Novel Real-Time Seam Tracking Algorithm Based on Vector Angle and Least Square Method

Real-time seam tracking can improve welding quality and enhance welding efficiency during the welding process in automobile manufacturing.However,the teaching-playing welding process,an off-line seam tracking method,is still dominant in automobile industry,which is less flexible when welding objects or situation change.A novel real-time algorithm consisting of seam detection and generation is proposed to track seam.Using captured 3D points,space vectors were created between two adjacent points along each laser line and then a vector angle based algorithm was developed to detect target points on the seam.Least square method was used to fit target points to a welding trajectory for seam tracking.Furthermore,the real-time seam tracking process was simulated in MATLAB/Simulink.The trend of joint angles vs.time was logged and a comparison between the off-line and the proposed seam tracking algorithm was conducted.Results show that the proposed real-time seam tracking algorithm can work in a real-time scenario and have high accuracy in welding point positioning.

Prediction of Logistics Demand via Least Square Method and Multi-Layer Perceptron

To implement the prediction of the logistics demand capacity of a certain region,a comprehensive index system is constructed,which is composed of freight volume and other eight relevant economic indices,such as gross domestic product(GDP),consumer price index(CPI),total import and export volume,port's cargo throughput,total retail sales of consumer goods,total fixed asset investment,highway mileage,and resident population,to form the foundation for the model calculation.Based on the least square method(LSM)to fit the parameters,the study obtains an accurate mathematical model and predicts the changes of each index in the next five years.Using artificial intelligence software,the research establishes the logistics demand model of multi-layer perceptron(MLP)neural network,makes an empirical analysis on the logistics demand of Quanzhou City,and predicts its logistics demand in the next five years,which provides some references for formulating logistics planning and development strategy.

STUDY ON FREQUENCY ESTIMATION BASED ON WEIGHTED LEAST SQUARE METHOD WITH THREE FOURIER COEFFICIENTS

In this paper,a sinusoidal signal frequency estimation algorithm is proposed by weighted least square method.Based on the idea of Provencher,three biggest Fourier coefficients in the maximum periodogram are considered,the Fourier coefficients can be written as three equations about the amplitude,phase,and frequency,and the frequency is estimated by solving equations.Because of the error of measurement,weighted least square method is used to solve the frequency equation and get the signal frequency.It is shown that the proposed estimator can approach the Cramer-Rao Bound(CRB)with a low Signal-to-Noise Ratio(SNR)threshold and has a higher accuracy.

A LEAST SQUARE BASED WEAK GALERKIN FINITE ELEMENT METHOD FOR SECOND ORDER ELLIPTIC EQUATIONS IN NON-DIVERGENCE FORM

This article is devoted to establishing a least square based weak Galerkin method for second order elliptic equations in non-divergence form using a discrete weak Hessian operator.Naturally,the resulting linear system is symmetric and positive definite,and thus the algorithm is easy to implement and analyze.Convergence analysis in the H2 equivalent norm is established on an arbitrary shape regular polygonal mesh.A superconvergence result is proved when the coefficient matrix is constant or piecewise constant.Numerical examples are performed which not only verify the theoretical results but also reveal some unexpected superconvergence phenomena.

Multi-Branch Fault Line Location Method Based on Time Difference Matrix Fitting

The distribution network exhibits complex structural characteristics,which makes fault localization a challenging task.Especially when a branch of the multi-branch distribution network fails,the traditional multi-branch fault location algorithm makes it difficult to meet the demands of high-precision fault localization in the multi-branch distribution network system.In this paper,the multi-branch mainline is decomposed into single branch lines,transforming the complex multi-branch fault location problem into a double-ended fault location problem.Based on the different transmission characteristics of the fault-traveling wave in fault lines and non-fault lines,the endpoint reference time difference matrix S and the fault time difference matrix G were established.The time variation rule of the fault-traveling wave arriving at each endpoint before and after a fault was comprehensively utilized.To realize the fault segment location,the least square method was introduced.It was used to find the first-order fitting relation that satisfies the matching relationship between the corresponding row vector and the first-order function in the two matrices,to realize the fault segment location.Then,the time difference matrix is used to determine the traveling wave velocity,which,combined with the double-ended traveling wave location,enables accurate fault location.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

A framework for computer vision-based health monitoring of a truss structure subjected to unknown excitations

Computer vision(CV)methods for measurement of structural vibration are less expensive,and their application is more straightforward than methods based on sensors that measure physical quantities at particular points of a structure.However,CV methods produce significantly more measurement errors.Thus,computer vision-based structural health monitoring(CVSHM)requires appropriate methods of damage assessment that are robust with respect to highly contaminated measurement data.In this paper a complete CVSHM framework is proposed,and three damage assessment methods are tested.The first is the augmented inverse estimate(AIE),proposed by Peng et al.in 2021.This method is designed to work with highly contaminated measurement data,but it fails with a large noise provided by CV measurement.The second method,as proposed in this paper,is based on the AIE,but it introduces a weighting matrix that enhances the conditioning of the problem.The third method,also proposed in this paper,introduces additional constraints in the optimization process;these constraints ensure that the stiffness of structural elements can only decrease.Both proposed methods perform better than the original AIE.The latter of the two proposed methods gives the best results,and it is robust with respect to the selected coefficients,as required by the algorithm.

An approach of dynamic response analysis of nonlinear structures based on least square Volterra kernel function identification

Analysis of the dynamic response of a complex nonlinear system is always a difficult problem.By using Volterra functional series to describe a nonlinear system,its response analysis can be similar to using Fourier/Laplace transform and linear transfer function method to analyse a linear system’s response.In this paper,a dynamic response analysis method for nonlinear systems based on Volterra series is developed.Firstly,the recursive formula of the least square method is established to solve the Volterra kernel function vector,and the corresponding MATLAB programme is compiled.Then,the Volterra kernel vector corresponding to the nonlinear response of a structure under seismic excitation is identified,and the accuracy and applicability of using the kernel vector to predict the response of a nonlinear structure are analysed.The results show that the Volterra kernel function identified by the derived recursive formula can accurately describe the nonlinear response characteristics of a structure under an excitation.For a general nonlinear system,the first three order Volterra kernel function can relatively accurately express its nonlinear response characteristics.In addition,the obtained Volterra kernel function can be used to accurately predict the nonlinear response of a structure under the similar type of dynamic load.

Fault Diagnosis and Separation for a Distributed Rotary-laser Scanning System

The wMPS is a laser-based measurement system used for large scale metrology.However,it is susceptible to external factors such as vibrations,which can lead to unreliable measurements.This paper presents a fault diagnosis and separation method which can counter this problem.To begin with,the paper uses simple models to explain the fault diagnosis and separation methods.These methods are then mathematically derived using statistical analysis and the principles of the wMPS.A comprehensive solution for fault diagnosis and separation is proposed,considering the characteristics of the wMPS.The effectiveness of this solution is verified through experimental observations.It can be concluded that this approach can detect and separate false observations,thereby enhancing the reliability of the wMPS.

EEMD and bidimensional RLS to suppress physiological interference for heterogeneous distribution in fNIRS study

Near-infrared spectroscopy(NIRS)can provide the hemodynamics information based on the hemoglobin concentration representing the blood oxygen metabolism of the cerebral cortical,which can be deployed for the cerebral function study.However,NIRS-based cerebral function detection accuracy can be signi¯cantly in°uenced by the physiological activities such as cardic cycle,respiration,spontaneous low-frequency oscillation and ultra-low frequency oscillation.The distribution difference of the capillary,artery and vein leads to the heterogeneity feature of the cerebral tissues.In the case that the heterogeneity is not serious,good detection accuracy and stable performance can be achieved through the regression analysis as the reference signal can well represent the interference in the measurement signal when conducting the multi-distance measurement approach.The direct use of the reference signal to estimate the interference is not able to achieve good performance in the case that the heterogeneity is serious.In this study,the cerebral function activity signal is extracted using recursive least square(RLS)method based on the multi-distance measurement method in which the reference signal is processed by ensemble empirical mode decomposition(EEMD)algorithm.The temporal and dimensional correlation of the neighboring sampling values are applied to estimate the interference in the measurement signal.Monte Carlo simulation based on a heterogeneous model is adopted here to investigate the effectiveness of this methodology.The results show that this methodology can effectively suppress the physiological interference and improve the detection accuracy of cerebral activity signal.

Four Wheel Independent Drive Electric Vehicle Lateral Stability Control Strategy

In this paper,a kind of lateral stability control strategy is put forward about the four wheel independent drive electric vehicle.The design of control system adopts hierarchical structure.Unlike the previous control strategy,this paper introduces a method which is the combination of sliding mode control and optimal allocation algorithm.According to the driver’s operation commands(steering angle and speed),the steady state responses of the sideslip angle and yaw rate are obtained.Based on this,the reference model is built.Upper controller adopts the sliding mode control principle to obtain the desired yawing moment demand.Lower controller is designed to satisfy the desired yawing moment demand by optimal allocation of the tire longitudinal forces.Firstly,the optimization goal is built to minimize the actuator cost.Secondly,the weighted least-square method is used to design the tire longitudinal forces optimization distribution strategy under the constraint conditions of actuator and the friction oval.Beyond that,when the optimal allocation algorithm is not applied,a method of axial load ratio distribution is adopted.Finally,Car Sim associated with Simulink simulation experiments are designed under the conditions of different velocities and different pavements.The simulation results show that the control strategy designed in this paper has a good following effect comparing with the reference model and the sideslip angle is controlled within a small rang at the same time.Beyond that,based on the optimal distribution mode,the electromagnetic torque phase of each wheel can follow the trend of the vertical force of the tire,which shows the effectiveness of the optimal distribution algorithm.

Online Identification of Power Battery Parameters for Electric Vehicles Using a Decoupling Multiple Forgetting Factors Recursive Least Squares Method

Li-ion batteries are widely used in electric vehicles(EVs).However,the accuracy of online SOC estimation is still challenging due to the time-varying parameters in batteries.This paper proposes a decoupling multiple forgetting factors recursive least squares method(DMFFRLS)for EV battery parameter identification.The errors caused by the different parameters are separated and each parameter is tracked independently taking into account the different physical characteristics of the battery parameters.The Thevenin equivalent circuit model(ECM)is employed considering the complexity of battery management system(BMS)on the basis of comparative analysis of several common battery ECMs.In addition,decoupling multiple forgetting factors are used to update the covariance due to different degrees of error of each parameter in the identification process.Numerous experiments are employed to verify the proposed DMFFRLS method.The parameters for commonly used LiFePO4(LFP),Li(NiCoMn)O2(NCM)battery cells and battery packs are identified based on the proposed DMFFRLS method and three conventional methods.The experimental results show that the error of the DMFFRLS method is less than 15 mV,which is significantly lower than the conventional methods.The proposed DMFFRLS shows good performance for parameter identification on different kind of batteries,and provides a basis for state of charge(SOC)estimation and BMS design of EVs.

Vehicle License Plate Character Segmentation

Vehicle license plate (VLP) character segmentation is an important part of the vehicle license plate recognition system (VLPRS). This paper proposes a least square method (LSM) to treat horizontal tilt and vertical tilt in VLP images. Auxiliary lines are added into the image (or the tilt-corrected image) to make the separated parts of each Chinese character to be an interconnected region. The noise regions will be eliminated after two fusing images are merged according to the minimum principle of gray values.Then, the characters are segmented by projection method (PM) and the final character images are obtained. The experimental results show that this method features fast processing and good performance in segmentation.

Probabilistic back analysis for geotechnical engineering based on Bayesian and support vector machine

Geomechanical parameters are complex and uncertain.In order to take this complexity and uncertainty into account,a probabilistic back-analysis method combining the Bayesian probability with the least squares support vector machine(LS-SVM) technique was proposed.The Bayesian probability was used to deal with the uncertainties in the geomechanical parameters,and an LS-SVM was utilized to establish the relationship between the displacement and the geomechanical parameters.The proposed approach was applied to the geomechanical parameter identification in a slope stability case study which was related to the permanent ship lock within the Three Gorges project in China.The results indicate that the proposed method presents the uncertainties in the geomechanical parameters reasonably well,and also improves the understanding that the monitored information is important in real projects.

Evaluation of dimension of fractal time series with the least square method

Properties of fractional Brownian motions(f Bms)have been investigated by researchers in different fields,e.g.statistics,hydrology,biology,finance,and public transportation,which has helped us better understand many complex time series observed in nature[1-4].The Hurst exponent H(0<H<1)is the most important parameter characterizing any given time series F(t),where t represents the time steps,and the

Experimental Investigation and Semi-Active Control Design of A Magnetorheological Engine Mount

In this paper;the dynamic characteristics of a semi-active magnetorheological fluid(MRF)engine mount are studied.To do so,the performance of the MRF engine mount is experimentally examined in higher frequencies(50~170 Hz)and the various amplitudes(0.01~0.2 mm).In such an examination,an MRF engine mount along with its magnetically biased is fabricated and successfully measured.In addition,the natural frequencies of the system are obtained by standard hammer modal test.For modelling the behavior of the system,a mass-spring-damper model with tuned PID coefficients based on Pessen integral of absolute error method is used.The parameters of such a model including mass,damping ratio,and stiffness are identified with the help of experimental modal tests and the recursive least square method(RLS).It is shown that using PID controller leads to reducing the vibration transmissibility in the resonance frequency(=93.45 Hz)with respect to the typical passive engine mount by a factor of 58%.The average of the vibration transmissibility decreasing is also 43%within frequency bandwidth(50~170 Hz).

MODIFIED LEAST SQUARE METHOD ON COMPUTING DIRICHLET PROBLEMS

The singularity theory of dynamical systems is linked to the numerical computation of boundary value problems of differential equations. It turns out to be a modified least square method for a calculation of variational problem defined on Ck(Ω), in which the base functions are polynomials and the computation of problems is transferred to compute the coefficients of the base functions. The theoretical treatment and some simple examples are provided for understanding the modification procedure of the metho...

Research on Hand-eye Calibration Technology of Visual Service Robot Grasping Based on ROS

For the hand-eye calibration of the vision service robot,the traditional hand-eye calibration tech-nology can’t be realized which because the service robot is independently developed and there is no teaching device to feed back the pose information of the service robot in real time.In this paper,a hand-eye calibration method based on ROS(Robot Operating System)is proposed.In this method,ROS system is used to accurately control the arm of the service robot to rotate in different positions for many times.Meanwhile,the head camera of the service robot takes images of a fixed point in the scene.Then,the nonlinear equations were established ac-cording to the homography matrix of the two images and the position and pose information of the ROS system,and the accurate hand-eye relationship was optimized by the least square method.Finally,an experimental platform is built and the proposed hand-eye calibration method is verified.The experiment results show that the method is easy to operate,simple algorithm and correct result,which verifies the effectiveness of the algorithm and provides conditions for the realization of humanoid grasping of visual service robot.

A multiplicative Gauss-Newton minimization algorithm:Theory and application to exponential functions

Multiplicative calculus(MUC)measures the rate of change of function in terms of ratios,which makes the exponential functions significantly linear in the framework of MUC.Therefore,a generally non-linear optimization problem containing exponential functions becomes a linear problem in MUC.Taking this as motivation,this paper lays mathematical foundation of well-known classical Gauss-Newton minimization(CGNM)algorithm in the framework of MUC.This paper formulates the mathematical derivation of proposed method named as multiplicative Gauss-Newton minimization(MGNM)method along with its convergence properties.The proposed method is generalized for n number of variables,and all its theoretical concepts are authenticated by simulation results.Two case studies have been conducted incorporating multiplicatively-linear and non-linear exponential functions.From simulation results,it has been observed that proposed MGNM method converges for 12972 points,out of 19600 points considered while optimizing multiplicatively-linear exponential function,whereas CGNM and multiplicative Newton minimization methods converge for only 2111 and 9922 points,respectively.Furthermore,for a given set of initial value,the proposed MGNM converges only after 2 iterations as compared to 5 iterations taken by other methods.A similar pattern is observed for multiplicatively-non-linear exponential function.Therefore,it can be said that proposed method converges faster and for large range of initial values as compared to conventional methods.

Simulation of Rock Complex Resistivity Using an Inversion Method

The complex resistivity of coal and related rocks contains abundant physical property information,which can be indirectly used to study the lithology and microstructure of these materials.These aspects are closely related to the fluids inside the considered coal rocks,such as gas,water and coalbed methane.In the present analysis,considering different lithological structures,and using the Cole-Cole model,a forward simulation method is used to study different physical parameters such as the zero-frequency resistivity,the polarizability,the relaxation time,and the frequency correlation coefficient.Moreover,using a least square technique,a complex resistivity“inversion”algorithm is written.The comparison of the initial model parameters and those obtained after inversion is used to verify the stability and accuracy of such approach.The method is finally applied to primary-structure coal considered as the experimental sample for complex resistivity measurements.