Accurate Compensation Research Articles

Neural Network Methods in the Development of MEMS Sensors

As a kind of long-term favorable device, the microelectromechanical system (MEMS) sensor has become a powerful dominator in the detection applications of commercial and industrial areas. There have been a series of mature solutions to address the possible issues in device design, optimization, fabrication, and output processing. The recent involvement of neural networks (NNs) has provided a new paradigm for the development of MEMS sensors and greatly accelerated the research cycle of high-performance devices. In this paper, we present an overview of the progress, applications, and prospects of NN methods in the development of MEMS sensors. The superiority of leveraging NN methods in structural design, device fabrication, and output compensation/calibration is reviewed and discussed to illustrate how NNs have reformed the development of MEMS sensors. Relevant issues in the usage of NNs, such as available models, dataset construction, and parameter optimization, are presented. Many application scenarios have demonstrated that NN methods can enhance the speed of predicting device performance, rapidly generate device-on-demand solutions, and establish more accurate calibration and compensation models. Along with the improvement in research efficiency, there are also several critical challenges that need further exploration in this area.

Research on design method of inertial interference self-compensating balance

This paper proposes a design method for a wind tunnel balance that can compensate for inertial interference, aiming to address the issue of short effective time in pulse wind tunnels. This method also tackles the problem of the force measuring system’s inability to dampen quickly and the coupling of inertia vibration signals with the output signal of the balance, which affects aerodynamic measurement accuracy. The method calculates vibration displacement at specific positions of the balance structure using its acceleration, constructs compensation signals based on the relationship between these displacements and balance outputs, and then superimposes them onto the balance signal for compensation. The effectiveness of this compensation method is evaluated through finite element simulations and wind tunnel tests. The results demonstrate that by compensating for the balance signal, there is a significant reduction in the amplitude of the inertia vibration signal, leading to an enhancement in the repeatability accuracy of the measurement. This compensation method effectively solves under-compensation or over-compensation issues when accelerometers are placed on complex models with various modes, where obtaining accurate compensation signals becomes challenging. It provides a new technical approach to improve aerodynamic measurement accuracy by mitigating inertial interference during pulse wind tunnel testing.

Two-level integrated verification evaluation method based on comprehensive weighted assessment in multiple scenarios

The complexity and diversity brought by the distributed architecture of the new generation electric information collection system have deepened the difficulty of constructing evaluation verification index systems and quality evaluation models. Moreover, the presence of differentiated components has made fair and scientific verification challenging. Therefore, leveraging graph neural networks and siamese networks, a integrated construction quality evaluation system based on comprehensive weighted assessment in multiple scenarios was developed. Firstly, a graph neural network was constructed based on terminal data of the branches electricity usage information collection system and the link topology structure. Subsequently, this network was deployed on the headquarters side to directly acquire terminal data and generate mirror network input from the branches data, enabling real-time acquisition of various system operational indicators. Finally, the similarity between the headquarters and branches data was calculated using siamese networks to compute accuracy compensation weights for checking and evaluating various indicators, thereby obtaining comprehensive weighted quality evaluation indicators of the branches new generation electric information collection system. We use three types of services including electricity data collection, load forecasting, and task scheduling as experimental scenarios. The results showed that the multidimensional comprehensive weighted quality assessment combined with accuracy compensation obtained from the siamese network resulted in business construction quality assessment values of 97.92%, 95.95%, and 99.96% in branch. This value is approximately equal to the quality evaluation value of manual work, so the method can effectively verify the construction quality of new systems in the branch.

Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles

The optimum performance position control of pneumatic artificial muscles (PAM) is restricted by their in-built hysteresis and nonlinearity. The hysteresis is usually depicted by a phenomenological model, while the model mentioned above always only describes the hysteresis phenomenon under certain conditions. Thus, the universality of the compensator is due to its weakness in handling disparate outside conditions. Our research employs the FN–QUPI (feedforward neural network–quadratic unparallel Prandtl–Ishlinskii) model to depict the phenomenon of pressure-displacement hysteresis in PAMs. This model has high-precision expression and generalization ability for the PAM hysteresis phenomenon. According to this, an inverse model of the QUPI operator is established as a feedforward control while combining with the feedback control of incremental PID-type iterative learning. The results show that due to the hysteresis of PAM, the compound control of feedforward control and iterative learning has better tracking performance than the ordinary PID compound control in terms of convergence rate and stability. According to the mean absolute error (MAE) and root mean square error (RMSE) of the tracking process, it can be seen that the control model can achieve accurate nonlinear compensation, and the control system shows excellent robustness to different input signals.

Integration of Metrology in Grinding and Polishing Processes for Rotationally Symmetrical Aspherical Surfaces with Optimized Material Removal Functions

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance.

High-resolution motion compensation for brain PET imaging using real-time electromagnetic motion tracking.

Substantial improvements in spatial resolution in brain positron emission tomography (PET) scanners have greatly reduced partial volume effect, making head movement the main source of image blur. To achieve high-resolution PET neuroimaging, precise real-time estimation of both head position and orientation is essential for accurate motion compensation. A high-resolution electromagnetic motion tracking (EMMT) system with an event-by-event motion correction is developed for PET-CTscanners. EMMT is comprised of a source, an array of sensors, and a readout electronic unit (REU). The source acts as a transmitter and emits an EM dipole field. It is placed in close proximity to the sensor array and detects changes in EM flux density due to sensor movement. The REU digitizes signals from each sensor and captures precise rotational and translational movements in real time. Tracked motion in the EMMT coordinate system is synchronized with the PET list-mode data and transformed into the scanner coordinate system by locating paired positions in both systems. The optimal rigid motion is estimated using singular value decomposition. The rigid motion and depth-of-interaction (DOI) parallax effect are corrected by event-by-event rebinning of mispositioned lines-of-response (LORs). We integrated the EMMT with our recently developed ultra-high resolution Prism-PET prototype brain scanner and a commercial Siemens Biograph mCT PET-CT scanner. We assessed the imaging performance of the Prism-PET/EMMT system using multi-frame motion of point sources and phantoms. The mCT/EMMT system was validated using a set of point sources attached to both a mannequin head and a human volunteer, for simulating multiframe and continuous motions, respectively. Additionally, a human subject for [18F]MK6240 PET imaging wasincluded. The tracking accuracy of the Prism-PET/EMMT system was quantified as a root-mean-square (RMS) error of 0.49 for 100 axial rotations, and an RMS error of 0.15 mm for 100 mm translations.The percent difference (%diff) in average full width at half maximum (FWHM) of point source between motion-corrected and static images, within a motion range of and 10 mm from the center of the scanner's field-of-view (FOV), was 3.9%. The measured recovery coefficients of the 2.5-mm diameter sphere in the activity-filled partial volume correction phantom were 23.9%, 70.8%, and 74.0% for the phantom with multi-frame motion, with motion and motion compensation, and without motion, respectively. In the mCT/EMMT system, the %diff in average FWHM of point sources between motion-corrected and static images, within a motion range of and 10 mm from the center of the FOV, was 14%. Applying motion correction to the [18F]MK6240 PET imaging reduced the motion-induced spill-in artifact in the lateral ventricle region, lowering its standardized uptake value ratio (SUVR) from 0.70 to 0.34. The proposed EMMT system is a cost-effective, high frame-rate, and none-line-of-sight alternative to infrared camera-based tracking systems and is capable of achieving high rotational and translational tracking accuracies for mitigating motion-induced blur in high-resolution brain dedicated PETscanners.

Influence of pushing speed on tube formability and accuracy compensation research based on free bending technology

Influence of pushing speed on tube formability and accuracy compensation research based on free bending technology

A non-iterative compensation method for machining errors of thin-walled parts considering coupling effect of tool-workpiece deformation

Thin-walled parts have significant application value in the aerospace industry due to their high strength-to-weight ratio. However, their low rigidity makes them susceptible to cutting force-induced error, which may seriously affect their machining accuracy. Error compensation is an effective method for addressing this issue. However, the commonly used mirror compensation method can cause residual errors due to the coupling effect of the tool-workpiece deformation. The influence mechanism of this coupling effect on error compensation is analyzed through an iterative compensation method. This method efficiently reduces residual errors. However, its computation efficiency is insufficient to meet the requirements of real-time compensation. Therefore, a non-iterative compensation method is proposed to directly calculate the compensation values considering the coupling effect of the tool-workpiece deformation. Through the approximate invariance of the overall cutting coefficient matrix and the pre-given system parameter, the proposed method avoids the repeated calculation of cutting forces and improves the computation efficiency. Experiment results of milling thin-walled blades show that after compensation using the proposed method, the machining accuracy of the thin-walled blade has seen a further increase of 18.1% in comparison to the mirror compensation method. Moreover, the proposed method achieves comparable compensation accuracy to the iterative method with a 66% reduction in computation time. The proposed method has significant potential for real-time compensation in the machining of complex 5-axis thin-walled parts.

Method for robot kinematic parameters identification based on position and orientation data obtained with laser tracker.

How to quickly and accurately identify the kinematic parameter errors is an important prerequisite for robot accuracy compensation. The laser tracker is used to measure and identify the kinematic parameters of the robot. The influence of laser tracker layout in robot pose measurement is analyzed, and the evaluation function of laser tracker layout is constructed to determine the optimal layout position. Then, the mapping relationship between the position and orientation deviation of the robot end and the deviation of each kinematic parameter is established, and the kinematic parameter identification model integrating the position and orientation information is constructed. The identification model is compared to the identification model based on position information to clarify the intrinsic reason of high accuracy. Next, aiming at the problem that the genetic algorithm is easy to prematurely converge to the local optimal solution, a hybrid genetic algorithm is constructed by introducing the simplex method to determine the optimal calibration pose set of the robot. On this basis, the results of robot kinematics parameter identification based on position measurement data, pose measurement data, and optimal calibration pose set are compared and analyzed through experiments, which verifies the effectiveness of the robot kinematics parameter identification model based on pose measurement data and optimization strategy.

Error Identification and Accuracy Compensation Algorithm for Improved 2RPU/UPR+R+P Hybrid Robot

Error Identification and Accuracy Compensation Algorithm for Improved 2RPU/UPR+R+P Hybrid Robot

Pt thin-film resistance thermo detectors with stable interfaces for potential integration in SiC high-temperature pressure sensors

Due to the excellent mechanical, chemical, and electrical properties of third-generation semiconductor silicon carbide (SiC), pressure sensors utilizing this material might be able to operate in extreme environments with temperatures exceeding 300 °C. However, the significant output drift at elevated temperatures challenges the precision and stability of measurements. Real-time in situ temperature monitoring of the pressure sensor chip is highly important for the accurate compensation of the pressure sensor. In this study, we fabricate platinum (Pt) thin-film resistance temperature detectors (RTDs) on a SiC substrate by incorporating aluminum oxide (Al2O3) as the transition layer and utilizing aluminum nitride (AlN) grooves for alignment through microfabrication techniques. The composite layers strongly adhere to the substrate at temperatures reaching 950 °C, and the interface of the Al2O3/Pt bilayer remains stable at elevated temperatures of approximately 950 °C. This stability contributes to the excellent high-temperature electrical performance of the Pt RTD, enabling it to endure temperatures exceeding 850 °C with good linearity. These characteristics establish a basis for the future integration of Pt RTD in SiC pressure sensors. Furthermore, tests and analyses are conducted on the interfacial diffusion, surface morphological, microstructural, and electrical properties of the Pt films at various annealing temperatures. It can be inferred that the tensile stress and self-diffusion of Pt films lead to the formation of hillocks, ultimately reducing the electrical performance of the Pt thin-film RTD. To increase the upper temperature threshold, steps should be taken to prevent the agglomeration of Pt films.

Preprocessing for Multi-Dimensional Enhancement and Reconstruction in Neural Video Compression

The surge in ultra-high-definition video content has intensified the demand for advanced video compression techniques. Video encoding preprocessing can improve coding efficiency while ensuring a high degree of compatibility with existing codecs. Existing video encoding preprocessing methods are limited in their ability to fully exploit redundant features in video data and recover high-frequency details, and their network architectures often lack compatibility with neural video encoders. To addressing these challenges, we propose a Multi-Dimensional Enhancement and Reconstruction (MDER) preprocessing method to improve the efficiency of deep learning-based neural video encoders. Firstly, our approach integrates a degradation compensation module to mitigate encoding noise and boost feature extraction efficiency. Secondly, a lightweight fully convolutional neural network is employed, which utilizes residual learning and knowledge distillation to refine and suppress irrelevant features across spatial and channel dimensions. Furthermore, to maximize the use of redundant information, we incorporate Dense Blocks, which can enhance and reconstruct important features in the video data during preprocessing. Finally, the preprocessed frames are then mapped from pixel space to feature space through the Dense Feature-Enhanced Video Compression (DFVC) module, which improves motion estimation and compensation accuracy. The experimental results demonstrate that, compared to neural video encoders, the MDER method can reduce bits per pixel (Bpp) by 0.0714 and 0.0536 under equivalent PSNR and MS-SSIM conditions, respectively. These results demonstrate significant improvements in compression efficiency and reconstruction quality, highlighting the effectiveness of the MDER preprocessing method and its compatibility with neural video codec workflows.

Virtual prototype modeling and fuzzy control of the heave compensation system for a 500-ton deep-sea mining vessel

The unexpected heave motion of vessels generated by waves has always threatened the safe operation of mining vessels. The heave compensation system is a critical equipment for offshore cranes to ensure the safety and stability of the deep-sea mining vessel operation. Unfortunately, the nominal load of the mining vessel transporting minerals has become increasingly large, which has led to the dynamic performance of the heave compensation decline and a significant increase in energy consumption. To solve these problems, a semiactive heave compensation system and fuzzy logic-based controller are proposed in this paper. First, an active-passive hybrid heave compensation system is designed to reduce energy consumption greatly. The virtual prototype models of the proposed heave compensation system in a 500-ton deep-sea mining vessel are also built. Then, an adaptive tuning PID controller is developed based on the Transfer function of the heave compensation system and Mamdani-type fuzzy logic. Virtual prototype simulation results of AMESim-Simulink joint simulation illustrate that the accuracy of compensation for the four or six sea states reaches more than 90% with the proposed method and the power of the active compensation system is about 21% of the total power. Finally, the experimental results are included to demonstrate the effectiveness of the proposed semiactive heave compensation system.

A dead-time compensation method for motor drive inverters based on nonlinear observer

Silicon carbide (SiC) inverters and interior permanent magnet synchronous motors (IPMSMs) are widely employed in electric vehicles (EVs) due to their superior efficiency and high power density. However, the harmonics generated by the high switching frequency and the dead-time effect in SiC inverters present a significant challenge. The time-varying nature of on–off delays and conduction voltage drops in power devices makes it difficult to establish a precise nonlinear model for inverters, thus limiting the effectiveness of traditional dead-time compensation methods. This paper introduces a dead-time compensation approach for voltage source inverters (VSIs) that leverages a nonlinear observer, thereby circumventing the need for a complex inverter model while enhancing compensation accuracy. The method determines the three-phase stator current polarity of the IPMSM by deriving the relationship between the current vector angle and the rotor position angle. Along with duty cycle variations computed by the PI controller, the inverter’s nonlinearity is adaptively compensated. Simulation and experimental results indicate that the proposed method outperforms the traditional method based on the error voltage model, effectively reducing the total harmonic distortion (THD) of the phase current.

Research on Motion Control and Compensation of UAV Shipborne Autonomous Landing Platform

As an important interface between unmanned aerial vehicles (UAVs) and ships, the stability and motion control compensation technology of the shipborne UAV landing platform are paramount for successful UAV landings. This paper has designed a new control compensation method for an autonomous UAV landing platform to address the impact of complex sea conditions on the stability of UAV landing platforms. Firstly, the parallel Stewart platform was introduced as the landing platform, and its structure was analyzed with forward and inverse kinematic calculations conducted in Matlab to verify its accuracy. Secondly, a least-squares recursive AR prediction algorithm was designed to predict the future attitudes of ships under varying sea conditions. Finally, the prediction algorithm was combined with the platform’s control strategy and a dual-sensor system was adopted to ensure the stability of the UAV landing process. The experimental results demonstrate that these innovative improvements enhanced the compensation accuracy by 59.6%, 60.3%, 48.4%, and 47.9% for the rolling angles of 5° and 10° and the pitching angles of 5° and 10°, respectively. Additionally, the compensation accuracy for the roll and pitch in sea states 2 and 5 improved by 51.2%, 59.4%, 58.7%, and 55.9%, respectively, providing technical support for UAV missions such as maritime rescue and exploration.

A novel temperature drift compensation method based on LSTM for NMR sensor

Nuclear Magnetic Resonance (NMR) sensors have become one of the best choices for angular velocity sensors in future Inertial Navigation Systems (INS) due to their small size and high precision. The performance of NMR sensors can be affected by temperature changes, resulting in temperature drift. Therefore, temperature compensation is essential. Accurate temperature compensation is challenging due to the lack of direct temperature observations in the core sensitive area (cell) and the complex temperature drift model. Considering the time correlation of temperatures inside and outside the cell, a new temperature drift compensation method based on the Long-Short Term Memory (LSTM) network is proposed. The memory characteristics of the LSTM enable it to fully utilize current and previous data to learn complex models. The main contributions of this study are as follows: (1) the mechanism of temperature drift in NMR sensors was analyzed; (2) a multi-time scale input–output model for temperature changes, temperature change rates, and ambient temperature versus sensor output bias was established; (3) a temperature drift model identification method based on LSTM was designed. Experimental results show that, compared to traditional polynomial fitting and memory-free methods, the proposed method improves temperature compensation accuracy by 26.0% to 47.0%. This method effectively reduces the adverse impact of temperature changes on the NMR sensor.

Upper limb power-assist wearable robot for handling repetitive medium- to low-weight loads in daily logistics tasks

In this study, we developed an upper-limb power-assisted wearable robot designed to reduce the burden of handling repetitive medium- to low-weight loads for daily logistics workers, thereby enhancing their work efficiency and overall safety. This study proposes a practical wearable robot with a well-designed structure for effectively supporting pick-and-place tasks at waist-to-shoulder height by applying a vertical force directly to the wearer’s wrist. The proposed robot features two active joints that are minimal for vertical assistance, resulting in a lightweight and compact structure. It offers six degrees of freedom per arm, including four passive joints, allowing free end-effector movement. Designed to connect only to the wearer’s wrist, the robot’s linkage is positioned along the wearer’s arm, not requiring alignment with the human–robot joint center, making it easy to wear and having a simple structure. This paper presents a method for calculating the joint torque that accounts for the deformation of the robot’s lightweight and slim links. This approach enhances the gravity compensation accuracy, and the proposed method demonstrates a lower RMS error compared to calculations based on the statics of the rigid link model. Experimental results demonstrated that the robot allowed for a wide range of motion and consistently applied an assistive force of 2 kgf per arm, facilitating the handling of objects weighing several kilograms.

Coarse frequency offset estimation and compensation based on short-time spectrum analysis in FSO communication system

Due to the high-speed movement of low-orbit satellites, the Doppler frequency offset in 1550-nm wavelength satellite-to-ground coherent laser communication is as high as ±4.5 GHz, which greatly increases the difficulty of carrier frequency acquisition. Low complexity, short capture time, and stable coarse frequency offset estimation and compensation algorithms are technical bottlenecks that must be addressed. In this work, a coarse frequency offset estimation and compensation algorithm based on short-time spectrum analysis is proposed. In the proof-of-principle system, the feasibility of the coarse frequency offset capture is verified based on a 2.5-GBaud data rate PM-QPSK modulation FPGA-based coherent receiver. The results show that the frequency offsets capture range is extended to ±4.5 GHz, covering the Doppler frequency offset range. Moreover, the standard deviation of the residual frequency offset after coarse frequency offset compensation is less than 140 MHz, which is smaller than the accurate frequency offset compensation range of 312.5 MHz.

A Proposed Condition-Based Risk Adjustment System for the Colombian Health Insurance Program

This article explores the development of a condition-based risk adjustment system for healthcare financing in Colombia, where the current age-, sex-, and geographic-based system faces significant challenges. These shortcomings, including inadequate compensation for high-risk members, necessitate workarounds that compromise efficiency. We propose a condition-based system that provides more accurate and equitable compensation, eliminating the need for special payments. This research discusses the existing issues in the current system, details the construction process of the condition-based risk adjustment system, and assesses its accuracy. This study not only offers an alternative to the current compensation system but also contributes to the broader discourse on health insurance reform.

Iterative outlier detection and refinement rule of compensation for phase aberrations in digital holographic microscopy.

We propose a robust and accurate compensation method for phase aberrations based on the iterative outlier detection and refinement (ODR) rule. This method does not require additional steps to select the known flat region manually or by image segmentation. Based on the proposed method, the phase aberration in regions of a specimen can be detected and refined iteratively. Then, the least squares fitting can be carried out to estimate the coefficients of Zernike polynomials and obtain the accurate phase aberration finally. Computer simulations and real experiments validate the feasibility and effectiveness, and the results show that the proposed method is robust to noise and has superior accuracy even when the specimen occupies half of the field of view.