A flexible multiscale algorithm based on an improved smoothed particle hydrodynamics method for complex viscoelastic flows

Viscoelastic flows play an important role in numerous engineering fields,and the multiscale algorithms for simulating viscoelastic flows have received significant attention in order to deepen our understanding of the nonlinear dynamic behaviors of viscoelastic fluids.However,traditional grid-based multiscale methods are confined to simple viscoelastic flows with short relaxation time,and there is a lack of uniform multiscale scheme available for coupling different solvers in the simulations of viscoelastic fluids.In this paper,a universal multiscale method coupling an improved smoothed particle hydrodynamics(SPH)and multiscale universal interface(MUI)library is presented for viscoelastic flows.The proposed multiscale method builds on an improved SPH method and leverages the MUI library to facilitate the exchange of information among different solvers in the overlapping domain.We test the capability and flexibility of the presented multiscale method to deal with complex viscoelastic flows by solving different multiscale problems of viscoelastic flows.In the first example,the simulation of a viscoelastic Poiseuille flow is carried out by two coupled improved SPH methods with different spatial resolutions.The effects of exchanging different physical quantities on the numerical results in both the upper and lower domains are also investigated as well as the absolute errors in the overlapping domain.In the second example,the complex Wannier flow with different Weissenberg numbers is further simulated by two improved SPH methods and coupling the improved SPH method and the dissipative particle dynamics(DPD)method.The numerical results show that the physical quantities for viscoelastic flows obtained by the presented multiscale method are in consistence with those obtained by a single solver in the overlapping domain.Moreover,transferring different physical quantities has an important effect on the numerical results.

Optimal Configuration of Fault Location Measurement Points in DC Distribution Networks Based on Improved Particle Swarm Optimization Algorithm

The escalating deployment of distributed power sources and random loads in DC distribution networks hasamplified the potential consequences of faults if left uncontrolled. To expedite the process of achieving an optimalconfiguration of measurement points, this paper presents an optimal configuration scheme for fault locationmeasurement points in DC distribution networks based on an improved particle swarm optimization algorithm.Initially, a measurement point distribution optimization model is formulated, leveraging compressive sensing.The model aims to achieve the minimum number of measurement points while attaining the best compressivesensing reconstruction effect. It incorporates constraints from the compressive sensing algorithm and networkwide viewability. Subsequently, the traditional particle swarm algorithm is enhanced by utilizing the Haltonsequence for population initialization, generating uniformly distributed individuals. This enhancement reducesindividual search blindness and overlap probability, thereby promoting population diversity. Furthermore, anadaptive t-distribution perturbation strategy is introduced during the particle update process to enhance the globalsearch capability and search speed. The established model for the optimal configuration of measurement points issolved, and the results demonstrate the efficacy and practicality of the proposed method. The optimal configurationreduces the number of measurement points, enhances localization accuracy, and improves the convergence speedof the algorithm. These findings validate the effectiveness and utility of the proposed approach.

A Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller Model Combined with an Improved Particle Swarm Optimization Method for Fall Detection

In many Eastern and Western countries,falling birth rates have led to the gradual aging of society.Older adults are often left alone at home or live in a long-term care center,which results in them being susceptible to unsafe events(such as falls)that can have disastrous consequences.However,automatically detecting falls fromvideo data is challenging,and automatic fall detection methods usually require large volumes of training data,which can be difficult to acquire.To address this problem,video kinematic data can be used as training data,thereby avoiding the requirement of creating a large fall data set.This study integrated an improved particle swarm optimization method into a double interactively recurrent fuzzy cerebellar model articulation controller model to develop a costeffective and accurate fall detection system.First,it obtained an optical flow(OF)trajectory diagram from image sequences by using the OF method,and it solved problems related to focal length and object offset by employing the discrete Fourier transform(DFT)algorithm.Second,this study developed the D-IRFCMAC model,which combines spatial and temporal(recurrent)information.Third,it designed an IPSO(Improved Particle Swarm Optimization)algorithm that effectively strengthens the exploratory capabilities of the proposed D-IRFCMAC(Double-Interactively Recurrent Fuzzy Cerebellar Model Articulation Controller)model in the global search space.The proposed approach outperforms existing state-of-the-art methods in terms of action recognition accuracy on the UR-Fall,UP-Fall,and PRECIS HAR data sets.The UCF11 dataset had an average accuracy of 93.13%,whereas the UCF101 dataset had an average accuracy of 92.19%.The UR-Fall dataset had an accuracy of 100%,the UP-Fall dataset had an accuracy of 99.25%,and the PRECIS HAR dataset had an accuracy of 99.07%.

Multi-target Collaborative Combat Decision-Making by Improved Particle Swarm Optimizer

A decision-making problem of missile-target assignment with a novel particle swarm optimization algorithm is proposed when it comes to a multiple target collaborative combat situation.The threat function is established to describe air combat situation.Optimization function is used to find an optimal missile-target assignment.An improved particle swarm optimization algorithm is utilized to figure out the optimization function with less parameters,which is based on the adaptive random learning approach.According to the coordinated attack tactics,there are some adjustments to the assignment.Simulation example results show that it is an effective algorithm to handle with the decision-making problem of the missile-target assignment(MTA)in air combat.

Solving Job-Shop Scheduling Problem Based on Improved Adaptive Particle Swarm Optimization Algorithm

An improved adaptive particle swarm optimization(IAPSO)algorithm is presented for solving the minimum makespan problem of job shop scheduling problem(JSP).Inspired by hormone modulation mechanism,an adaptive hormonal factor(HF),composed of an adaptive local hormonal factor(H l)and an adaptive global hormonal factor(H g),is devised to strengthen the information connection between particles.Using HF,each particle of the swarm can adjust its position self-adaptively to avoid premature phenomena and reach better solution.The computational results validate the effectiveness and stability of the proposed IAPSO,which can not only find optimal or close-to-optimal solutions but also obtain both better and more stability results than the existing particle swarm optimization(PSO)algorithms.

Multi-path routing algorithm in WSN using an improvedparticle swarm optimization

To slove the problems of constrained energy and unbalanced load of wireless sensor network(WSN)nodes,a multipath load balancing routing algorithm based on neighborhood subspace cooperation is proposed.The algorithm adopts the improved particle swarm optimization(PSO)algorithm,takes the shortest distance and minimum energy consumption as optimization target and divides the nodes in one-hop neighborhood near the base station area into different regions.Furthermore,the algorithm designs a fitness function to find the best node in each region as a relay node and forward the data in parallel through the different paths of the relay nodes.The simulation results show that the proposed algorithm can reduce energy consumption and average end-to-end delay,balance network load and prolong network lifetime effectively.

Improved Particle Swarm Optimization for Solving Transient Nonlinear Inverse Heat Conduction Problem in Complex Structure

Accurately solving transient nonlinear inverse heat conduction problems in complex structures is of great importance to provide key parameters for modeling coupled heat transfer process and the structure’s optimization design.The finite element method in ABAQUS is employed to solve the direct transient nonlinear heat conduction problem.Improved particle swarm optimization(PSO)method is developed and used to solve the transient nonlinear inverse problem.To investigate the inverse performances,some numerical tests are provided.Boundary conditions at inaccessible surfaces of a scramjet combustor with the regenerative cooling system are inversely identified.The results show that the new methodology can accurately and efficiently determine the boundary conditions in the scramjet combustor with the regenerative cooling system.By solving the transient nonlinear inverse problem,the improved particle swarm optimization for solving the transient nonlinear inverse heat conduction problem in a complex structure is verified.

A FastSLAM Algorithm Based on the Improved Auxiliary Particle Filter with Stirling Interpolation

The choice of the particle's distribution model and the consistency of the result are very important for FastSLAM.The improved auxiliary variable model with FastSLAM,and Stirling Interpolation which is used to approximate the nonlinear functions are provided.This approach improves the precision of the approximation for the nonlinear functions,conquers the drawback of the FastSLAM1.0 by using a model ignoring the measurement data,enhances the estimation consistency of the robot pose,and reduces the degradation speed of the particle in FastSLAM algorithm.Simulation results demonstrate the excellence of the proposed algorithm and give the noise parameter influence on the proposed algorithm.

Improved Model for Soil as a Two-Phase Mixture Based on Smoothed Particle Hydrodynamics (SPH)

It is desired to resolve soil contamination with reduced costs. “Insoluble treatment” is a soil improvement method for heavy metal containing soil, which uses soil mixers to mix soil and soil improvement liquid agents. To reduce the costs of this method, soil mixers have to be optimized. However, it is not achieved due to the lack of theoretical knowledge on mixing solid with liquid. Therefore, a numerical model to simulate the dynamic behavior of solid and liquid is on the development in this study using Smoothed Particle Hydrodynamics (SPH) method. To validate the numerical model, several experiments were carried out and numerically reproduced. The comparisons of the results showed that the numerical model replicated a liquid flow with an error rate of 2.1% and a seepage flow with an error rate up to 26.1%. Especially, the water distribution in the soil pores was highly improved with absolute gaps in volumetric water content up to 4.4% in the porosity range of 10% - 90%. For the water absorption into dry sand, the simulation result became more realistic by concerning soil suction.

Angular insensitive nonreciprocal ultrawide band absorption in plasma-embedded photonic crystals designed with improved particle swarm optimization algorithm

Using an improved particle swarm optimization algorithm(IPSO)to drive a transfer matrix method,a nonreciprocal absorber with an ultrawide absorption bandwidth and angular insensitivity is realized in plasma-embedded photonic crystals arranged in a structure composed of periodic and quasi-periodic sequences on a normalized scale.The effective dielectric function,which determines the absorption of the plasma,is subject to the basic parameters of the plasma,causing the absorption of the proposed absorber to be easily modulated by these parameters.Compared with other quasi-periodic sequences,the Octonacci sequence is superior both in relative bandwidth and absolute bandwidth.Under further optimization using IPSO with 14 parameters set to be optimized,the absorption characteristics of the proposed structure with different numbers of layers of the smallest structure unit N are shown and discussed.IPSO is also used to address angular insensitive nonreciprocal ultrawide bandwidth absorption,and the optimized result shows excellent unidirectional absorbability and angular insensitivity of the proposed structure.The impacts of the sequence number of quasi-periodic sequence M and collision frequency of plasma1ν1 to absorption in the angle domain and frequency domain are investigated.Additionally,the impedance match theory and the interference field theory are introduced to express the findings of the algorithm.

Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind farms.To improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind farms.First,the power flow of offshore wind farms is modeled,analyzed and calculated.To improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning factor.Taking the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering needs.Finally,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of SVC.Comparing the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is determined.Meanwhile,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

Development of re-crosslinkable dispersed particle gels for conformance improvement in extremely high-temperature reservoirs

Micro-scale and nano-scale dispersed gel particles(DPG)are capable of deep migration in oil reservoirs due to their deformability,viscoelasticity,and suitable particle size.Therefore,it has been widely studied and applied in reservoir conformance control in recent years.However,for highly permeable channels,their plugging performance is still limited.In addition,conventional in situ cross-linked polymer gels(ISCPGs)have fast gelation time under extremely high-temperature conditions,which often causes problems such as difficulty in pumping.Therefore,a re-cross linkable dispersed particle gel(RDPG)system applied for conformance control in highly permeable channels of extremely high-temperature petroleum reservoirs was investigated.The particle size distribution,gelation time,gel strength,injection performance,and perfo rmance strength in po rous media were investigated using a laser particle size meter,the Sydansk bottle test method,rheometer,and core displacement experiments,respectively.Results show that the RDPG suspension can be stable for more than 6 months at room temperature with storage modulus G’much lower than 10 Pa.It can pass through the pore throat by elastic deformation effect and does not cause strong blockage.Moreover,it can undergo re-crosslinking reaction at 150℃to form a strong bulk gel.The gel strength G’of re-crosslinked RDPG can be as high as 69.3 Pa,which meets the strength requirement of conformance control.The RDPG suspension has the properties of easy injection,and it also has strong plugging,and high-temperature resistance after re-crosslinked in the core,which can be a very promising material for conformance improvement in extremely high-temperature reservoirs.

Study of a New Improved PSO-BP Neural Network Algorithm

In order to overcome shortcomings of traditional BP neural network,such as low study efficiency, slow convergence speed,easily trapped into local optimal solution,we proposed an improved BP neural network model based on adaptive particle swarm optimization( PSO) algorithm. This algorithm adjusted the inertia weight coefficients and learning factors adaptively and therefore could be used to optimize the weights in the BP network. After establishing the improved PSO-BP( IPSO-BP) model,it was applied to solve fault diagnosis of rolling bearing. Wavelet denoising was selected to reduce the noise of the original vibration signals,and based on these vibration signals a wide set of features were used as the inputs in the neural network models. We demonstrate the effectiveness of the proposed approach by comparing with the traditional BP,PSO-BP and linear PSO-BP( LPSO-BP) algorithms. The experimental results show that IPSO-BP network outperforms other algorithms with faster convergence speed,lower errors,higher diagnostic accuracy and learning ability.

Hybrid particle swarm optimization for multiobjective resource allocation

Resource allocation （RA） is the problem of allocating resources among various artifacts or business units to meet one or more expected goals, such a.s maximizing the profits, minimizing the costs, or achieving the best qualities. A complex multiobjective RA is addressed, and a multiobjective mathematical model is used to find solutions efficiently. Then, all improved particie swarm algorithm （mO_PSO） is proposed combined with a new particle diversity controller policies and dissipation operation. Meanwhile, a modified Pareto methods used in PSO to deal with multiobjectives optimization is presented. The effectiveness of the provided algorithm is validated by its application to some illustrative example dealing with multiobjective RA problems and with the comparative experiment with other algorithm.

Improved algorithms to plan missions for agile earth observation satellites

This study concentrates of the new generation of the agile （AEOS）. AEOS is a key study object on management problems earth observation satellite in many countries because of its many advantages over non-agile satellites. Hence, the mission planning and scheduling of AEOS is a popular research problem. This research investigates AEOS characteristics and establishes a mission planning model based on the working principle and constraints of AEOS as per analysis. To solve the scheduling issue of AEOS, several improved algorithms are developed. Simulation results suggest that these algorithms are effective.

Water Quality Evaluation Using Back Propagation Artificial Neural Network Based on Self-Adaptive Particle Swarm Optimization Algorithm and Chaos Theory

To overcome the shortcomings of the traditional methods of water quality evaluation, in this paper, a novel model combines particle swarm optimization (PSO), chaos theory, self-adaptive strategy and back propagation artificial neural network (BP ANN) that was proposed to evaluate the water quality of Weihe River in China. An improved PSO algorithm with a self-adaptive inertia weight and a chaotic learning factor tuned by logistic function was developed and used to optimize the network parameters of BP ANN. The values of average absolute deviation (AAD), root mean square error of prediction (RMSEP) and squared correlation coefficient are 0.0061, 0.0163 and 0.9903, respectively. Compared with other methods, such as BP ANN, and PSO BP ANN, the proposed model displays optimal prediction performance with high precision and good correlation. The results show that the proposed method has the good prediction ability for evaluating water quality. It is convenient, reliable and high precision, which provides good analysis and evaluation method for water quality.

Prediction of Parkinson’s Disease Using Improved Radial Basis Function Neural Network

Parkinson’s disease is a neurogenerative disorder and it is difficult to diagnose as no therapies may slow down its progression.This paper contributes a novel analytic system for Parkinson’s Disease Prediction mechanism using Improved Radial Basis Function Neural Network(IRBFNN).Particle swarm optimization(PSO)with K-means is used to find the hidden neuron’s centers to improve the accuracy of IRBFNN.The performance of RBFNN is seriously affected by the centers of hidden neurons.Conventionally K-means was used to find the centers of hidden neurons.The problem of sensitiveness to the random initial centroid in K-means degrades the performance of RBFNN.Thus,a metaheuristic algorithm called PSO integrated with K-means alleviates initial random centroid and computes optimal centers for hidden neurons in IRBFNN.The IRBFNN uses Particle swarm optimization K-means to find the centers of hidden neurons and the PSO K-means was designed to evaluate the fitness measures such as Intracluster distance and Intercluster distance.Experimentation have been performed on three Parkinson’s datasets obtained from the UCI repository.The proposed IRBFNN is compared with other variations of RBFNN,conventional machine learning algorithms and other Parkinson’s Disease prediction algorithms.The proposed IRBFNN achieves an accuracy of 98.73%,98.47%and 99.03%for three Parkinson’s datasets taken for experimentation.The experimental results show that IRBFNN maximizes the accuracy in predicting Parkinson’s disease with minimum root mean square error.

Prediction Model for Gas Outburst Intensity of Coal Mining Face Based on Improved PSO and LSSVM

For the problems of nonlinearity,uncertainty and low prediction accuracy in the gas outburst prediction of coal mining face,the least squares support vector machine(LSSVM)is proposed to establish the prediction model.Firstly,considering the inertia coefficients as global parameters lacks the ability to improve the solution for the traditional particle swarm optimization(PSO),an improved PSO(IPSO)algorithm is introduced to adjust different inertia weights in updating the particle swarm and solve the fitness to stagnate.Secondly,the penalty factor and kernel function parameter of LSSVM are searched automatically,and the regression accuracy and generalization performance is enhanced by applying IPSO.Finally,to verify the proposed prediction model,the model is applied for gas outburst prediction of Jiuli Hill coal mine in Jiaozuo City,and the results are compared with that of PSO-SVM model,IGA-LSSVM model and BP model.The results show that the relative errors of the proposed model are not greater than 2.7%,and the prediction accuracy is higher than other three prediction models.The IPSO-LSSVM model can be used to predict the intensity of gas outburst of coal mining face effectively.

Improved Bacterial Foraging Optimization Algorithm Based on Fuzzy Control Rule Base

Manual construction of a rule base for a fuzzy system is the hard and time-consuming task that requires expert knowledge.In this paper we proposed a method based on improved bacterial foraging optimization（IBFO）,which simulates the foraging behavior of “E.coli” bacterium,to tune the Gaussian membership functions parameters of an improved Takagi-Sugeno-Kang fuzzy system（C-ITSKFS） rule base.To remove the defect of the low rate of convergence and prematurity,three modifications were produced to the standard bacterial foraging optimization（BFO）.As for the low accuracy of finding out all optimal solutions with multi-method functions,the IBFO was performed.In order to demonstrate the performance of the proposed IBFO,multiple comparisons were made among the BFO,particle swarm optimization（PSO）,and IBFO by MATLAB simulation.The simulation results show that the IBFO has a superior performance.

The impacts of ambiguity in preparation of 80% sulfuric acid solution and shaking time control of calibration solution on the determination of transparent exopolymer particles

The quantification of transparent exopolymer particles(TEP) by colorimetric method is of large error and low repeatability,one major reason of which is related to the absence of clear definition and evaluation for part steps of the original method.It is obscure that the 80% sulfuric acid solution,acted as the extraction solution in the determination of TEP,is prepared based on a volume ratio or mass ratio.Furthermore,the change of solubility of recently available Gum Xanthan(GX) from the market means that the original protocol is no longer applicable,and the grinding of GX stock solution with a tissue grinder is replaced by shaking with a rotating shaker in the study to prevent the excessive dissolution of GX.We found that different preparation techniques could result in the varied concentrations of 80% H_(2)SO_(4).The duration of shaking during the preparation of standard solution significantly affected the slope of the calibration curve,which caused different correction results of TEP.The impacts of different extraction solution concentrations and shaking time of GX solution on the quantification of TEP were investigated based on the field sampling and laboratory analysis.The extraction capacities of H_(2)SO_(4) with different concentrations for Alcian Blue were distinct,but had limited effect on the final measuring result of TEP.The change of the standard curve slope came along with the variation of shaking time,which markedly altered the detection limit and calibration result,and the extended shaking time was in favor of the determination of low-concentration TEP.It was suggested that the extraction solution concentration,shaking time and filtration volume of standard solution are required to be well controlled and selected to obtain more accurate results for TEP with different concentrations.