Structural physical parameter identification based on evolutionary-simplex algorithm and structural dynamic response

Evolutionary computation based on the idea of biologic evolution is one type of global optimization algorithm that uses self-adaptation,self-organization and random searching to solve optimization problems.The evolutionary-simplex algorithm is introduced in this paper.It contains floating encoding which combines the evolutionary computation and the simplex algorithm to overcome the problems encountered in the genetic algorithm and evolutionary strategy methods. Numerical experiments are performed using seven typical functions to verify the algorithm.An inverse analysis method to identify structural physical parameters based on incomplete dynamic responses obtained from the analysis in the time domain is presented by using the evolutionary-simplex algorithm.The modal evolutionary-simplex algorithm converted from the time domain to the modal domain is proposed to improve the inverse efficiency.Numerical calculations for a 50-DOF system show that when compared with other methods,the evolutionary-simplex algorithm offers advantages of high precision, efficient searching ability,strong ability to resist noise,independence of initial value,and good adaptation to incomplete information conditions.

Intelligent direct analysis of physical and mechanical parameters of tunnel surrounding rock based on adaptive immunity algorithm and BP neural network

Because of complexity and non-predictability of the tunnel surrounding rock, the problem with the determination of the physical and mechanical parameters of the surrounding rock has become a main obstacle to theoretical research and numerical analysis in tunnel engineering. During design, it is a frequent practice, therefore, to give recommended values by analog based on experience. It is a key point in current research to make use of the displacement back analytic method to comparatively accurately determine the parameters of the surrounding rock whereas artificial intelligence possesses an exceptionally strong capability of identifying, expressing and coping with such complex non-linear relationships. The parameters can be verified by searching the optimal network structure, using back analysis on measured data to search optimal parameters and performing direct computation of the obtained results. In the current paper, the direct analysis is performed with the biological emulation system and the software of Fast Lagrangian Analysis of Continua (FLAC3D. The high non-linearity, network reasoning and coupling ability of the neural network are employed. The output vector required of the training of the neural network is obtained with the numerical analysis software. And the overall space search is conducted by employing the Adaptive Immunity Algorithm. As a result, we are able to avoid the shortcoming that multiple parameters and optimized parameters are easy to fall into a local extremum. At the same time, the computing speed and efficiency are increased as well. Further, in the paper satisfactory conclusions are arrived at through the intelligent direct-back analysis on the monitored and measured data at the Erdaoya tunneling project. The results show that the physical and mechanical parameters obtained by the intelligent direct-back analysis proposed in the current paper have effectively improved the recommended values in the original prospecting data. This is of practical significance to the appraisal of stability and informationization design of the surrounding rock.

A Physical Layer Network Coding Based Tag Anti-Collision Algorithm for RFID System

In RFID(Radio Frequency IDentification)system,when multiple tags are in the operating range of one reader and send their information to the reader simultaneously,the signals of these tags are superimposed in the air,which results in a collision and leads to the degrading of tags identifying efficiency.To improve the multiple tags’identifying efficiency due to collision,a physical layer network coding based binary search tree algorithm(PNBA)is proposed in this paper.PNBA pushes the conflicting signal information of multiple tags into a stack,which is discarded by the traditional anti-collision algorithm.In addition,physical layer network coding is exploited by PNBA to obtain unread tag information through the decoding operation of physical layer network coding using the conflicting information in the stack.Therefore,PNBA reduces the number of interactions between reader and tags,and improves the tags identification efficiency.Theoretical analysis and simulation results using MATLAB demonstrate that PNBA reduces the number of readings,and improve RFID identification efficiency.Especially,when the number of tags to be identified is 100,the average needed reading number of PNBA is 83%lower than the basic binary search tree algorithm,43%lower than reverse binary search tree algorithm,and its reading efficiency reaches 0.93.

Conducting a Correlation Model Between TCM Constitution and Physical Examination Index Based on BPNN Algorithm

This paper studies the correlation between Traditional Chinese Medicine(TCM)Constitution discrimination and physical examination index based on BPNN algorithm.253cases of routine urine test were used to build a linkage model between TCM Constitution and physical indicators via BPNN algorithm.According to the test,the correct rate of learning and test group are60%and40%,respectively.A strong correlation was found between TCM Constitution and physical examination indexes.By applying cutting-edge knowledge and technologies,the development and modernization process of TCM can be greatly promoted.

A quasi-physical algorithm for solving the linear separation problem in n-dimensional space

A quasi physical algorithm was proposed for solving the linear separation problem of point set in n dimensional space.The original idea of the quasi physical algorithm is to find an equivalent physical world for the primitive mathematical problem and to observe the vivid images of the motion of matter in it so as to be inspired to obtain an algorithm for solving the mathematical problem. In this work, the electrostatics with two kinds of matter is found to be the equivalent physical world. As a result,the proposed algorithm is evidently more efficient and robust than the famous LMS algorithm and ETL algorithm. The efficiency of the quasi physical algorithm is about 10-50 times of the LMS algorithm’s for representative instances. A typical Boolean valued instance shows that it is hard for ETL algorithm but very easy for the quasi physical algorithm.In this instance, point set A and B is {000, 010, 011, 111} and {001,100}, respectively.

Heuristic Quasi-physical Algorithm for Protein Structure Prediction

A three-dimensional off-lattice protein model with two species of monomers, hydrophobic and hydrophilic, is studied. Enligh- tened by the law of reciprocity among things in the physical world, a heuristic quasi-physical algorithm for protein structure prediction problem is put forward. First, by elaborately simulating the movement of the smooth elastic balls in the physical world, the algorithm finds low energy configurations for a given monomer chain. An ＂off-trap＂ strategy is then proposed to get out of local minima. Experimental results show promising performance. For all chains with lengths 13≤n ≤55, the proposed algorithm finds states with lower energy than the putative ground states reported in literatures. Furthermore, for chain lengths n = 21, 34, and 55, the algorithm finds new low energy configurations different from those given in literatures.

FREE VIBRATION ANALYSIS AND PHYSICAL PARAMETER IDENTIFICATION OF NON-UNIFORM BEAM CARRYING SPRING-MASS SYSTEMS

To analyze a multibody system composed of non-uniform beam and spring-mass subsystems, the model discretization is carried on by utilizing the finite element method（FEM）, the dynamic model of non-uniform beam is developed by using the transfer matrix method of multibody system（MS-TMM）, the transfer matrix of non-u- niform beam is derived, and the natural frequencies are computed. Compared with the numerical assembly method （NAM）, the results by MS-TMM have good agreement with the results by FEM, and are better than the results by NAM. When using the high precision method, the global dynamic equations of the complex multibody system are not needed and the orders of involved system matrices are decreased greatly. For the investigation on the re- verse problem of the physical parameter identification of multibody system, MS-TMM and the optimization tech- nology based on genetic algorithms（GAs） are combined and extended. The identification problem is exchanged for an optimization problem, and it is formulated as a global minimum solution of the objective function with respect to natural frequencies of multibody system. At last, the numerical example of non-uniform beam with attach- ments is discussed, and the identification results indicate the feasibility and the effectivity of the proposed aop- proach.

Influential Node Ranking and Invulnerability of Air Traffic Cyber Physical System

To ensure flight safety,the complex network method is used to study the influence and invulnerability of air traffic cyber physical system(CPS)nodes.According to the rules of air traffic management,the logical coupling relationship between routes and sectors is analyzed,an air traffic CPS network model is constructed,and the indicators of node influence and invulnerability are established.The K-shell algorithm is improved to identify node influence,and the invulnerability is analyzed under random and selective attacks.Taking Airspace in Eastern China as an example,its influential nodes are sorted by degree,namely,K-shell,the improved K-shell(IKS)and betweenness centrality.The invulnerability of air traffic CPS under different attacks is analyzed.Results show that IKS can effectively identify the influential nodes in the air traffic CPS network,and IKS and betweenness centrality are the two key indicators that affect the invulnerability of air traffic CPS.

A quantitative analysis model of grid cyber physical systems

Conventional power systems are being developed into grid cyber physical systems(GCPS) with widespread application of communication, computer, and control technologies. In this article, we propose a quantitative analysis method for a GCPS. Based on this, we discuss the relationship between cyberspace and physical space, especially the computational similarity within the GCPS both in undirected and directed bipartite networks. We then propose a model for evaluating the fusion of the three most important factors: information, communication, and security. We then present the concept of the fusion evaluation cubic for the GCPS quantitative analysis model. Through these models, we can determine whether a more realistic state of the GCPS can be found by enhancing the fusion between cyberspace and physical space. Finally, we conclude that the degree of fusion between the two spaces is very important, not only considering the performance of the whole business process, but also considering security.

Study and application of an improved four-dimensional variational assimilation system based on the physical-space statistical analysis for the South China Sea

The four-dimensional variational assimilation(4D-Var)has been widely used in meteorological and oceanographic data assimilation.This method is usually implemented in the model space,known as primal approach(P4D-Var).Alternatively,physical space analysis system(4D-PSAS)is proposed to reduce the computation cost,in which the 4D-Var problem is solved in physical space(i.e.,observation space).In this study,the conjugate gradient(CG)algorithm,implemented in the 4D-PSAS system is evaluated and it is found that the non-monotonic change of the gradient norm of 4D-PSAS cost function causes artificial oscillations of cost function in the iteration process.The reason of non-monotonic variation of gradient norm in 4D-PSAS is then analyzed.In order to overcome the non-monotonic variation of gradient norm,a new algorithm,Minimum Residual(MINRES)algorithm,is implemented in the process of assimilation iteration in this study.Our experimental results show that the improved 4D-PSAS with the MINRES algorithm guarantees the monotonic reduction of gradient norm of cost function,greatly improves the convergence properties of 4D-PSAS as well,and significantly restrains the numerical noises associated with the traditional 4D-PSAS system.

Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical processes.The smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical system.At the same time,the machine learning(ML)modelsfind useful for the smart grids integrated into the CPES for effective decision making.Also,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity utilization.In this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in CPES.The proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter optimization.The OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing step.Besides,the MBLSTM model is applied for the prediction of stability level of the smart grids in CPES.At the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM model.To ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several aspects.The experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

Selected topics of quantum computing for nuclear physics

Nuclear physics,whose underling theory is described by quantum gauge field coupled with matter,is fundamentally important and yet is formidably challenge for simulation with classical computers.Quantum computing provides a perhaps transformative approach for studying and understanding nuclear physics.With rapid scaling-up of quantum processors as well as advances on quantum algorithms,the digital quantum simulation approach for simulating quantum gauge fields and nuclear physics has gained lots of attention.In this review,we aim to summarize recent efforts on solving nuclear physics with quantum computers.We first discuss a formulation of nuclear physics in the language of quantum computing.In particular,we review how quantum gauge fields(both Abelian and non-Abelian)and their coupling to matter field can be mapped and studied on a quantum computer.We then introduce related quantum algorithms for solving static properties and real-time evolution for quantum systems,and show their applications for a broad range of problems in nuclear physics,including simulation of lattice gauge field,solving nucleon and nuclear structures,quantum advantage for simulating scattering in quantum field theory,non-equilibrium dynamics,and so on.Finally,a short outlook on future work is given.

THE EFFECTIVENESS OF GENETIC ALGORITHM IN CAPTURING CONDITIONAL NONLINEAR OPTIMAL PERTURBATION WITH PARAMETERIZATION “ON-OFF” SWITCHES INCLUDED BY A MODEL

In the typhoon adaptive observation based on conditional nonlinear optimal perturbation (CNOP), the ‘on-off’ switch caused by moist physical parameterization in prediction models prevents the conventional adjoint method from providing correct gradient during the optimization process. To address this problem, the capture of CNOP, when the "on-off" switches are included in models, is treated as non-smooth optimization in this study, and the genetic algorithm (GA) is introduced. After detailed algorithm procedures are formulated using an idealized model with parameterization "on-off" switches in the forcing term, the impacts of "on-off" switches on the capture of CNOP are analyzed, and three numerical experiments are conducted to check the effectiveness of GA in capturing CNOP and to analyze the impacts of different initial populations on the optimization result. The result shows that GA is competent for the capture of CNOP in the context of the idealized model with parameterization ‘on-off’ switches in this study. Finally, the advantages and disadvantages of GA in capturing CNOP are analyzed in detail.

Concrete Physics Method for Solving NP hard Problem

With a NP hard problem given, we may find a equivalent physical world. The rule of the changing of the physical states is simply the algorithm for solving the original NP hard problem .It is the most natural algorithm for solving NP hard problems. In this paper we deal with a famous example , the well known NP hard problem——Circles Packing. It shows that our algorithm is dramatically very efficient. We are inspired that, the concrete physics algorithm will always be very efficient for NP hard problem.

Physics of Failure(PoF)-Based Reliability and Performance Integrating Modeling Method and Software for Electromechanical Product

Electromechanical product's reliability is affected by uncertainty as well as performance degeneration during its life cycle.The present reliability and performance integrating modeling methods have obvious deficiencies in long period reliability analysis and assessment when applied to such system.A novel integrating modeling method based on physics of failure(PoF)and a simulation algorithm that considers uncertainty and degeneration are proposed in this paper to compute maintenance free operation period or maintenance free operation period survivability which is used to assess long period reliability of system.Furthermore,the concept design of this kind of software based on the above theory is also introduced.A case study of servo valve demonstrates the feasibility of the method and usability of the software in this research.

基于物理模型的ISTAVS-Net多线圈MRI图像重建

提高磁共振成像速度是磁共振领域待解决的问题,采用多线圈扫描是一种常用加速方式.但在加速因子大于4时,传统压缩感知磁共振(CS-MRI)重建算法得到的图像质量不高.为此,本文提出一种基于物理模型的ISTAVS-Net多线圈MRI图像重建方法.将ISTA算法与VS-Net网络拆分思想相结合,提出ISTAVS算法,并展开成ISTAVS-Net网络.将每步迭代与网络模块结合,比黑盒U-Net网络的可解释性更强.网络中引入残差机制,增加了网络的非线性表达能力和稳定性.稀疏变换、收缩阈值以及正则化参数在训练中自动学习,提高了重建的灵活性.采用Globus膝关节数据集的测试结果表明不同加速因子下ISTAVS-Net网络效果均优于传统的L1-ESPIRiT和ISTA迭代算法,图像质量和性能指标比U-Net、ISTA-Net+和VS-Net网络提升明显,在高加速因子下对组织细节恢复能力更强.该网络鲁棒性强,更适合对临床扫描数据进行快速高质量重建,可拓宽MRI应用范围.

基于物理驱动深度学习的结构形状优化设计

结构形状的优化设计本质上是一类泛函极值求解问题.在求解高维度泛函极值问题时,传统的变分法往往面临着求解目标函数类型有限、求解过程呈现振荡行为等问题.利用深度学习模型的高维非线性映射能力,建立了一种基于物理驱动深度学习的泛函极值求解模型.首先将描述结构形状优化问题的物理信息(控制方程、初始条件和边界条件等)作为正则化项嵌入深度学习模型中,基于性能目标构建损失函数;采用随机梯度下降法完成深度学习模型的训练,进而实现泛函极值的求解和结构形状的优化设计;通过分析最优曲面和最优拱轴线问题验证模型的有效性,并与遗传算法进行对比,结果表明该模型在小样本的目标任务上具有较高的预测精度和效率.作为一种非参数模型化技术,物理驱动深度学习模型对解决数据采集成本高、难度大的工程问题具有重要意义.

PI环境下考虑节点失效与动态需求的供应链网络韧性优化研究

在逆全球化、百年变局的大背景下,追求低成本精益化管理、零库存目标造成的供应链网络拉伸,使得供应链的局部脆弱性问题得以暴露,研究如何建立韧性供应链网络并进行优化变得至关重要。实物互联网(Physical Internet,PI)环境下的物流模式具有很强的开放性、全局性和协作性,文章结合实际供应链运转过程中存在的节点失效以及市场动态需求变化等问题,采用情景法对可能的影响因素进行描述,构建了PI环境下考虑节点应变能力的韧性供应链网络优化模型,并应用改进后的GA-BPSO算法对模型进行求解。通过实证算例证明了模型的可行性及算法的有效性,可以为企业进行韧性供应链优化提供科学决策参考。

改进鲸鱼算法优化多微波源功率组合的微波加热系统温度均匀性研究

多微波源组合加热系统内电磁场、温度场的均匀性不仅影响物料的加热质量,而且影响物料加热的能源效率。本文研究了多源微波加热系统中各微波源的功率分配问题,应用鲸鱼优化算法,探索提升多微波源加热物料的温度均匀性的调控方法。将加热系统中的多微波源的功率组合构建成一个鲸鱼种群的优化个体。设定微波总功率为约束条件并将物料温度均匀性作为目标函数,实现多微波源功率分配达到温度均匀分布的充分条件。对鲸鱼优化算法进行改进以提高搜索精度和收敛速度,提出实现微波源跟踪到对应分配功率的一个种群初始化迭代算法,从而实现温度均匀的目标。通过Matlab和COMSOL软件对模型进行联合仿真,结合算法的寻优迭代过程和模拟加热温度求解过程,验证了改进鲸鱼优化算法相较于各对比算法对温度均匀性有更好的效果。