Leapfrog Algorithm Research Articles

Monitoring and analysis of settlement and deformation status of high-rise buildings based on nonlinear regression

In order to solve the problems of low reliability and poor prediction accuracy in traditional building structure settlement monitoring, the author proposes a monitoring and analysis of high-rise building settlement deformation status based on nonlinear regression. The author collected and wirelessly transmitted building settlement information through various hardware devices such as sensors and GPRS communication modules. The monitoring data collected by sensors were compared and analyzed to determine the settlement situation of the building. An RBF neural network prediction model was constructed for possible settlement points. Then, the leapfrog algorithm is used to optimize the structural parameters of the RBF neural network. The experimental results show that this method can accurately evaluate the possible settlement of building structures in actual environments, and the prediction error is small, with a maximum relative error of 4.83 %, indicating good warning ability. This method achieved the best actual value fitting curve results, verifying its feasibility in settlement prediction. Subsequently, a more widely applicable settlement detection and prediction system for building complex structures will be established based on the proposed method, in order to promote its large-scale application.

Retracted: Data Envelopment Analysis Algorithm of Enterprise Economic Benefits Based on Leapfrog Algorithm

Retracted: Data Envelopment Analysis Algorithm of Enterprise Economic Benefits Based on Leapfrog Algorithm

Simulation of electron transport and secondary emission in a photomultiplier tube and experimental validation

The electron amplification and transport within a photomultiplier tube (PMT) has been investigated by developing an in-house Monte Carlo simulation code. The secondary electron emission in the dynodes is implemented via an effective electron model and the Modified Vaughan’s model, whereas the transport is computed with the Boris leapfrog algorithm. The PMT gain, rise time and transit time have been studied as a function of supply voltage and external magnetostatic field. A good agreement with experimental measurements using a Hamamatsu R13408-100 PMT was obtained. The simulations have been conducted following different treatments of the underlying geometry: three-dimensional, two-dimensional and intermediate (2.5D). The validity of these approaches is compared. The developed framework will help in understanding the behavior of PMTs under highly intense and irregular illumination or varying external magnetic fields, as in the case of prompt gamma-ray measurements during pencil-beam proton therapy; and aid in optimizing the design of voltage dividers with behavioral circuit models.

A Niche Adaptive Elite Evolutionary Algorithm for the Clustering Optimization of Intelligent Unmanned Agricultural Unmanned Aerial Vehicle Swarm Collaboration Networks

Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be realized to expand the detection range, and more complex tasks can be completed without human participation, so as to improve work efficiency and reduce the consumption of manpower and material resources. In AUSCNs, clustering is a key method to lower energy consumption. However, there is a challenge to select cluster heads in AUSCNs because of the limitation of transmission distances and the complexity of network topological structures. In addition, this problem has been confirmed as NP-hard. In this paper, a new niche adaptive elite evolutionary algorithm (NAEEA) is proposed to solve this problem. NAEEAs can search within various complicated stochastic situations at high speeds with characterized high precision and fast convergence. This algorithm integrates the merits of elite selection and adaptive adjusting to achieve high performance. In NAEEAs, a new adaptive operator is designed to speed up the convergence rate, while a novel elite operator is proposed to avoid local optima and raise the exploration ability. Furthermore, a new niche operator is also proposed to increase the population diversity. The simulation results show that, compared with an evolutionary algorithm (EA), a simulated annealing algorithm (SA) and a leapfrog algorithm (SFLA), clustering energy consumption based on an NAEEA is significantly reduced, and the network energy consumption of the AUSCN is up to 21.43%, 25.00% and 25.76% lower than the other three algorithms, respectively.

Random packing dynamics of [Formula: see text]-triplets.

In this paper, we used a combination of DEM and the multi-sphere method to investigate the random packing dynamics of [Formula: see text]-triplets. These triplets consist of three overlapping primary spheres, forming a bent structure with a bond angle of [Formula: see text] and belonging to the [Formula: see text] symmetry group. The motivation for choosing such a structural arrangement is twofold: first, to understand how bent-shaped structures influence packing dynamics, and secondly, to investigate how mesoscopic or macroscopic particles possessing symmetry similar to that found in more elementary particles impact packing observables. To ensure non-overlapping particles at the beginning of the simulations, the confinement box was divided into basic cells. Each triplet was then inserted into a basic cell with a random orientation. After that, the system is allowed to settle under gravity toward the bottom of the box. An implicit leapfrog algorithm with quaternion acceleration was used to numerically integrate the rotational motion equations. Through a molecular approach, we consider the impact of long-range cohesive forces by employing a Lennard-Jones (LJ)-type potential. Packing processes are studied assuming different long-range interaction strengths. Different quantities are studied including packing density and orientation pair correlation function. In addition, the force probability distributions in the random packing structures have been analyzed.

A Moment-Fitted Extended Spectral Cell Method for Structural Health Monitoring Applications

The spectral cell method has been shown as an efficient tool for performing dynamic analyses over complex domains. Its good performance can be attributed to the combination of the spectral element method with mesh-independent geometrical descriptions and the adoption of customized mass lumping procedures for elements intersected by a boundary, which enable it to exploit highly efficient, explicit solvers. In this contribution, we introduce the use of partition-of-unity enrichment functions, so that additional domain features, such as cracks or material interfaces, can be seamlessly added to the modeling process. By virtue of the optimal lumping paradigm, explicit time integration algorithms can be readily applied to the non-enriched portion of a domain, which allows one to maintain fast computing simulations. However, the handling of enriched elements remains an open issue, particularly with respect to stability and accuracy concerns. In addressing this, we propose a novel mass lumping method for enriched spectral elements in the form of a customized moment-fitting procedure and study its accuracy and stability. While the moment-fitting equations are deployed in an effort to minimize the lumping error, stability issues are alleviated by deploying a leap-frog algorithm for the solution of the equations of motion. This approach is numerically benchmarked in the 2D and 3D modeling of damaged aluminium components and validated in comparison with experimental scanning laser Doppler vibrometer data of a composite panel under piezo-electric excitation.

Research on welding path modification of welding industrial robot based on leapfrog algorithm

Research on welding path modification of welding industrial robot based on leapfrog algorithm

An optimisation method of urban road green space landscape layout based on leapfrog algorithm

An optimisation method of urban road green space landscape layout based on leapfrog algorithm

Hybrid Leapfrog Algorithm in Feature Selection Optimization of Text Classification

Hybrid Leapfrog Algorithm in Feature Selection Optimization of Text Classification

Multi-objective optimization of construction progress based on improved Leapfrog algorithm

With the steady development of China's construction industry, construction projects continue to tend to be large-scale and complex, and the corresponding multi-objective construction schedule optimization level is also improving day by day. The research on multi-objective optimization of construction schedule has always been the focus and hotspot of construction project management. This paper summarizes the principle of leapfrog algorithm, analyzes the characteristics of the improved leapfrog algorithm, puts forward the multi-objective optimization theory, and tests the original leapfrog algorithm and the improved leapfrog algorithm. The results show that the improved shuffle leapfrog algorithm is better than the original shuffle leapfrog algorithm when all basic parameters are the same.

Classical molecular dynamics study of structural properties of pristine and defective mono-layer 2D h-BNNSs

The structural, stability and vacancy properties of both defect-free and defected 2D h-BNNSs were studied using the classical molecular dynamics (MD) approach. The calculations were performed in the canonical (NVT Evans) and isothermal-isobaric (NPT Hoover) ensembles using the Tersoff potentials with the Verlet leapfrog algorithm to obtain reliable structural properties and energies for defect-free, boron (B) and nitrogen (N) vacancies. B and N defect energies were calculated relative to the bulk defect-free total energies, and the results suggest that N vacancy is the most stable vacancy as compared to the B vacancy. The radial distribution functions (RDF) and structure factors were used to predict the most probable structural form. Mean square displacement (MSD) suggests that the mobility of B and N atoms in the system increases with an increase in the surface area of the nanosheets. Results obtained were compared with the bulk defect-free h-BNNSs.

Finding geodesics joining given points

Finding a geodesic joining two given points in a complete path-connected Riemannian manifold requires much more effort than determining a geodesic from initial data. This is because it is much harder to solve boundary value problems than initial value problems. Shooting methods attempt to solve boundary value problems by solving a sequence of initial value problems, and usually need a good initial guess to succeed. The present paper finds a geodesic gamma :[0,1]rightarrow M on the Riemannian manifold M with γ(0) = x0 and γ(1) = x1 by dividing the interval [0,1] into several sub-intervals, preferably just enough to enable a good initial guess for the boundary value problem on each subinterval. Then a geodesic joining consecutive endpoints (local junctions) is found by single shooting. Our algorithm then adjusts the junctions, either (1) by minimizing the total squared norm of the differences between associated geodesic velocities using Riemannian gradient descent, or (2) by solving a nonlinear system of equations using Newton’s method. Our algorithm is compared with the known leapfrog algorithm by numerical experiments on a 2-dimensional ellipsoid Ell(2) and on a left-invariant 3-dimensional special orthogonal group SO(3). We find Newton’s method (2) converges much faster than leapfrog when more junctions are needed, and that a good initial guess can be found for (2) by starting with Riemannian gradient descent method (1).

Optimization of Multi-Objective Mobile Emergency Material Allocation for Sudden Disasters.

The mobile emergency system is a new emergency mode that provides a solution to deal with increasingly frequent sudden disasters by reasonably allocating mobile emergency facilities and optimizing the allocation of mobile emergency materials. We consider mobile emergency cost and mobile emergency time as two objective functions. This paper establishes a multi-objective mobile emergency material allocation model, and transforms the multi-objective. We choose the emergency material transportation path for coding, and apply the hybrid leapfrog algorithm for material allocation to obtain the optimal solution. Finally, the feasibility of the model is verified by taking Zhengzhou urban area under the “21.7” severe rainstorm and flood disaster in Henan Province. The result analyses show that the model can correspond to each stage of mobile emergency material allocation based on the value of cost preference, and put forward suggestions on the location of mobile emergency facilities and the amount of material allocation.

Initial Clustering Based on the Swarm Intelligence Algorithm for Computing a Data Density Parameter.

To improve the accuracy and efficiency of cluster startup using data density parameters, the author proposes a large data cluster extraction algorithm based on a herd intelligence algorithm. Since clustering to initiate data density parameters is primarily data mining, the author explores data mining clustering based primarily on herd intelligence algorithms. First, the obscure c-key cluster algorithm in the clustering algorithm is analyzed, and then the hybrid jump algorithm in the sub-heuristic herd intelligence optimization technology is optimized in the case of a few parameters by combining the obscure c-means cluster algorithm. The simulation results show that the convergence speed of the fuzzy C-means clustering algorithm and hybrid leapfrog algorithm is slow; the convergence rate of the PSO-FCM algorithm has been improved. Since the fusion algorithm requires fewer adjustment parameters, the cluster centers can be obtained more accurately and quickly with strong robustness and fast convergence. Compared with other algorithms, the fusion algorithm proposed by the author has the best performance in clustering effect, accuracy, convergence rate, and robustness. It is proved that the swarm intelligence algorithm can effectively perform density parameter initialization clustering on computational data.

Time-discretization of a plasma-neutral MHD model with a semi-implicit leapfrog algorithm

The semi-implicit leapfrog time-discretization is a workhorse algorithm for initial-value MHD codes to bridge between vastly separated time scales. Inclusion of atomic interactions with neutrals breaks the functional structure of the MHD equations that exploited by the leapfrog. We address how to best integrate atomic physics into the semi-implicit leapfrog. Following the Crank-Nicolson method, one approach is to time-center the atomic interactions in the linear solver and use a Newton method to include the nonlinear contributions. Alternatively, another family of methods are based on operator-splitting the terms associated with the atomic interactions using a Strang-splitting technique. These methods naturally break equations into constituent ODE and PDE parts and preserve the structure exploited by the semi-implicit leapfrog. We study the accuracy and efficiency of these methods through a battery of 0D and 1D cases and show that a second-order-in-time Douglas-Rachford inspired coupling between the ODE and PDE advances is effective in reducing the time-discretization error to be comparable to that of Crank-Nicolson with Newton iteration of the nonlinear terms. Splitting ODE and PDE parts results in independent matrix solves for each field which reduces the computational cost considerably and provides parallelization over species relative to Crank-Nicolson.

Time-domain Analysis of Traveling Wave Switches based on Time-variant Transmission Line Model

Background and Objectives: Switches play an important role in controlling the signal flow in telecommunication systems. The traveling wave switch structure is introduced based on active transmission lines. By applying the gate voltage (Vg), the transfer of signal through the drain transmission line is controlled. By increasing operating frequency, lumped model is unreliable and semi and fully distributed modeling should be applied for the analysis of these elements. Methods: Traveling wave switches can be analyzed based on the lossy transmission line model, in linear and non-linear modes in the time and frequency domains. The study of transient behavior and time domain response of switch is very important. Switching and transient from on to off state and vice versa affect the performance of telecommunication systems.In the proposed method, the switch is modeled as the lossy transmission line that the primary elements of this model change with time based on the control voltage applied to the gate and are considered as variable with time. The structure is discretized in the space and time domains with ∆z and ∆t steps. The Finite-difference time-domain (FDTD) method is utilized to study the transient response of switches. By using the leap-frog algorithm, the new voltages and currents of the transmission line are calculated based on the values of adjacent spatial and temporal steps.Results: The transient response of the switch is analyzed in transition from off to on states and vice versa, for the 800µm switch at 60GHz, based on the parameters of the passive transmission line and nonlinear Curtice2 FET model.Conclusion: For transient analysis of the structure, the time-variant lossy transmission line model was used that its elements changed based on the applied control voltage. The results of FDTD method were investigated with the transient analysis of commercial software that show good agreement with each other and validated the proposed method.

An optimisation method of urban road green space landscape layout based on leapfrog algorithm

An optimisation method of urban road green space landscape layout based on leapfrog algorithm

A wireless sensor network node redeployment method based on improved leapfrog algorithm

A wireless sensor network node redeployment method based on improved leapfrog algorithm

Solving No-Wait Flow Shop Scheduling Problem Based on Discrete Wolf Pack Algorithm

Aiming at the no-wait flow shop scheduling problem with the goal of minimizing the maximum makespan, a discrete wolf pack algorithm has been proposed. First, the methods for solving the no-wait flow shop scheduling problem and the application research of the wolf pack algorithm were summarized, and it was pointed out that there was lack of research on the application of the wolf pack algorithm to solve the no-wait flow shop scheduling problem. According to the analysis of characteristics of the no-wait flow shop scheduling problem, the individual wolf was coded by a decimal integer; wolf searching behavior was realized through the exchange of different code bits in the individual wolf, and the continuous code segment of the head wolf was randomly selected to replace the corresponding code of the fierce wolf, by which the behaviors of wolves raiding and sieging were realized, and the population was updated according to the rule of “survival of the strong.” In particular, to fully explore the potential optimal solution in the solution space, loop operations were added to the wandering, summoning, and siege processes. Finally, based on a comparison with the leapfrog algorithm and the genetic algorithm, the effectiveness of the algorithm was verified.

Data Envelopment Analysis Algorithm of Enterprise Economic Benefits Based on Leapfrog Algorithm

In order to deal with the problems of low analysis accuracy and poor security in the current enterprise economic benefit data analysis, this paper proposes an enterprise economic benefit data envelopment analysis algorithm based on leapfrog algorithm. According to the proposed method, with the help of comparing and analyzing different analysis algorithms of enterprise economic benefit data, the data envelopment analysis algorithm with strong usability is selected firstly. After that, the effectiveness of DEA is judged by introducing relaxation variables with the help of the data envelopment analysis algorithm, and the non-Archimedean infinitesimal model is used to build the basic operation mechanism. On this basis, the overall operation mechanism of leapfrog algorithm component is used to clarify the value range of relevant economic benefit data and the number of global mixed iterations. The experimental results show that the envelope analysis of enterprise economic benefit-related data using this method can not only improve the accuracy of data analysis but also effectively improve the security of relevant business data.