Sensor Measurement Errors Research Articles

Perencanaan dan Pembuatan Real Time Monitoring System Dari Pada Modul Active Solar Photo Voltaic Tracker Berbasis Internet Of Things

There are still monitoring system in Solar Power Plant that do it manually using measuring and recording tools directly. This makesthe data obtained is also limited. From here the idea emerged to develop a monitoring system using the IoT concept so that monitoring onSolar Power Plant that can be monitored just by looking at the website page, so current and voltage data can be easily monitored. In addition,the resulting display is easy to analyze because the display can be digital and can be monitored with a PC or Smart Phone. This monitoringsystem is used to monitor the current and voltage of a solar power plant with solar cell capacities of 50WP, 80WP, and 100WP. The SolarPower Plant that used is an active type of solar tracker. In the application of using Solar Power Plant that, there is a current and voltagestarting from the solar cell output, accumulator output, AC load input, and DC load input. The monitoring results that have been carried out willthen be automatically sent to the Thingspeak application using the SIM800L Module. The thingspeak application will send data in the form ofnumbers and graphics in real time. By comparing the value of the measurement results on the Arduino and the Thingspeak application, thevalue of the percentage of reading errors on the measuring instrument is obtained. 3. In accordance with the tests that have been carried out,the percentage of read errors / errors can occur because of the reading accuracy of the current sensor measurements and AC-DC voltagesensors with different thingspeak applications. Each sensor has a percentage value of 0.16% AC & DC voltage sensor measurement error,1.2% AC & DC current sensor, and 2.8% data transmission observation.

Development of personal comfort model and its use in the control of air conditioner

The accurate identification of the real-time thermal state of personnel working indoors has been the focus of researchers. In this study, a detailed examination of the entire process of developing a personal comfort model, from theory to practice, is conducted. In particular, the subject of this investigation is a smart air conditioner capable of predicting thermal sensation based on an infrared (IR) sensor that measures the skin temperature of personnel and then implements automatic control. Random forest algorithm was employed to establish a thermal sensation prediction model, which accepts skin and air temperatures as input parameters. The model achieves a high prediction accuracy value of 0.84. Given the measurement errors of low-cost IR sensors, a simulated temperature measurement experiment based on the manikin was conducted, and an error calibration method based on multiple linear regression was developed through the experiment. 30 subjects participated in another prototype test experiment, which proved that the air conditioner can accomplish effective automatic control based on the prediction of thermal sensation, avoid the occurrence of extreme temperatures, and ensure the thermal comfort of personnel. The possible causes of remaining problems and potential improvement schemes are discussed in detail.

A Review: Design and Build Damage Detection Equipment on Sensors and Power Supply Automatic Rain Gauge (ARG) With Long Short Term Memory Integrated

The Meteorology, Climatology, and Geophysics Agency (BMKG) technicians have successfully developed automatic rain measuring devices. This tool is called Automatic Rain Gauge - BMKG (ARG-BMKG). The existence of this instrument can replace conventional rain measuring observation systems or public rain stations in Indonesia. ARG – BMKG consists of a tipping bucket sensor, solar panels, GPRS modem, dry battery, and data logger. This repeated operation causes sensor measurement errors due to damage to the sensor due to the sensor voltage supply not meeting specifications, resulting in inaccurate data sent. Predicting sensor damage can be done with predictive maintenance. The results of field tests in previous studies showed that the system could operate properly where the device could measure the voltage of each sensor and send data to the database A sensor damage prediction model was designed and implemented using long-sort term memory (LSTM) by generating root mean square error (rmse). The system can provide damage prediction information on the sensor, and the power supply is displayed through the website properly

A hybrid polynomial-based optimization method for underwater gliders with parameter uncertainty

As a type of enduring platform for ocean exploration, underwater glider usually contains some uncertain parameters in actual engineering applications, which are related to manufacturing errors, assembly errors, and sensor measurement errors. Uncertain parameters will make the glider performance show uncertain characteristics, so it is necessary to consider their effects in the design optimization process for an underwater glider. In this paper, a hybrid polynomial-based optimization method is proposed to solve optimization problems of underwater gliders with parameter uncertainty. The hybrid polynomial consists of power function terms and trigonometric function terms, and it is used for fitting surrogate models of the glider dynamic models. The fitted surrogate models are used for quickly calculating the performance evaluation parameters of underwater gliders. Specifically, power function terms and trigonometric function terms correspond to optimization design variables and uncertain parameters, respectively. The hybrid polynomial method covers the convenience of the traditional response surface method and the advantage of the Chebyshev polynomial in interval analysis. Based on surrogate models and interval analysis, we can estimate boundary functions of performance evaluation parameters, and they are employed to construct optimization objective functions. Then, the optimization iteration calculation can be carried out. Here, the proposed method is used for determining the optimal control parameter values of the Petrel-II glider with the uncertainty of some configuration and measurement parameters. The optimization objectives are to minimize the specific energy consumption and maximize the average advance velocity simultaneously. Finally, some laws of the glider performance are summarized.

Research on Tip Clearance Measurement Technology of Aero-engine Blade Based on Optical Fiber Sensing System

A novel non-contact detecting method of aero-engine blade tip clearance based on an optical fiber sensing system is presented. To eliminate the sensor measurement error caused by the slight change of the reflectivity value of the mechanical end face when the light source angle fluctuates and rotates, a three-coil coaxial fiber bundle sensor is proposed. Relevant experimental results show that the system has the advantages of high measurement sensitivity, small sensing probe volume, strong electromagnetic interference resistance, and high reliability. The application of this technology provides an effective method for dynamic radial gap detection of high-speed rotating machinery.

An Adaptive-Frequency Harmonic Suppression Strategy Based on Vector Reconstruction for Current Measurement Error of PMSM Drives

The errors of gain and offset caused by the current sensor measurement can lead to the first and second harmonics in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis currents of the permanent magnet synchronous motor (PMSM) drives. An adaptive-frequency harmonic suppression strategy based on the vector reconstruction for current measurement error is proposed. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dq</i> -axis current vector is recoordinate transformed using the division-frequency position as the transformation matrix angle. Thus, the frequency of the fundamental component can be decoupled from that of the harmonics in the current vector. In this way, the single-frequency harmonic suppressor can be established in the reconstructed coordinate frame. The current vector error is converged by adaptively adjusting the filtering weight and frequency so as to eliminate the harmonics. In view of the change of fundamental frequency, the variable step of the convergence is used to obtain the ideal filtering effect at different speeds and reduce the negative impact of current sensor measurement errors. The effectiveness of the proposed method is verified on the 11.7-kW PMSM drive platform.

Analysis and Correction of Measurement Error of Spherical Capacitive Sensor Caused by Assembly Error of the Inner Frame in the Aeronautical Optoelectronic Pod.

The ball joint is a multi-degree-of-freedom transmission pair, if it can replace the inner frame in the aviation photoelectric pod to carry the optical load, which will greatly simplify the system structure of the photoelectric pod and reduce the space occupied by the inner frame. However, installation errors in ball joint siting introduce nonlinear errors that are difficult to correct and two degree of freedom angular displacement of the ball joint is difficult to detect, which limits application in the precision control of two degrees of freedom systems. Studies of spherical capacitive sensors to date have not tested sensors for use in an inner frame stabilisation mechanism nor have they analysed the influence of installation error on sensor output. A two-axis angular experimental device was designed to measure the performance of a ball joint capacitive sensor in a frame stabilisation mechanism in an aeronautical optoelectronic pod, and a mathematical model to compensate for ball joint capacitive sensor installation error was created and tested. The experimental results show that the resolution of the capacitive sensor was 0.02° in the operating range ±4°, the repeatability factor was 0.86%, and the pulse response time was 39 μs. The designed capacitive sensor has a simple structure, high measurement accuracy, and strong robustness, and it can be integrated into ball joint applications in the frames of aeronautical photoelectric pods.

Trajectory tracking of a quadrotor using extend state observer based U-model enhanced double sliding mode control

This paper develops a novel U-model enhanced double sliding mode controller (UDSMC) for a quadrotor based on multiple-input and multiple-output extended-state-observer (MIMO-ESO). UDSMC is designed using Lyapunov synthesis and Hurwitz stability to not only cancel the complex dynamics and nonlinearity, but also stabilize the uncertainty and external disturbance of the underlying quadrotors. MIMO-ESO is designed to estimate the unmeasurable velocities which can reduce the impact of sensor measurement errors in practice. The difficulties associated with quadrotor velocity's measurement disturbances and uncertain aerodynamics are successfully addressed in this control design. Rigorous theoretical analysis has been carried out to determine whether the proposed control system can achieve stable trajectory tracking performance, and a comparative real-time experimental study has also been carried out to verify the better effectiveness of the proposed control system than the built-in PID control system.

A spatio-temporal prediction model theory based on deep learning to evaluate the ecological changes of the largest reservoir in North China from 1985 to 2021

Miyun Reservoir, located in the Miyun District, Beijing, China, is the largest comprehensive water conservancy project and is an important ecological protection area in the North China region. Changes within the basin are the driving factors affecting the ecosystem in the watershed; therefore, it is important to analyze the changes in the ecological environment of Miyun Reservoir. For the analysis of a long time series of image data remotely sensed by satellite, the outliers caused by atmospheric, lighting, and sensor measurement errors are significant, and it is difficult for traditional algorithms to effectively recover the true image value. To address this, this paper proposes a theoretical model for predicting spatio-temporal variation based on deep learning to identify and correct invalid and anomalous values in extended time series data. This study corrected and analyzed the results of Remote Sensing based Ecological Index inversion of Landsat data of the Miyun Reservoir watershed from 1985 to 2021. The findings and conclusions of this study are important for the analysis of long time series image data from satellite remote sensing and for improving regional ecological evaluation and sustainable development planning.

An Active Perception Framework for Autonomous Underwater Vehicle Navigation Under Sensor Constraints

Inertial navigation for autonomous underwater vehicles (AUVs) is challenging because of the drift error caused by the noise and measurement errors of inertial sensors, typically packaged as an inertial measurement unit (IMU), integrated over time. To mitigate the drift error, recent AUV state estimation approaches incorporate external references or environmental information obtained from exteroceptive sensors, with increased costs and limited operational domains. For improved navigation under sensor constraints, this article proposes an active perception framework that exploits vehicle motion to estimate the flow state together with the vehicle state using IMU and depth sensors only. The proposed framework uses the estimated flow state as external information to improve vehicle state estimation. We construct a linear time-varying system for the flow state, separated from a nonlinear system for the vehicle state. This formulation allows us to analyze uniform complete observability for the flow state, which is found to depend on vehicle motion. Then, along with vehicle and flow state estimators, we design a vehicle controller to enable vehicle motion to maximize an information metric pertaining to estimation performance based on either observability or constructability Gramian for the flow state. The proposed framework is validated through simulations for a case study with a vehicle descending through the water column in a time-varying flow field. The effectiveness of the framework is demonstrated by comparing results obtained from its four implementations with those from baseline approaches without active perception.

Fault Tolerance Analysis of Car-Following Models for Autonomous Vehicles

Microscopic car-following models can be applied in Autonomous Vehicles (AVs) to control the real-time longitudinal interactions among individual vehicles. Moreover, they can have a vital role in Advanced Vehicle Control and Safety Systems (AVCSSs) such as collision warning, adaptive cruise control, lane guidance driver assistance, brake assist as well as in modeling simulation of safety studies and capacity analysis in transportation science. In reality, sensor measurements are generally inaccurate. Surprisingly no comparative assessment of the car-following models in presence of sensor (measurement) errors for AVs exist till now to the best of our knowledge. Therefore, in this paper we evaluate the prominent car-following models for Avs in presence of sensor errors in terms of safety, trip times, flow and fuel efficiency through simulations. We show that sensor errors significantly impact safety and flow in all models, while they do increase trip times and fuel consumption of some of the models. None of the models is completely fault-tolerant and suitable for AVs as some models produce collisions and/or negative velocity while all models violate traffic light. Nonetheless, the k-leader Fuel-efficient Traffic Model (kFTM) is the most fault-tolerant negative velocity and collision free model having reasonable trip times and energy consumption among the investigated models.

Improving the precision of parallel registration by incorporating a priori information.

This paper presents an algorithm for the precise registration of optical wavefronts. A wavefront exceeding the spatial or dynamic measurement range of a wavefront sensor, e.g. a Shack-Hartmann sensor, can be measured in multiple sub-measurements, each providing a segment of the wavefront. Sensor misalignment during the measurements results in the demand for registration algorithms to precisely reconstruct the entire wavefront from the segments. The proposed algorithm registers the segments in parallel and incorporates a priori information about the uncertainty of the sensor misalignment obtaining high-quality registration. A simulative analysis of the algorithm with respect to sensor misalignment and measurement errors is presented together with an application of the algorithm to a measured divergent wavefront. In the scope of the analysis, the algorithm is compared to state-of-the-art registration algorithms, such as the iterative closest point (ICP) algorithm, where an improvement of the registration performance by a factor of 3 is obtained. Results show that the algorithm is able to reconstruct a divergent and a freeform wavefront with an RMS registration error of a few tens of nanometers with a standard deviation of 80 µm and 2.4 mrad.

Статистическое уточнение импульса последействия маршевого двигателя разгонного блока «Фрегат» для повышения точности выведения космических аппаратов

As it is known from practice, the most important influence on the accuracy of the payload launching into the target orbit is exerted by measurement errors of the command instrumentation system sensors, as well as by the sustainer afteraction pulse spread. A method is considered to increase accuracy of launching into a given orbit using the Fregat upper stage by refining the sustainer afteraction pulse. According to the results of launches, actual errors in the realized orbit linear parameters were recalculated in the afteraction pulse deviations. Then, the afteraction pulse average value was refined by statistical analysis in several iterations, which made it possible to significantly reduce the launching errors and confirm correctness of the developed technique.

Impact of PM10 Particles on the Measurement Error of SO2 Electrochemical Gas Sensor

To address the problems of poor measurement accuracy and long service life of SO2 electrochemical gas sensors when used in thermal power plant areas, fly ash emitted from a thermal power plant in China was used as the research object. Based on the analysis of the morphological characteristics of fly ash particles, theoretical calculations were used to obtain the settling speed of fly ash particles and the amount of fly ash deposited at different times, and then the impact of fly ash on the measurement error of a SO2 electrochemical gas sensor was investigated by experimental tests. The research results show that the particle size distribution of fly ash is 2–11 μm, the average settling speed of fly ash particles is 1.34 × 10−3 m/s, and the deposition amount of fly ash on the surface of the sensor inlet film is 0.95 mg per day. The deposition time of fly ash affects the sensor measurement error, and the longer the deposition time, the larger the sensor measurement error, which is due to the reduction of gas diffusion area S and diffusion coefficient K in the sensor caused by fly ash deposition. Fly ash deposition has a greater impact on the sensor when measuring low concentration gases. The higher the gas concentration, the lower the measurement error, because the higher the gas concentration, the faster the gas reaches the working electrode area and the higher the effective SO2 concentration detected in the limited response time. When using SO2 electrochemical sensors in environments with high concentrations of fly ash or dust, it is recommended to install dust-proof devices (such as air-permeable filter membranes with a pore size of less than 4 μm) and regularly clean the deposited fly ash, which can improve the accuracy of the sensor measurement and extend the service life.

Rotor Winding Short-Circuit-Fault Protection Method for VSPSGM Combining the Stator and Rotor Currents

Rotor winding short circuit faults are common faults for variable-speed pumped-storage generator-motors (VSPSGM). At present, the exciting rotor fault protection of VSPSGM is simple and has low sensitivity. It can only act when the instantaneous value of the rotor phase current reaches three times the rated current. Therefore, it is difficult to cover some rotor winding short-circuit faults with weak fault characteristics. It is urgent to study a novel rotor winding short-circuit-fault protection method for VSPSGM. In this paper, a protection method that combines the stator and rotor currents with different frequencies is proposed. The characteristics of the stator and rotor currents before and after the fault is analyzed by using Clark transformation. On this basis, a specific protection criterion is constructed based on the discrete integral operation, which is easy to implement and not affected by the change of rotor speed. Then, the calculation method of the protection setting is proposed, considering the effect of unbalanced voltage and sensor measurement error. Simulation results show that the proposed method can reliably realize the protection of rotor winding faults. It has faster protection action speed than other methods in the same field. The protection coverage rate is over 90%.

A Novel Portable Soil Water Sensor Based on Temperature Compensation

Soil water sensors based on the standing wave rate (SWR) principle are affected by temperature in long-term operation. To address this problem, a temperature compensation model based on the binary regression analysis method is proposed. The measurement results of the temperature-compensated standing wave rate (TCSWR) sensor at different temperatures and soil volumetric water content are analyzed, and the least-squares principle is used to identify the parameters to be determined in the compensation model for temperature for the SWR soil water sensor. A portable tapered TCSWR sensor with built-in temperature compensation model was developed on this basis. The calibration results show that the standing wave measurement circuit of the TCSWR sensor can effectively respond to changes in soil water, and the coefficient of the fitted equation exceeds 0.95. A comparison of the results before and after temperature compensation proves that compensation can significantly reduce the measurement error of the TCSWR sensor and improve the measurement accuracy. The static and dynamic characteristics of the TCSWR sensor show that the measurement range of the TCSWR sensor is7.50%-31.50%, the measurement accuracy is ±0.63%, the stability is good, the resolution is a minimum of 0.05%, and the dynamic response time is less than 1 s. The absolute error of the TCSWR sensor measurement is less than 1% in comparison with similar sensors, demonstrating that the measurement results of the TCSWR sensor are reliable.

Adaptive Output Feedback Compensation for a Class of Nonlinear Systems With Actuator and Sensor Failures

In this article, an adaptive output feedback compensation scheme is proposed for a class of uncertain nonlinear systems with a set of redundant actuators and a sensor. The sensor output is the only information available for output feedback. Both the actuators and the sensor may suffer from failures during operation and, therefore, all the system states are not available. To estimate the unmeasured states, a modified high-gain observer is designed which can not only ensure the closed-loop system stability but also improve the tracking performance. In the adaptive controller design, to overcome the complexity due to the unknown actuator and sensor failures coupled with the nonlinear functions and unknown parameters, an upper bound estimation method is adopted, which greatly simplifies the design procedure. It is proved that with the proposed scheme, the actuator and sensor failures can be compensated simultaneously, the reconfiguration can be adaptively updated without fault detection and isolation, and the real-tracking error can converge to a residual set, that is, mainly proportional to the sensor gain variation and measurement error.

An All-Fiber Optical Sensor Combined with FBG and CFBG-FP for Attitude Estimation.

In order to meet the needs of attitude correction of the optical fiber sensor array, an all-fiber optical sensor combined with FBG and CFBG-FP is proposed to monitor its attitude angle. The FBG sensor for pitch and roll angles detection employs a geometric model solution to shift the center wavelength of the three fiber Bragg gratings connected to it through the mass sphere, allowing for pitch and roll angle detection. Based on the magnetostrictive effect, the CFBG-FP sensor for heading angle detection employs changes of the cavity length of the chirped grating Fabry-Pérot cavity connected to its surface. The method of interpolation data processing and derivation of the attitude angle matrix is used to realize heading angle detection. The experimental results indicate that the FBG sensor for the pitch and roll angles detection has an effective detection range of 180 degrees and an angle measurement error of less than 1.5 degrees. Meanwhile, in the region beyond 10 degrees in the geomagnetic north-south direction, the angle measurement error of the CFBG-FP sensor for the heading angle detection is less than 1.3 degrees. Moreover, the impact of temperature in the system is minimized, demonstrating the validity of this combined fiber optical approach in the attitude angle measurements.

Study on Measurement Error Reduction Using the Internal Interference Light Reduction Structure of a Time-of-Flight Sensor

In recent years, the demand for the installation of Time-of-Flight (ToF) sensors in mobile phones and other devices to send and receive distance information has increased. A ToF sensor is a system that measures the distance to an object by calculating the elapsed time of an infrared (IR) light emitted from a vertical-cavity surface-emitting laser to the moment it returns to the single-photon avalanche diode after being reflected by the object. In this study, we analyzed the IR optical path of a ToF sensor structure using optical simulation. In addition, we analyzed the field of view, amount of light source, and intensity of the internal interference light. Moreover, the internal structure of the ToF sensor, which can minimize the intensity of interference light, was optimized and designed. Consequently, the internal interference light intensity decreased by 84% compared to the structure designed using conventional method. Furthermore, a structure was optimized using a polarizing film and quarter-wave plate, the internal interference light intensity of this structure reduced by 88% compared to that of the conventional method, and the distance measurement error of the ToF sensor decreased significantly.

Event-triggered-based cooperative game optimal tracking control for modular robot manipulator with constrained input

In this paper, a cooperative game optimal tracking control method based on event-triggered mechanism for constrained input modular robot manipulators (MRMs) system is introduced. According to the joint torque feedback technique, the dynamics model of the constrained input subsystem is established and the global state space equation is derived. The control inputs of n joints in the MRM system with constrained input are taken as n participants of cooperative game, and the tracking control problem of the manipulator system is transformed into the optimal control problem based on the cooperative game. Next, a fusion function containing position and velocity errors is defined to construct the performance index function. In order to improve the control performance and robustness of the manipulator system, part of the known model information is used to devise controller, the model uncertainty is dealt by the neural network (NN) observer, and the optimal compensation control strategy is used to deal with internal disturbance such as sensor measurement error and transmission ripple due to power fluctuations, electromagnetic effects, noise and vibration. Based on the adaptive dynamic programming algorithm and event-triggered mechanism, the optimal tracking control strategy is obtained by approximately solving the event-triggered Hamilton-Jacobi-Bellman equation with the critic NN. The Lyapunov theory proves that trajectory tracking error of MRM system with constrained input is uniformly ultimately bounded. Finally, the experimental results demonstrate the effectiveness of the proposed control method.