Accuracy Of Sensor Research Articles

Condition Monitoring System: A Flexible Hybrid Electronics Approach for Sealed Container Applications

A comparative study is presented between two advanced flexible hybrid electronics (FHE) systems designed for monitoring temperature within storage containers across various industries, including food, pharmaceuticals, agriculture, automotive, and defense. The first system, a copper-flex system (CFS), employs an 88.9 µm polyimide substrate with 35 µm thick copper traces, coated with a 12.5 µm polyimide solder mask. The second system, a printed-flex system (PFS), utilizes a 127 µm polyimide substrate with high-temperature resistance and tensile strength. Conductive silver ink was screen-printed on the PFS substrate, and electronic components including a temperature sensor were attached. Functional and reliability tests were performed to check the accuracy and durability of the temperature sensor. Further, environmental and mechanical characterizations including moisture and insulation resistance, corrosion, elongation, bending, and terminal bond strength tests were performed based on ASTM and IPC-TM650 standards. Moisture and insulation resistance test on the PFS test coupons without a coating layer indicated stable resistance of approximately 18 MΩ, while permanent color change indicated oxidation of copper on uncoated CFS test coupons. After 72 hours of corrosion test, both the CFS and PFS “meander line” test coupons covered with polyimide showed negligible change in weight and resistance, approximately 0.35%. A Young’s modulus of 7.17 GPa and 2.6 GPa was calculated from the elongation test for the CFS and PFS, respectively. Bending tests on PFS revealed negligible average resistance change (0.1%) during 180° bending cycles, while no impact was recorded on the CFS. During the terminal bond strength test, soldered wires detached from the CFS test coupons at an average force of 43 N, while it was 3.8 N on the PFS test coupons. Both systems with polyimide coating layer demonstrate robustness and reliability for diverse applications in various industries.

Stretchable Piezoresistive Pressure Sensor Array with Sophisticated Sensitivity, Strain-Insensitivity, and Reproducibility.

This study delves into the development of a novel 10 by 10 sensor array featuring 100 pressure sensor pixels, achieving remarkable sensitivity up to 888.79kPa-1, through the innovative design of sensor structure. The critical challenge of strain sensitivity inherent is addressed in stretchable piezoresistive pressure sensors, a domain that has seen significant interest due to their potential for practical applications. This approach involves synthesizing and electrospinning polybutadiene-urethane (PBU), a reversible cross-linking polymer, subsequently coated with MXene nanosheets to create a conductive fabric. This fabrication technique strategically enhances sensor sensitivity by minimizing initial current values and incorporating semi-cylindrical electrodes with Ag nanowires (AgNWs) selectively coated for optimal conductivity. The application of a pre-strain method to electrode construction ensures strain immunity, preserving the sensor's electrical properties under expansion. The sensor array demonstrated remarkable sensitivity by consistently detecting even subtle airflow from an air gun in a wind sensing test, while a novel deep learning methodology significantly enhanced the long-term sensing accuracy of polymer-based stretchable mechanical sensors, marking a major advancement in sensor technology. This research presents a significant step forward in enhancing the reliability and performance of stretchable piezoresistive pressure sensors, offering a comprehensive solution to their current limitations.

Reliability Assessment and Multi-Physics Simulation of Additively Printed Wearable Humidity Sensor with Super Capacitive Material for Astronaut in Extreme Condition

Abstract Additive technologies, such as aerosol jet printing, direct write printing are increasingly being used in the production of printed circuit boards because they eliminate the need for costly tooling, such as photomasks or etching containers. This is because additive methods allow for the direct deposition of printing materials onto a substrate. This versatility in a broad variety of applications allows engineers to create diverse applications, such as sensing devices with ECG sensors, pulse-oxygen sensors, galvanic skin response sensors, body temperature sensors, humidity sensors, and so on. Due to its potential for adaptability and integration, the development of additively printed humidity sensors has been the subject of several prior investigations. There are still issues with the reliability of current humidity sensor technology when flexing force is coupled with the humidity sensor. For the avoidance of stability issues, it is required to develop a better printing technique, process recipe, and sensing material encapsulation. In this research, the direct-write (D-write) printing approach with a nScrypt printer was employed to print the humidity sensor as a test vehicle in a laboratory setting. The sensor was characterized by analyzing the print recipe and its interaction with humidity in regards to resistance and humidity sensitivity. Additionally, the characterization of sensor accuracy, hysteresis, linearity, and stability in relation to temperature and humidity variation has been measured. Furthermore, a multi-physics simulation model was created in order to comprehend the electrochemical processes that occur when the humidity sensor is exposed to a very humid environment.

Advancements of Glucose Monitoring Biosensor: Current State, Generations of Technological Progress, and Innovation Dynamics.

Glucose monitoring is essential for managing diabetes, and continuous glucose monitoring biosensors can offer real-time monitoring with little invasiveness. However, challenges remain in improving sensor accuracy, selectivity, and overall performance. This article aims to review current trends and recent advancements in glucose-monitoring biosensors while evaluating their benefits and limitations for diabetes monitoring. An analysis of current literature on transdermal glucose sensors was conducted, focusing on detection techniques, novel nanomaterials, and integrated sensor systems. Recent research has led to advancements in electrochemical, optical, electromagnetic, and sonochemical sensors for transdermal glucose detection. The use of novel nanomaterials and integrated sensor designs has improved sensitivity, selectivity, and accuracy. However, issues like calibration requirements, motion artifacts, and skin irritation persist. Transdermal glucose sensors show promise for non-invasive, convenient diabetes monitoring but require further enhancements to address limitations in accuracy, reliability, and biocompatibility. Continued research and innovation focusing on sensor materials, designs, and surface chemistry is needed to optimize biosensor performance and utility. The study offers a comprehensive analysis of the present status of technological advancement and highlights areas that need more research.

Integration of a Smart Power Meter for Monitoring Household Energy Consumption in Prepaid Electrical Systems

Indonesian households have widely embraced the prepaid electricity payment system. The system relies on electricity credits, with each electrical device consuming credits as a unit of measurement for energy usage. A common issue is the automatic cutoff of electricity supply when the credits are depleted. This research designed a smart power meter using Current Transformer and voltage sensors. The electrical token value is then stored in the Arduino's EEPROM before being transmitted to the NodeMCU. The NodeMCU transfers the data to Antares using the MQTT protocol to forward it to the subscriber, typically an Android device. The data sent to the Android application includes current, voltage, active power, frequency, cos φ, and electrical credits. The measurement of electricity consumption on a kWh meter involves subtracting the value of the input electricity token from the device-measured electricity usage. The device sends a WhatsApp message when the remaining credit exceeds 10 kWh. The prototype of the smart power meter demonstrates practical functionality, with the current sensor accuracy at 99.983% and the voltage sensor accuracy at 99.999%. The largest measurement difference of the electric credit balance between the PLN Meter and the prototype is 0.04 kWh over a test period of 72 hours.

Data-driven solutions and parameter discovery of the extended higher-order nonlinear Schrödinger equation in optical fibers

In this paper, we investigate an extended higher-order nonlinear Schrödinger equation that includes third-order and fourth-order dispersion terms. By considering higher-order effects, the equation can more accurately simulate and predict the propagation behavior of ultrashort pulses in optical fibers, e.g., pulse splitting, compression and stability. In the optical fiber sensors, the sensitivity and resolution of the sensor can be improved by utilizing nonlinear effects. The analysis of the extended higher-order nonlinear Schrödinger equation helps to understand how nonlinear effects affect the sensing signal, thereby designing more accurate sensors. Specific coefficients of each term in the equation are studied for different scenarios, such as optical fiber design and signal propagation in optical communications. Under certain conditions, the extended higher-order nonlinear Schrödinger equation can be degenerated into the Hirota equation or the Lakshmanan–Porsezian–Daniel equation. With the development of numerical algorithms, the utilization of deep neural networks for solving equations has become increasingly attractive. With given initial conditions and boundary conditions, the physical information is embedded into the neural network in the form of loss functions. We efficiently derive numerical solutions to the extended equation for one-soliton, two-soliton and rogue-wave cases by training weights and biases in physics-informed neural networks. Compared with traditional numerical methods, physics-informed neural networks yield lower prediction errors with less data volume, thus will reduce the cost and time consumptions for data sampling in practical engineering. Furthermore, we solve the inverse problem associated with the extended equation. This approach is critical for extracting fundamental parameters of optical systems from observable data, thereby deepening our understanding of complex optical behavior and helping to improve the performance of optical fiber communication technologies.

Flexible self-powered triboelectric nanogenerator sensor for wind speed measurement driven by moving trains

Flexible self-powered sensors have been extensively applied to the Internet of Things, structural health monitoring (SHM), and intelligent transportation. It would be more demanding for the power supply to these sensors during the long-term maintenance of the rail transit system. The wind pressure/velocity generated by high-speed trains poses a substantial threat to safety of human, and new sensors without an external power supply should be developed to monitor wind pressure/velocity in the trackside. Flexible self-powered wind triboelectric nanogenerator (W-TENG) sensor with a single-electrode mode based on conductive hydrogel is designed to wind pressure/velocity monitoring without power supply by harvesting wind energy. It is devoted the relationship between the output voltage of the sensors and the wind pressure/velocity driven by high-speed trains. Material selection and structural design methods are adopted to enhance the energy harvesting efficiency and sensing accuracy of self-powered W-TENG sensors. Open-circuit current of 2.8 μA and open-circuit voltage of 12 V are achieved, and the output voltage signal has the linear relationship with trackside wind pressure/velocity. Field tests are implemented to evaluate the performance of self-powered W-TENG sensors in wind pressure/velocity measurement caused by moving trains, providing an idea to SHM application in intelligent transmit systems.

Space-Based Passive Orbital Maneuver Detection Algorithm for High-Altitude Situational Awareness

Orbital maneuver detection for non-cooperative targets in space is a key task in space situational awareness. This study develops a passive maneuver detection algorithm using line-of-sight angles measured by a space-based optical sensor, especially for targets in high-altitude orbit. Emphasis is placed on constructing a new characterization for maneuvers as well as the corresponding detection method. First, the concept of relative angular momentum is introduced to characterize the orbital maneuver of the target quantitatively, and the sensitivity of the proposed characterization is analyzed mathematically. Second, a maneuver detection algorithm based on the new characterization is designed in which sliding windows and correlations are utilized to determine the mutation of the maneuver characterization. Subsequently, a numerical simulation system composed of error models, reference missions and trajectories, and computation models for estimating errors is established. Then, the proposed algorithm is verified through numerical simulations for both long-range and close-range targets. The results indicate that the proposed algorithm is effective. Additionally, the sensitivity of the proposed algorithm to the width of the sliding window, accuracy of the optical sensor, magnitude and number of maneuvers, and different relative orbit types is analyzed, and the sensitivity of the new characterization is verified using simulations.

Enhancing the estimation of direct normal irradiance for six climate zones through machine learning models

The evaluation of solar radiation is essential for large-scale solar energy systems, as assessing economic feasibility early on depends on accurate solar radiation data. Accurate sensors are needed to characterize the solar resource. Due to a scarcity of solar radiation data, numerical models are commonly used to estimate solar radiation components using meteorological variables that are simple or cheap to measure. In recent years, the use of machine learning (ML) algorithms has gained significant popularity in the estimation of solar radiation components. In this study it is proposed a post-processing approach using the separation model outcomes as input variables to estimate the diffuse fraction. Three ML models are employed (XGBoost, Random Forest, and Multilayer Perceptron) to boost the accuracy in terms of three statistical indicators: nRMSE, nMBE, and R2. The employed technique takes a distinctive approach by using reference stations to train the machine learning models and, afterward, make the assessment at the site under study. The results show an improvement in terms of precision of individual separation model outcomes. Thus, the proposed methodology may serve as a reliable approach for estimating solar radiation components in cases where historical data for a specific place of interest is not accessible.

A data-driven LSTMSCBLS model for soft sensor of industrial process

Abstract In the chemical industry, data-driven soft sensor modeling plays a crucial role in efficiently monitoring product quality and status. Industrial data in these applications typically exhibit significant temporal characteristics, meaning that current information is influenced by data from previous periods. Effectively extracting and utilizing these temporal features is essential for achieving accurate soft sensor modeling in complex chemical scenarios. To address this challenge, this study proposes a data-driven Broad Learning System (BLS) model, which combines Long Short-Term Memory (LSTM) networks with an adaptive algorithm known as the Stochastic Configuration Algorithm (SC), referred to as LSTMSCBLS. The model operates in two stages: temporal feature extraction and final prediction. In the temporal feature extraction stage, the integration of the LSTM network with a feature attention mechanism allows for efficient extraction of temporal features from high-dimensional time-series data. In the final prediction stage, the SC is integrated into the BLS, effectively mitigating issues related to node space redundancy and the determination of the number of nodes. The effectiveness and superiority of the proposed model are demonstrated through two industrial case studies involving a debutanizer column and a sulfur recovery unit.

Ultrasonic Sensor-based Water Leveling for Three-dimensional Water Phantom: Prototype Development

Objectives: The purpose of this study was to develop a prototype for controlling the water level of a three-dimensional (3D) water phantom using ultrasound sensors and Arduino technology and evaluate its performance in setting up the 3D water phantom for radiation beam measurements. Materials and Methods: A prototype consisted of an Arduino Nano board and two types of ultrasound sensors (US015 and SR04). The accuracy of both sensors was tested at various distances and the performance was evaluated through statistical analysis. The distance measurement test was performed rigorously at intervals of 2 cm from 5 cm to 21 cm, measuring an average error and a maximum deviation for each sensor. Results: Both sensors demonstrated the measurement accuracy within 2 mm. When using the traditional and prototype-based setup methods, the measured photon and electron beam profiles did not show any significant difference. This result suggests the equivalent setup capability when using these two different 3D water phantom setup methods. Conclusion: The ultrasound sensor-based prototype is demonstrated as a more effective device in maintaining the 3D water phantom setup consistently compared to the traditional method, which is prone to human error, and it will aid in facilitating precise phantom setup during the commissioning and routine quality assurance (QA) of linear accelerators in radiotherapy clinics.

Evaluation of Non-Invasive Wearable Diabetes Sensors

<b>Introduction</b> Diabetes management has increasingly emphasised the need for continuous glucose monitoring (CGM) systems, promoting advancements in non-invasive wearable diabetes sensors. This comprehensive review explores the latest developments in this field, focusing on the types, technological advancements, and challenges associated with these devices. The review is structured into distinct sections that examine the current state and future directions of optical, electromagnetic, and transdermal sensors, along with emerging technologies in non-invasive glucose monitoring. The review examines the technological enhancements that have improved sensor accuracy and precision, ergonomic designs for increased comfort, and advancements in data analytics that integrate machine learning for predictive analytics. Comparison of the major challenges such as maintaining sensor accuracy and reliability, ensuring user compliance, safeguarding data privacy, and overcoming cost-related barriers are explored. Furthermore, the paper discusses the promising future directions like the use of innovative materials, the integration of artificial intelligence, and the importance of regulatory and ethical considerations in the development of CGM technologies. This review not only underscores the significant progress made in the field but also highlights the critical need for ongoing research to overcome existing limitations. The implications of these technologies extend beyond individual patient management to broader applications in healthcare and lifestyle monitoring, promoting crucial shift towards more personalised and accessible diabetes management solutions. <b>Material and Methods</b> <b>Results</b> <b>Conclusions</b> <b></b> <b></b>

Implementation of the Fuzzy Logic Mamdani Method in the KUB Chicken Egg Incubator

Poultry farming plays an important role in rural Indonesia's economy, with increasing demand for poultry meat and protein-rich eggs. One of the main challenges for farmers is the limited production of chicken seeds and suboptimal egg incubation methods. Modern egg incubators offer a solution with higher ease and efficiency compared to traditional methods. However, existing machines on the market have weaknesses, such as less accurate temperature and humidity control, and less optimal power source switching. The use of fuzzy logic methods in egg incubators has proven to be more efficient than manual methods, with a hatching success rate of 100% for 10 eggs. Fuzzy logic-based egg incubators start hatching earlier and more on days 18 and 19, while manual methods begin on days 19 and 20. The automatic egg-turning process in fuzzy logic machines saves labor and reduces the risk of error. This research highlights the importance of using accurate sensors and optimal temperature and humidity control systems to improve the success rate of chicken egg hatching. Index Terms—poultry farming; egg hatching; temperature; humidity; fuzzy logic

Comparative Analysis of LiDAR and CCTV Sensor Accuracy at Signalized Intersections Under Varied Weather Conditions

Comparative Analysis of LiDAR and CCTV Sensor Accuracy at Signalized Intersections Under Varied Weather Conditions

Dynamic Position Accuracy of Low-Cost Global Navigation Satellite System Sensors Applied in Road Transport for Precision and Measurement Reliability

Low-cost Global Navigation Satellite System (GNSS) sensors have been successfully applied in commercial vehicles’ position monitoring, and they continually raise interest among research audiences both in theoretical and practical aspects. While numerous studies have applied simulations and numerical methods to evaluate the accuracy of the sensors, this paper presents an analysis, supported by actual measurements collected under diversified conditions. The measurements were collected under a variety of conditions, including urban and suburban routes of considerable length, and in accordance with the position in lane applied in most European countries, which is considerably related to the sustainability of road transport. The measurements were collected during driving of three different passenger vehicles, and the response of the measurements to correct, partially correct and incorrect vehicle positions was recorded. Differentiated kinematic conditions and actual dynamic performance during driving were analyzed. This research compared the position accuracy of a low-cost GNSS sensor and a dual-antenna GNSS/INS sensor for vehicle dynamics monitoring. Both types of sensors were operated on all the passenger vehicles and with the same measurement conditions. Statistical hypothesis tests have been considered to compare the results, in accordance with the latest guidelines for carrying out such tests. Studies have indicated that a low-cost GNSS sensor also has satisfactory accuracy. However, this paper points out additional considerations and conclusions. Both the positive and negative results are described and commented on in the paper, including research limitations and suggestions for future measurement and future research agendas, both by the authors and as an inspiration for other researchers.

A Compensation Method for Full-Field-of-View Energy Nonuniformity in Dark-and-Weak-Target Simulators.

Dark-and-weak-target simulators are used as ground-based calibration devices to test and calibrate the performance metrics of star sensors. However, these simulators are affected by full-field-of-view energy nonuniformity. This problem impacts the quality of output images and the calibration accuracy of sensors and inhibits further improvements in navigational accuracy. In the study reported in this paper, we sought to analyze the factors which affect full-field-of-view energy uniformity in dark-and-weak-target simulators. These include uneven irradiation in backlight sources, the leakage of light from LCD display panels, and the vignetting of collimating optical systems. We then established an energy transfer model of a dark-and-weak-target simulator based on the propagation of a point light source and proposed a self-adaptive compensation algorithm based on pixel-by-pixel fitting. This algorithm used a sensor to capture the output image of a dark-and-weak-target simulator and iteratively calculated the response error matrix of the simulator. Finally, we validated the feasibility and effectiveness of the compensation algorithm by acquiring images using a self-built test system. The results showed that, after compensating an output image of the dark-and-weak-target simulator, the grayscale standard display function (SDF) of the acquired sensor image was reduced by about 50% overall, so the acquisition image was more accurately compensated, and the desired level of grayscale distribution was obtained. This study provides a reference for improving the quality of output images from dark-and-weak-target simulators, so that the working environments of star sensors may be more realistically simulated, and their detection performance improved.

Impact of bindarit, a CCL2 chemokine synthesis inhibitor, on macrophage-based biofouling and continuous glucose monitoring invivo

Continuous glucose monitoring (CGM) using implantable glucose sensors is a critical tool in the management of diabetes. Unfortunately, current commercial glucose sensors have limited performance and lifespans in vivo, considered to be due to sensor-induced tissue reactions (inflammation, fibrosis, and vessel regression). Previously, our laboratory utilized monocyte/macrophage (Mo/MQ) deficient and depleted mice to establish a causal relationship between Mo/MQ accumulation and inflammation in glucose sensor performance in vivo. Using C–C chemokine ligand-2 (CCL2) and C–C chemokine receptor-2 (CCR2) knockout mice, we next established that deletion of this Mo/MQ chemokine family, suppressed inflammation at the sensor-tissue interface in these mice, while improving sensor performance over a 4-week post-sensor implantation, compared to normal mice. These studies underscore the importance of the CCL2 family of chemokines and receptors in Mo/MQ recruitment/activation, and sensor performance in vivo. In the present study, we systemically administered Bindarit, a CCL2 synthesis inhibitor, to assess the role of CCL2 chemokines, Mo/MQ recruitment and inflammation at sensor implantation sites, on CGM performance in vivo. These studies demonstrate that systemic administration of Bindarit substantially reduced sensor-induced inflammation, particularly MQ recruitment, preventing sensor biofouling in our CGM mouse model. These results not only confirm the major role monocytes/macrophages play, but directly demonstrate that CCL2 drives Mo/MQ recruitment and biofouling of glucose sensors in vivo. These findings support future studies incorporating Mo/MQ migration/chemotaxis inhibitors, like CCL2, on sensor coatings to improve glucose sensor accuracy and lifespan in vivo.

Enhanced drift self-calibration of low-cost sensor networks based on cluster and advanced statistical tools

The escalating use of low-cost sensors in environmental monitoring demands effective management of sensor drift and errors. To tackle this challenge, lightweight calibration techniques are essential for accurate sensor readings. This paper presents a novel calibration method for low-cost sensors, prioritizing the detection and correction of sensor drifts. The approach incorporates clustering for efficient task distribution and leverages Inverse Distance Weighting (IDW) for calibration precision. Also, advanced statistical tools, including the Two-Sample Kolmogorov–Smirnov test (TSKS test) and Exponential Moving Average (EMA), assess and enhance sensor data stability based on historical measurements. Additionally, the Root Update Estimator (RUE) is utilized to adjust predicted values and improve the model’s adaptability to changes in the underlying data distribution. Extensive simulation experiments using an Intel Berkeley Research Laboratory (IBRL) dataset affirm the method’s effectiveness in swiftly addressing sensor drifts. These findings advance low-cost sensor calibration, boosting reliability and precision in environmental monitoring.

Design and Build Noise and CO2 Control Devices in IOT (Internet of Things) Based Libraries

The library is a source of knowledge, information, and support for learning. It is equipped with facilities to support comfort and maintain concentration while carrying out activities in the library. For human resources to progress further, libraries must be made as comfortable as possible to support learning. This study aims to design a noise and CO2 control device in an IOT (Internet of Things) based library by using 4 sound sensors KY-037 to detect noise and 1 MQ-135 sensor to detect CO2 gas. This research method uses a literature review study. The noise and CO2 control device in the IOT (Internet Of Things) based library has been completed and can work as planned, the accuracy obtained from the KY-037 sound sensor is quite high, which is 97.6%. However, the good thing is that if the sensor accuracy is improved again, the accuracy obtained from the MQ-135 sound sensor is also quite high, which is 99.16%. Conclusion If the accuracy of the sensor is improved again, the web server runs smoothly using AJAX (Asynchronous et al.) so that the resulting website does not need to load the entire page and the ESP32 as a microcontroller can control the entire system well.

Introducing the double validation metric for radar sensor models

In automated vehicles, environment perception is performed by various sensor types, such as cameras, radars, lidars, and ultrasonics. Simulation models of these sensors, as required in virtual validation methods, are available in various degrees of detail. However, proving the validity of such models is a subject of research. New metrics and methods for credibility assessment of simulation are needed to standardize the validation process in the future. The so-called double validation metric (DVM) has shown advantages and allows an intuitive interpretability of the validation results. The DVM has so far only been applied to lidar sensor models. In this paper, an extension to the DVM is introduced, which is called the DVM Map. A static measurement scenario is conducted in reality and transferred into simulation. The novel method is demonstrated on the obtained real and simulated radar sensor data. In this simple scenario special focus is put on the position accuracy of GNSS reference sensors. Therefore, their impact on the result of sensor model validation is discussed. The paper shows that the method provides a more detailed and accurate validation in comparison to the state of the art of a radar simulation, revealing previously undetected simulation errors. Errors due to the environment model, signal propagation, and signal processing are separated and satellite imagery is used for intuitive visualization of the results. This method is a complementary tool to existing validation techniques to improve the interpretability and judging the trustworthiness of radar simulations.