Wave Azimuth Cutoff Compensation Method and the Multisatellite Networking Mode of SAR

Synthetic aperture radars(SARs)encounter the azimuth cutoff problem when observing sea waves.Consequently,SARs can only capture the waves with wavelengths larger than the cutoff wavelength and lose the information of waves with smaller wavelengths.To increase the accuracy of SAR wave observations,this paper investigates an azimuth cutoff compensation method based on the simulated multiview SAR wave synchronization data obtained by the collaborative observation via networked satellites.Based on the simulated data and the equivalent multiview measured data from Sentinel-1 virtual networking,the method is verified and the cutoff wavelengths decrease by 16.40%and 14.00%.The biases of the inversion significant wave height with true values decrease by 0.04 m and 0.14 m,and the biases of the mean wave period decrease by 0.17 s and 0.22 s,respectively.These results demonstrate the effectiveness of the azimuth cutoff compensation method.Based on the azimuth cutoff compensation method,the multisatellite SAR networking mode for wave observations are discussed.The highest compensation effect is obtained when the combination of azimuth angle is(95°,115°,135°),the orbital intersection angle is(50°,50°),and three or four satellites are used.The study of the multisatellite networking mode in this paper can provide valuable references for the compensation of azimuth cutoff and the observation of waves by a multisatellite network.

CNC Thermal Compensation Based on Mind Evolutionary Algorithm Optimized BP Neural Network

Thermal deformation error is one of the most important factors affecting the CNCs’ accuracy, so research is conducted on the temperature errors affecting CNCs’ machining accuracy;on the basis of analyzing the unpredictability and pre-maturing of the results of the genetic algorithm, as well as the slow speed of the training speed of the particle algorithm, a kind of Mind Evolutionary Algorithm optimized BP neural network featuring extremely strong global search capacity was proposed;type KVC850MA/2 five-axis CNC of Changzheng Lathe Factory was used as the research subject, and the Mind Evolutionary Algorithm optimized BP neural network algorithm was used for the establishment of the compensation model between temperature changes and the CNCs’ thermal deformation errors, as well as the realization method on hardware. The simulation results indicated that this method featured extremely high practical value.

Strip Thickness Control of Cold Rolling Mill with Roll Eccentricity Compensation by Using Fuzzy Neural Network

In rolling mill, the accuracy and quality of the strip exit thickness are very important factors. To realize high accuracy in the strip exit thickness, the Automatic Gauge Control (AGC) system is used. Because of roll eccentricity in backup rolls, the exit thickness deviates periodically. In this paper, we design PI controller in outer loop for the strip exit thickness while PD controller is used in inner loop for the work roll actuator position. Also, in order to reduce the periodic thickness deviation, we propose roll eccentricity compensation by using Fuzzy Neural Network with online tuning. Simulink model for the overall system has been implemented using MATLAB/SIMULINK software. The simulation results show the effectiveness of the proposed control.

Simulation Study on Series Capacitor Compensation to Improve the Voltage Quality of Rural Power Distribution Network

In order to improve the voltage quality of rural power distribution network, the series capacitor in distribution lines is proposed. The principle of series capacitor compensation technology to improve the quality of rural power distribution lines voltage is analyzed. The real rural power distribution network simulation model is established by Power System Power System Analysis Software Package (PSASP). Simulation analysis the effect of series capacitor compensation technology to improve the voltage quality of rural power distribution network, The simulation results show that the series capacitor compensation can effectively improve the voltage quality and reduce network losses and improve the transmission capacity of rural power distribution network.

Neural Network Robust Control Based on Computed Torque for Lower Limb Exoskeleton

The lower limb exoskeletons are used to assist wearers in various scenarios such as medical and industrial settings.Complex modeling errors of the exoskeleton in different application scenarios pose challenges to the robustness and stability of its control algorithm.The Radial Basis Function(RBF)neural network is used widely to compensate for modeling errors.In order to solve the problem that the current RBF neural network controllers cannot guarantee the asymptotic stability,a neural network robust control algorithm based on computed torque method is proposed in this paper,focusing on trajectory tracking.It innovatively incorporates the robust adaptive term while introducing the RBF neural network term,improving the compensation ability for modeling errors.The stability of the algorithm is proved by Lyapunov method,and the effectiveness of the robust adaptive term is verified by the simulation.Experiments wearing the exoskeleton under different walking speeds and scenarios were carried out,and the results show that the absolute value of tracking errors of the hip and knee joints of the exoskeleton are consistently less than 1.5°and 2.5°,respectively.The proposed control algorithm effectively compensates for modeling errors and exhibits high robustness.

Research on motion compensation method based on neural network of radial basis function

The machining precision not only depends on accurate mechanical structure but also depends on motion compensation method. If manufacturing precision of mechanical structure cannot be improved, the motion compensation is a reasonable way to improve motion precision. A motion compensation method based on neural network of radial basis function(RBF) was presented in this paper. It utilized the infinite approximation advantage of RBF neural network to fit the motion error curve. The best hidden neural quantity was optimized by training the motion error data and calculating the total sum of squares. The best curve coefficient matrix was got and used to calculate motion compensation values. The experiments showed that the motion errors could be reduced obviously by utilizing the method in this paper.

Study on Adaptive Control with Neural Network Compensation

A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.

Time-delay Positive Feedback Control for Nonlinear Time-delay Systems with Neural Network Compensation

新适应时间延期积极反馈控制器(ATPFC ) 为非线性的时间延期系统的一个班被介绍。建议控制计划由神经基于网络的鉴定和时间延期组成积极反馈控制器。与一个特殊动态鉴定模型一起合并的二个高顺序的神经网络(HONN ) 被采用识别非线性的系统。基于识别模型，本地 linearization 赔偿被用来处理系统的未知非线性。线性化的系统的一个 time-delay-free 逆模型和一个需要的引用模型被利用组成反馈控制器，它能导致系统输出追踪一个引用模型的轨道。为鉴定和靠近环的控制系统的追踪的错误的严密稳定性分析借助于 Lyapunov 稳定性标准被提供。模拟结果被包括表明建议计划的有效性。

Design of Dispersion Compensation and Nonlinear Distortion Self-compensationin Co -network Transmission Systems

Dispersion and nonlinear distortion have an effect on transmission performanc es of optic al fiber transimission systems. The schemes of dispersion compensation and nonli near distortion self-compens ation in telecom-CATV co-network transmission systems are reported, followed by investigation on (1) the impact of dispersion compensation fiber (DCF) on fi ber nonlinear effects with a cascade of erbium-doped fiber amplifiers (EDFAs) an d different dispersion compensation schemes, (2) the impact of the complex on th e total nonlinear distortion induced by EDFA gain tilt and the light source. As a result , dispersion compensation optimal scheme and EDFA negative gain tilt are suggest ed as a solution to dispersion compensation and the nonlinear distortion self-c ompensation.

Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

:Machine Learning(ML)algorithms have been widely used for financial time series prediction and trading through bots.In this work,we propose a Predictive Error Compensated Wavelet Neural Network(PEC-WNN)ML model that improves the prediction of next day closing prices.In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs.An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence.The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange.The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs.The RMSE error is 33%improved when the proposed PEC-WNN model is used compared to the Long ShortTerm Memory(LSTM)model.Furthermore,through the analysis of training mechanisms,we found that using the updated training the performance of the proposed model is improved.The contribution of this study is the applicability of simultaneously different time frames as inputs.Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.

Optimal Reactive Power Compensation of Distribution Network to Prevent Reactive Power Reverse

The capacitive reactive power reversal in the urban distribution grid is increasingly prominent at the period of light load in the last years.In severe cases,it will endanger the security and stability of power grid.This paper presents an optimal reactive power compensation method of distribution network to prevent reactive power reverse.Firstly,an integrated reactive power planning(RPP)model with power factor constraints is established.Capacitors and reactors are considered to be installed in the distribution system at the same time.The objective function is the cost minimization of compensation and real power loss with transformers and lines during the planning period.Nodal power factor limits and reactor capacity constraints are new constraints.Then,power factor sensitivity with respect to reactive power is derived.An improved genetic algorithm by power factor sensitivity is used to solve the model.The optimal locations and sizes of reactors and capacitors can avoid reactive power reversal and power factor exceeding the limit.Finally,the effectiveness of the model and algorithm is proven by a typical high-voltage distribution network.

Aeromagnetic Compensation Algorithm Based on Levenberg-Marquard Neural Network

The magnetic compensation of aeromagnetic survey is an important calibration work,which has a great impact on the accuracy of measurement.In an aeromagnetic survey flight,measurement data consists of diurnal variation,aircraft maneuver interference field,and geomagnetic field.In this paper,appropriate physical features and the modular feedforward neural network(MFNN)with Levenberg-Marquard(LM)back propagation algorithm are adopted to supervised learn fluctuation of measuring signals and separate the interference magnetic field from the measurement data.LM algorithm is a kind of least square estimation algorithm of nonlinear parameters.It iteratively calculates the jacobian matrix of error performance and the adjustment value of gradient with the regularization method.LM algorithm’s computing efficiency is high and fitting error is very low.The fitting performance and the compensation accuracy of LM-MFNN algorithm are proved to be much better than those of TOLLES-LAWSON(T-L)model with the linear least square(LS)solution by fitting experiments with five different aeromagnetic surveys’data.

Adaptive Real-time Predictive Compensation Control for 6-DOF Serial Arc Welding Manipulator

Because of long driving chain and great system load inertia, the serial manipulator has a serious time delay problem which leads to significant real-time tracking control errors and damages the welding quality finally. In order to solve the time delay problem and enhance the welding quality, an adaptive real-time predictive compensation control（ARTPCC） is presented in this paper. The ARTPCC technique combines offline identification and online compensation. Based on the neural network system identification technique, the ARTPCC technique identifies the dynamic joint model of the 6-DOF serial arc welding manipulator offline. With the identified dynamic joint model, the ARTPCC technique predicts and compensates the tracking error online using the adaptive friction compensation technique. The ARTPCC technique is proposed in detail in this paper and applied in the real-time tracking control experiment of the 6-DOF serial arc welding manipulator. The tracking control experiment results of the end-effector reference point of the manipulator show that the presented control technique reduces the tracking error, enhances the system response and tracking accuracy efficiently. Meanwhile, the welding experiment results show that the welding seam turns more continuous, uniform and smooth after using the ARTPCC technique. With the ARTPCC technique, the welding quality of the 6-DOF serial arc welding manipulator is highly improved.

Dispersion compensation for an ultrathin metal film using LCD-CCD system

A dispersion compensation method is introduced to correct the distorted image passing through an ultrathin metal film.An LCD-CCD system is modeled by the back propagation network and used to evaluate the transmittance of the ultrathin metal film.Training samples for the network come from 729 images captured by shooting test patches,in which the RGB values are uniformity distributed between 0 and 255.The RGB value of the original image that will be distorted by the dispersion is first transformed by mapping from the LCD to the CCD,multiplied by the inverse matrix of the transmittance matrix,and finally transformed by mapping from the CCD to the LCD,then the corrected image is obtained.In order to verify the effectiveness of the proposed method,ultrathin aluminum films with different thicknesses are evaporated on glass substrates and laid between the CCD and LCD.Experimental results show that the proposed method compensates for the dispersion successfully.

Space radiation effect on fibre optical gyroscope control circuit and compensation algorithm

The process of a γ-irradiation experiment of fibre optical gyroscope （FOG） control circuit was described, in which it is demonstrated that the FOG control circuit, except for D/A converter, could endure the dose of 10krad with the protection of cabin material. The distortion and drift in D/A converter due to radiation, which affect the performance of FOG seriously, was indicated based on the elemental analysis. Finally, a compensation network based on adaptive neuro-fuzzy inference system is proposed and its function is verified by simulation.

Laser Interferometer Based Measurement for Positioning Error Compensation in Cartesian Multi-Axis Systems

Accuracy is one of the most important key indices to evaluate multi-axis systems’ (MAS’s) characteristics and performances. The accuracy of MAS’s such as machine tools, measuring machines and robots is adversely affected by various error sources, including geometric imperfections, thermal deformations, load effects, and dynamic disturbances. The increasing demand for higher dimensional accuracy in various industrial applications has created the need to develop cost-effective methods for enhancing the overall performance of these mechanisms. Improving the accuracy of a MAS by upgrading the physical structure would lead to an exponential increase in manufacturing costs without totally eliminating geometrical deviations and thermal deformations of MAS components. Hence, the idea of reducing MAS’s error by a software-based alternative approach to provide real-time prediction and correction of geometric and thermally induced errors is considered a strategic step toward achieving the full potential of the MAS. This paper presents a structured approach designed to improve the accuracy of Cartesian MAS’s through software error compensation. Four steps are required to develop and implement this approach: (i) measurement of error components using a multidimensional laser interferometer system, (ii) tridimensional volumetric error mapping using rigid body kinematics, (iii) volumetric error prediction via an artificial neural network model, and finally (iv) implementation of the on-line error compensation. An illustrative example using a bridge type coordinate measuring machine is presented.

Eccentric error and compensation in rotationally symmetric laser triangulation

Rotationally symmetric triangulation(RST)sensor has more flexibility and less uncertainty limits because of the abaxial rotationally symmetric optical system.But if the incident laser is eccentric,the symmetry of the image will descend,and it will result in the eccentric error especially when some part of the imaged ring is blocked.The model of rotationally symmetric triangulation that meets the Schimpflug condition is presented in this paper.The error from eccentric incident laser is analysed.It is pointed out that the eccentric error is composed of two parts,one is a cosine in circumference and proportional to the eccentric departure factor,and the other is a much smaller quadric factor of the departure.When the ring is complete,the first error factor is zero because it is integrated in whole ring, but if some part of the ring is blocked,the first factor will be the main error.Simulation verifies the result of the a- nalysis.At last,a compensation method to the error when some part of the ring is lost is presented based on neural network.The results of experiment show that the compensation will make the absolute maximum error descend to half,and the standard deviation of error descends to 1/3.

Compensated methods for networked control system with packet drops based on compressed sensing

Due to unreliable and bandwidth-limited characteristics of communication link in networked control systems,the realtime compensated methods for single-output systems and multioutput systems are proposed in this paper based on the compressed sensing(CS)theory and sliding window technique,by which the estimates of dropping data packets in the feedback channel are obtained and the performance degradation induced by packet drops is reduced.Specifically,in order to reduce the cumulative error caused by the algorithm,the compensated estimates for single-output systems are corrected via the regularization term;considering the process of single-packet transmission,a new sequential CS framework of sensor data streams is introduced to effectively compensate the dropping packet on single-channel of multi-output systems;in presence of the medium access constraints on multi-channel,the communication sequence for scheduling is coupled to the algorithm and the estimates of the multiple sensors for multi-output systems are obtained via the regularization term.Simulation results illustrate that the proposed methods perform well and receive satisfactory performance.

Fuzzy-GA based algorithm for optimal placement and sizing of distribution static compensator (DSTATCOM) for loss reduction of distribution network considering reconfiguration

This work presents a fuzzy based methodology for distribution system feeder reconfiguration considering DSTATCOM with an objective of minimizing real power loss and operating cost. Installation costs of DSTATCOM devices and the cost of system operation, namely, energy loss cost due to both reconfiguration and DSTATCOM placement, are combined to form the objective function to be minimized. The distribution system tie switches, DSTATCOM location and size have been optimally determined to obtain an appropriate operational condition. In the proposed approach, the fuzzy membership function of loss sensitivity is used for the selection of weak nodes in the power system for the placement of DSTATCOM and the optimal parameter settings of the DFACTS device along with optimal selection of tie switches in reconfiguration process are governed by genetic algorithm(GA). Simulation results on IEEE 33-bus and IEEE 69-bus test systems concluded that the combinatorial method using DSTATCOM and reconfiguration is preferable to reduce power losses to 34.44% for 33-bus system and to 45.43% for 69-bus system.

A Graphical Analysis on Compensation Designs of Large-gap CPT systems for EV Charging Applications

Capacitive power transfer(CPT)technology is a newly emerging research focus for EV charging applications.Due to the absence of eddy current loss and light weight of the capacitive coupling metal plates,CPT technology is considered to be a promising alternative to the inductive power transfer(IPT)technology.In this paper,the recent designs of large-gap CPT systems designed for EV charging applications are summarized.And a graphical method based on Smith chart is proposed to analyze the characteristics of the compensation designs.With the design objectives graphically represented,the Smith chart can be used to visually analyze the performance of a compensation design,which could be helpful in multi-objectives design of the CPT system.The graphic features of three major design objectives on the Smith chart are investigated.Several guidelines of a good compensation design are concluded.