Sensor Measurements Research Articles

Experimental Observations on Film Cooling Effectiveness on Aerodynamic Heating in Hypersonic Flow

An experimental campaign was carried out to examine the effectiveness of film cooling on aerodynamic heating at hypersonic velocities. A flat-faced cylindrical model was tested at flow velocity of 2.5 km/s in a hypersonic shock tunnel. The test model was equipped with temperature sensors for convective heat flux measurement. The coolant gas, nitrogen, was injected through a 2 mm orifice housed at the center of the test model at different pressure ratios. Two different flow regimes were observed, steady and unsteady, with the steady flowfield having overall reduction in heat transfer compared to the unsteady one.

Vapor bubble on a single nucleation site : Temperature and heat flux measurements

This work aimed at gaining a detailed understanding of heat transfer when boiling occurs on a heated substrate. More specifically, this work focuses on thermal transfers occurring at wall-fluid interfaces at the transition between natural convection and nucleate boiling regimes in pool boiling experiments. To avoid parasitic effects, the vapor bubble is created at a single artificial nucleation site. The boiling cell is instrumented with sensors for temperature measurement, pressure control, and parietal heat flux measurement. The fluid tested is Fluorinert FC-72. For providing a boiling surface and measuring heat flux, a boiling-meter consisting of a heating resistance, heat flux sensors and two thermocouples is used. This boiling-meter enables heat flux generation, the measurement of both temperature and heat flux, and can be rotated through 360°, enabling the influence of inclination to be studied. The boiling-meter is indented at its center to create a single vapor bubble. Thermal measurements are obtained and studied for different inclination angles between 0° and 180° at the saturation conditions. The results showed the nucleation site’s recurrent pattern of being active and inactive for whatever is the boiling surface inclination. Preliminary results are presented and discussed.

Quantitative validation of the suprasternal pressure signal to assess respiratory effort during sleep

Objective. Intra-esophageal pressure (Pes) measurement is the recommended gold standard to quantify respiratory effort during sleep, but used to limited extent in clinical practice due to multiple practical drawbacks. Respiratory inductance plethysmography belts (RIP) in conjunction with oronasal airflow are the accepted substitute in polysomnographic systems (PSG) thanks to a better usability, although they are partial views on tidal volume and flow rather than true respiratory effort and are often used without calibration. In their place, the pressure variations measured non-invasively at the suprasternal notch (SSP) may provide a better measure of effort. However, this type of sensor has been validated only for respiratory events in the context of obstructive sleep apnea syndrome (OSA). We aim to provide an extensive verification of the suprasternal pressure signal against RIP belts and Pes, covering both normal breathing and respiratory events. Approach. We simultaneously acquired suprasternal (207) and esophageal pressure (20) signals along with RIP belts during a clinical PSG of 207 participants. In each signal, we detected breaths with a custom algorithm, and evaluated the SSP in terms of detection quality, breathing rate estimation, and similarity of breathing patterns against RIP and Pes. Additionally, we examined how the SSP signal may diverge from RIP and Pes in presence of respiratory events scored by a sleep technician. Main results. The SSP signal proved to be a reliable substitute for both esophageal pressure (Pes) and respiratory inductance plethysmography (RIP) in terms of breath detection, with sensitivity and positive predictive value exceeding 75%, and low error in breathing rate estimation. The SSP was also consistent with Pes (correlation of 0.72, similarity 80.8%) in patterns of increasing pressure amplitude that are common in OSA. Significance. This work provides a quantitative analysis of suprasternal pressure sensors for respiratory effort measurements.

JITL-MBN: A Real-Time Causality Representation Learning for Sensor Fault Diagnosis of Traction Drive System in High-Speed Trains.

A traction drive system (TDS) in high-speed trains is composed of various modules including rectifier, intermediate dc link, inverter, and others; the sensor fault of one module will lead to abnormal measurement of sensor in other modules. At the same time, the fault diagnosis methods based on single-operating condition are unsuitable to the TDS under multi-operating conditions, because a fault appears various in different conditions. To this end, a real-time causality representation learning based on just-in-time learning (JITL) and modular Bayesian network (MBN) is proposed to diagnose its sensor faults. In specific, the proposed method tracks the change of operating conditions and learns potential features in real time by JITL. Then, the MBN learns causality representation between faults and features to diagnose sensor faults. Due to the reduction of the nodes number, the MBN alleviates the problem of slow real-time modeling speed. To verity the effectiveness of the proposed method, experiments are carried out. The results show that the proposed method has the best performance than several traditional methods in the term of fault diagnosis accuracy.

Temperature-Decoupled Single-Crystal MgO Fiber-Optic Fabry-Perot Vibration Sensor Based on MEMS Technology for Harsh Environments.

A high-temperature-resistance single-crystal magnesium oxide (MgO) extrinsic Fabry-Perot (FP) interferometer (EFPI) fiber-optic vibration sensor is proposed and experimentally demonstrated at 1000 °C. Due to the excellent thermal properties (melting point > 2800 °C) and optical properties (transmittance ≥ 90%), MgO is chosen as the ideal material to be placed in the high-temperature testing area. The combination of wet chemical etching and direct bonding is used to construct an all-MgO sensor head, which is favorable to reduce the temperature gradient inside the sensor structure and avoid sensor failure. A temperature decoupling method is proposed to eliminate the cross-sensitivity between temperature and vibration, improving the accuracy of vibration detection. The experimental results show that the sensor is stable at 20-1000 °C and 2-20 g, with a sensitivity of 0.0073 rad (20 °C). The maximum nonlinearity error of the vibration sensor measurement after temperature decoupling is 1.17%. The sensor with a high temperature resistance and outstanding dynamic performance has the potential for applications in testing aero-engines and gas turbine engines.

A novel joint depth sensor calibration method without fixture for mobile robots’ navigation

AbstractCommercial mobile robots are usually equipped with multiple depth sensors that can measure the point cloud information around the robot's environment. The installation process of these sensors contains assembly error and sensor measurement error, so it is necessary to calibrate each sensor to align the point cloud. In order to obtain the sensor calibration results of commercial robots under normal working conditions, this study proposes a fixture free multi depth sensor joint calibration method that can be deployed on low‐cost embedded computing units, which efficiently aligns the point clouds of each sensor. During the calibration process, the robot is placed in the center of three upright thin plates perpendicular to the ground. 2D LIDAR depicts high‐precision contours of the upright thin plates. In the calibration process of each depth sensor, the roll angle and pitch angle of the sensor point cloud are first calibrated to make it perpendicular to the ground, and then the yaw angle and position of the point cloud are calibrated to fit the high‐precision contour of the upright thin plate. The results show that this method can be deployed on low‐cost embedded computing units, with real‐time and accurate calibration results. The convergence of calibration results can be achieved through up to 5 iterations, and the average running time is less than 120 ms. This research achievement provides a reference for multi‐sensor calibration of commercial robots.

Corrections to “Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications”

Corrections to “Fusing Channel and Sensor Measurements for Enhancing Predictive Beamforming in UAV-Assisted Massive MIMO Communications”

Intelligent data-aided semantic sensing with variational deep embedding

This paper proposes an intelligent sensing framework for Internet-of-Things platforms, where sensor measurements stem from multiple causes. Sensors are selectively chosen for data collection to identify the cause with partial measurements. We employ variational deep embedding, a generative model capable of clustering and generation, to identify causes, cluster measurements accordingly, and determine causes for estimating complete measurements from partial data. These estimates aid in efficient sensor selection for data collection. Results demonstrate early and reliable cause sensing and complete measurement estimation using the proposed framework.

Exploring Practical Acoustic Transduction Attacks on Inertial Sensors in MDOF Systems

In cyber-physical systems, inertial sensors are the basis for identifying motion states and making actuation decisions. However, extensive studies have proved the vulnerability of those sensors under acoustic transduction attacks, which leverage malicious acoustics to trigger sensor measurement errors. Unfortunately, the threat from such attacks is not assessed properly because of the incomplete investigation on the attack's potential, especially towards multiple-degree-of-freedom systems, e.g., drones. To thoroughly explore the threat of acoustic transduction attacks, we revisit the attack model and design a new yet practical acoustic modulation-based attack, named KITE. Such an attack enables stable and controllable injections, even under frequency offset based distortions that limit the effect of prior attacking approaches. KITE exploits the potential threat of transduction attacks without the need of strengthening attackers' abilities. Furthermore, we extend the attack surface to multiple-degree-of-freedom (MDOF) systems, which are more widely deployed but ignored by prior work. Our study also covers the scenario of attacking moving targets. By revealing the practical threat from acoustic transduction attacks, we appeal for both the attention to their harm and necessary countermeasures.

An Adaptive Extended Kalman Filter With Passivity-Based Control for DC-DC Converter in DC Microgrids Supplying Constant Power Loads

This paper proposes an adaptive nonlinear control strategy to eliminate the instability problem of buck converters in DC microgrids (DCMG) considering constant power load (CPL). The strategy includes an Adaptive Passivity-Based Controller (PBC) and an Adaptive Extended Kalman Filter (AEKF). The proposed AEKF performs faster and more accurately than the conventional EKF and adjusts the covariance matrices adaptively. Furthermore, this AEKF directly estimates the unknown CPL power supplied by the converter and prevents the number of measurement sensors from increasing. Hence, PBC ensures system stability alongside AEKF. The controller guarantees Lyapunov stability and fast dynamic response in the presence of uncertainties and perturbations compared to other nonlinear methods. MATLAB simulations and laboratory implementation are provided to confirm the performance and robustness of the proposed control strategy and validate the analysis.

Painting robot with three axis motion

Paint was applied directly to the painted surface while painting with a brush. There are occasionally unsatisfactory outcomes, such as paint stacking or streaks remaining. Spray guns yield better paint results, therefore a robot was developed that can regulate the spray gun's movement in order to apply paint. A painting robot that resembles an arm is one that has three axes of movement. The robot can move left-right on the x axis, up-down on the y axis, and back-front on the z axis. Its end effector is a spray cannon. An ultrasonic sensor is used to measure the distance between components and the spray gun; when the sensor measures near distances, such as between 4 and 8 cm, the test sensor distance measurements have a larger average error. When the test reading distance is increased from 10 cm to 14 cm, the average error reduces. Superior painting outcomes can be achieved with a paint distance of 10 cm, an air pressure of 80 psi, and a spray gun arm movement speed of 25 rpm. With average x-axis movement of 74.6 cm, y-axis movement of 14.8 cm, and z-axis movement of 88.2 cm, the largest volume that can be painted is 97,517.5 cm3. The rotation of the arm is used to determine the angular rotation needed to paint components. Painting the left side of the component at an angle of 10°, the back side at an angle of 100°, the right side at an angle of 190°, and only painting the front side of the component at the required angle of 90° require the rotational movement of the arm to the left.

Developing a Nutritional Assessment Tool for Toddlers Using Anthropometry and IoT Technology

Data from the Indonesian Toddler Nutrition Status Survey (SSGBI) in 2021, the prevalence of stunting in Indonesia is still quite high, at 24.4%, which is equivalent to 5.33 million toddlers. This figure is still above the standard tolerated by WHO, which is below 20%. Therefore, efforts are needed to accelerate stunting reduction so that the prevalence of stunting in toddlers decreases to 19.4% by 2024. This research aims to develop a tool for measuring toddlers' height and weight to assess their nutritional status promptly, aiming to preemptively address any nutritional abnormalities and prevent exacerbation. Anthropometry serves as the primary method for assessing toddlers' nutritional status in this study. The tool's design incorporates the ESP32 as the main control unit, the HC-SR04 sensor for height measurements, and the HX711 module and loadcell sensor as weight sensors. Data from the sensor are transmitted from the ESP32 master to the ESP32 slave for processing, culminating in a nutritional status assessment. Notably, the tool boasts a minimal error rate of 0.18% for weight measurement with 99.82% accuracy and a 2.66% error rate for height measurement with 97.34% accuracy. Furthermore, the tool's integration with IoT technology offers additional advantages. It facilitates real-time data transmission and analysis, enabling healthcare professionals to promptly identify any nutritional issues in toddlers. This, in turn, allows for timely intervention and appropriate management strategies to prevent the development or exacerbation of stunting. Overall, the benefits of this research for the Anthropometry Stunting Monitor based on IoT are manifold. It enhances accuracy and efficiency in measuring toddlers' height and weight, enables early detection of stunting, and facilitates timely intervention to address nutritional abnormalities. This holds significant promise for improving pediatric healthcare outcomes and reducing the prevalence of stunting among children.

Personalized Weather Station

Abstract: This research outlines the conception, design, and implementation of a personalized weather monitoring system integrating multiple sensors for comprehensive environmental data collection. The project aims to create a sophisticated weather station utilizing the DHT11 sensor for temperature and humidity measurements, MQ135 sensor for gas detection, dust sensor for particulate matter assessment, and rain sensor for precipitation detection. The system's architecture incorporates Esp32-based data acquisition, employing sensor interfacing and data processing to obtain real-time environmental parameters. The project's significance lies in providing localized, accurate, and immediate weather insights beyond the capabilities of conventional smartphone applications.

Multi-Purpose Tracker Robot

Abstract: This paper introduces a multi-purpose tracking robot designed with Arduino, ultrasonic, infrared sensors, and motor drivers. The robot is capable of performing diverse tasks such as obstacle avoidance, line following, and object tracking. Its hardware comprises an Arduino Uno microcontroller, ultrasonic sensors for distance measurement, infrared sensors for line detection and obstacle avoidance, and motor drivers for controlling DC motors. The software, developed using Arduino IDE, includes algorithms for sensor data processing, decision-making logic, and motor control. By implementing PID control algorithms, the robot achieves enhanced stability and accuracy in navigation tasks like motor speed control and line following. Experimental results validate its effectiveness in various scenarios, showcasing its potential for advanced robotics and automation applications through intelligent sensor integration and control strategies.

Battery Management System Using IoT

Abstract: This study presents an Arduino-based system for advising users of nearby EV stations and tracking the status of their automobile batteries. The MQ-2 gas sensor, DHT11 temperature sensor, and DC voltage sensor are the three main sensors that the system interfaces with through the use of an Arduino Uno microcontroller as the central controller. When the battery temperature rises over safe limits, the DHT11 sensor monitors it and sends out alarms. By detecting gases being released into the atmosphere, the MQ-2 gas sensor warns customers of possible battery problems. Through the use of a Java web application, the DC voltage sensor suggests local EV stations and notifies users when levels are low. The sensor measures voltage and percentage. By delivering timely alerts and making charging facilities easier to use, this integrated solution raises user awareness of battery health and promotes safer driving. By fusing Arduino technology with sensor data and web application capability, it illustrates how IoT can be used practically in the automotive industry.

A novel resonator cavity microwave sensor for high-temperature blade tip clearance measurement

The blade tip clearance is the key to efficiency and safety of rotating engines with accurate and real-time measurement. Existing microwave sensors can only withstand 900 °C. This paper introduces a novel resonant cavity sensor, withstanding up to 1300 °C. Using a high-impedance compensation method, the high-temperature section for the sensor has been designed. The sensor’s structure is designed based on the principle of electromagnetic field superposition and optimized by electromagnetic field simulation. A phase compensation method of transmission path based on dual-frequency technology has been proposed. Prototypes are developed and the performance tests are completed. Results show a monotonic relationship between the phase difference measurements and actual blade tip clearance values. The novel sensor is suitable for dynamic measurement of blade tip clearance. Additionally, the sensor operates reliably at high temperatures up to 1300 °C. The measurement deviation of blade tip clearance between high-temperature and room-temperature is always within ±10 %.

Uncertainty evaluation for wind speed measurement part (2): “Input quantity correlation”

When the “Guide to the expression of uncertainty in measurement” (GUM) is used to evaluate the uncertainty of wind speed sensors, there is a lack of input quantities correlation research, which leads to the deviation of the uncertainty evaluation result. This paper takes the calibration result of an automatic weather station's three-cup wind speed sensor as an example to evaluate the uncertainty of wind speed sensor measurement based on the correlation of input quantities. First, the calibration point of 10.00 m/s wind speed is taken as an example, and the original wind speed record is obtained by calibrating the three-cup wind speed sensor. Then, according to the GUM evaluation method, the uncertainty of the wind speed measurement result is analyzed and evaluated, And analyze and explore the correlation between the input quantities (considering the influence of the stability and uniformity of the wind tunnel on the measurement result, and the existence of a certain interdependence between them, an experimental plan was developed to test the stability and uniformity of the wind tunnel, and the correlation coefficient between them was calculated to be 0.1561 at the wind speed point of 10.00 m/s). Finally, the input quantities correlation (the correlation coefficient of wind tunnel stability and uniformity) is integrated into the uncertainty evaluation, and the uncertainty evaluation result is obtained at 10.00 m/s wind speed point (where U = 0.22 m/s, U95 = 0.22 m/s). Through comparison, the uncertainty results evaluated without considering the correlation of input quantities have generated negative deviation, with deviation values being −0.00022 m/s. Due to the relatively small deviation, in practical applications, it can be determined whether to correct it based on the accuracy requirements of the measurement result and actual needs.

Microwave-assisted hydrothermal synthesis and gas sensing properties of ZnSn(OH)6, ZnSnO3, and Zn2SnO4/SnO2 hierarchical nano-/hetero-structures

Although semiconducting metal oxide sensors present reasonable sensitivity, an improved lower detection limit and/or selectivity would allow broadening real-time monitoring applications. This work reports the growth mechanism and gas sensing performance of zinc tin oxide-based structures synthesised via a microwave-assisted hydrothermal route. The synthesised materials were characterised by X-ray diffraction (XRD), Raman and Fourier-transform infrared (FTIR) spectroscopy, scanning and scanning transmission electron microscopy (SEM and STEM), X-ray photoelectron spectroscopy (XPS), energy-dispersive X-ray spectroscopy (EDS), and nitrogen adsorption/desorption experiments. Gas sensor measurements showed that ZnSnO3 presents an outstanding lower detection limit to nitrogen dioxide (NO2), in which a 10-fold increase in electrical resistance is expected in the presence of 1 ppb NO2 at an operating temperature of 150 ˚C. Moreover, the Zn2SnO4/SnO2 heterostructure exhibited superior selectivity to NO2 relative to hydrogen (H2) and carbon monoxide (CO), exhibiting a sensor response ∼1500 times higher for the oxidising gas. Hence, it is demonstrated that nanostructures’ growth engineering can realise lower detection limits and ultra-selective high-performance gas sensor devices through a greater surface area and enhanced contact potential barriers.

Sensors and Sensing Devices Utilizing Electrorheological Fluids and Magnetorheological Materials-A Review.

This paper comprehensively reviews sensors and sensing devices developed or/and proposed so far utilizing two smart materials: electrorheological fluids (ERFs) and magnetorheological materials (MRMs) whose rheological characteristics such as stiffness and damping can be controlled by external stimuli; an electrical voltage for ERFs and a magnetic field for MRMs, respectively. In this review article, the MRMs are classified into magnetorheological fluids (MRF), magnetorheological elastomers (MRE) and magnetorheological plastomers (MRP). To easily understand the history of sensing research using these two smart materials, the order of this review article is organized in a chronological manner of ERF sensors, MRF sensors, MRE sensors and MRP sensors. Among many sensors fabricated from each smart material, one or two sensors or sensing devices are adopted to discuss the sensing configuration, working principle and specifications such as accuracy and sensitivity. Some sensors adopted in this article include force sensors, tactile devices, strain sensors, wearable bending sensors, magnetometers, display devices and flux measurement sensors. After briefly describing what has been reviewed in a conclusion, several challenging future works, which should be undertaken for the practical applications of sensors or/and sensing devices, are discussed in terms of response time and new technologies integrating with artificial intelligence neural networks in which several parameters affecting the sensor signals can be precisely and optimally tuned. It is sure that this review article is very helpful to potential readers who are interested in creative sensors using not only the proposed smart materials but also different types of smart materials such as shape memory alloys and active polymers.

Ai Driven Interactive Agri Bot Providing Realtime Assistance in Cultivation and Market Linkages

This project introduces an integrated system for smart agriculture, employing Internet of Things (IoT) technology for soil type analysis and deep learning methodologies for pest detection. The proposed system leverages a specialized NPK sensor for real-time measurement of soil nutrient levels, facilitating precision agriculture practices. Additionally, a Convolutional Neural Network (CNN) algorithm is employed to detect pests in crops, enhancing crop management efficiency and yield optimization. The IoT-based NPK sensor enables farmers to monitor essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) levels remotely and in real-time. This data empowers farmers to make informed decisions regarding fertilization strategies, ensuring optimal nutrient balance for healthy plant growth while minimizing resource wastage and environmental impact. To the deep learning framework, specifically CNN, is utilized for pest detection in crops. By analyzing images captured from smart agricultural cameras, the CNN model can identify and classify various pests and diseases affecting crops. This enables early detection and intervention, thereby mitigating potential crop damage and yield losses. The integration of CNN-based pest detection with IoT infrastructure enables timely and targeted pest management actions, reducing reliance on chemical pesticides and promoting sustainable agricultural practices.