Sensor Errors Research Articles

Modeling Urban Temperature Using Measurements from Mobile and Stationary Monitoring Stations

Heat waves are occurring more frequently worldwide as global warming continues, and urban heat islands can threaten conventional life in cities. Measuring, analyzing, and simulating weather data at fine spatial and temporal scales are essential to prevent and reduce the damage caused by extreme heat waves. In urban environments, handling complex micrometeorological situations using current meteorological stations and global simulation models (e.g., weather research forecasting models) is challenging. In this study, the thermal environments of urban areas were measured using a mobile meteorological measurement platform. Both mobile and stationary datasets were incorporated into the meteorological modeling process to simulate the spatial and temporal distribution of temperature. Additionally, various mobile observation implementation scenarios for temperature modeling were examined. We compared simulation combinations with the temperature field generated from the total dataset to obtain a better sampling campaign and properly incorporate mobile data scenarios. When collecting mobile data, it is important to consider spatial features to improve the efficiency of sampling programs. This can substantially reduce the cost of mobile data collection, together with the sensor error bound.

High-precision geometric positioning for optical remote sensing satellite in dynamic imaging

ABSTRACT Dynamic imaging of optical remote sensing satellites refers to the active acquisition of images while the satellite is maneuvering at a high angular velocity, which significantly enhances the efficiency and application value of remote sensing imagery. However, due to the influence of rapid maneuvering, the random error of star sensors significantly increases, resulting in a decrease in the geometric positioning accuracy of dynamic imaging remote sensing images. This paper proposes a dynamic fusion method for multisource attitude measurement data based on the noise adaptive estimation and bidirectional filter to achieve high-precision attitude determination and geometric positioning in dynamic imaging. Based on the measurement error model of star sensors, the weights of the star sensor and gyroscope are adaptively adjusted in multisource data fusion to reduce the impact of star sensor measurement errors on the gyroscope. Moreover, a bidirectional fusion filter that includes a low-velocity maneuvering stage is proposed to realize the optimal estimation of the satellite attitude parameter. The simulation data and onboard data of the Luojia3-01 (LJ3–01) satellite were tested to verify the effectiveness of the proposed method. The geometric positioning accuracy of the staring images of LJ3–01 improved from 10.048 m to 7.538 m. The registration accuracy of the sequential images improved from 3.568 pixels to 1.179 pixels. The proposed method can significantly improve the attitude determination accuracy and the geometric positioning accuracy of LJ3–01 satellite staring images. Moreover, for simulation data with various angular velocities, the attitude determination accuracies of the proposed method are better than 0.93”. The experimental results show that the proposed method can achieve high-precision attitude determination in dynamic imaging, reaching the accuracy in the traditional passive imaging.

Analysis of orientation errors in triaxial fluxgate sensors and research on their calibration methods

Abstract. Three-axis magnetic flux gate sensors are widely used in Chinese geomagnetic observatories, but due to their directional errors it is necessary to study error correction methods to improve measurement accuracy. Firstly, the mechanism of directional errors produced by three-axis magnetic flux gate sensors is analyzed, followed by the development of measurement tools for conducting directional error measurement experiments on the high-precision three-axis magnetic flux gate sensors of the Chinese FGM-01 series. Experimental results show that correcting the Z-axis and D-axis directional errors is essential. The observation data after error correction, whether in terms of the standard deviation of its all-day baseline values or the relative difference magnitude with the reference instrument, significantly decrease, demonstrating the clear correction effect and proving the effectiveness of this correction method.

Influence of Temperature Sensor (Pt100) Accuracy on the Interpretation of Experimental Results of Measuring Temperature on the Surface

This article presents the impact of accuracy of sensors, or more specifically Pt100 temperature sensors, on result analysis of experimental studies. For this purpose, an experiment was carried out consisting in measuring the temperature on the surface of a partition - a concrete wall, beneath its insulation layer. The tested surface was separated from external environment and could only be influenced by the wall structure. Therefore, the expected result of the experiment, i.e. the difference in temperature sensor readings in identical locations on both sides of the partition, should reach a value close to 0. This article also presents the values of absolute error for sensors which were determined before their installation on the surface, and on which their location depended. The obtained deviations were included in the results of the experiment, which led to a decrease in temperature differences on both sides of the partition, in some cases even reaching the expected value of 0. This analysis showed how important it is to know the measurement error and then eliminate it in result interpretation.

Research on ultra-high temperature sensor and cold end compensation error

Abstract An ultra high temperature sensor and cold end compensation error are proposed. Aiming at the ultra-high temperature and relatively complex harsh environment of spacecraft, a sensor probe structure which can withstand the high temperature of 2500°C is presented in this paper. According to the practical application characteristics of the sensor with high measurement accuracy, the author proposed a high-precision cold end compensation method. Through this compensation technology, the ultra-high temperature sensor maintains high-precision temperature measurement in the full temperature range. The simulation analysis of the sensor structure and temperature test show that the sensor can not only realize temperature measurement at ultra-high temperature, but also control the nonlinear error to 0.6% of the full scale. The accuracy of the whole temperature range is controlled within 0.92% of the full scale, which meets the practical requirements.

UAV clusters information geometry fusion positioning method with LEO satellite system

The existing Low-Earth-Orbit (LEO) positioning performance cannot meet the requirements of Unmanned Aerial Vehicle (UAV) clusters for high-precision real-time positioning in the Global Navigation Satellite System (GNSS) denial conditions. Therefore, this paper proposes a UAV Clusters Information Geometry Fusion Positioning (UC-IGFP) method using pseudo-ranges from the LEO satellites. A novel graph model for linking and computing between the UAV clusters and LEO satellites was established. By utilizing probability to describe the positional states of UAVs and sensor errors, the distributed multivariate Probability Fusion Cooperative Positioning (PF-CP) algorithm is proposed to achieve high-precision cooperative positioning and integration of the cluster. Criteria to select the centroid of the cluster were set. A new Kalman filter algorithm that is suitable for UAV clusters was designed based on the global benchmark and Riemann information geometry theory, which overcomes the discontinuity problem caused by the change of cluster centroids. Finally, the UC-IGFP method achieves the LEO continuous high-precision positioning of UAV clusters. The proposed method effectively addresses the positioning challenges caused by the strong direction of signal beams from LEO satellites and the insufficient constraint quantity of information sources at the edge nodes of the cluster. It significantly improves the accuracy and reliability of LEO-UAV cluster positioning. The results of comprehensive simulation experiments show that the proposed method has a 30.5% improvement in performance over the mainstream positioning methods, with a positioning error of 14.267 m.

A novel trident shaped microstrip antenna sensor for glucose detection in human blood

A novel trident-shaped microstrip patch antenna sensor with great sensitivity is developed to estimate the amounts of glucose in blood samples of human. The suggested sensor is built on a 30 × 24 × 1.6 mm3 FR-4 substrate layer with a quality factor of 64 and a dielectric constant value of 4.3 resonating at 4.5 GHz. A finger phantom is the replica of the human finger and it is originated in the electromagnetic simulator in order to forecast the glucose content. By positioning the phantom replica at various localities on the constructed antenna sensor, the frequency shifts are observed for various levels of glucose concentration in different degrees, ranging from 0 to 1000 mg/dL are detected. The proposed sensor can identify diabetic conditions in patients as it has a finger phantom positioned parallel to the feed at the top of the antenna giving a 156 MHz paramount shift in frequency, sensitivity of 156 kHz/(mg/dL), and a minimum frequency shift of 24 MHz and sensitivity of 24 kHz/(mg/dL). To confirm the suggested sensor's functionality in a real-time environment, its performance is examined for various actual human finger postures, and the resulting resonance frequencies are observed. In order to detect the levels of glucose concentration, the suggested sensor's error performance is calculated and are found to be near to 1%.

Kalman filter-driven state observer for thermal error compensation in machine tool digital twins

Sustainable reduction of thermal errors during production is the key challenge in modern high-precision manufacturing. Numerical compensation models provide an energy-efficient solution, but in the case of data-driven models, high-quality experimental data must be time-consuming and expensive to produce, negatively impacting overall productivity. Furthermore, robustness concerns arise in the case of new operating conditions, which were not contained in the training data. This paper presents a novel use of a Kalman filter together with model order reduced finite element models to observe the entire thermal state, which allows the subsequent solution of the mechanical model and computation of the thermal errors in real-time without requiring any training data but instead purely based on the physical system model. The effectiveness of this approach is evaluated using experiments on a thermal test bench with 16 out of 40 temperature sensors employed for observation and demonstrated on a 5-axis machine tool (MT) with 13 out of 25 temperature sensors used. Due to the combination of the reduced order model and Kalman filter these 13 temperature sensors are sufficient to represent a MT mesh of more than 350’000 elements. The entire temperature profile of the thermal test bench is reconstructed to achieve a root mean square error (RMSE) of the unobserved temperature sensors of only 2.7 °C, which accounts for more than 83% of all temperature variations and 1.3 °C for the 5-axis MT. For the thermal error of the thermal test bench, the RMSE could be reduced from 67.4μm to 33.3μm, corresponding to a reduction of 52.7 %. This could be achieved without the need for experimental data for model calibration, in a real-time capable physics-based model.

Intelligent System Design for Oyster Mushroom Cultivation Using Mamdani Fuzzy Inference with Internet of Things (IoT)

Oyster mushroom (Pleurotus ostreatus) has great potential in agricultural development with increasing global market demand. This research develops an intelligent cultivation system based on Mamdani fuzzy and IoT to control air temperature, air humidity, and soil moisture automatically and in real-time, using DHT22 sensor, resistive soil moisture sensor, and ESP32 as microcontroller. The test results show a high level of accuracy, with an average sensor error of 1% for air temperature, 3.8% for air humidity, and 9.3% for soil moisture. The validation of watering, humidification, and fan prediction times compared to MATLAB calculations is excellent, with MAPE values of 1.52%, 1.43%, and 1.43%, respectively. This system offers a more accurate and responsive automated solution compared to conventional cultivation management. Further testing on actual oyster mushroom farms is required to determine whether the system can adapt to varying environmental conditions. The system relies heavily on a stable internet connection to optimally perform the real-time Mamdani fuzzy calculation function.

Enhancing hotel efficiency and environmental health with biosensors and big data analytics

The hospitality industry embraces digital technologies to enhance efficiency, guest satisfaction, and environmental sustainability. Integrating biosensors and big data analytics allows hotels to monitor operational processes while maintaining high ecological health standards. However, the impact on environmental health is not fully understood, leading to suboptimal decisions and missed opportunities. This research proposes a novel Tabu Search Drove-Extended Multi-Layer Perceptron (TSD-EMLP) to evaluate the effectiveness of digital operations in hotels through the use of biosensors and big data for predicting hotel environmental conditions. Initially, smart biosensors are deployed in key areas and heating, ventilation, and air conditioning denoted as HVAC systems in the hotel, then the water quality data, noise levels, lighting quality, waste management data, operational data, financial and operational effectiveness data are collected and transmitted to the Internet of Things (IoT) cloud for further process. Interquartile Range (IQR) utilizes the IoT cloud data to remove sensor errors and anomalous events to frame outlier data for the Wavelet Packet Transform (WPT) feature extraction process to decompose sensor data into detailed frequency bands, allowing for precise analysis of complex environmental signals, TSD-EMLP model predicts the environmental health in hotels using the decomposed data. The results demonstrate reducing energy consumption, ventilation systems, indoor environment control, and guest satisfaction, improving air quality, and adjusting environmental settings based on real-time environmental conditions through TSD-EMLP optimized settings. TSD-EMLP classification model achieved high accuracy in predicting hotel environmental health, with a low improvement in guest satisfaction metrics.

Leakage identification and correlation coefficient method for industrial workshop production process combining with computational fluid dynamics

Identifying leakage sources in industrial factory production is crucial to improving air quality, ensuring people’s health and safety and preventing safety accidents. In this study, a method for leakage source identification in industrial factories combining with computational fluid dynamics (CFD) and correlation coefficient was proposed and validated. The study first experimentally validated the numerical methods, which were fundamental to the leakage identification method. Then impacts of leakage sources, sensor errors and number of sensors on the source identification results were evaluated. The results showed that the identification accuracy could be significantly improved by refining the step size of the coefficient a r in this method. When the number of leakage sources was unknown, the accuracy of this method in identifying the number and location of leakages was 93.5%. The computation time spent on source identification depended on the maximum number of leakage sources. Using four sensors with errors were enough to identify the number of unknown leakage sources. The number of leakage sources did not exceed three at the same time. Overall, coupled CFD and correlation coefficients method could effectively identify the number, location and intensity of leakages.

Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data

Accurate traffic prediction is crucial for urban planning, especially in rapidly growing cities. Traditional models often struggle to account for sudden traffic pattern changes, such as those caused by the COVID-19 pandemic. Neural networks offer a powerful solution, capturing complex, non-linear relationships in traffic data for more precise prediction. This study aims to create a neural network model for predicting vehicle numbers at main intersections in the city. The model is created using real data from the sensors placed across the city of Zilina, Slovakia. By integrating pandemic-related variables, the model assesses the COVID-19 impact on traffic flow. The model was developed using neural networks, following the data-mining methodology CRISP-DM. Before the modelling, the data underwent thorough preparation, emphasising correcting sensor errors caused by communication failures. The model demonstrated high prediction accuracy, with correlations between predicted and actual values ranging from 0.70 to 0.95 for individual sensors and vehicle types. The results highlighted a significant pandemic impact on urban mobility. The model’s adaptability allows for easy retraining for different conditions or cities, making it a robust, adaptable tool for future urban planning and traffic management. It offers valuable insights into pandemic-induced traffic changes and can enhance post-pandemic urban mobility analysis.

Model, design, and experiments of a field mill sensor for measuring high-voltage DC electric fields

Abstract In an attempt to measure the DC field intensity in the space below the high voltage transmission line, based on the principium of electrostatic induction, the DC-induced signal is transformed into an AC signal by continuously changing the relative area of the parallel plate capacitor, and then the signal is filtered and amplified. The measured value of the field to be measured is obtained from the amplitude of the output signal. The influences of the number of openings on the electric field distribution, the induced charge, and the output current in the adjacent space of the capacitive plate are studied by modeling and simulation. An experimental device is built to study the influence of the number of plate openings on the measurement results, and the simulation results are compared and analyzed. The calibration error of the field grinding sensor designed in this paper is about 6%, and the results show that the method can be applicable to the measurement of electric fields in DC transmission engineering.

Online state of charge estimation of LiFePO4 battery based on EKF-AUKF algorithm with reference compensation for estimation results

The state of charge(SOC) is an important index in Battery Management System(BMS) for smart grids and electric vehicles, whose accuracy is affected by model and sensor errors. In this paper, an online SOC estimation method based on EKF-AUKF algorithm with reference compensation for estimation results is proposed. Firstly, a joint estimation method based on extended Kalman filter(EKF) and adaptive unscented Kalman filter(AUKF) algorithm is adopted to estimate SOC under Beijing bus dynamic stress test(BBDST) condition. The result shows that the estimation error is within 2 %. Secondly, the characteristics of EKF-AUKF algorithm under voltage and current sampling errors are analyzed over the whole SOC range. The results indicate that voltage measurement error increases SOC estimation error. Then, an online reference is built by calculating the sum of the estimation result of ampere-hour counting(AHC) method and constant deviation, and then the difference between the estimation result of EKF-AUKF algorithm and online reference can be obtained. The absolute value of the difference is used to compare with limit deviation value and compensate the SOC of EKF-AUKF method by different compensation coefficients. Finally, the effectiveness of the proposed method is verified under different operating conditions. The experimental results show that the proposed method can greatly improve the SOC estimation accuracy under large voltage error.

Novel energy savings method considering extra sensor battery discharge time for fish farming applications

Energy savings in Wireless Sensor Networks for fish farms is necessary and beneficial. We present a novel energy savings method that minimizes the interpolation errors of sensors' measurements. Sensors follow a working cycle in which they are active when measuring data, and inactive or suspended when data is interpolated from the above measurements and consumed energy is reduced. To our knowledge, we are the first to implement interpolation for energy savings. We improved that model to describe the non-linear property of the consumed energy in the batteries, adding a new variable that explains their real behavior. Several experiments with a prototype of a Wireless Sensor Network with pH, water temperature, and ambient temperature sensors are implemented to validate our method. We made a series of measurements to determine the actual energy savings at each sensor and compared these savings with those predicted by each theory model. The results show that the model is more accurate, presenting less than 5 % prediction errors which does not affect fish growth. Furthermore, our paper introduces an energy-saving method for extending WSN lifetime by modeling the non-linear power consumption of sensors' batteries. We propose a new mathematical optimization formulation using an efficient interpolation mechanism that operates in real-time. A real-scale WSN prototype installed over water validates and refines our method. Finally, we showed that the number of interpolated values is of a broader range for aquatic sensors than for outdoor sensors such as ambient temperature. That is, energy savings for fish farming is acceptable.

A low-complexity parallel local remote microphone technology for multichannel narrowband active noise control systems

Narrowband active noise control (NANC) systems can effectively eliminate periodic disturbances at its error sensors, but sometimes the error sensors are inconvenient to be placed at target locations for a long period. Remote microphone technology (RMT) can tackle this problem by moving quiet zones to the target areas. Nevertheless, the RMT-NANC system has significantly higher computational complexity than conventional NANC systems, particularly for multichannel systems. This hinders their implementation in real-time systems with limited computational resources. The additional computational burden stems from multiple convolutions involving the estimated global high-order secondary paths and observation filters when estimating the virtual error signals. To address this problem, a low-complexity parallel local RMT is proposed based on the narrowband filtered-x least mean square (PLRMT-NFxLMS) algorithm in this paper. Using a two-step local modeling approach, multiple local modeling low-order filters for both secondary paths and observation filters are constructed during the training stage. In the subsequent control stage, virtual error signals are estimated in parallel for different frequency components using these filters instead of global modeling filters, thereby alleviating the substantial computational cost arising from convolutions between lengthy vectors. Moreover, a filtered-error structure, termed the PLRMT-NFeLMS algorithm, is introduced in the proposed algorithm to further reduce the computational complexity. A comprehensive analysis of computational complexity is provided to demonstrate the superiority of the two proposed algorithms. Extensive simulations and real-time experiments were conducted to validate the feasibility and practicability of these proposed methods.

Research on Corn Leaf and Stalk Recognition and Ranging Technology Based on LiDAR and Camera Fusion.

Corn, as one of the three major grain crops in China, plays a crucial role in ensuring national food security through its yield and quality. With the advancement of agricultural intelligence, agricultural robot technology has gained significant attention. High-precision navigation is the basis for realizing various operations of agricultural robots in corn fields and is closely related to the quality of operations. Corn leaf and stalk recognition and ranging are the prerequisites for achieving high-precision navigation and have attracted much attention. This paper proposes a corn leaf and stalk recognition and ranging algorithm based on multi-sensor fusion. First, YOLOv8 is used to identify corn leaves and stalks. Considering the large differences in leaf morphology and the large changes in field illumination that lead to discontinuous identification, an equidistant expansion polygon algorithm is proposed to post-process the leaves, thereby increasing the average recognition completeness of the leaves to 86.4%. Secondly, after eliminating redundant point clouds, the IMU data are used to calculate the confidence of the LiDAR and depth camera ranging point clouds, and point cloud fusion is performed based on this to achieve high-precision ranging of corn leaves. The average ranging error is 2.9 cm, which is lower than the measurement error of a single sensor. Finally, the stalk point cloud is processed and clustered using the FILL-DBSCAN algorithm to identify and measure the distance of the same corn stalk. The algorithm combines recognition accuracy and ranging accuracy to meet the needs of robot navigation or phenotypic measurement in corn fields, ensuring the stable and efficient operation of the robot in the corn field.

Radiation Anomaly Detection of Sub-Band Optical Remote Sensing Images Based on Multiscale Deep Dynamic Fusion and Adaptive Optimization

Radiation anomalies in optical remote sensing images frequently occur due to electronic issues within the image sensor or data transmission errors. These radiation anomalies can be categorized into several types, including CCD, StripeNoise, RandomCode1, RandomCode2, ImageMissing, and Tap. To ensure the retention of image data with minimal radiation issues as much as possible, this paper adopts a self-made radiation dataset and proposes a FlexVisionNet-YOLO network to detect radiation anomalies more accurately. Firstly, RepViT is used as the backbone network with a vision transformer architecture to better capture global and local features. Its multiscale feature fusion mechanism efficiently handles targets of different sizes and shapes, enhancing the detection ability for radiation anomalies. Secondly, a feature depth fusion network is proposed in the Feature Fusion part, which significantly improves the flexibility and accuracy of feature fusion and thus enhances the detection and classification performance of complex remote sensing images. Finally, Inner-CIoU is used in the Head part for edge regression, which significantly improves the localization accuracy by finely adjusting the target edges; Slide-Loss is used for classification loss, which enhances the classification robustness by dynamically adjusting the category probabilities and markedly improves the classification accuracy, especially in the sample imbalance dataset. Experimental results show that, compared to YOLOv8, the proposed FlexVisionNet-YOLO method improves precision, recall, mAP0.5, and mAP0.5:0.9 by 3.5%, 7.1%, 4.4%, and 13.6%, respectively. Its effectiveness in detecting radiation anomalies surpasses that of other models.

Measurement System for the Calibration of Accelerometer Arrays

This paper addresses accelerometer array calibration, focusing on determining the errors between multiple sensors. Micro-electromechanical system (MEMS) based triaxial accelerometers, key components of Inertial Measurement Units (IMUs), are used in localization, robotics, and navigation systems. The requirements of these applications necessitate low-cost sensors, which makes MEMS IMUs a reasonable choice. However, these low-cost IMUs are significantly affected by systematic (i.e., bias, misalignment, scale-factor) and random errors. Achieving reliable sensor output depends on the precision of the executed calibration method. While traditional laboratory-based sensor calibration using specialized equipment (i.e., three-axis turntable) is accurate, it is time-consuming and costly. In contrast, in-field calibration techniques, which can be performed using a mechatronic actuator or a robotic arm, have gained popularity. These techniques involve comparing sensor measurements to established reference values. The MEMS sensors are increasingly being used in multi-sensor applications, which demands not only individual sensor error calibration but also important to determine the axis misalignment between the used sensors. During calibration process, various optimization algorithms (e.g., GA, PSO) can also be used to find the error parameters. The proposed measurement system allows for individual calibration of misalignment, bias, and scale factor of the sensor array, and eliminates between-sensor misalignment errors.

Robust optimized weights spectrum: Enhanced interpretable fault feature extraction method by solving frequency fluctuation problem

Machine condition monitoring (MCM) plays a pivotal role in ensuring the reliability, safety, and efficiency of a production and operation system. Fault feature extraction (FFE), as an important step within MCM, aims to filter out interference components (ICs) and extract fault components (FCs) from raw signals. Consequently, it facilitates incipient fault detection and performance degradation assessment. A recently proposed optimized weights spectrum (OWS) provided a data-driven FFE methodology with a strong theoretical foundation, but it was found that the OWS is highly sensitive to a frequency fluctuation problem caused by rotational speed fluctuations and sensor errors, which may result in fake fault signatures. Therefore, a robust optimized weights spectrum (ROWS) is proposed in this paper to solve the frequency fluctuation problem. The ROWS is inspired by an intuitive idea that employs functions with adaptive frequency bandwidths instead of isolated spectral lines to represent frequency components, thus mitigating the impact of the frequency fluctuation problem. To adaptively determine the optimal parameters for these functions and estimate the corresponding ROWS, an alternative optimization strategy is adopted. Then, the proposed ROWS can be obtained when convergence conditions of this strategy are satisfied. Finally, the effectiveness and superiority of the proposed ROWS are verified by three real-world cases, i.e., the proposed ROWS can solve the frequency fluctuation problem and provide robust results for interpretable FFE.