Compensation Approach Research Articles

Adaptive decorrelation compensation approach in digital-image-correlation-assisted phase-sensitive optical coherence tomography

Adaptive decorrelation compensation approach in digital-image-correlation-assisted phase-sensitive optical coherence tomography

A hybrid active control method for a 3-DOF wave-compensated gangway

ABSTRACT Wave-compensated gangways are affected by forces from internal joint coupling and vessel motion, making the control process complex and challenging. Previously, three-degree-of-freedom gangways adopted passive compensation methods, utilizing end-effector sensor feedback for control. However, these methods are constrained by slow response times and significant lag. This study proposes an active hybrid compensation approach to enhance response speed and reduce tracking errors. The kinematics and dynamics of the gangway under vessel motion were analyzed. Subsequently, an improved velocity feedforward second-order super-twisting sliding mode control scheme was designed, complemented by fuzzy control for small torque compensation. Two experiments were conducted to evaluate and compare the performance of three control methods. Experiments demonstrated that the proposed hybrid control method exhibited superior performance in joint trajectory tracking, significantly reducing both root mean square and maximum errors, validating its effectiveness in improving tracking speed and accuracy.

An efficient compensation approach for fiber impairments in high-capacity system based on NARX neural network algorithms

An efficient compensation approach for fiber impairments in high-capacity system based on NARX neural network algorithms

Application of Compensation Algorithms to Control the Speed and Course of a Four-Wheeled Mobile Robot.

This article presents a tuned control algorithm for the speed and course of a four-wheeled automobile-type robot as a single nonlinear object, developed by the analytical approach of compensation for the object's dynamics and additive effects. The method is based on assessment of external effects and as a result new, advanced feedback features may appear in the control system. This approach ensures automatic movement of the object with accuracy up to a given reference filter, which is important for stable and accurate control under various conditions. In the process of the synthesis control algorithm, an inverse mathematical model of the robot was built, and reference filters were developed for a closed-loop control system through external effect channels, providing the possibility of physical implementation of the control algorithm and compensation of external effects through feedback. This combined approach allows us to take into account various effects on the robot and ensure its stable control. The developed algorithm provides control of the robot both when moving forward and backward, which expands the capabilities of maneuvering and planning motion trajectories and is especially important for robots working in confined spaces or requiring precise movement into various directions. The efficiency of the algorithm is demonstrated using a computer simulation of a closed-loop control system under various external effects. It is planned to further develop a digital algorithm for implementation on an onboard microcontroller, in order to use the new algorithm in the overall motion control system of a four-wheeled mobile robot.

Using the reverse geometry method for warpage compensation on changing meshes with interpolation methods

AbstractIn the manufacturing process of injection molding, the geometric accuracy of the produced part is affected by shrinkage and warpage. To support efforts to compensate for these effects, this paper presents an extension of the numerical compensation approach known as the reverse geometry method. The reverse geometry method is based on the numerical forward simulation of the displacement field resulting from shrinkage and warpage. It is an iterative method that performs node‐based compensation in each step. The mention of node‐based compensation already indicates that the method is associated with a computational mesh. In particular, in its basic version, it is linked to a specific mesh that must be kept identical during all iteration steps. This requirement is not always compatible with commercial simulation tools for injection molding, which enforce automatic remeshing between simulations. We present an interpolation method that allows to handle changing meshes and illustrate the method with two practical examples.

Burnt out and still single: Susceptibility to dating app burnout over time

Despite the ubiquity of dating apps, there is little longitudinal research examining the mental health and well-being of dating app users. To fill this void, this study takes a social compensation approach to exploring dating app users’ burnout experiences (i.e., emotional exhaustion, inefficacy, and depersonalization) over time. Four hundred ninety-three active single dating app users were surveyed over four waves across 12 weeks, with a final sample of 190. Multilevel growth curve models showed that dating app users experienced increased emotional exhaustion and inefficacy over time. Furthermore, depression, anxiety, and loneliness predicted more emotional exhaustion and inefficacy, while problematic dating app use predicted more emotional exhaustion and less inefficacy. The results suggest that dating apps may exacerbate existing difficulties with relationship initiation among vulnerable users by increasing their risk of burnout. Dating app burnout is a potential intervention target to improve users’ dating success.

Compensation of phased array antenna electrical performance based on improved genetic algorithm

Phased array antennas are frequently subjected to surface distortions due to environ-mental factors during operation. Such distortions can lead to a deterioration in the electrical performance of the phased array antenna. This study provides an in-depth analysis of the impact of array distortions on electrical performance indicators. An electronic compensation approach, employing an enhanced genetic algorithm, is proposed to mitigate the effects of these distortions. The conventional genetic algorithm has been augmented with an elimination mechanism to boost convergence. This method compensates for amplitude and phase by calculating each element's amplitude and phase of the excitation current, thereby enhancing the convergence performance. Simulations this approach, simulations of rectangular microstrip patch antennas are conducted to validate the effectiveness of this approach. The simulation outcomes reveal that the proposed method significantly improves the antenna's radiation pattern and all performance indicators, confirming its efficacy.

Influences of Variability in Attenuation Compensation on the Estimation of Backscatter Coefficient of Median Nerves in Vivo.

Peripheral nerves remain a challenging target for medical imaging, given their size, anatomical complexity, and structural heterogeneity. Quantitative ultrasound (QUS) applies a set of techniques to estimate tissue acoustic parameters independent of the imaging platform. Many useful medical and laboratory applications for QUS have been reported, but challenges remain for deployment in vivo, especially for heterogeneous tissues. Several phenomena introduce variability in attenuation estimates, which may influence the estimation of other QUS parameters. For example, estimating the backscatter coefficient (BSC) requires compensation for the attenuation of overlying tissues between the transducer and the underlying tissue of interest. The purpose of this study is to extend prior studies by investigating the efficacy of several analytical methods of estimating attenuation compensation on QUS outcomes in the human median nerve. Median nerves were imaged at the volar wrist in vivo and beam-formed radiofrequency (RF) data were acquired. Six analytical approaches for attenuation compensation were compared: 1-2) attenuation estimated by applying spectral difference method (SDM) and spectral log difference method (SLDM) independently to regions of interest (ROIs) overlying the nerve and to the nerve ROI itself; 3-4) attenuation estimation by applying SDM and SLDM to ROIs overlying the nerve, and transferring these properties to the nerve ROI; and 5-6) methods that apply previously published values of tissue attenuation to the measured thickness of each overlying tissue. Mean between-subject estimates of BSC-related outcomes as well as within-subject variability of these outcomes were compared among the 6 methods. Compensating for attenuation using SLDM and values from the literature reduced variability in BSC-based outcomes, compared to SDM. Variability in attenuation coefficients contributes substantially to variability in backscatter measurements. This work has implications for the application of QUS to in vivo diagnostic assessments in peripheral nerves and possibly other heterogeneous tissues.

A Swarm Intelligent Metaheuristic Approach for Efficient Series Compensation Resulting in System Loadability Enhancement

A Swarm Intelligent Metaheuristic Approach for Efficient Series Compensation Resulting in System Loadability Enhancement

Machine learning approaches for ecological compensation in transboundary waters

The Tongshun River, a vital waterway facing severe pollution, requires effective strategies for environmental management and sustainable resource use. This study utilizes an LSTM-XGBoost composite model to predict key water quality indicators—CODMn, NH3-N, TP, and TN—at the Gangzhou Village and Huangling Bridge monitoring sections. Using historical data from January 2021 to November 2023, the model demonstrates significant predictive accuracy, outperforming traditional CNN, LSTM, and XGBoost models by achieving lower MSE, RMSE, and MAE values, and higher R2 values. These results facilitate the implementation of ecological compensation mechanisms, enabling proactive interventions and efficient resource allocation. By integrating predictive modeling with ecological compensation frameworks, this approach enhances transboundary water pollution management, supporting the preservation of water quality and aquatic ecosystem health. The study highlights the potential of machine learning models in advancing water pollution control and informs effective policy formulation in environmental protection.

Fund allocation modeling of compensation subjects for various protection levels of the ecological flow in rivers under runoff variations

Ecological compensation for protecting of river ecological flow (e-flow) is crucial to maintaining the health of river ecosystems; however, determining the fund sharing for ecological compensation can be challenging. To fill this scientific gap, we herein developed a specific framework to determine the fund-sharing of ecological compensation subjects. Such a framework mainly consists of three parts:1) Identifying the subjects who benefit from the consumption and eco-products provided by e-flow protection, and determining the sharing coefficient of the compensation subjects based on the ratio of their economic benefit to the total economic benefit; 2) Evaluating the ecological compensation resulting from the e-flow protection with the help of the production function method; 3) Combining the ecological compensation and compensation sharing coefficient to determine the fund sharing of compensation subjects. We have successfully applied this approach to the Baoji Section of the Weihe River and the Baojixia Yuanshang Irrigation District (BYID). The obtained results revealed that the compensation subjects were Baoji and Xianyang City, and their corresponding fund-sharing coefficients in 2010 in order were 0.71 and 0.29. It was predicted that ecological compensation for e-flow protection would be 209 million yuan in 2010 year, and the corresponding values of capital sharing are 148 million and 61 million yuan. Over the last 23 years (in the time interval of 2000–2022), the share of compensation funds has gradually decreased, indicating an increasing trend with the improvement of the security level. Join us in our mission to protect river ecosystems and maintain a healthy balance by using this approach for ecological compensation.

Dynamic decoupling and compensation based on the inverted network technique and experiment research for multi-axis hydraulic road simulator

This paper presents a dynamic decoupling and compensation approach to eliminate channel interaction for a multi-axis hydraulic road simulator. Misalignment of the concentrated moving mass centroid on the entire test bench with the control point and inconsistencies in actuator dynamics leads to dynamic coupling, which deteriorates the motion performance of the system. To solve the problem, an inverted decoupling and compensation network based on an identification model is proposed. Firstly, the system is modeled using the recursive extended least square (RELS) identification method. In the feedforward compensation modeling process, the steady-state inverse is obtained using zero-magnitude error technology control (ZMETC). In addition, a finite impulse response (FIR) filter with an adaptive algorithm is employed to reduce the adverse impact of the modeling error. The proposed decoupling strategy is validated by conducting experiments using a multi-axis hydraulic road simulator. The experimental results indicate that the designed compensator can effectively decrease the cross-coupling of multi-input and multi-output (MIMO) systems. The suggested approach is also applicable to applications that necessitate the elimination of interactions between different control variables.

Control of hole rolling on 3D Servo Presses

Innovative press concepts with multiple degrees of freedom such as the 3D Servo Press (3DSP) allow the implementation of incremental forming processes, thus the production of previously unfeasible workpiece geometries. This publication demonstrates that elliptical double-sided collars can be formed out of sheet metal through closed-loop control of the ram pose and controlled ram tilting. For this purpose, a new tool has been developed that enables the process of flexible hole rolling on a 3DSP. We show that the geometry of produced parts can be influenced by adapting the tool path trajectories and present a compensation approach that ensures the highly accurate insertion of the collars into the sheet metal. The geometries as well as the resulting courses of the collar height over the circumference of the hole, are analysed using experimental and FEM-based simulation results.

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.

Neural Network Approach for Modelling and Compensation of Local Surface-Tilting-Dependent Topography Measurement Errors in Coherence Scanning Interferometry

The topography measurement accuracy of coherence scanning interferometry (CSI) suffers from the local characteristic of micro-structured surfaces, such as local surface slopes. A cylindrical reference artefact made of single-mode fiber with high roundness and low roughness has been proposed in this manuscript to traceably investigate the surface tilting induced measurement deviations using coherence scanning interferometry with high NA objectives. A feed-forward neural network (FF-NN) is designed and trained to model and thereafter compensate the systematic measurement deviations due to local surface tilting. Experimental results have verified that the FF-NN approach can well enhance the accuracy of the CSI for radius measurement of cylindrical samples up to 0.3%. Further development of the FF-NN for modelling of the measurement errors in CSI due to the optical properties of surfaces including areal roughness is outlined.

Turbulence compensation with pix-to-pix generative adversarial networks in vector vortex beams

Orbital angular momentum (OAM) has significantly propelled free space optical communication (FSOC) towards achieving ultra-large transmission capacities, but mode-crosstalk in atmospheric turbulence limits its application. Here, we propose a proof-of-concept turbulence compensation approach utilizing pix-to-pix generative adversarial networks (pix2pixGAN) that does not rely on the wavefront sensor. The model captures the complex relationships between distorted optical fields and phase screens through extensive training, after which the phase screen is directly recovered from the well-trained model by identifying the corresponding distorted image to compensate for distortions. Using this model, the Strehl ratio improvement is measured at 35.7%, 8.9%, and 1.7% under three distinct turbulence conditions, respectively. Furthermore, the recognition of vector vortex beams (VVBs) integrating with the pix2pixGAN significantly improves average mode accuracy from 2% to over 99%. Additionally, the exploration of VVB-based communication further elucidates pix2pixGAN's role in enhancing communication quality. These findings suggest a potential advancement in developing a novel neural network-based strategy to compensate for transmission distortions under intense turbulence.

Locomotion trajectory optimization for quadruped robots with kinematic parameter calibration and compensation

It is significant to improve the locomotion trajectory accuracy of a quadruped robot for more applications. A compensation approach with kinematic parameter calibration is proposed to enhance the trajectory accuracy. The motion equation of the foot ends in coordinated gait is given. The related kinematic error model is deduced. The model for identifying the joint parameter errors is implemented via the trajectory error in the Z-direction. The trunk trajectory accuracy enhancement is facilitated by compensating the joint parameter error with joint angles as tuning variables. The performance of the methodology is validated in the simulation system by predetermining ten random sets of joint variable errors. The results indicate that both the trajectory and the attitude accuracy of the calibrated robot can be significantly improved. In practical experiments, it is shown that an error reduction rate of 80% in positional deviation and 58% in attitude error can be achieved.

Transforming Advanced Practice RN Compensation: Strategies for Equity in Total Pay.

As the advanced practice RN (APRN) workforce rapidly expands, nursing leaders are grappling with the challenge of establishing equitable and competitive compensation models. This column explores the lessons learned by 2 pioneering APRN leaders who navigated these workforce and compensation challenges within their own healthcare organizations. By sharing their experiences building sustainable APRN infrastructure and addressing pay discrepancies, the authors provide valuable insights and strategies that can help other nursing leaders transform their APRN compensation approaches and ensure their advanced clinicians are appropriately valued and rewarded.

A Bilevel Approach for Compensation and Routing Decisions in Last-Mile Delivery

In last-mile delivery logistics, peer-to-peer logistic platforms play an important role in connecting senders, customers, and independent carriers to fulfill delivery requests. As the carriers are not under the platform’s control, the platform has to anticipate their reactions while deciding how to allocate the delivery operations. Indeed, carriers’ decisions largely affect the platform’s revenue. In this paper, we model this problem using bilevel programming. At the upper level, the platform decides how to assign the orders to the carriers; at the lower level, each carrier solves a profitable tour problem to determine which offered requests to accept, based on her own profit maximization. Possibly, the platform can influence carriers’ decisions by determining also the compensation paid for each accepted request. The two considered settings result in two different formulations: the bilevel profitable tour problem with fixed compensation margins and with margin decisions, respectively. For each of them, we propose single-level reformulations and alternative formulations where the lower-level routing variables are projected out. A branch-and-cut algorithm is proposed to solve the bilevel models, with a tailored warm-start heuristic used to speed up the solution process. Extensive computational tests are performed to compare the proposed formulations and analyze solution characteristics. Funding: The research of E. Fernandez was partially funded through the Spanish Ministerio de Ciencia y Tecnología and European Regional Development Funds (ERDF) [Grant MTM2019-105824GB-I00]. The research of C. Archetti, M. Cerulli, and I. Ljubić was partially funded by CY Initiative of Excellence, France [Grant “Investissements d’Avenir ANR-16-IDEX-0008”]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0129 .

Machine learning for aspherical lens form accuracy improvement in precision molding of infrared chalcogenide glass

Precision glass molding (PGM) is an effective approach to manufacturing infrared chalcogenide glass (ChG) aspherical lens with complex shapes. However, infrared ChG aspherical lens often experiences form error in the designed profile and the final profile obtained by PGM. To reduce the form error of infrared ChG aspherical lens in the PGM process, a form error compensation model based on the random forest (RF) algorithm is proposed. The infrared ChG aspherical lens profile was first machined on an electroless nickel-phosphorus (Ni–P) plating to serve as the mold for PGM. After molding, the profile data of the lens was extracted, and a compensation model based on RF was constructed to optimize the model parameters using the evaluation parameters such as root mean square error (RMSE), coefficient of determination (R2), and out-of-bag (OOB). Finally, the generated compensated profile based on the compensation model was used for the compensation machining of the mold. Through this compensation approach, we have demonstrated a substantial 63.5 % reduction in the form error of the fabricated infrared ChG aspherical lens, decreasing the Peak-to-Valley (PV) value from 1.04 μm to 0.38 μm.