Position Sensor Research Articles

Multi-Channel Differential Synchronous Demodulator for Linear Inductive Position Sensor

Multi-Channel Differential Synchronous Demodulator for Linear Inductive Position Sensor

Fault-Tolerant Closed-Loop Controller Using Online Fault Detection by Neural Networks

This paper presents an online model-free sensor fault-tolerant control scheme capable of tolerating the most common faults affecting an induction motor. This approach involves using neural networks for fault detection to provide the controller with sufficient information to counteract adverse consequences due to sensor faults, such as degradation in performance, reliability, and even failures in the control system. The proposed approach does not consider the knowledge of the nominal model of the system or when the fault may occur. Therefore, a high-order recurrent neural network trained online by the Extended Kalman Filter is used to obtain a mathematical model of the system. The obtained model is used to synthesize a discrete-time sliding mode control. Then, the fault-detection and -isolation stage is performed by independent neural networks, which have as input the signal from the current sensor and the position sensor, respectively. In this way, the neural classifiers continuously monitor the sensors, showing the ability to know the sensor status. The combination of controller and fault detection maintains the operation of the motor during the time of the fault occurrence, whether due to sensor disconnection, degradation, or connection failure. In fact, the MLP neural network achieves an accuracy between 95% and 99% and shows an AUC of 97% to 99%, and this neural network correctly classifies true positives with acceptable performance. The Recall value is high, between 97% and 99%, and the F1 score confirms a good performance. In contrast, the CNN shows a higher accuracy, between 96% and 99% in accuracy and 98% to 99% in AUC. In addition, its Recall and F1 reflect a better balance and capacity to handle complex data, demonstrating its superiority to MLP in fault classification. Therefore, neural networks are a promising approach in areas such as fault-tolerant control.

Measuring position sense.

Position sense is arguably more important than any of the other proprioceptive senses, because it provides us with information about the position of our body and limbs in relationship to one another and to our surroundings; it has been considered to contribute to our self-awareness. There is currently no consensus over the best method of measuring position sense. We have recently measured position sense with three commonly used methods. These were two-arm matching, one-arm pointing and one-arm repositioning, all carried out by blindfolded subjects with their lightly loaded forearms moving in the sagittal plane. It is currently believed that muscle spindles are the principal position sensors. We posed the question, was there evidence for spindles participating in the generation of position sense with each method? The indicator of spindle activity we used was the presence of thixotropic errors in the position signal, in response to conditioning voluntary contractions of forearm muscles. Based on this criterion, there was evidence of spindles contributing to position sense with all three methods. It was concluded that the spindle contribution to the position signal and the extent to which this was processed centrally was different with each method. It is argued that a case could be made for the existence of more than one position sense. Differences between the methods have implications for their meaning in a clinical setting.

Dual-Frequency Multi-Constellation Global Navigation Satellite System/Inertial Measurements Unit Tight Hybridization for Urban Air Mobility Applications

A global navigation satellite system (GNSS) for remotely piloted aircraft systems (RPASs) positioning is essential, thanks to the worldwide availability and continuity of this technology in the provision of positioning services. This makes the GNSS technology a critical element as malfunctions impacting on the determination of the position, velocity and timing (PVT) solution could determine safety issues. Such an aspect is particularly challenging in urban air mobility (UAM) scenarios, where low satellite visibility, multipath, radio frequency interference and cyber threats can dangerously affect the PVT solution. So, to meet integrity requirements, GNSS receiver measurements are augmented/fused with other aircraft sensors that can supply position and/or velocity information on the aircraft without relying on any other satellite and/or ground infrastructures. In this framework, in this paper, the algorithms of a hybrid navigation unit (HNU) for UAM applications are detailed, implementing a tightly coupled sensor fusion between a dual-frequency multi-constellation GNSS receiver, an inertial measurements unit and the barometric altitude from an air data computer. The implemented navigation algorithm is integrated with autonomous fault detection and exclusion of GPS/Galileo/BeiDou satellites and the estimation of navigation solution integrity/accuracy (i.e., protection level and figures of merit). In-flight tests were performed to validate the HNU functionalities demonstrating its effectiveness in UAM scenarios even in the presence of cyber threats. In detail, the navigation solution, compared with a real-time kinematic GPS receiver used as the reference centimetre-level position sensor, demonstrated good accuracy, with position errors below 15 m horizontally and 10 m vertically under nominal conditions (i.e., urban scenarios characterized by satellite low visibility and multipath). It continued to provide a valid navigation solution even in the presence of off-nominal events, such as spoofing attacks. The cyber threats were correctly detected and excluded by the system through the indication of the valid/not valid satellite measurements. However, the results indicate a need for fine-tuning the EKF to improve the estimation of figures of merit and protection levels associated to the navigation solution during the cyber-attacks. In contrast, solution accuracy and integrity indicators are well estimated in nominal conditions.

Design of magneto-inductive waveguide in 2-d magnetic metamaterial structure for wireless power transfer and near-field communications

<p>Recently, significant research has been conducted on magnetic metamaterials that exhibit negative permeability and operate within the GHz and MHz frequency ranges. These metamaterial structures can be utilized to improve the efficiency of near-field wireless power transfer systems, subterranean communication, and position sensors. However, in most cases, they are only designed to work for a single application. This study focuses on examining the transmission of magneto-inductive waves in magnetic metamaterial structures with ordered arrangements. This structure can be used simultaneously for wireless power transfer and near-field communications. The unit cell is formed by a spiral with five turns that is implanted on a FR-4 substrate. An external capacitor was used to regulate the resonant frequency of the magnetic metamaterial unit cell. The properties of magneto-inductive waves, including reflection, transmission response, and field distribution on the waveguide, have been extensively computed and simulated. The obtained results indicate that both 1-dimensional and 2-dimensional magnetic metamaterial configurations possess the ability to conduct electromagnetic waves and propagate magnetic field energy at a frequency of 13.56 MHz. The straight and cross path configurations were also investigated to identify the optimal configuration on the 2-dimensional metamaterial slab.</p>

Predicting Barbell Release Speed from Peak Speed in the Bench Press Throw via a Linear Position Transducer

ABSTRACT Upper body force-velocity profiles to assess musculoskeletal performance are created using release speed (RS) of the barbell in a bench press throw (BPT). A more easily obtained variable is peak speed (PS) measured by a linear position transducer. We assessed the validity of predicting RS from measured PS. One hundred and seventy-eight throws from ten male participants age (mean ± SD) 27 ± 5 yrs, mass 88 ± 13 kg with minimum one year of resistance training performed the BPT with increasing loads on a Smith machine. Correlation revealed an exponential relationship of RS = 0.26e0.9(PS), (R2 = 0.96, p < .05). We assessed predictive validity by comparing the measured RS of the barbell in each throw to the RS estimated by this formula. Bland-Altman analysis showed the 95% limit of agreement was 0.26 m∙s−1 to 0.18 m∙s−1, with a mean difference of 0.04 m∙s−1 (2.92%), determining that PS may be used to estimate RS.

Space Debris In-Orbit Detection with Commercial Automotive LiDAR Sensors.

This article presents an alternative approach to detecting and mapping space debris in low Earth orbit by utilizing commercially available automotive LiDAR sensors mounted on CubeSats. The main objective is to leverage the compact size, low weight, and minimal power consumption of these sensors to create a "Large Cosmic LiDAR" (LCL) system. This LCL system would operate similarly to a giant radar circling the Earth, with strategically positioned LiDAR sensors along the target orbit. The article examines the feasibility of this concept by analyzing the relative orbital velocity between the sensor and debris objects, and calculating the time required to scan a complete orbit.

A Bayesian inverse approach to proton therapy dose delivery verification

This study presents a proof-of-concept for a novel Bayesian inverse method in a one-dimensional setting, aimed at proton beam therapy treatment verification. Our methodology is predicated on a hypothetical scenario wherein strategically positioned sensors detect prompt- γ s emitted from a proton beam when it interacts with defined layers of tissue. Using these data, we employ a Bayesian framework to estimate the proton beam’s energy deposition profile. We validate our Bayesian inverse estimations against a closed-form approximation of the Bragg Peak in a uniform medium and a layered lung tumour.

A Study on Ways to Expand the Speed Operation Range of Dental Laboratory Handpiece Motors Using Low-Cost Hall Sensors

In the dental prosthetics field, BLDC micro-motors are primarily used for implant procedures. As dental materials become increasingly harder, there is a growing demand for higher performance motors to precisely process these materials. However, difficulties in motor development arise due to price competitiveness. Dental motors require capabilities such as low-speed, high-torque for drilling operations and high-speed rotation for precise machining of high-strength dental materials. Additionally, there is a requirement to address thermal issues to prevent user burns due to motor-generated heat. Typically, motors requiring precision control utilize high-resolution position sensors like encoders or resolvers to acquire and control the rotor’s position. However, the high cost of these sensors and constraints such as increased device size pose challenges in maintaining price competitiveness. In this paper, we propose a control algorithm that satisfies the requirements for low-speed, high-torque and high-speed operation requirements for precision machining without hardware modifications using a BLDC micro-motor equipped with an inexpensive Hall sensor commonly used in the dental field. Furthermore, the algorithm is designed to ensure stable operation even in the event of Hall sensor failure or malfunction, and its effectiveness is validated through experiments.

Intelligence data acquisition based on embedded system in Chinese cuisine cooker (CCICR V1.0)

The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ C$$\\end{document} and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.

Velocity-Based Training With Weightlifting Derivatives: Barbell and System Velocity Comparisons.

Suchomel, TJ, Kissick, CR, Techmanski, BS, Mann, JB, and Comfort, P. Velocity-based training with weightlifting derivatives: Barbell and system velocity comparisons. J Strength Cond Res XX(X): 000-000, 2024-The aim of this study was to examine the differences in barbell and system (i.e., subject + load) velocity during weightlifting derivatives performed across a spectrum of relative loads. 14 resistance-trained men participated in 6 testing sessions, which included 1 repetition maximum hang power clean (HPC) testing and individual jump shrug (JS), hang high pull (HHP), HPC, hang clean pull (HCP), and countermovement shrug (CMS) sessions. The order of the exercise testing sessions was randomized and required the subjects to perform either JS, HHP, HPC, HCP, or CMS repetitions while standing on a force platform with a linear position transducer attached to the barbell. The JS and HHP were performed with 20, 40, 60, 80, and 100% of their 1 repetition maximum HPC, HPC with 20, 40, 60, and 80% 1RM, and the HCP and CMS performed with 20, 40, 60, 80, 100, 120, and 140% 1RM. Mean and peak barbell and system velocities were determined across all exercises and loads using either 2 × 5, 2 × 4, or 2 × 7 repeated measures ANOVA depending on the number of loads performed. Significantly ( p < 0.001) and meaningfully ( g ≥ 1.49) greater mean and peak barbell velocities existed at every exercise and load combination compared with the mean and peak system velocities produced. Barbell and system velocity are distinct characteristics that should not be substituted for one another. Owing to the characteristics of the transition phase, mean barbell and system velocity may not provide strength and conditioning practitioners with meaningful information related to load prescription.

Improving the accuracy of dynamic inclination measurement by machine learning

With the diminishing availability of oil and gas resources, the Rotary Steerable System has become increasingly important. However, the vibrations and shocks during the drilling process pose challenges to the Measurement While Drilling. In recent years, the application of machine learning in the field of petroleum exploration has gradually expanded, especially in the estimation of geological parameters and lithology discrimination. However, there is still limited research on drilling tool attitude measurement. To address the above-mentioned issues, this study proposes a method that combines drilling tool attitude sensor data with artificial neural networks to improve the accuracy of dynamic inclination measurement. This method utilizes machine learning techniques, combining real-time z-axis acceleration signals and magnetic induction signals, and employs a deep learning model to invert the x and y-axis acceleration signals, thereby achieving high-precision measurement of dynamic inclination angles. Experimental results show that Long Short-Term Memory model, under simulated measurement conditions with different rotational speeds, yields dynamic inclination curve errors ranging from 0.4° to 0.7°, significantly reducing the errors compared to the original measurements. This method not only improves the accuracy of inclination angle measurement but also demonstrates strong adaptability to different rotational speeds, providing more accurate data support for drilling operations.

Acute Effect of Fixed vs. Self-Selected Rest Interval Between Sets on Physiological and Performance-Related Responses.

Background: Although the comparison between self-managed rest and fixed rest periods in subjects experienced in lower-limb strength training has been investigated, the results remain unclear due to controversies among some studies. Therefore, the present study aimed to analyze the role of self-managed rest versus fixed rest in athletic performance, mean propulsive velocity, velocity loss, muscle oxygen saturation, and rest time in trained subjects; Methods: Thirteen subjects with a minimum of one year of training experience (age (years): 26.31 ± 3.84; height (cm): 175.46 ± 5.61; weight (kg): 79.24 ± 6.83) were randomly assigned to two groups (self-selected rest group [SR] = 7 and fixed rest group [FR] = 6). The subjects underwent a session for evaluation (one maximum repetition (1RM) estimation, familiarization, and data collection) and another day for a traditional strength training session for the back squat, consisting of five sets of four repetitions at 80% of 1RM. One group took a fixed 2 min break, while the other group managed their breaks autonomously (resuming when they felt ready to perform the next set at maximum velocity). Mean propulsive velocity (MPV) was monitored using a linear position transducer, and muscle oxygen saturation (SmO2) was measured with a near-infrared spectroscopy device; Results: Significant differences between the groups were found for the rest time between the first and second sets (SR 97.29 ± 23.70 seg vs. FR 120 ± 0.00 seg). However, no differences were found for MPV, velocity loss, or SmO2; Conclusions: Given the similarities in performance and physiological outcomes between fixed and self-selected rest conditions, both can be used equally depending on the preferences and training goals of coaches and athletes.

Adaptive Fault‐Tolerant Multiplicative Attitude Filtering for Small Satellites

ABSTRACTThis study tackles the problem of fault‐tolerant attitude estimation for small satellites. A probabilistic adaptive technique is presented for the multiplicative extended Kalman filter (MEKF) algorithm that is used in attitude estimation. The presented method is based on tracking the normalized measurement innovations in the filter and calculating the probability of the normal operation of the estimation system. Using this probability, the filter gain is corrected to maintain the tracking performance of the filter despite faulty measurements. In order to evaluate the performance of this method, several simulations are performed where different types of faults are introduced to the synthetic attitude sensor measurements (magnetometer and sun sensor) at different times. Simulation results are compared not only with a conventional EKF but also with another popular adaptive Kalman filter, an adaptive Kalman filter with multiple scaling factors (MSFs).

Artificial Neural Network-based Control of a Switched Reluctance Motor for a High-precision Positioning System

This paper presents a approach to control a switched reluctance motor (SRM) in the context of a high-precision positioning system using artificial neural networks (ANNs). The SRM is known for its robustness and simplicity, making it suitable for various applications, including positioning systems where precision is paramount. Traditional control methods often struggle to achieve the desired level of accuracy due to the non-linear and dynamic nature of the SRM. In this study, we propose an advanced control strategy leveraging the adaptive learning capabilities of ANNs. The neural network is trained to capture the intricate relationships between the motor's inputs and outputs, allowing for precise control in real-time. By measuring the electromagnetic torque and phase currents, the neural network is able to estimate the rotor position, facilitating the elimination of the rotor position sensor. The training data set of the neural network consists of magnetization data for the SRM with the electromagnetic torque and current as inputs and the corresponding position as outputs in this set. With a sufficiently large training data set, the artificial neural networks (ANN) can be correlated for appropriate network architecture.

Design and performance evaluation of embedded micro stiffness sensor for On-Orbit service tools

On-orbit service of the space station is a vital mission. The space station will be in orbit for many years, and parts would break down and instruments could fail in tight space. This research focuses on the needs of on-orbit maintenance operations in tight space and designs an end tool with embedded micro-sensors. The pressure & deformation sensing module is designed based on the principle of polyurethane flexible plate and metal strain film. The sensor module uses alloy as the housing to form a stiffness sensor, built into the end tool to achieve a single sensor envelope size in the range of 15*15*13 mm3. Calibration results indicate that the overall stiffness of this sensing unit is approximately 12000 N/mm, capable of sensing material stiffness within the range of 50 N/mm ∼ 12000 N/mm. Through multiple measurements and root mean square error (RMSE) analysis, it is shown that the uncertainty of different sensors is within the acceptable range compared with the standard data. The results of the fatigue test and simulated temperature test in the space station show that the performance of the stiffness sensor can meet the normal operation requirements in the space station environment. Comparisons with geometric dimensions, accuracy, sensitivity, and measurement precision show that the uncertainty between different position sensors, metal strain film sensors, and standard sensors does not exceed 1.8 %. The research results confirm the potential of this stiffness-sensing unit in future space missions and dexterous operations applications.

Application and Research of Intelligent Multi-source Information Fusion Technology in Firefighting Equipment

As modern firefighting environments become increasingly complex, traditional firefighter equipment and operational methods are no longer adequate to address the high-risk challenges of fire rescue operations. Fire scenes are characterized by complex and hazardous environmental factors, with firefighters facing multiple threats such as extreme temperatures, dense smoke, and toxic gases. Therefore, ensuring the safety and efficiency of firefighters is of paramount importance. This study integrates multiple sensor technologies to enable real-time monitoring of firefighters' physiological status, posture changes, and fire scene conditions. The system incorporates ErgoLAB wireless ECG and PPG sensors, the BWT61CL posture sensor, and the MAG14mini infrared camera, which are capable of simultaneously capturing key physiological data and heat distribution in the fire scene. To ensure data accuracy and real-time performance, the study employs efficient signal preprocessing techniques, including noise reduction, baseline correction, and time synchronization. In addition, wireless transmission technology and multimodal data fusion algorithms are utilized to comprehensively analyze the firefighters' status and fire scene conditions. This approach significantly enhances the precision of firefighter safety monitoring and operational efficiency, demonstrating substantial innovation and practical value.

Position measurement and output feedback tracking method of hydraulic servo system based on pressure sensor

Instead of using position sensor, a position measurement and output feedback tracking method is proposed based on pressure sensor for a class of hydraulic servo system. First, the desired accumulator pressure is calculated and obtained in an open-loop way according to the desired cylinder position. Then, the position tracking task can be indirectly realized by achieving the pressure control. As a result, only one pressure sensor is required instead of position sensor, which is different from almost all the traditional output feedback position controller of hydraulic system. For the closed-loop pressure tracking, a desired compensation method and an auxiliary variable-based state observer are integrated to form the nonlinear controller. The theoretical and numerical simulation results demonstrate that the presented controller can attain satisfactory pressure tracking performance.

Synthetic image data generation via rendering techniques for training AI-based Instance Segmentation

Within the scope of the development of the project, the acceleration and gyro signal, VR implementation, and User Control for VR were realized. The starting point was a list of MPU9250 data, which are acceleration and gyro data. The goal is to do the signal processing of the signal that is got from MPU9250 and implement the data with a specific format so that the LINAK LA36 actuator can be controlled in such a way that it performs as desired position. For this purpose, a multi-axis acceleration and position sensor is mounted on a surfing board which then collects the acceleration and Gyro data. A MATLAB Program had been written to convert the desired position into a CAN message. Before that, the raw acceleration data and gyro must be converted and filtered into actual data. First, the acceleration data was collected from another group. A signal processing method has been implemented. This includes gravitational force filter, noise filter, integration, and complementary filter method was used in order to get the accurate actual data that is able to be implemented into the system. For those methods, it was necessary to deal extensively with the characteristics of signal processing. VR application was implemented in this project to give the user a sensational surfing experience in the virtual world. User control has been implemented as well to allow users to control the video. Unity with Arduino work together so that both Arduino and Unity can communicate with each other, and signal messages will be sent to the Arduino from Unity and vice versa to achieve the synchronization of the video signal and Unity VR application.

An electromagnetic indirect-driving scanning mirror for wide-field coaxial LiDAR applications

This paper reports an electromagnetic indirect-driving scanning mirror with an enlarged mirror plate (17mm×17mm) supported by high-strength polymer hinges for wide-field coaxial LiDAR (Light Detection and Ranging) applications. An indirect-driving mechanism was developed to achieve large tilting angle through mechanical amplification, while maintaining a relatively high resonance frequency of the enlarged mirror plate. A prototype mirror was designed, fabricated, and tested. A Hall scan position sensor was integrated to monitor the pose of the mirror in real time. The testing results show a coupled resonance frequency of 54.9 Hz with an optical tilting angle of ±60°, corresponding to a field of view (FoV) of 120°. A wide-field coaxial LiDAR system was also built based on the indirect-driving scanning mirror, and 2D imaging was demonstrated.