Sensor Errors Research Articles

Construction of Probability Graph Optimization Model on Basis of Localization Algorithm

Accurate positioning is crucial in fields such as indoor positioning, autonomous driving, and robot navigation. However, traditional base station positioning algorithms are often affected by factors such as sensor errors, environmental noise, and uncertainty, resulting in certain errors in the positioning results. The article constructed a probability graph model using Radio Frequency Identification (RFID) by using position estimation results as variables. In this model, each position estimation result was represented as a node, and the dependency relationship between positions was represented as an edge. This article observed sensor data and uses Bayesian inference methods to update the probability distribution of each node. The experimental results showed that compared to traditional base station positioning algorithms, the probability graph optimization model based on radio frequency identification can significantly improve the accuracy and reliability of positioning. The highest values for both reached 0.989 and 0.95, respectively. This model has significant advantages in positioning tasks and provides an effective solution for practical applications.

Equilibrium Compensation Based Control: A Universal Control Scheme for Systems With Mismatched Disturbances and Sensor Errors

Equilibrium Compensation Based Control: A Universal Control Scheme for Systems With Mismatched Disturbances and Sensor Errors

Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors

Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors

Mid-infrared auto-correction on-chip waveguide gas sensor based on 2f/1f wavelength modulation spectroscopy.

Compared to the most commonly used on-chip direct absorption spectroscopy (DAS) gas detection technique, the second harmonic (2f) based on-chip wavelength modulation spectroscopy (WMS) proposed by our group has the faculty to suppress noise and improve performance, but the accuracy of 2f WMS is easily affected by optical power variation. A mid-infrared auto-correction on-chip gas sensor based on 2f/1f WMS was proposed for decreasing the influence of the variation of optical power. The limit of detection of methane (CH4) obtained by a chalcogenide waveguide with a length of 10 mm is 0.031%. Compared with the 2f WMS, the maximum relative concentration error of the auto-correction on-chip gas sensor was decreased by ∼5.6 times. The measurement error is ≤2% in a temperature variation range of 30°C. This auto-correction sensor without a complicated manual calibration is helpful to the high accuracy measurement for on-chip integrated gas sensing.

A microwave resonance thin-film sensor for real-time wireless temperature monitoring in composites manufacturing

Composites are of growing importance to today's industries. Curing or consolidation under certain temperatures is a vital process to manufacture composite parts. Great attention has been paid to monitoring the temperature of composite parts being processed. Wireless monitoring methods are extensively explored for their unique advantages in realizing real-time monitoring of specific temperature distribution of the part. Here, a flexible microwave resonance sensor is first introduced to achieve real-time wireless temperature monitoring of composites. When applied to curing processes with different heating rates, it was found that the sensing relationship of the sensor was varied. Through in-depth analysis, the relationship between peak frequencies and temperature was established with consideration of different heating rates. In several curing processes with different heating rates, the mean error of the thin film sensor using a heating rate-related relationship was about 4.23 °C. which can be reduced by more than 43 % compared with the method using a static relationship.

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.

Development of a Systematic Methodology for Active Noise Control with Vehicle Engine Mounts Excitation

Active noise control (ANC) technology using speakers as actuators has an excellent performance, but since it generates anti-noise signals in a path completely different from the vehicle's noise source, performance can be greatly reduced at a location far from the error sensor, and this problem can get worse as the frequency to be controlled increases. Another approach is the vibration-based ANC technology that excites the body member around the engine mount and reduces acoustic error signals in a passenger compartment. This approach has the advantage of being effective even at a distance from the error sensor and reducing vibration and noise together when trying to cancel out the structure borne noise generated by the engine because the paths from the primary source and the secondary source to the interior are almost identical. This paper presents a systematic methodology for this vibration-based ANC technology to achieve high performance in vehicles. First, through noise reduction simulation, the number of error sensors, the location of actuators, the force requirements, and an appropriate system are proposed. Next, to improve performance even with a small number of actuators and limited power when applying this technology in vehicles, an algorithm using the control effort weighting parameter is introduced into the existing FxLMS algorithm and modelled on the controller. Finally, a noise reduction control test is performed in a vehicle under static and driving conditions, and the results are reviewed.

Development Edge Device Monitoring System Stunting and Malnutrition in Golden age 0–5 years Integrated with AI

Golden age is the best period for a child's growth, monitoring of growth and development must be carried out regularly. Growth and development disorders in children are stunting and malnutrition. This incident also prompted the government through the Ministry of Health to create a special program towards a golden Indonesia 2045 to monitor stunting and disease, especially in children. The prevalence of stunting decreased to 21.6% from 24.4% in 2022. Early detection of stunting and malnutrition, where the research object is children aged 0-5 years. This prototype was built using a load cell sensor, a study of the use of optical sensors and ultrasonic sensors to measure body height, and a MAX sensor to detect children's anemia. The integration of this tool combines IoT and AI. The results obtained to validate the use of load cells have a reading error of 0.01% with an accuracy of 99%. Comparison using optical sensors and ultrasonic sensors. Optical sensors have result average error of 0.01, accuracy 98.99%, ultrasonic sensors error was 0. 15 with 85% accuracy. To measure malnutrition, the anemia parameters were processed using the Dense Neural Network (DNN) model with 256 neurons showing an accuracy of 98.03%.

Autonomous method for a strapdown inertial navigation system calibration in operation in operation

Strapdown inertial navigation systems (SINS) are widely used in modern air transport. The service life of such systems is decades. During this time, the characteristics of such systems may deteriorate due to the degradation of inertial sensors - gyroscopes and accelerometers. Under these conditions, in order to maintain the required accuracy of the SINS during the entire period of operation, it is necessary either to periodically carry out routine maintenance with the onboard system, or to continuously adapt the used sensor measurement model in terms of compensating for errors that arise. The second direction, which we will call calibration, has the advantage over the first one that it is carried out automatically by the system itself and does not require any additional equipment or additional maintenance work. The known method of SINS recalibration is based on the integration of information from the SINS and from the receiver of satellite radio navigation signals (SRNS). Such calibration, firstly, significantly depends on the availability and quality of satellite information, and secondly, it is possible only for integrated navigation systems in which the inertial subsystem is supplemented by a SRNS receiver. In contrast to the known method, this article solves the problem of autonomous SINS calibration, which is carried out after each flight. The solution is based on a linearized inertial navigation error model based on the boundary conditions of the state vector (at the beginning and end of the flight). The external information used consists only of the initial values of the coordinates and the current measurements of the baroaltimeter. As a result of the decision at the end of the flight, the errors of gyroscopes and accelerometers are determined, as well as the error in determining the final values of latitude and longitude in the SINS. Sensor errors can be used in subsequent system activations, which improves the accuracy of its operation as a whole. The analysis of the suitability and effectiveness of the developed methodology was carried out on a typical flight simulation program.

Predictive Maintenance in IoT Devices using Time Series Analysis and Deep Learning

The pervasive integration of Internet of Things (IoT) devices across industries has ushered in a new era of data-driven operational efficiency. However, the reliability and uninterrupted functionality of these interconnected devices necessitate innovative approaches to maintenance. This research focuses on the development and implementation of a predictive maintenance framework for IoT devices, leveraging the synergies between Time Series Analysis (TSA) and Deep Learning (DL) techniques. The primary objective of this study is to enhance the accuracy and efficiency of predictive maintenance processes, ultimately minimizing downtime and optimizing resource utilization. The research methodology involves the collection of diverse data types from IoT devices, encompassing sensor readings, error logs, and historical maintenance records. A meticulous data preprocessing stage follows, involving cleaning, normalization, and feature extraction to prepare the dataset for analysis. The core analytical components of the proposed framework include Time Series Analysis for uncovering temporal patterns in the IoT data. Statistical methods and time series decomposition are applied to identify trends and seasonality, providing valuable insights into the device's performance over time. Concurrently, Deep Learning models, specifically recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), are employed to predict maintenance needs based on historical patterns. Results obtained from the application of the predictive maintenance framework to real-world IoT datasets demonstrate promising accuracy and efficiency in anticipating maintenance requirements. The paper identifies existing challenges in predictive maintenance for IoT devices and suggests future research directions. These include the exploration of edge computing, federated learning, and the integration of explainable AI to enhance model interpretability. In conclusion, the study underscores the significance of predictive maintenance in ensuring the reliability of IoT devices, offering a roadmap for industries seeking to harness the full potential of data analytics and artificial intelligence for operational excellence.

Towards high-sensitivity and high-accuracy forward Brillouin scattering-based optomechanical temperature sensing in thin-diameter fibers.

We numerically and experimentally demonstrated a high-sensitivity and high-accuracy temperature sensor based on guided acoustic radial modes of forward stimulated Brillouin scattering (FSBS)-based optomechanics in thin-diameter fibers (TDF). The dependence of the FSBS-involved electrostrictive force on the fiber diameter is systematically investigated. As the diameters of the fiber core and cladding decrease, the intrinsic frequency of each activated acoustic mode and corresponding FSBS gain are expected to be accordingly increased, which benefits the significant enhancement of its temperature sensitivity as well as the optimization of the measurement accuracy. In validations, by utilizing TDFs with fiber diameters of 80 µm and 60 µm, the proof-of-concept experiments proved that sensitivities of the TDF-based FSBS temperature sensor with radial modes from R0,4 to R0,15 increased from 35.23 kHz/°C to 130.38kHz/°C with an interval of 8.74 kHz/°C. The minimum measurement error (i.e., 0.15 °C) of the temperature sensor with the 60 µm-TDF is 2.5 times lower than that of the 125 µm-SSMF (i.e., 0.39 °C). The experimental and simulated results are consistent with theoretical predictions. It is believed that the proposed approach with high sensitivity and accuracy could find potential in a wide range of applications such as environmental monitoring, chemical engineering, and cancer detection in human beings.

A scalable parallel computing method for autonomous platoons

This paper developed a scalable parallel computing method that can be used for platoon simulations and controller validations. A scalable adaptive platooning control law was firstly designed, which accommodates a variety of vehicle-to-vehicle communication topologies. A road vehicle dynamics model that considered the Magic Formula tyre model and suspension dynamics was then derived and validated. The parallel computing method adopted the Message Passing Interface technique to allow fast and scalable simulations. Platoon length changes do not require controller and algorithm changes. An 11-vehicle platoon on a real-world 10 km long road section was simulated. Different localisation sensor errors, communication delays, heterogenous vehicle masses and driving modes were considered. Results show that localisation errors have negligible influences on space errors. Aggressive driving and heterogeneous vehicle masses slightly increase space errors (increases less than 0.23 m). Communication delays are the greatest influencer for space errors. Increases for 15, 45 and 75 ms delays were 0.43, 1.41 and 2.41 m, respectively. It is further shown that parallel computing can improve the computing speed by three times on personal computers and seven to 12 times on workstations.

Adaptive Delay-Free Filtering Based on IMU for Improving Ship Heave Measurement.

Ship heave motion measurement is crucial for ensuring vessel stability, navigation precision, and maritime engineering safety. In order to achieve accurate heave motion measurement, a method based on an adaptive digital high-pass filter is proposed. The approach involves constructing a ship heave motion model, conducting an analysis of heave motion, determining the optimal cutoff frequency for the adaptive filter based on an analysis of filtering and sensor errors, and designing an adaptive delay-free digital high-pass filter. Through simulation experiments in various sea conditions and platform tests, the method demonstrates superior performance. In comparison to fixed-parameter complementary filters, it exhibits a reduction of over 50% in maximum error and mean square error.

Research on temperature compensation method for photoelectric sensors

In order to make the temperature error of fiber-optic current sensor (FGB) based on polarization modulation principle meet the requirements of engineering applications, the temperature error characteristics of FGB are analyzed theoretically, and the optimized BP neural network is used for the temperature compensation of FGB, which realizes the nonlinear temperature error correction of the sensor, and compares and analyzes the experimental results with those of other types of temperature compensation algorithms. The results show that the temperature compensation results based on the neural network algorithm are better than other compensation effects. Finally, the repeatability of the FGB was experimentally verified using its full temperature experiment, and the temperature errors of the FGB in the range of 20 ℃~ 100 ℃ were less than 0.5% after the correction of the neural network algorithm.

CrowdLOC-S: Crowdsourced seamless localization framework based on CNN-LSTM-MLP enhanced quality indicator

Seamless positioning ability has become an essential requirement in large-scale smart city scenes with the development of Artificial Intelligence of Things technology. The performance of seamless positioning is limited by the inaccurate crowdsourced navigation database, cumulative error of built-in sensors, and changeable measurement errors of different location sources. In order to solve these problems, this paper presents the CrowdLOC-S framework, which provides a concrete and accurate indoor/outdoor localization performance using the combination of crowdsourced Wi-Fi fingerprinting, Global Navigation Satellite System (GNSS), and low-cost sensors. A data and model dual-driven based trajectory estimator is developed for improving the long-term positioning performance of built-in sensors, and a hybrid one-dimensional convolutional neural network (1D-CNN), Bi-directional Long Short-Term Memory (Bi-LSTM), and Multilayer Perceptron (MLP) enhanced quality indicator is proposed for quality evaluation of crowdsourced trajectories and further Wi-Fi fingerprinting database construction. Besides, the transfer learning approach is applied in the quality indicator for autonomously predicting the location errors towards different indoor and outdoor location sources and realizing seamless scenes switching. Finally, a unified extended Kalman filter is developed to realize multi-source integration-based seamless localization using the positioning information provided by indoor and outdoor location sources and corresponding quality indicator results. Comprehensive experiments demonstrate that the presented CrowdLOC-S system is proven to realize precise and efficient indoor and outdoor positioning performance in complex and large-scale urban environments.

An efficient offline scheme to compute an FIR controller for active reduction of acoustic reflections in an anechoic chamber

This paper presents an offline iterative scheme to efficiently obtain a fixed controller for larger-scale active noise control systems. The approach reduces the computational complexity of the scheme by applying preconditioning and prewhitening via the frequency domain, followed by conversion of the filters to the time domain using the inverse Fourier transform. Although the filters are not necessarily causal, causality is ensured by utilizing delay and truncation techniques. The use of these filters in the iterative scheme leads to improved convergence, lowering computational effort while also being memory efficient. Regularization is applied to maintain convergence while allowing for larger step sizes between iterations. The controller minimizes the reflected sound field, which is measured by the performance sensors. The signals of the performance sensors are computed with the Kirchhoff–Helmholtz integral. The results show that this allows for global control of the reflected sound field within the microphone area. A simulation is shown of a noise control setup with 12 primary sources, 12 secondary sources, 12 reference sensors, 12 error sensors and 37 performance sensors. Although the delay of the filters in the direct path is reflected in the filter coefficients, this delay can be truncated to obtain filter coefficients without any delay. Despite the truncation resulting in an average loss in performance of 1.6 dB, the control system without any delays is able to reduce the average reflected sound at the performance sensors by 13.8 dB, when the reference signal is known.

Improving Strapdown Inertial Navigation System Performance by Self-Compensation of Inertial Sensor Errors

Abstract Microelectromechanical systems (MEMS)-based strapdown navigation systems offer advantages such as small size, low cost and minimal power consumption. However, MEMS sensors are prone to significant low-frequency noise and poor bias repeatability, which can lead to navigational errors over time. These errors make them unsuitable for autonomous navigation applications, even with frequent recalibration. One way in which to solve this problem is by using the rotation modulation (RM) method. This approach is widely recognised but has only been successful with precise laser and fiber optic gyroscopes equipped with precise rotating platforms. This article focuses on the potential of adapting the RM method for the case of inexpensive MEMS sensors that can significantly improve navigation performance, while maintaining the benefits of microelectromechanical technologies. Potential issues of implementation were discussed, and corresponding requirements were formulated. The proposed optimal computation scheme was verified during static tests of the developed inertial measurement unit (IMU). Further steps in studying the adaptation of the RM method for MEMS sensors have also been outlined.

Online Distributed Relative Positioning Utilizing Multiple Cooperative Autonomous Agents

Currently, an unmanned aerial vehicle (UAV) utilizes global navigation satellite systems (GNSS) in conjunction with other modalities for localization purposes. Nevertheless, this approach faces robustness issues when GNSS signals become unavailable or sensors malfunction. Clearly, the robustness of the system increases considerably when multiple UAV agents are employed to perform collaborative positioning. In this work, an online distributed solution is proposed for relative localization, which incorporates multiple UAVs together with Signals of Opportunity (SOPs) as well as inertial, visual, and optical flow measurements. The proposed localization system includes relative self-localization of each UAV agent, as well as a reliable distributed relative positioning system (DRPS) for each UAV based on the relative positions from other UAV agents in its vicinity. The latter positioning strategy is required in case the relative self-localization fails, mainly due to such problems as inertial measurement unit (IMU) accumulated error drift, camera sensor errors, or SOP shortfalls due to multipath or antenna obstruction. Extensive field experiments validate the proposed technique and demonstrate increased localization accuracy and robustness when compared to the benchmark approach that does not include cooperation between UAVs.

Multi-channel active noise reduction system realizes the local area sound field control of the subway driver's cabin

Aerodynamic noise, air conditioning fan noise, and wheel-rail noise during subway operation have an adverse effect on drivers' physical and emotional well-being and can jeopardize the safe operation of vehicles. On the basis of the original passive acoustic treatment, multi-channel active control technology is used to manage the local sound field in order to further enhance the acoustic comfort of subway drivers' working environment. The multi-channel active noise reduction system installed on the driver's seat is described in this paper. In order to properly prevent the issues of ear pain and obstruction brought on by prolonged use of noise-canceling headphones, the system integrates reference sensors, error sensors, speakers, and a power amplifier within the headrest. The noise in the carriage is picked up using a multi-channel reference signal, and a low delay and anti-interference algorithm is investigated in accordance with the wide frequency band's characteristics and the abrupt change in sound pressure level when the vehicle is running. The test results demonstrate the system's ability to stabilize under situations of abrupt changes in driving noise and in-car broadcast, as well as to successfully minimize noise in the head area of subway drivers.

A Computation-efficient Online Secondary Path Modeling Technique for Modified FXLMS Algorithm

This paper proposes an online secondary path modelling (SPM) technique to improve the performance of the modified filtered reference Least Mean Square (FXLMS) algorithm. It can effectively respond to a time-varying secondary path, which refers to the path from a secondary source to an error sensor. Unlike traditional methods, the proposed approach switches modes between adaptive ANC and online SPM, eliminating the use of destabilizing components such as auxiliary white noise or additional filters, which can negatively impact the complexity, stability, and noise reduction performance of the ANC system. The system operates in adaptive ANC mode until divergence is detected due to secondary path changes. At this moment, it switches to SPM mode until the path is remodeled and then returns to ANC mode. Furthermore, numerical simulations in the paper demonstrate that the proposed online technique effectively copes with the secondary path variations.