Traditional Compensation Method Research Articles

A novel IQ mismatch compensation method in wideband direct-conversion receivers.

Direct-conversion receivers (DCRs) have been widely used in recent years due to their small size and low power consumption. However, the mismatch between the in-phase (I) and the quadrature (Q) branches will seriously affect the performance of the DCRs. This paper proposes a novel blind compensation method to suppress the interference introduced by IQ mismatch. Based on the Hilbert transform, our proposed method can obtain the orthogonal signal of the I-channel signal by utilizing the Weaver architecture. Compared with traditional compensation methods, the main difference of the proposed method is that it ignores prior information, training sequences, and additional hardware circuits. Furthermore, the complexity of the proposed blind compensation method is low because no iterative operations are involved in the compensation process. The simulation results show that the proposed method has an excellent compensation performance, especially in wideband applications.

Automated surface profile measurement with digital holographic microscopy and large mask inpainting networks

Digital holographic microscopy (DHM) allows for highly precise 3D surface measurements in a non-invasive way, but phase aberrations from off-axis DHM recordings can compromise image accuracy. Traditional compensation methods require manual intervention, hindering further automated use of DHM. Other methods based on background segmentation and Zernike polynomials have been proposed, but identifying the sample and background regions can lead to inaccurate compensation results. Additionally, traditional image restoration algorithms struggle with restoring sample-free holograms involving large or multiple vacant areas. A new automated aberration compensation method is proposed using large-mask inpainting networks. This method restores sample-free holograms and compensates for phase aberrations, leveraging deep learning to enable real-time measurements. In the study, the network was trained with holograms of varying fringes, and experimental results show its effectiveness in improving image accuracy and detail. This approach could have wide applications in industries such as micro-electromechanical systems and micro- integrated circuits.

A geomagnetic vector evolutionary compensation method using Woodbury equation

High-accuracy geomagnetic vector measurements inevitably suffer from the magnetic interference derived from ferromagnetic materials and electric equipment. Traditionally, to eliminate the influence of magnetic interference, a compensation process can be implemented to estimate the interference parameters that contain hard-iron and soft-iron parameters. The traditional compensation method did not consider the situation where the interference parameters would vary over time due to the change in magnetic properties. To overcome this problem, we proposed an evolutionary compensation method based on the Woodbury equation. Using this approach, the interference parameters can be iteratively updated during the measurement process by combining the previous information and the latest measurement data. Both simulation and experimentation were undertaken to evaluate the effectiveness of our proposed approach. The results demonstrate that the proposed method is successful in minimizing the impact of magnetic interference variations. The experiment shows that the RMS errors of geomagnetic filed total intensity and three components can be decreased from 4042.57 nT, 1425.84 nT, 5342.40 nT, and 2921.34 nT to 170.57 nT, 58.39 nT, 207.99 nT and 137.75 nT respectively by traditional method, while with evolutionary method the errors can be reduced to 47.37 nT, 16.19 nT, 57.61 nT and 38.38 nT respectively.

A Differentiated Dynamic Reactive Power Compensation Scheme for the Suppression of Transient Voltage Dips in Distribution Systems

At present, with the construction of new power systems, the uncertainty of the system is increasing and the transient voltage stability of the power grid is becoming more and more complicated. For this reason, this paper proposes a differentiated dynamic reactive power compensation configuration method for suppressing transient voltage dip instability. The method first establishes a dynamic reactive power device configuration indicator system based on multiple-two-element notation, and then establishes a differentiated dynamic reactive power compensation device configuration model based on the node instability risk characteristics. The model takes the investment cost and suppression effect as the optimization objectives, and flexibly adjusts the optimization weights of its cost and suppression effect according to the high or low risk of the transient voltage instability of the compensation node, so as to realize the differentiated dynamic reactive power compensation. Compared with traditional compensation methods, differentiated compensation can significantly improve the economic benefits of reactive power compensation configuration. Finally, taking the IEEE39 node test system as an example, the improved TLBO algorithm is used to solve the model. The cost of differentiated compensation is reduced by 22.13%, with a similar suppression effect, which verifies the validity of the method proposed in this paper.

The Conjunctive Compensation Method Based on Inertial Navigation System and Fluxgate Magnetometer

Eliminating the magnetic interference of the carrier platform is an important technical link and plays a vital role in aeromagnetic survey. The traditional compensation method is based on the Tolles–Lawson (T-L) model and establishes the linear relationship between the aircraft interference magnetic field and the aircraft attitude. The compensation coefficients are solved by designing the calibration flight. At present, almost all aeromagnetic systems use the fluxgate magnetometer fixed to the aircraft to realize the attitude measurement of the flight platform. However, the fluxgate magnetometer has problems, such as non-orthogonal error, zero drift error, and linearity error limited by the production process, and the fluxgate magnetometer is also very susceptible to external magnetic interference as a magnetic sensor. These lead to the aircraft attitude calculated by the fluxgate magnetometer being inaccurate, thus reducing the compensation effect. In this article, we analyze the influence of the fluxgate magnetometer noise on compensation and propose a new conjunctive compensation method based on inertial navigation systems (INS) and fluxgate magnetometer information to improve the compensation effect. The flight experiment data show that the proposed method can significantly improve the quality of aeromagnetic data. Compared with the traditional compensation method only based on fluxgate magnetometer information, the improved ratio is increased by 30–60%, and it is a real-time compensation method. It shows that the proposed method has a remarkable compensation effect for aeromagnetic interference.

A Compensation Method in Magnetic Distortion Through Regularized Inverse Problems

In magnetic surveys, three-axis magnetometers (TAMs) are usually installed on large carriers, and the TAM outputs are inevitably subjected to errors from TAMs and the carrier (including scale factor, nonorthogonality, bias error, temperature error, soft iron error, and hard iron error). Traditional compensation methods may result in unreliable and unsuccessful solutions when maneuvers of the vehicle are constrained. Therefore, a compensation method based on regularized inverse problems is proposed. We inspect the performance of the proposed method by conducting simulated and semiphysical experiments. All the results indicate that the proposed calibration method can still achieve high performance in the case of narrow-range attitudes and obtain more accurate magnetic components.

Study on FOG scale factor error calibration in start-up stage based on GWO-GRU

The fiber optic gyroscope (FOG) is the core component of the inertial system, which has been widely utilized in aviation, maritime, automotive, and other fields. The scale factor is one of the critical parameters to characterize the dynamic performance of the FOG. Due to the temperature sensitivity of the optoelectronic devices, the scale factor errors will be caused with the temperature change. The traditional compensation method usually compensates the output of the FOG in a stable working state. Without considering the difference between the characteristics of the FOG in the start-up stage and the stable working state, the compensation accuracy of the traditional method cannot meet the demand for quick start-up in application areas. According to the above analysis, a novel scale factor error compensation method based on the grey wolf optimization (GWO) and gated recurrent unit (GRU) neural network is proposed in this paper. Furthermore, a new experiment is designed to calibrate the scale factor error in the start-up stage, which can reduce the calibration time and ensure the calibration accuracy. The experimental results demonstrate the proposed method significantly improves the compensation accuracy compared with other methods, such as the multilayer perceptrons (MLPs) and the traditional GRU network.

High-Efficiency Mitigation of Nonlinear Distortion in Microwave Photonics Link Assisted by Artificial Neural Network

A microwave photonics radio over fiber link can deliver the radio frequency (RF) signal to realize the antenna remote. When the signal is a broadband and multicarrier RF signal, there are some linear distortions in the link, such as the crossmodulation distortion (XMD) and third-order intermodulation distortion (IMD3). It will destroy the wide-bandwidth performance of the link. Traditional postdigital compensation methods for XMD and IMD3 mitigation extract the baseband signal and reconstruct the compensation signal with calculated compensation factor. Here, a kind of artificial neural network genetic algorithm (ANN-GA) distortion compensation technique is proposed to seek the compensation factor instead of calculations. The neural network algorithm is used to fit the mapping function between the compensation factor and the distortion and give a set of predicted data as the original individual fitness value for the genetic algorithm. Based on the original value, the minimal distortion, the corresponding optimal compensation factors γ and α are found with optimization iterations. Taking advantage of the optimal compensation factors, based on the traditional postdigital compensation method, the distortion is mitigated with a suppression ratio of -84.4 dB. In our paper, the mitigation technology of the XMD and IMD3 can be applied for any kinds of link instead of mathematically modelling the link and calculating the compensation factor. The technology can improve the intelligence and flexibility of microwave photonic link linearization design.

A Current-Sharing Compensation Method for High-Power-Medium-Frequency Coils Composed of Multiple Branches Connected in Parallel

In inductive power transfer (IPT) systems, the coils with paralleled multiple branches are commonly used in high power applications where the frequency range is between 20 and 90 kHz. The traditional compensation method takes multiple branches as a whole and the compensation capacitors are connected outside the coils, which suffers from current unbalance among multiple branches, but currently few researchers analyze it. Therefore, this article illustrates the current-unbalance mechanism and proposes a current-sharing compensation method for IPT coils. The current-unbalance characteristics of coils with paralleled multiple branches are illustrated to prove the current-sharing necessity. Then, based on the relation between the impedance of branch and current unbalance, a compensation capacitor with a specially designed value is connected in each branch, so that the current unbalance can be suppressed by the increased branch impedance. Besides, the cost, volume, and weight of IPT systems remain unchanged approximately. Finally, a 30-kW IPT prototype is built up to verify the availability of the proposed method, and the currents and temperatures of multiple branches are equally distributed within wide power range with this method.

Temperature Compensation of an Eddy-Current Displacement Sensor Using an Improved Sparrow Search Algorithm Method

Eddy-current displacement sensor (ECS) has been applied widely to the production of modern industry by reason of its characteristics of high sensitivity, good reliability, powerful anti-interference capacity, and noncontact measurement. However, it cannot be used when severe temperature drift occurs at high temperature. Some traditional compensation methods are difficult to achieve good performance with neglecting the nonlinearity. Hence, it is essential to propose a better method for temperature compensation. A novel temperature compensation approach for ECS problem using an improved sparrow search algorithm (ISSA) and radial basis function neural network (RBFNN) is proposed in this article. In the ISSA, a chaos strategy is introduced in the algorithm for avoiding local optimal point, and an elite opposition-based learning strategy is integrated to promote global search ability of ISSA with high efficiency. RBFNN is elected to model the temperature drift, and its parameters are determined by the proposed ISSA. The proposed method compensates the significant deterministic errors caused by temperature variation within a wide temperature range. The experimental schemes were designed to the effectiveness of the proposed method according to data fusion technology. The various test results obtained confirm the potential and effectiveness of the proposed approach compared to some other traditional temperature compensation methods presented in the literatures.

Adapted part design methods for springback minimization of stamped sheet metal car body components

In sheet metal forming, both the dimensional accuracy of the manufactured parts and the robustness of the forming process are significantly compromised by elastic springback effects. In contrast to traditional compensation methods mostly relying on modifications of the tool geometry, a part geometry-based approach reduces the amount of springback by appropriately manipulating the part stiffness through slight modifications of the part geometry. To date, part-geometric measures are mostly based on practical experience and trial-and-error processes resulting in a need for simple and effective simulation-based approaches. In the present work three novel simulation-based approaches for a suitable modification of structural component design are suggested relying on unconventional topology optimization, the so-called line-of-force method and shape optimization. First tests with a classical hat profile reveal that the optimized geometries, compared to the nominal one, lead to significant reductions of the springback after stamping and simultaneously to a notable decrease of the process variation when parameters of the forming process are varied slightly. For the adapted topology optimization approach the excellent results are also confirmed when applied to the industry-relevant case of a truck rear panel.

Deep Learning in Sheet Metal Bending With a Novel Theory-Guided Deep Neural Network

Sheet metal forming technologies have been intensively studied for decades to meet the increasing demand for lightweight metal components. To surmount the springback occurring in sheet metal forming processes, numerous studies have been performed to develop compensation methods. However, for most existing methods, the development cycle is still considerably time-consumptive and demands high computational or capital cost. In this paper, a novel theory-guided regularization method for training of deep neural networks (DNNs), implanted in a learning system, is introduced to learn the intrinsic relationship between the workpiece shape after springback and the required process parameter, e.g., loading stroke, in sheet metal bending processes. By directly bridging the workpiece shape to the process parameter, issues concerning springback in the process design would be circumvented. The novel regularization method utilizes the well-recognized theories in material mechanics, Swift's law, by penalizing divergence from this law throughout the network training process. The regularization is implemented by a multi-task learning network architecture, with the learning of extra tasks regularized during training. The stress-strain curve describing the material properties and the prior knowledge used to guide learning are stored in the database and the knowledge base, respectively. One can obtain the predicted loading stroke for a new workpiece shape by importing the target geometry through the user interface. In this research, the neural models were found to outperform a traditional machine learning model, support vector regression model, in experiments with different amount of training data. Through a series of studies with varying conditions of training data structure and amount, workpiece material and applied bending processes, the theory-guided DNN has been shown to achieve superior generalization and learning consistency than the data-driven DNNs, especially when only scarce and scattered experiment data are available for training which is often the case in practice. The theory-guided DNN could also be applicable to other sheet metal forming processes. It provides an alternative method for compensating springback with significantly shorter development cycle and less capital cost and computational requirement than traditional compensation methods in sheet metal forming industry.

A New Dead-time Compensation Method Based on LMS Algorithm for PMSM

This paper presents a new dead-time compensation method for permanent-magnet synchronous motor (PMSM) at low speed. Based on least-mean-square (LMS) algorithm and combined with the traditional average voltage compensation method, the proposed method can determine the current polarity through extracting the fundamental current. Compared with traditional compensation methods, the proposed method can detect current polarity more accurately at low speed due to the narrower notch bandwidth. Moreover, this method is robust to motor parameters and easy to implement. The proposed method is simulated in a voltage-source inverter PMSM drive controlled by I/F and the results show that it meets the requirement of dead-time compensation and eliminates the influence of dead-time effect.

Short-range clutter suppression method combining oblique projection and interpolation in airborne CFA radar

The airborne conformai array (CFA) radar's clutter ridges are range-modulated, which result in a bias in the estimation of the clutter covariance matrix (CCM) of the cell under test (CUT), further, reducing the clutter suppression performance of the airborne CFA radar. The clutter ridges can be effectively compensated by the space-time separation interpolation (STS-INT) method, which costs less computation than the space-time interpolation (STINT) method, but the performance of interpolation algorithms is seriously affected by the short-range clutter, especially near the platform height. Location distributions of CFA are free, which yields serious impact that range spaces of steering vector matrices are non-orthogonal complement and even no longer disjoint. Further, a new method is proposed that the short-range clutter is pre-processed by oblique projection with the intersected range spaces (OPIRS), and then clutter data after being pre-processed are compensated to the desired range bin through the STSINT method. The OPIRS also has good compatibility and can be used in combination with many existing methods. At the same time, oblique projectors of OPIRS can be obtained in advance, so the proposed method has almost the same computational load as the traditional compensation method. In addition, the proposed method can perform well when the channel error exists. Computer simulation results verify the effectiveness of the proposed method.

The New Component Compensation Method and Field Application of Portable Geomagnetic Field Measurement Instrument

Magnetic field measurement is an important analysis method in mining industry. Magnetic distortion of inertial navigation system (INS) is one severe error of portable geomagnetic field vector measurement instrument. A correlation algorithm is proposed for component compensation, in which magnetometer attitude information is provided by INS, and the geomagnetic field vector without magnetic anomaly is measured. Experiment result shows that distortion field vector of INS can reach hundreds of nT, which are suppressed to about 4.8 % after compensation, thus it is effective for component compensation. The magnetic anomaly measurement using magnetic intensity test (TMI) method and the proposed instrument are analyzed. In application, the proposed compensation way shows effectiveness comparing with several traditional compensation methods. In addition, the potential applications in mining industry are discussed.

Fast Progressive Compensation Method for Externally Compensated AMOLED Displays

The externally compensated display system has been proved to have greater potential to completely solve aging and uniformity issues of the AMOLED display system than the internally compensated display system. However, the traditional compensation method is time consuming and responses slowly to the mobility change of TFT. The traditional compensation method performs calibration during the inter-frame blanking period, the length of which is limited. Only a few rows of pixel circuits can be calibrated per frame. Furthermore, each pixel circuit needs multiple times of calibration to reach compensated state. In this article, a fast progressive compensation method is proposed. The calibration of a row of pixel circuits is performed K rows ahead of the display line during the display data updating period. By doing so, the proposed fast progressive compensation method can approach compensated state faster than the traditional method because display data updating period is significantly longer than the inter-frame blanking period. For the case of 10% inter-frame blanking period and 20 μs waiting delay for feedback data line to be stable, the proposed method reduces the compensation time by ~14x than the traditional method.

Improving the performance of drifted/shifted electronic nose systems by cross-domain transfer using common transfer samples

Sensor drift/shift is a challenging and high-profile issue in the field of sensors and measurements. Because of the time variability and unpredictable properties of drift/shift, traditional compensation methods are costly and laborious. Considering the distribution alignments of different domains at both the feature and decision level, a unified double-level drift/shift compensation framework is presented to address sensor drift/shift. The framework consists of cross-domain feature subspace transfer learning coupled with a cross-domain adaptation extreme learning machine (CFST-AELM), which learns a low-dimensional discriminant subspace and a robust classifier using small numbers of labeled samples selected from the target domain. In general, the unified framework has the following advantages: (1) implementing anti-drift/shift at both the feature and decision level by cross-domain transfer learning which takes full advantage of transfer samples; (2) the subspace learning is a joint distribution adaptation algorithm and can be easily realized by eigenvalue decomposition; (3) more discriminative information of the source and target datasets remain after linear mapping; and (4) there is enhancement of the generalization performance of the model by adding the prediction error of transfer samples as a constraint of extreme learning machines (ELM) classification. The results suggest that compared with several state-of-the-art strategies, the CFST-AELM framework has superior performance in terms of classification accuracy.

Effects of partial tool engagement in micro-EDM milling and adaptive tool wear compensation strategy for efficient milling of inclined surfaces

Abstract Compensation of tool wear in micro-EDM milling of inclined surfaces is a difficult task due to variations of the radial engagement conditions of the tool with the workpiece. Traditional compensation methods based on off-line wear prediction are likely to be inaccurate, while methods involving tool measurements are time-consuming. In this research, the process dynamics when the tool is not fully engaged with the workpiece are investigated by performing repeated milling tests and analysing the effects on the discharging process and tool wear behaviour. An adaptive tool wear compensation strategy is accordingly developed, combining traditional linear compensation method (LCM) and tool wear sensing. In this strategy, simultaneous monitoring of the normal discharge pulses and tool displacement is used for determining the tool engagement conditions. This allows to adapt the milling process to the expected tool wear behaviour when the tool is in partial engagement. The proposed control strategy has been tested for an industrially-relevant case, i.e. the shaping of a diffuser for a turbine blade. A machining accuracy in the range of the applied milling layer thickness was obtained without the need of tool measurements.

Adaptive [formula omitted] Kalman filter based random drift modeling and compensation method for ring laser gyroscope

This paper investigates the random drift influence on ring laser gyroscope (RLG) and proposes a random drift modeling and compensation method to improve the measurement accuracy of RLG. The traditional compensation method for random drift is the standard Kalman filter. However, it needs to know the accurate discrete-time state-space model and the statistic characteristics of process and measurement noises, which limits the performance of Kalman filter. Therefore, based on the autoregressive moving average (ARMA) model, this paper incorporates the H∞ technique and adaptive law design into the standard Kalman filter to deal with the system uncertainty and estimate the statistic characteristics of unknown noises. To test the performance of our proposed filter, the Allan variance method is adopted to analyze the random drift influence under different Kalman-type filters. The experimental results have been given to demonstrate that under the proposed adaptive H∞ Kalman filter, the influence of random drift on RLG output is largely reduced and the calibration accuracy for turntable is improved significantly.

Fast Commutation Error Compensation for BLDC Motors Based on Virtual Neutral Voltage

Precise commutation signal is the prerequisite for the efficient operation of brushless direct current (BLDC) motors. However, the low-pass filter, nonideal current, circuit, and software retarding can bring about the commutation errors that will deteriorate the performance of BLDC motors, for example, resulting in larger current ripple and noise. To eliminate the commutation errors quickly and improve the performance of the motor, a novel compensation method is proposed in this letter. The compensation time is calculated by a counter. And the start-stop signals of the counter are obtained by the virtual neutral voltage. The proposed method requires none voltage or current sensor and has a faster compensation speed and higher precision than the traditional compensation method based on the integration of virtual neutral voltage. The effectiveness of the proposed method is verified by comparable exp eriments using an 80-W BLDC motor.