Accuracy Of Sensor Research Articles

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.

Dynamic Validation of Calibration Accuracy and Structural Robustness of a Multi-Sensor Mobile Robot.

For mobile robots, the high-precision integrated calibration and structural robustness of multi-sensor systems are important prerequisites for ensuring healthy operations in the later stage. Currently, there is no well-established validation method for the calibration accuracy and structural robustness of multi-sensor systems, especially for dynamic traveling situations. This paper presents a novel validation method for the calibration accuracy and structural robustness of a multi-sensor mobile robot. The method employs a ground-object-air cooperation mechanism, termed the "ground surface simulation field (GSSF)-mobile robot -photoelectric transmitter station (PTS)". Firstly, a static high-precision GSSF is established with the true north datum as a unified reference. Secondly, a rotatable synchronous tracking system (PTS) is assembled to conduct real-time pose measurements for a mobile vehicle. The relationship between each sensor and the vehicle body is utilized to measure the dynamic pose of each sensor. Finally, the calibration accuracy and structural robustness of the sensors are dynamically evaluated. In this context, epipolar line alignment is employed to assess the accuracy of the evaluation of relative orientation calibration of binocular cameras. Point cloud projection and superposition are utilized to realize the evaluation of absolute calibration accuracy and structural robustness of individual sensors, including the navigation camera (Navcam), hazard avoidance camera (Hazcam), multispectral camera, time-of-flight depth camera (TOF), and light detection and ranging (LiDAR), with respect to the vehicle body. The experimental results demonstrate that the proposed method offers a reliable means of dynamic validation for the testing phase of a mobile robot.

Mesoporous CdO/CdGa2O4 microsphere for rapidly detecting triethylamine at ppb level

Developing fast, accurate and sensitive triethylamine gas sensors with low detection limits is paramount to ensure the safety of workers and the public. However, sensors based on single metal oxide materials still suffer from drawbacks such as low response sensitivity and long response and recovery times. To address these challenges, in this work, a series of mesoporous CdO/CdGa2O4 microspheres were synthesized. We optimized the sensor's sensing performance to triethylamine by fine-tuning the ratio of CdO to CdGa2O4. Among them, CdO:3CdGa2O4-based sensor demonstrates a rapid response time of 2 s to detect 100 ppm of triethylamine, with a high response value of 211 and exceptional selectivity. Furthermore, it exhibits a low detection limit of 20 ppb for triethylamine, making it suitable for practically testing fish freshness. Crucially, electron transfer between the heterojunctions increases the chemically adsorbed oxygen on the materials’ surface, thereby enhancing the sensor's response sensitivity to triethylamine. This discovery provides new insights and methodologies for the design of highly efficient triethylamine gas sensors.

Agreement between Vital Signs Measured Using Mat-Type Noncontact Sensors and Those from Conventional Clinical Assessment.

Vital signs are crucial for assessing the condition of a patient and detecting early symptom deterioration. Noncontact sensor technology has been developed to take vital measurements with minimal burden. This study evaluated the accuracy of a mat-type noncontact sensor in measuring respiratory and pulse rates in patients with cardiovascular diseases compared to conventional methods. Forty-eight hospitalized patients were included; a mat-type sensor was used to measure their respiratory and pulse rates during bed rest. Differences between mat-type sensors and conventional methods were assessed using the Bland-Altman analysis. The mean difference in respiratory rate was 1.9 breaths/min (limits of agreement (LOA): -4.5 to 8.3 breaths/min), and proportional bias existed with significance (r = 0.63, p < 0.05). For pulse rate, the mean difference was -2.0 beats/min (LOA: -23.0 to 19.0 beats/min) when compared to blood pressure devices and 0.01 beats/min (LOA: -11.4 to 11.4 beats/min) when compared to 24-h Holter electrocardiography. The proportional bias was significant for both comparisons (r = 0.49, p < 0.05; r = 0.52, p < 0.05). These were considered clinically acceptable because there was no tendency to misjudge abnormal values as normal. The mat-type noncontact sensor demonstrated sufficient accuracy to serve as an alternative to conventional assessments, providing long-term monitoring of vital signs in clinical settings.

A Low-Frequency Vibration Sensor Based on Ball Triboelectric Nanogenerator for Marine Pipeline Condition Monitoring.

Marine pipeline vibration condition monitoring is a critical and challenging issue, on account of the complex marine environment, while powering the required monitoring sensors remains problematic. This study introduces a vibration sensor based on a ball triboelectric nanogenerator (B-TENG) for marine pipelines condition monitoring. The B-TENG consists of an acrylic cube, polyester rope, aluminum electrodes, and PTFE ball, which converts vibration signals into electrical signals without the need for an external energy supply. The experimental results show that B-TENG can accurately monitor the frequency, amplitude, and direction of vibration in the range of 1-5 Hz with a small error of 0.67%, 4.4%, and 5%, and an accuracy of 0.1 Hz, 0.97 V/mm, and 1.5°, respectively. The hermetically sealed B-TENG can monitor vibration in underwater environments. Therefore, the B-TENG can be used as a cost-effective, self-powered, highly accurate vibration sensor for marine pipeline monitoring.

IoT revolutionizes orchid cultivation in Indonesia with precision and efficiency

The Orchidaceae family is highly valued in ornamental, perfume, medical, and cosmetic industries, but cultivation often fails due to human error. This research aims to design an IoT-integrated automation system to monitor and control key parameters such as air temperature, humidity, and moss moisture for orchid cultivation. Using the research and development method, we observed plant needs, reviewed relevant literature, designed an effective system, and tested sensor accuracy and system reliability. Results showed the DHT-22 sensor had a minimal error in temperature (0.9%) and humidity (2.1%) readings, and the soil moisture sensor had a 3.65% error. The system maintained optimal conditions, and the Blynk app effectively displayed data. This innovation reduces human error and enhances orchid cultivation efficiency, with potential applications in other sensitive plant systems. Highlight: Error Reduction: IoT system minimizes human error in orchid cultivation. Sensor Accuracy: DHT-22 sensors show minimal temperature (0.9%) and humidity (2.1%) errors. Optimal Conditions: Maintains air humidity 40%-80%RH and moss moisture 50%-80%RH. Keyword: Orchidaceae, IoT automation, sensor accuracy, environmental control, orchid cultivation

Seamless Solar-Powered Hydroponics Revolutionizes Urban Farming in Indonesia

This study explores the use of PLTS (solar power) as an alternative energy source for urban hydroponic systems. Due to PLTS's limited capacity, an automatic transfer switch (ATS) is utilized to seamlessly switch between PLTS and PLN (public electricity) based on sensor readings. The system uses an INA219 sensor to monitor PLTS voltage, a TDS sensor for water quality, and a NodeMCU microcontroller, with data displayed on an LCD and Blynk for remote monitoring. Testing showed a PLTS voltage of 12.0V, with sensor accuracies of 96% (INA219) and 98.59% (TDS). Overall, the system demonstrated reliable and optimal performance, providing a sustainable solution for urban hydroponic farming. Highlights: 1. PLTS and PLN integration ensures reliable power for urban hydroponics.2. High sensor accuracy supports precise system monitoring.3. Remote monitoring via LCD and Blynk enhances control and sustainability. Keywords: PLTS, hydroponic systems, automatic transfer switch, NodeMCU, sustainability

Continuous glucose monitoring and recreational scuba diving in type 1 diabetes: head-to-head comparison between Free Style Libre 3 and Dexcom G7 performance.

Background Scuba diving was previously excluded because of hypoglycemic risks for type 1 diabetic patients (T1DM). Specific eligibility criteria and a safety protocol have been defined, while continuous glucose monitoring (CGM) systems have enhanced diabetes management. This study aims to assess the feasibility and accuracy of CGM Dexcom G7 (DG7) and Free Style Libre 3 (FSL3) in a setting of repetitive scuba diving in T1DM, exploring the possibility of non-adjunctive use. Material and methods The study was conducted during an event of Diabete Sommerso® association in November 2023. Participants followed a safety protocol, with capillary glucose as reference standard (Beurer GL50Evo). Sensors accuracy was evaluated through Median and Mean Absolute Relative Difference (MeARD and MARD) and Surveillance Error Grid (SEG) Analysis. Sub-analyses were performed by glycemic range, timing, gender. Data distribution and correlation was estimated by Spearman test and Bland-Altman plots. The ability of sensors to identify hypoglycaemia was assessed by contingency tables. Participants completed a satisfation questionnaire. Results Data from 202 dives of 13 patients were collected. The overall MARD was 31% for DG7 and 14.2% for FSL3, MeARD 19.7% and 11.6% respectively. FSL3 exhibited better accuracy in normoglycemic and hyperglycemic ranges. SEG analysis showed 82.1% (DG7) and 97.4% (FSL3) data on no-risk zone. FSL3 better performed on hypoglycemia identification (diagnostic odds ratio of 254.10 vs 58.95). Neither of sensors reached the MARD for non-adjunctive use. Patient satisfaction favored FSL3 for size and minimal bulk, while DG7 received positive feedback for versatility. Conclusions The study reveals FSL3 superior accuracy compared to DG7 in a setting of repetitive scuba diving in T1DM, except for hypoglycemic range. Both sensors fails to achieve accuracy for non-adjunctive use. Capillary tests remain crucial for safe dive planning, and sensor data should be interpreted cautiously. We suggest exploring additional factors potentially influencing sensor performance.

Optical fiber sensor for water velocity measurement in rivers and channels

In this work, optical fiber Bragg grating sensors were used to measure water velocity and examine how it was distributed in open channels. Several types of coatings were incorporated into the design of the sensors to examine their effects on the strain that the fibers experienced as a result of the water flow. Due to their low elastic coefficient, which reduced the hysteresis, the results indicated that the aluminum- and acrylate-coated fibers had the best performance. ANSYS-CFX V2020 R2 software was used to model the strain encountered by the fibers under various flow rates to assess the performance of the FBG sensors. The calculations and actual data exhibited good convergence, demonstrating the accuracy of the FBG sensors in determining water velocity. The study illustrated the usability of the proposal in both scenarios by contrasting its application in rivers and channels.