Sensor Errors Research Articles

GO/CNT−OH/Nafion Nanocomposite Humidity Sensor Based on the LC Wireless Method

In recent years, LC resonant sensors have gained widespread attention for their extensive applications in industries such as pharmaceutical storage and food transportation. A wireless passive sensor with a good sensing performance is proposed based on a GO/CNT−OH/Nafion nanocomposite. The sensor was fabricated via inkjet printing technology, and the surface morphology of the GO/CNT−OH/Nafion nanocomposite was characterized by SEM measurement. It is found that the MWCNTs support the GO layer and the hydrophobic chains of Nafion interact with the hydrophobic layer of GO, resulting in a larger cavity and hydrophilic surface of the entire material. This structure well reflects the fact that the mixing of MWCNTs and Nafion provides the entire material with a stronger water absorption. The experimental study shows that the proposed humidity sensor has a frequency variation of 103 kHz/%RH at low humidity (30–60% RH) and a sensitivity of 931 kHz/%RH at high humidity (60–95% RH), while the sensitivity value from 30–95% RH is 547 kHz/% RH. The response time and recovery time are 110 s and 115 s, respectively. In addition, the tests showed that the GO/CNT−OH/Nafion nanocomposite applied to the humidity sensor had a maximum humidity hysteresis of about 3% RH at 30–95% RH, the resonant frequency remained basically unchanged after 50 h of testing, and the whole sensor possessed a good stability. After conducting several repeated experiments, it was found that the resonant frequency error of the whole sensor was low and did not affect the overall sensing test, which proved the reproducible preparation of the sensor. Finally, the humidity-sensing mechanism of the proposed sensor was analyzed in this paper, and it was found that GO enhanced the hygroscopic properties of GO/CNT−OH/Nafion nanocomposite when it was supported by MWCNT-OH and included uniformly dispersed Nafion. Therefore, our proposed humidity sensor is suitable for humidity detection above 30% RH in both sealed and open environments.

Strategies for sensor virtual in-situ calibration in building energy system: Sensor evaluation and data-driven based methods

Sensor errors greatly affect the optimal control, safe operation, and energy efficiency of the building energy system. The sensor virtual in-situ calibration (VIC) method based on Bayesian inference can calibrate the sensor errors and does not need to remove existing sensors or add redundant sensors. In the previous study, the calibration of all sensors (non-differential calibration) and the physical model-driven based methods are applied in the simulation system. However, in the practical building energy system, these reduce calibration accuracy and efficiency. To solve these two negative factors, two calibration strategies are proposed: (1) sensor evaluation, and (2) data-driven based methods. Four cases are designed to demonstrate calibration accuracy and efficiency improvement by applying the calibration strategies in the variable air volume system. The results show that non-differential calibration causes difficulties in practical applications because of its low accuracy (38.10 %) and long calibration time (5136.35 s). The calibration accuracy of the physical model-driven based methods is 9.06 %, which completely deviates from the true value. The two calibration strategies are applied to improve the sensor calibration accuracy (up to 91.88 %) and reduce the calibration time (28.99 %) although it will increase some replacement costs. Meantime, the calibration accuracy of the temperature sensor is further improved (exceeding 94 %) by dividing the operating conditions according to the compressor power. In summary, the application of the calibration strategies can effectively overcome the negative impacts on calibration accuracy and efficiency, and increase the robustness of the VIC method in the practical building energy system.

Simulation of The System of Provision of Target Braking of Rolling Stock

This research presents a framework simulation model that can be used to simulate target braking of rail rolling stock, with a particular focus on target braking in metro systems. The model takes into account various factors that might affect stopping accuracy, including errors in speed measurement and track sensors' positioning. The research considered the possibility to minimize the required equipment for the target braking system, with its conclusions providing insights for possible solutions that would require minimal additional equipment or infrastructure modifications. Through simulation in MATLAB®/Simulink® environment, this study investigates the individual impact of these factors on the target braking precision of rail rolling stock. By identifying these critical factors that affect braking accuracy and quantifying their impact, this research provides valuable insights into strategies for optimizing braking performance and enhancing passenger safety and comfort.

Design Of Multi Sensor Quadcopter Navigation Data Acquisition For Early Fire Detection and Treatment

Recently, forest and land fires have become an event that has become a concern for the national and international community. This research creates a quadcopter as a tool for detecting forest and land fires. This system is an alternative tool for detecting forest and land fires that already exist. The advantage of this quadcopter is that it can collect data before the fire, during the fire, and after the fire. This quadcopter is designed and realized the developm­­­­­ent of an automatic navigation system for unmanned aircraft. This system is controlled by a microcontroller that processes IMU (Inertial Measurement Unit) sensor data in which there is a gyroscope and accelerometer, GPS, and magnetometer so that it can fly automatically. The purpose of this study is to acquire data on the navigation sensors used in the unmanned aerial vehicle (UAV) design, then the data from the navigation sensors will be combined using complement and median filters as position control input for the quadcopter. Based on this research, processing from sensor readings to sensor integration is able to produce better data, where the quadcopter's angle of view has an error percentage of 1.10° and 0.68°. This value is smaller than the percentage error of the magnetometer sensor alone or the IMU sensor alone which results in an error percentage of 1.18° and 0.76°.

Design of Hydroponic System Using NFT (Nutrient Film Technique) Method for Lettuce (Lactuca Sativa L.) Plant

In this study, the hydroponic system for lettuce using the Nutrient Film Technique (NFT) method has been designed. The NFT method is a nutrient delivery method that flows nutrient-containing water and circulates it continuously, so that the roots do not easily rot. The hydroponic system designed in this study uses a DHT11 sensor to detect temperature and a pH sensor to detect pH. In addition, this study also varied the color of the light in the hydroponic system to determine the effect of light color on the growth of lettuce plants. The results of the DHT11 sensor testing showed that the temperature readings were approximately the same as the reference instrument, and the average error in the sensor readings was 1.89%. The average error of the pH sensor was 1.65%, and the average pH sensor value was 1.3%. Based on the measurement of lettuce plant growth, the highest growth was found in plants without using any light, and the widest plant was the one using yellow light with a width of 8.5 cm on day 14. The number of leaves for plants using red and yellow lights was the same, with 8 leaves.

The Behavior of Bp He-weak Stars in the Far UV—Paper VI: HD 21699

The analysis of spectra of the B8 He-weak Si star HD 21699 recorded by the SWP camera on board the International Ultraviolet Explorer in 1987 reveals large amplitude variations of the far-ultraviolet spectral energy distribution over a time interval of about four days in 1987 October. In contrast, the level of the mid-UV flux did not vary between phases of FUV flux maximum and minimum which suggests that changes of the silicon continuous and line opacity play an important role in the variations. The lines of C iv doublet near 1550 Å are stronger and have an extended blue wing at phase of far-UV maximum. The optical brightness recorded by the Fine Error Sensor on board IUE is probably anticorrelated with the FUV flux, reflecting the redistribution of the FUV flux toward longer wavelengths.

Shoulder Range of Motion Measurement Using Inertial Measurement Unit—Validation with a Robot Arm

The invention of inertial measurement units allowed the construction of sensors suitable for human motion tracking that are more affordable than expensive optical motion capture systems, but there are a few factors influencing their accuracy, such as the calibration methods and the fusion algorithms used to translate sensor readings into angles. The main purpose of this study was to test the accuracy of a single RSQ Motion sensor in comparison to a highly precise industrial robot. The secondary objectives were to test how the type of sensor calibration affects its accuracy and whether the time and magnitude of the tested angle have an impact on the sensor’s accuracy. We performed sensor tests for nine repetitions of nine static angles made by the robot arm in eleven series. The chosen robot movements mimicked shoulder movements in a range of motion test (flexion, abduction, and rotation). The RSQ Motion sensor appeared to be very accurate, with a root-mean-square error below 0.15°. Furthermore, we found a moderate-to-strong correlation between the sensor error and the magnitude of the measured angle but only for the sensor calibrated with the gyroscope and accelerometer readings. Although the high accuracy of the RSQ Motion sensors was demonstrated in this paper, they require further study on human subjects and comparisons to the other devices known as the gold standards in orthopedics.

Innovation-saturated Koopman Kalman filter for distribution system dynamic state estimation against measurement outliers

Sensor errors, model uncertainty, changes in the surrounding environment, data loss, or malicious network attacks can all result in outliers, which can pollute the measurement process of the distribution system. In this study, to address this problem, a dynamic state estimation method for distribution systems based on innovation-saturated Koopman Kalman filter (IS-KKF) was proposed. The Koopman Kalman filter (KKF) is model-free and data-driven, and estimates nonlinear dynamics through the conventional linear Kalman filter. To make the Koopman Kalman filter robust to measurement outliers, the innovation saturation mechanism applies a saturation function to it. To correct the state estimation, this mechanism is applied to the filtering process. When outliers occur, the distorted innovation is saturated to prevent the state estimation results from being destroyed. Adaptive adjustment of the saturated boundary is a feature of this mechanism. Extensive simulations were carried out on the IEEE 118-bus test system to verify the effectiveness and robustness of the proposed method. The simulation results demonstrate that the proposed method can effectively reject outliers with different amplitudes, types, and continuous times, has significant computational efficiency, and does not require additional measurement redundancy.

Improving the thermo-electro-mechanical responses of MEMS resonant accelerometers via a novel multi-layer perceptron neural network

Microelectromechanical systems (MEMS) accelerometers have been considerably developed since their wide use in current industrial applications. MEMS resonant accelerometers have attracted considerable attention in recent years by introducing some advantages over analog ones in terms of supporting a large range of dynamic movements, providing good sensitivity, considerably reducing the effects of interferences, etc. However, since silicon-based designs have shown some temperature-related disadvantages, there are always significant errors in such sensors due to the variable temperature. In this paper, we propose a novel multi-layer perceptron (MLP) neural network to improve the accuracy temperature compensation model for MEMS resonant accelerometers. To do so, we propose a novel metaheuristic algorithm by improving the searching ability of an artificial bee colony (ABC), named global search artificial bee colony (GSABC) to optimally train the MLP for gaining a better temperature compensation model. We then first evaluate GSABC on engineering and some well-known benchmark datasets in comparison with some widely-used and novel metaheuristic algorithms. After that, we perform various experiments to train the proposed MLP. Our experiments show that the proposed GSABC-derived MLP outperforms the state-of-the-art in providing higher accuracy for temperature compensation of MEMS resonant accelerometers. It is shown that within the calibration measuring experiment of the MEMS system, the environmental temperature was varied from 0 °C to 90 °C with a temperature interval of 10 °C.

Experimental method for estimating the dynamic error of devices and sensors under their operating conditions

Experimental method for estimating the dynamic error of devices and sensors under their operating conditions

Automotive Radar Modeling for Virtual Simulation Based on Mixture Density Network

Road safety is the fundamental purpose of advanced driver assistance systems (ADAS), and automotive Radar often plays a significant role in reliable environment perception. Therefore, as the complexity of system integration continues to increase, the development quality and speed become increasingly crucial. Nowadays, sensor virtualization helps expedite the development process. Since Radar must perform several measurements in a short period to resolve ambiguity, the resulting radar signal is multi-dimensional and data-intensive. Therefore, analyzing these signals and generating models from them are not an easy task. To overcome the challenges, we present a Radar model based on Mixture Density Network (MDN) to generate production sensor errors that exhibit varied input correlations. Meanwhile, the errors modify a specific object list of Radar during the virtualized sensing process. The results show that MDN networks can better express the uncertainty of sensor detection errors and can be generalized to a generic sensor model. Finally, the MDN-based radar model is integrated into a multi-body simulation virtual platform with an open simulation interface to validate its performance.

RENAL6: Movement Abnormality Quantitation: Comparison of On-Body vs. Around-Body Systems for Assessment of Movement Abnormalities in Chronic Kidney Disease

Background: Chronic kidney disease (CKD) causes a range of derangements including movement abnormalities. Presently, motion disorders are largely described qualitatively. Our group developed quantitative measurement devices: 1. wearable digital patch (Biostamp) (stretchable electronics) measuring motion with 6 degrees of freedom – an “on body” (contact) method, and 2. video telemedicine system using external markers (MOCA 2.0) – an “around-body” (non-contact) method, measuring body element movement, e.g. arms, fingers. We compared the relative performance of each system assessing a series of defined movements. We studied normal volunteers – free of CKD, to be utilized as controls for a next step patient CKD study. Methods: Six subjects (3:3 male:female) had both wearable digital sensor patches (on-body method) and small color paper markers (around-body method) applied. Subjects performed 6 defined movements, categorized as either small (<1 sq ft), medium (1-5 sq ft), or large (>5 sq ft). Each movement was performed for 30 seconds at both slow and fast pace, (n=3), for a total of 36 movement sessions. Digital signals for the on-body method were streamed and recorded contemporaneous with around-body video recording of subject movement. Analyzed data for each system was also compared to physical measurement of movements and distance traveled. Results: Simultaneous recording and quantitation of movements with both systems allowed for comparison of the two systems. The on-body digital patch system was able to detect all movements through positional acceleration and angular velocity which was processed into positional and angular data vs. time (Fig A, B.). The around-body system generated positional data vs. time, along with angles of movements for all movements performed (Fig C.). A limitation of the around-body system became apparent with small fast movements. For fast movement, camera lag frequently lost track of markers and did not accurately track movements, compared to the high level of fidelity and sensitivity seen with the digital patch system. Discussion: Both on-body and around body systems offer readily deployable means for capturing nuances of human motion useful for movement disorder diagnosis and monitoring. The on-body system reports data in angular velocities and accelerations which is converted into positional data. However, due to interpretation of forces registered by incorporated sensors, instead of true positional data, small errors may be introduced. In contrast the around-body video capture system utilizes video processing to directly yield positional data with respect to time. As actual positional data is measured this method is free of introduced sensor error, though has the limitation of inefficacy with movements too small or fast for camera capture. Understanding system constraints will allow future patient studies to advance with greater accuracy based on system choice.

Observability Analysis and Optimization of Cooperative Navigation System With a Low-Cost Inertial Sensor Array

Low-cost inertial measurement unit (IMU) is gradually applied for providing reliable positioning and navigation information in the area of Internet of Things (IoT) applications recently. However, the accuracy of IMU is highly influenced by inertial sensor errors in GNSS-denied and indoor navigation environment. In order to improve the accuracy and robustness of IMU, rotation modulation and cooperative navigation techniques can serve as effective ways for indoor and outdoor seamless navigation and positioning. In this paper, we propose a cooperative navigation system with a low-cost inertial sensor array composed of four IMUs. For optimizing the configuration and information processing of this system, observability analysis is carried out based on the concept of the degree of observability. Furthermore, a criterion for calculating the degree of observability is formulated to simplify the observability analysis of rotational IMUs. According to the simulation experiments of unmanned vehicle at low, medium and high driving speeds, the IMU rotation technique can improve the observability of inertial sensor errors and thus increase the accuracy of orientation, while the cooperative navigation technique can highly improve the accuracy of positioning.

Small Dataset-Based Closed-Loop State of Charge Estimation for Pure Electric Construction Machinery With Large Sensor Error: A Case Study of 5-ton Loader

Pure electric construction machinery (PECM) is the future development trend for construction machinery. Compared with other electric drive systems, the PECM systems are often accompanied by complex working conditions, frequent high current pulse signals, limited memory sources, and heavier sensor noise. To improve the robustness and accuracy of state of charge (SOC) estimation for the PECM using small dataset with large sensor error, a hybrid approach combined machine learning method with physical method is proposed in this paper. Firstly, the Gaussian process regression (GPR) is employed to train an open-loop SOC estimation model with a simple moving window method. Secondly, a framework of hybrid SOC estimation is proposed based on an improved version of the Kalman filter to improve the robustness of the GPR model with large sensor error. Finally, a series of experiments are carried out to evaluate the proposed approach considering temperature variation, measurement drift noise, and users’ habits. It is worth noting that the sampling frequency of training data is only 0.1 Hz. The proposed approach is compared with the most widely used machine learning methods, which verifies the high accuracy and strong robustness of the proposed approach.

Modeling and Compensation of Inertial Sensor Errors in Measurement Systems

In the field of surveying and mapping, inertial sensor deterministic errors are poorly understood, and error calibration and compensation are not carried out. Thus, in this study, the effects of three types of deterministic errors (i.e., bias, scale factor error, and installation error) in a conventional inertial measurement unit (IMU) error model on a navigation system are theoretically deduced and verified by simulation. Subsequently, navigation experiments are carried out to investigate the effects of the three deterministic errors on the navigation system. The experimental results show that the gyro bias has the strongest influence on the navigation and positioning accuracy of the system. Consequently, we designed a two-position continuous calibration scheme to calibrate the IMU. The calibration scheme can simultaneously calibrate the bias error of the gyroscope and the accelerometer. When calibrating the bias error of the 0.005°/h order of magnitude, the maximum relative error is 13.16%, and the rest of the calibration relative errors are less than 10%, which verifies the effectiveness of the calibration path designed in this paper. The system is compensated by using the IMU bias calibration results, and the navigation experiment results show that the position accuracy and heading accuracy are improved by 72.68% and 79.65%, respectively, through the calibration and compensation of IMU bias error. Therefore, the position and heading accuracy of the system will be greatly improved by calibrating and compensating the bias error through the two-position calibration path before the IMU is used.

Asynchronous Anti-Bias Track-to-Track Association Algorithm Based on Nearest Neighbor Interval Average Distance for Multi-Sensor Tracking Systems

Due to sensor characteristics, geographical environment, electromagnetic interference, electromagnetic silence, information countermeasures, and other reasons, there may be significant system errors in sensors in multi-sensor tracking systems, resulting in poor track-to-track association (TTTA) effect of the system. In order to solve the problem of TTTA under large system errors, this paper proposes an asynchronous anti-bias TTTA algorithm that utilizes the average distance between the nearest neighbor intervals between tracks. This algorithm proposes a systematic error interval processing method to track coordinates, and then defines the nearest neighbor interval average distance between interval coordinate datasets and interval coordinate points, and then uses grey theory to calculate the correlation degree between tracks. Finally, the Jonker–Volgenant algorithm is combined to use the canonical allocation method for TTTA judgment. The algorithm requires less prior information and does not require error registration. The simulation results show that the algorithm can ensure a high average correct association rate (over 98%) of asynchronous unequal rate tracks under large system errors, and achieve stable association, with good association and anti-bias performance. Compared with other algorithms, the algorithm maintains good performance for different target numbers and processing cycles, and has good superiority and robustness.

The Behavior of Bp Si Stars in the far-UV—Paper VI: HD 215441

The analysis of spectra of the B9 Si star HD 215441 recorded by the SWP camera on board the International Ultraviolet Explorer reveals large amplitude variations of the far-ultraviolet spectral energy distribution over a time interval of about half the rotation period. In contrast, the level of the mid-UV flux does not vary between phases of FUV flux maximum and minimum which suggests that changes of the silicon continuous and line opacity play an important role in the variations. The optical brightness recorded by the Fine Error Sensor on board IUE is anticorrelated with the FUV flux, reflecting the redistribution of the FUV flux towards longer wavelengths.

Recent Applications of System Identification Techniques at IPEV

This paper presents an overview of the system identification techniques developed and applied at the Flight Test and Research Institute during the last decade. System identification techniques have been used extensively to obtain aerodynamic models for a flight-test simulator, which was developed in house and specifically tailored for flight-test instruction and preparation for actual test flights. The methods applied were mostly the output error for data compatibility check and equation error in the time domain for model structure selection and parameter estimation. The most relevant applications are summarized in this paper, including identification of a high angle of attack model with compressibility effects for a jet trainer; estimation of drag polars at high subsonic regime for a jet fighter; identification of aerodynamic model in real-time for a propeller-driven fighter; application of flight-path reconstruction techniques to spin tests, in order to correct errors in the air data sensors; and identification of power effects on the longitudinal stability of a propeller-driven trainer. The examples show that system identification has become crucial to 1) obtain accurate aerodynamic models for training in the flight-test simulator and 2) develop more efficient flight-test techniques.

Influence of buffer layer on nitrogen doped AZO relative humidity sensor with p-n heterojunction structure

A nitrogen doped AlZnO (N-doped AZO) relative humidity (RH) sensing p-n heterojunction structure on n-type silicon substrate using DC sputtering system and lift-off technique has been proposed in this research. N-doped AZO RH sensing film was adopted to enhance photocatalyst effect and realize the heterojunction structure in humidity sensing device. The carrier properties of N-doped AZO RH sensing film were evaluated in the carrier type, carrier mobility, carrier concentration, and carrier conductivity using Hall effect measurement. The N-doped AZO RH sensors were designed with a p-n heterojunction structure and embedded a buffer layer structure. The sensing properties of N-doped AZO RH sensors both with polymer buffer layer and without polymer buffer layer were respectively explored and evaluated in the hysteresis effect, sensitivity, linearity, sensing response and recover response characteristics using environment measurement system and measurement firmware. The research results revealed the carrier type, carrier mobility, carrier concentration, and carrier resistivity of the N-doped AZO RH sensing film deposited on p-n heterojunction structure with buffer layer respectively reached as p-type, 9.6✕101 cm2/Vs, 3.3✕1017 cm−3, and 5.0✕100 Ω-cm. The RH sensing hysteresis error of N-doped AZO RH sensor with buffer layer is approximately 4.57% at 60 %RH which is less than those at other relative humidity values. The sensitivity of N-doped AZO RH sensor with buffer layer reaches as more than 15.7 kΩ/%RH at desorption process which is better than that of sensor without buffer layer either at relative humidity value from 30 %RH to 90 %RH and from 30 %RH to 75 %RH. The adsorption linearity and desorption linearity of N-doped AZO RH sensor with buffer layer are respectively 0.31 and 0.27 between 30 %RH and 75 %RH which are better than those without buffer layer. The adsorption time and desorption time responses of N-doped AZO RH sensor with buffer layer measured during the measurement time of 2000 s with two measuring cycles were estimated about 235 s and 295 s at first cycle respectively less than those of the RH sensor without buffer layer. The design of polymer buffer layer embedded between N-doped AZO RH sensing p-n heterojunction structure and use of n-type silicon substrate revealed the better sensing characteristics and electrical properties both the molecular adsorption/desorption effects in chemical property and the carrier transport phenomena in physical property respectively.

Simplified Real-Time Flush Air-Data Sensing System for Sharp-Nosed Hypersonic Vehicles

This paper discusses a flush air-data sensing (FADS) system for a sharp-nosed hypersonic vehicle designed to estimate flight air data in real time at hypersonic speeds. The design’s target condition is Mach 5.0 to 7.0 with an angle of attack within 5 deg. The FADS system estimates air data by relating them to surface pressures measured from surface-mounted ports on the vehicle forebody. Unique combinations of pressure ratios were used to estimate the freestream dynamic pressure, which was a primary parameter for the flight experiment, the angle of attack, and the Mach number. The proposed FADS estimation algorithm was validated through numerical simulation, which was also used to generate datasets of surface pressures for given flight conditions. To handle possible sensor errors related to estimation accuracy in real-time estimation for a flight experiment, redundant systems were implemented. The results indicated that the designed FADS system can estimate air data within the uncertainty of 4.8% for a single estimator by considering sensor errors for freestream dynamic pressures in the range of 0–100 kPa, including targeted and off-design flight conditions. The proposed algorithm can estimate the air data with an acceptable level of uncertainty while retaining the robustness of estimation to sensor failures with low computational cost.