard sensor suite. This OAFE method mainly consists of a cubature Kalman filter and an optimizer. The filter serves as the main loop with which to generate flow angles in real time by fusing the acceleration, angular rate, attitude, and airspeed. Without flow angle measurements, the optimizer generates approximate aerodynamic derivatives, which serve as pseudo-measurements with which to refine the performance of the filter. The results demonstrate that the estimated angle of attack and side slip angle displayed root mean square errors of around 0.11° and 0.24° in the simulation. The feasibility was also verified in field tests. The OAFE method does not require flow angle measurements, the prior acquisition of aerodynamic parameters, or model training, making it suitable for quick deployment on different SUAVs. Full article

The last decade has seen significant progress in the development of flexible electronics and sensors, particularly for display technologies and healthcare applications. Advancements in scalable manufacturing, miniaturization, and integration have further extended the use of this new class of devices to smart agriculture, where multimodal sensors can be seamlessly attached to plants for continuous and remote monitoring. Among the various types of sensing devices for agriculture, flexible mechanical sensors have emerged as promising candidates for monitoring vital parameters, including growth rates and water flow, providing a new avenue for understanding plant health and growth under varied environmental conditions. This perspective provides a snapshot of recent progress in this exciting and unconventional area of research and highlights potential opportunities for the future. Full article

The healthcare sector is undergoing a transformation with the integration of cutting-edge technologies such as machine learning (ML), the Internet-of-Things (IoT), and Cyber–Physical Systems (CPS). However, traditional ML systems often face challenges in real-time processing and resource efficiency, limiting their application in life-critical scenarios. This research explores the potential of edge ML, particularly TinyML with TensorFlow Lite, implemented on microcontroller-based AI sensors for real-time health monitoring. By leveraging model quantization, the system analyzes heart rate variability (HRV) data to deliver continuous and personalized insights into stress levels and sleep quality. Trained on SWELL and ISRUC datasets, the system is highly energy-efficient, consuming 33 mW in idle mode, 66 mW during data collection, and 99 mW during real-time inference, making it suitable for resource-constrained environments. Performance analysis reveals significant demographic variations: younger individuals (18–25) achieved 90% accuracy due to higher HRV and lower baseline stress, while middle-aged (26–50) and older adults (50+) demonstrated declining HRV, reducing accuracy to 82% for the latter. Gender differences were also observed, with males exhibiting greater stress response sensitivity and better accuracy (89%) compared to females. This study underscores the transformative potential of TinyML for real-time, energy-efficient health monitoring and emphasizes the need for demographic-specific optimizations to enhance system reliability and accessibility. Full article

Traditional network architectures in smart ship communication systems struggle to efficiently manage the integration of heterogeneous sensor data. Additionally, conventional end-to-end transmission algorithms that rely on single-metric and single-path selection are inadequate in fulfilling the high reliability and real-time transmission requirements essential for high-priority service data. This inadequacy results in increased latency and packet loss for critical control information. To address these challenges, this study proposes an innovative ship network framework that synergistically integrates Software-Defined Networking (SDN) and Time-Sensitive Networking (TSN) technologies. Central to this framework is the introduction of a redundant multipath selection algorithm, which leverages Double Dueling Deep Q-Networks (D3QNs) in conjunction with Graph Convolutional Networks (GCNs). Initially, an optimization function encompassing transmission latency, bandwidth utilization, and packet loss rate is formulated within a software-defined time-sensitive network transmission framework tailored for smart ships. The proposed D3QN-GCN-based algorithm effectively identifies optimal working and redundant paths for TSN switches. These dual-path configurations are then disseminated by the SDN controller to the TSN switches, enabling the TSN’s inherent reliability redundancy mechanisms to facilitate the simultaneous transmission of critical service flows across multiple paths. Experimental evaluations demonstrate that the proposed algorithm exhibits robust convergence characteristics and significantly outperforms existing algorithms in terms of reducing network latency and packet loss rates. Furthermore, the algorithm enhances bandwidth utilization and promotes balanced network load distribution. This research offers a novel and effective solution for shipboard switch path selection, thereby advancing the reliability and efficiency of smart ship communication systems. Full article

Accurate knowledge of the flow rate is essential for hydraulic systems, enabling the calculation of hydraulic power when combined with pressure measurements. These data are crucial for applications such as predictive maintenance. However, most flow rate sensors in fluid power systems operate invasively, disrupting the flow and producing inaccurate results, especially under transient conditions. Utilizing pressure transducers represents a non-invasive soft sensor approach since no physical flow rate sensor is used to determine the flow rate. Usually, this approach relies on the Hagen–Poiseuille (HP) law, which is limited to steady and incompressible flow. This paper introduces a novel soft sensor with an analytical model for transient, compressible pipe flow based on two pressure signals. The model is derived by solving fundamental fluid equations in the Laplace domain and converting them back to the time domain. Using the four-pole theorem, this model contains a relationship between the pressure difference and the flow rate. Several unsteady test cases are investigated and compared to a steady soft sensor based on the HP law, highlighting our soft sensor’s promising capability. It exhibits an overall error of less than 0.15% for the investigated test cases in a distributed-parameter simulation, whereas the HP-based sensor shows errors in the double-digit range. Full article

Drip irrigation pressure is considered a key parameter for controlling and designing the drip irrigation system in sandy soils. Understanding soil water content (SWC) movements under varying pressures can enhance water use efficiency and support sustainable irrigation strategies for crops in arid regions. The objectives of this study were to investigate the effects of irrigation pressure on the spatial, vertical, and temporal variability of SWC in sandy loam soil using surface drip irrigation. Experiments were carried out in a soil bin located in a greenhouse. SWC sensors were placed at depths 10, 20, 30, 40, and 50 cm to monitor SWC variability under low, medium, and high drip irrigation pressures (25, 50, and 75 kPa) at a constant emitter flow rate of 3 L/h. A pressure controller was used to regulate drip irrigation pressure, while microcontrollers communicated with SWC sensors, collected experimental data, and automatically recorded the outputs. At low irrigation pressure, water content began to increase at 0.53 h and saturated at 3.5 h, with both values being significantly lower at medium and high pressures. The results indicated that lower pressures led to significant variability in water movement at shallow depths (10 to 30 cm), becoming uniform at deeper layers but requiring longer irrigation times. Competitively higher pressures showed uniform water distribution and retention statistically throughout the soil profiles with shorter irrigation times. The variation in water distribution resulting in non-uniform coverage across the irrigated area demonstrates how pressure changes affect the flow rate of the emitter. The results provide information maps with soil water data that can be adjusted with irrigation pressure to maximize water use efficiency in sandy loam soils, aiding farmers in better irrigation scheduling for different crops using surface drip irrigation techniques in arid environments. Full article

Effective water management is crucial in urban and rural settings, requiring efficient usage and timely detection of issues like leakages for sustainability. This paper introduces an integrated framework that combines Digital Twin technology with a multimodal transformer-based model for accurate water usage prediction and leakage detection. The system synchronizes real-time data from various sensors including flow meters, pressure sensors, and thermal imaging devices with a Digital Twin of the water network. Advanced transformer models, specifically the Informer model for long-term time-series prediction and a Water Multimodal Transformer for anomaly detection, process these data to capture complex patterns and dependencies. Experimental results demonstrate the framework’s effectiveness: the Informer model achieved an R² score of 0.9995 and a Mean Squared Error (MSE) of 2.2, outperforming traditional models. For leakage detection, the model attained 98.4% accuracy and precision, an F1 score of 97.6%, a low False Positive Rate of 0.0019, and an Area Under the Curve (AUC) of 0.984. By fusing diverse sensor data and utilizing advanced transformer architectures, the framework provides a comprehensive view of the water network, enabling real-time decision-making, enhancing forecasting accuracy, and reducing water waste. This scalable solution supports sustainable water management practices in both urban and industrial contexts. Full article

In recent years, significant efforts have been dedicated to understanding the growth mechanisms behind the synthesis of vertically aligned nanocarbon structures using plasma-enhanced chemical vapor deposition (PECVD). This study explores how varying synthesis conditions, specifically hydrocarbon flow rate, hydrocarbon type, and plasma power,—affect the microstructure, properties, and electrochemical performance of nitrogen-doped vertically aligned graphene (NVG) and nitrogen-doped vertically aligned carbon nanofibers (NVCNFs) hybrids. It was observed that adjustments in these synthesis parameters led to noticeable changes in the microstructure, with particularly significant alterations when changing the hydrocarbon precursor from acetylene to methane. The electrochemical investigation revealed that the sample synthesized at higher plasma power exhibited enhanced electron transfer kinetics, likely due to the higher density of open edges and nitrogen doping level. This study contributes to better understanding the PECVD process for fabricating nanocarbon materials, particularly for sensor applications. Full article