ITERATIVE ALGORITHM FOR AXIALLY ACCELERATING STRINGS WITH INTEGRAL CONSTITUTIVE LAW

A numerical method is proposed to simulate the transverse vibrations of a viscoelastic moving string constituted by an integral law. In the numerical computation, the Galerkin method based on the Hermite functions is applied to discretize the state variables, and the Runge- Kutta method is applied to solve the resulting differential-integral equation system. A linear iterative process is designed to compute the integral terms at each time step, which makes the numerical method more efficient and accurate. As examples, nonlinear parametric vibrations of an axially moving viscoelastic string are analyzed.

Dosimetric Comparison of Integral Radiation Dose: Anisotropic Analytical Algorithm and Acuros XB in Breast Radiotherapy

The impact of the difference between Anisotropic Analytical Algorithm (AAA) and Acuros XB (AXB) in breast radiotherapy is not clearly due to different uses and further research is required to explain this effect. The aim of this study is to investigate the contribution of calculation differences between AAA and AXB to the integral radiation dose (ID) on critical organs. Seven field intensity modulated radiotherapy (IMRT) plans were generated using with AAA and AXB algorithms for twenty patients with early stage left breast cancer after breast conserving surgery. Volumetric and dosimetric differences, as well as, the Dmean, V5, V20 doses of the left and right-sided lung, the Dmean, V10, V20, V30 doses of heart and the Dmean, V5, V10 doses of the contralateral breast were investigated. The mean dose (Dmean), V5, V20 doses of the left-sided lung, the Dmean, V5, V10 doses of right-sided lung, the Dmean, V10, V20, V30 doses of heart and the Dmean, V5, V10 doses of the contralateral breast were found to be significantly higher with AAA. In this research integral dose was also higher in the AAA recalculated plan and the AXB plan with the average dose as follows left lung 2%, heart 2%, contralateral breast 8%, contralateral lung 4% respectively. Our study revealed that the calculation differences between Acuros XB (AXB) and Anisotropic Analytical Algorithm (AAA) in breast radiotherapy caused serious differences on the stored integral doses on critical organs. In addition, AXB plans showed significantly dosimetric improvements in multiple dosimetric parameters.

Design of Wind Turbine Torque Controller with Second‑Order Integral Sliding Mode Based on VGWO Algorithm

A robust control strategy using the second-order integral sliding mode control(SOISMC)based on the variable speed grey wolf optimization(VGWO)is proposed.The aim is to maximize the wind power extraction of wind turbine.Firstly,according to the uncertainty model of wind turbine,a SOISMC torque controller with fast convergence speed,strong robustness and effective chattering reduction is designed,which ensures that the torque controller can effectively track the reference speed.Secondly,given the strong local search ability of the grey wolf optimization(GWO)and the fast convergence speed and strong global search ability of the particle swarm optimization(PSO),the speed component of PSO is introduced into GWO,and VGWO with fast convergence speed,high solution accuracy and strong global search ability is used to optimize the parameters of wind turbine torque controller.Finally,the simulation is implemented based on Simulink/SimPowerSystem.The results demonstrate the effectiveness of the proposed strategy under both external disturbance and model uncertainty.

Highly Accurate Golden Section Search Algorithms and Fictitious Time Integration Method for Solving Nonlinear Eigenvalue Problems

This study sets up two new merit functions,which are minimized for the detection of real eigenvalue and complex eigenvalue to address nonlinear eigenvalue problems.For each eigen-parameter the vector variable is solved from a nonhomogeneous linear system obtained by reducing the number of eigen-equation one less,where one of the nonzero components of the eigenvector is normalized to the unit and moves the column containing that component to the right-hand side as a nonzero input vector.1D and 2D golden section search algorithms are employed to minimize the merit functions to locate real and complex eigenvalues.Simultaneously,the real and complex eigenvectors can be computed very accurately.A simpler approach to the nonlinear eigenvalue problems is proposed,which implements a normalization condition for the uniqueness of the eigenvector into the eigenequation directly.The real eigenvalues can be computed by the fictitious time integration method(FTIM),which saves computational costs compared to the one-dimensional golden section search algorithm(1D GSSA).The simpler method is also combined with the Newton iterationmethod,which is convergent very fast.All the proposed methods are easily programmed to compute the eigenvalue and eigenvector with high accuracy and efficiency.

Design of a Proportional-Integral-Derivative Controller for an Automatic Generation Control of Multi-area Power Thermal Systems Using Firefly Algorithm

Essentially, it is significant to supply the consumer with reliable and sufficient power. Since, power quality is measured by the consistency in frequency and power flow between control areas. Thus, in a power system operation and control,automatic generation control(AGC) plays a crucial role. In this paper, multi-area(Five areas: area 1, area 2, area 3, area 4 and area 5) reheat thermal power systems are considered with proportional-integral-derivative(PID) controller as a supplementary controller. Each area in the investigated power system is equipped with appropriate governor unit, turbine with reheater unit, generator and speed regulator unit. The PID controller parameters are optimized by considering nature bio-inspired firefly algorithm(FFA). The experimental results demonstrated the comparison of the proposed system performance(FFA-PID)with optimized PID controller based genetic algorithm(GAPID) and particle swarm optimization(PSO) technique(PSOPID) for the same investigated power system. The results proved the efficiency of employing the integral time absolute error(ITAE) cost function with one percent step load perturbation(1 % SLP) in area 1. The proposed system based FFA achieved the least settling time compared to using the GA or the PSO algorithms, while, it attained good results with respect to the peak overshoot/undershoot. In addition, the FFA performance is improved with the increased number of iterations which outperformed the other optimization algorithms based controller.

PRECISE INTEGRAL ALGORITHM BASED SOLUTION FORTRANSIENT INVERSE HEAT CONDUCTION PROBLEMSWITH MULTI-VARIABLES

By modeling direct transient heat conduction problems via finite element method (FEM) and precise integral algorithm, a new approach is presented to solve transient inverse heat conduction problems with multi-variables. Firstly, the spatial space and temporal domain are discretized by FEM and precise integral algorithm respectively. Then, the high accuracy semi-analytical solution of direct problem can be got. Finally, based on the solution, the computing model of inverse problem and expression of sensitivity analysis are established. Single variable and variables combined identifications including thermal parameters, boundary conditions and source-related terms etc. are given to validate the approach proposed in 1-D and 2-D cases. The effects of noise data and initial guess on the results are investigated. The numerical examples show the effectiveness of this approach.

General and efficient parallel approach of finite element-boundary integral-multilevel fast multipole algorithm

A general and efficient parallel approach is proposed for the first time to parallelize the hybrid finiteelement-boundary-integral-multi-level fast multipole algorithm （FE-BI-MLFMA）. Among many algorithms of FE-BI-MLFMA, the decomposition algorithm （DA） is chosen as a basis for the parallelization of FE-BI-MLFMA because of its distinct numerical characteristics suitable for parallelization. On the basis of the DA, the parallelization of FE-BI-MLFMA is carried out by employing the parallelized multi-frontal method for the matrix from the finiteelement method and the parallelized MLFMA for the matrix from the boundary integral method respectively. The programming and numerical experiments of the proposed parallel approach are carried out in the high perfor- mance computing platform CEMS-Liuhui. Numerical experiments demonstrate that FE-BI-MLFMA is efficiently parallelized and its computational capacity is greatly improved without losing accuracy, efficiency, and generality.

A Super-High-Efficiency Algorithm for the Calculation of the Correlation Integral

When the chaotic characteristics of manufacturing quality level are studied, it is not practical to use chaotic methods because of the low speed of calculating the correlation integral. The original algorithm used to calculate the correlation integral is studied after a computer hardware upgrade. The result is that calculation of the correlation integral can be sped up only by improving the algorithm. This is accomplished by changing the original algorithm in which a single distance threshold-related correlation integral is obtained from one traversal of all distances between different vectors to a high-efficiency algorithm in which all of the distance threshold-related correlation integrals are obtained from one traversal of all of the distances between different vectors. For a time series with 3000 data points, this high-efficiency algorithm offers a 3.7-fold increase in speed over the original algorithm. Further study of the high-efficiency algorithm leads to the development of a super-high-efficiency algorithm, which is accomplished by changing the original and high-efficiency algorithms, in which the add-one operation of the Heaviside function is executed n times, such that the execution of the add-one operation occurs only once. The super-high-efficiency algorithm results in increases in the calculation speed by up to 109 times compared with the high-efficiency algorithm and by approximately 404 times compared with the original algorithm. The calculation speed of the super-high-efficiency algorithm is suitable for practical use with the chaotic method.

A model for extracting large deformation mining subsidence using D-InSAR technique and probability integral method

Due to the difficulties in obtaining large deformation mining subsidence using differential Interferometric Synthetic Aperture Radar （D-InSAR） alone, a new algorithm was proposed to extract large deformation mining subsidence using D-InSAR technique and probability integral method. The details of the algorithm are as follows：the control points set, containing correct phase unwrapping points on the subsidence basin edge generated by D-InSAR and several observation points （near the maximum subsidence and inflection points）, was established at first; genetic algorithm （GA） was then used to optimize the parameters of probability integral method; at last, the surface subsidence was deduced according to the optimum parameters. The results of the experiment in Huaibei mining area, China, show that the presented method can generate the correct mining subsidence basin with a few surface observations, and the relative error of maximum subsidence point is about 8.3%, which is much better than that of conventional D-InSAR （relative error is 68.0%）.

INTEGRATED OPERATOR GENETIC ALGORITHM FOR SOLVING MULTI-OBJECTIVE FLEXIBLE JOB-SHOP SCHEDULING

In the flexible job-shop scheduling problem （FJSP）, each operation has to be assigned to a machine from a set of capable machines before alocating the assigned operations on all machines. To solve the multi-objective FJSP, the Grantt graph oriented string representation （GOSR） and the basic manipulation of the genetic algorithm operator are presented. An integrated operator genetic algorithm （IOGA） and its process are described. Comparison between computational results and the latest research shows that the proposed algorithm is effective in reducing the total workload of all machines, the makespan and the critical machine workload.

L^p Boundedness of Commutators of Calderón-Zygmund Singular Integral Operators

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AN NUMERICAL ALGORI THM FOR LAPLACE′S INTEGRAL BY B-SPLINE

Algorithm for Laplace ′s integral is given when the inverse image function has high order discontinui ty. The multi-node technique of B-spline is used to describe the interruption point, cusp and non-smooth point of the inverse image function. The difference quotient and de Boor algorithm are used to derive the image function of the Lapl ace′s integral under non-uniform partition. And a set of practical formula is got when the partition is quasi-uniform. The scheme enables the image function to be approximated within any prescribed tolerance. Experiments also show that g ood result is achieved. It is much faster than that of Simpsons rule, and much s impler than that of Berge method, the traditional efficient method. It is no lon ger to find the zero points and coefficients of Gauss-Laguerre or Gauss-Legend re polynomials. The image function of Laplace′s integral can also be computed while the inverse image function is hyper-function with high order discontinuity.

Dynamic and Integrated Load-Balancing Scheduling Algorithm for Cloud Data Centers

One of the challenging scheduling problems in Cloud data centers is to take the allocation and migration of reconfigurable virtual machines as well as the integrated features of hosting physical machines into consideration. We introduce a Dynamic and Integrated Resource Scheduling algorithm (DAIRS) for Cloud data centers. Unlike traditional load-balance scheduling algorithms which often consider only one factor such as the CPU load in physical servers, DAIRS treats CPU, memory and network bandwidth integrated for both physical machines and virtual machines. We develop integrated measurement for the total imbalance level of a Cloud datacenter as well as the average imbalance level of each server. Simulation results show that DAIRS has good performance with regard to total imbalance level, average imbalance level of each server, as well as overall running time.

Stability of an explicit time-integration algorithm for hybrid tests, considering stiffness hardening behavior

An explicit unconditionally stable algorithm for hybrid tests,which is developed from the traditional HHT-α algorithm,is proposed.The unconditional stability is first proven by the spectral radius method for a linear system.If the value of α is selected within [-0.5,0],then the algorithm is shown to be unconditionally stable.Next,the root locus method for a discrete dynamic system is applied to analyze the stability of a nonlinear system.The results show that the proposed method is conditionally stable for dynamic systems with stiffness hardening.To improve the stability of the proposed method,the structure stiffness is then identified and updated.Both numerical and pseudo-dynamic tests on a structure with the collision effect prove that the stiffness updating method can effectively improve stability.

CHARACTERISTIC GALERKIN METHOD FOR CONVECTION-DIFFUSION EQUATIONS AND IMPLICIT ALGORITHM USING PRECISE INTEGRATION

This paper presents a finite element procedure for solving transient, multidimensional convection-diffusion equations. The procedure is based on the characteristic Galerkin method with an implicit algorithm using precise integration method. With the operator splitting procedure, the precise integration method is introduced to determine the material derivative in the convection-diffusion equation, consequently, the physical quantities of material points. An implicit algorithm with a combination of both the precise and the traditional numerical integration procedures in time domain in the Lagrange coordinates for the characteristic Galerkin method is formulated. The stability analysis of the algorithm shows that the unconditional stability of present implicit algorithm is enhanced as compared with that of the traditional implicit numerical integration procedure. The numerical results validate the presented method in solving convection-diffusion equations. As compared with SUPG method and explicit characteristic Galerkin method, the present method gives the results with higher accuracy and better stability.

Integrity Monitoring in Navigation Systems: Fault Detection and Exclusion RAIM Algorithm Implementation

The use of GPS is becoming increasingly popular for real-time navigation systems. To ensure that satellite failures are detected and excluded at the receiver is of high importance for the integrity of the satellite navigation system. The focus of this paper is to implement a fault detection and exclusion algorithm in a software GPS receiver in order to provide timely warnings to the user when it is not advisable to use the GPS system for navigation. The GPS system currently provides some basic integrity information to users via the navigation message, but it is not timely enough for safety-critical applications. RAIM is a means of providing integrity with the capability of detecting when a satellite failure or a measurement error has occurred. It is the simplest and most cost effective technique for integrity monitoring. After applying the iterative fault detection and the exclusion algorithm, a significant improvement in positioning accuracy is achieved.

An Integrating Algorithm and Theoretical Analysis for Fully Rheonomous Affine Constraints: Completely Integrable Case

This paper develops an integrating algorithm for fully rheonomous affine constraints and gives theoretical analysis of the algorithm for the completely integrable case. First, some preliminaries on the fully rheonomous affine constraints are shown. Next, an integrating algorithm that calculates independent first integrals is derived. In addition, the existence of an inverse function utilized in the algorithm is investigated. Then, an example is shown in order to evaluate the effectiveness of the proposed method. By using the proposed integrating algorithm, we can easily calculate independent first integrals for given constraints, and hence it can be utilized for various research fields.

Optimization of press bend forming path of aircraft integral panel

In order to design the press bend forming path of aircraft integral panels,a novel optimization method was proposed, which integrates FEM equivalent model based on previous study,the artificial neural network response surface,and the genetic algorithm.First,a multi-step press bend forming FEM equivalent model was established,with which the FEM experiments designed with Taguchi method were performed.Then,the BP neural network response surface was developed with the sample data from the FEM experiments.Furthermore,genetic algorithm was applied with the neural network response surface as the objective function. Finally,verification was carried out on a simple curvature grid-type stiffened panel.The forming error of the panel formed with the optimal path is only 0.098 39 and the calculating efficiency has been improved by 77%.Therefore,this novel optimization method is quite efficient and indispensable for the press bend forming path designing.

A Line-Surface Integrated Algorithm for Underwater Terrain Matching

The current underwater terrain surface matching algorithm,which uses Hu moment as the similarity index,cannot gain accurate location due to the algorithm’s disadvantage in detecting slight differences.To solve this problem,a line-surface integrated terrain matching algorithm is presented.First,the similarity evaluation method of the traditional Terrain Contour Matching(TERCOM)algorithm is improved,and the strategy used to select the matching regions is developed.Then,a surface matching algorithm based on the geometric similarity is established to determine the optimum match between the reference maps and the actual measured terrains.Finally,a means of“line matching algorithm”and“surface matching algorithm”integration is proposed based on a fixed threshold.The experimental results show that the proposed algorithm can obtain a more accurate location and has greater robustness than that of the surface underwater matching algorithm based on Hu moment.

Intelligent Small Sample Defect Detection of Concrete Surface Using Novel Deep Learning Integrating Improved YOLOv5

Dear Editor,This letter presents an intelligent small sample defect detection of concrete surface using novel deep learning integrating the improved YOLOv5 based on the Wasserstein GAN(WGAN)enhancement algorithm.The proposed method is capable of producing top-notch data sets to address the issues of insufficient samples and substandard quality.