An Optimal Algorithm for the Retrieval of Chlorophyll,Suspended Sediments and Gelbstoff of Case Ⅱ Waters in the Pearl River Estuary

An optimal algorithm for the retrieval of chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary was established with the optical parameters derived from the in-situ data obtained in Jan. 2003 in the same area. And then, the chlorophyll, suspended sediments and gelbstoff of the SeaWiFS pixels on Jan. 29, 2003 corresponding to the in-situ sites of Jan. 25 and 26, 2003 were synchronously retrieved, with average relative errors of 14.9%, 12.1% and 13.6% for chlorophyll, suspended sediments and gelbstoff, respectively. The research results indicated that the optimal retrieval algorithm established here was relatively fit for the retrieval of the chlorophyll, suspended sediments and gelbstoff of case Ⅱ waters in the Pearl River estuary, and had quite good retrieval accuracy.

IMPROVED AERODYNAMIC APPROXIMATION MODEL WITH CASE-BASED REASONING TECHNIQUE FOR MDO OF AIRCRAFT

To increase the efficiency of the multidisciplinary optimization of aircraft, an aerodynamic approximation model is improved. Based on the study of aerodynamic approximation model constructed by the scaling correction model, case-based reasoning technique is introduced to improve the approximation model for optimization. The aircraft case model is constructed by utilizing the plane parameters related to aerodynamic characteristics as attributes of cases, and the formula of case retrieving is improved. Finally, the aerodynamic approximation model for optimization is improved by reusing the correction factors of the most similar aircraft to the current one. The multidisciplinary optimization of a civil aircraft concept is carried out with the improved aerodynamic approximation model. The results demonstrate that the precision and the efficiency of the optimization can be improved by utilizing the improved aerodynamic approximation model with ease-based reasoning technique.

Optimal Matching Analysis of Net Buoyancy and Pitching Angle for Underwater Gliders

Control parameter optimization is an efficient way to improve the endurance of underwater gliders(UGs),which influences their gliding efficiency and energy consumption.This paper analyzes the optimal matching between the net buoyancy and the pitching angle and proposes a segmented control strategy of Petrel-L.The optimization of this strategy is established based on the gliding range model of UG,which is solved based on the approximate model,and the variations of the optimal control parameters with the hotel load are obtained.The optimization results indicate that the segmented control strategy can significantly increase the gliding range when the optimal matching between the net buoyancy and the pitching angle is reached,and the increase rate is influenced by the hotel load.The gliding range of the underwater glider can be increased by 10.47%at a hotel load of 0.5 W.The optimal matching analysis adopted in this study can be applied to other UGs to realize endurance improvement.

Time -Sharing Power Supply Optimal Decision forElectrolytic Deposition Process of Zinc Based onSimulated Annealing Algorithm

According to time-sharing valuation principle (TSVP) of power supply, the relationships of current density and current efficiency at different acidities are obtained based on the processed data of electrolytic deposition process of zinc (EDPZ) with the least square method (LSM). Thus an optimal model of time-sharing power supply system for EDPZ is established, which has been optimized by use of an improved efficient simulated annealing algorithm (SAA). Practical results show that industrial and mining enterprises can obtain enormous economic benefits every year.

Domain Decomposition of an Optimal Control Problem for Semi-Linear Elliptic Equations on Metric Graphs with Application to Gas Networks

We consider optimal control problems for the flow of gas in a pipe network. The equations of motions are taken to be represented by a semi-linear model derived from the fully nonlinear isothermal Euler gas equations. We formulate an optimal control problem on a given network and introduce a time discretization thereof. We then study the well-posedness of the corresponding time-discrete optimal control problem. In order to further reduce the complexity, we consider an instantaneous control strategy. The main part of the paper is concerned with a non-overlapping domain decomposition of the semi-linear elliptic optimal control problem on the graph into local problems on a small part of the network, ultimately on a single edge.

Proximal Point-Like Method for Updating Simultaneously Mass and Stiffness Matrices of Finite Element Model

The problem of correcting simultaneously mass and stiffness matrices of finite element model of undamped structural systems using vibration tests is considered in this paper.The desired matrix properties,including satisfaction of the characteristic equation,symmetry,positive semidefiniteness and sparsity,are imposed as side constraints to form the optimal matrix pencil approximation problem.Using partial Lagrangian multipliers,we transform the nonlinearly constrained optimization problem into an equivalent matrix linear variational inequality,develop a proximal point-like method for solving the matrix linear variational inequality,and analyze its global convergence.Numerical results are included to illustrate the performance and application of the proposed method.

Short-Term Precipitation Forecasting Rolling Update Correction Technology Based on Optimal Fusion Correction

In order to improve the availability of regional model precipitation forecast, this project intends to use the measured heavy rainfall data of dense automatic stations to carry out historical precipitation in the high resolution: the Severe Weather Automatic Nowcast System (SWAN) quantitative precipitation forecast and the High-Resolution Rapid Refresh (HRRR) regional numerical model precipitation forecast in short-term nowcasting aging. Based on the error analysis, the grid fusion technology is used to establish the measured rainfall, HRRR regional model precipitation forecast, and optical flow radar quantitative precipitation forecast (QPF) three-source fusion correction scheme, comprehensively integrate the revised forecasting effect, adjust the fusion correction parameters, establish an optimal correction plan, generate a frozen rolling update revised product based on measured dense data and short-term forecast, and put it into business operation, and perform real-time effect rolling test evaluation on the forecast product.

Cross Validation Based Model Averaging for Varying-Coefficient Models with Response Missing at Random

In this paper, a model averaging method is proposed for varying-coefficient models with response missing at random by establishing a weight selection criterion based on cross-validation. Under certain regularity conditions, it is proved that the proposed method is asymptotically optimal in the sense of achieving the minimum squared error.

APPROXIMATION TECHNIQUES FOR APPLICATION OF GENETIC ALGORITHMS TO STRUCTURAL OPTIMIZATION

Although the genetic algorithm (GA) has very powerful robustness and fitness, it needs a large size of population and a large number of iterations to reach the optimum result. Especially when GA is used in complex structural optimization problems, if the structural reanalysis technique is not adopted, the more the number of finite element analysis (FEA) is, the more the consuming time is. In the conventional structural optimization the number of FEA can be reduced by the structural reanalysis technique based on the approximation techniques and sensitivity analysis. With these techniques, this paper provides a new approximation model-segment approximation model, adopted for the GA application. This segment approximation model can decrease the number of FEA and increase the convergence rate of GA. So it can apparently decrease the computation time of GA. Two examples demonstrate the availability of the new segment approximation model.

RBMDO Using Gaussian Mixture Model-Based Second-Order Mean-Value Saddlepoint Approximation

Actual engineering systems will be inevitably affected by uncertain factors.Thus,the Reliability-Based Multidisciplinary Design Optimization(RBMDO)has become a hotspot for recent research and application in complex engineering system design.The Second-Order/First-Order Mean-Value Saddlepoint Approximate(SOMVSA/-FOMVSA)are two popular reliability analysis strategies that are widely used in RBMDO.However,the SOMVSA method can only be used efficiently when the distribution of input variables is Gaussian distribution,which significantly limits its application.In this study,the Gaussian Mixture Model-based Second-Order Mean-Value Saddlepoint Approximation(GMM-SOMVSA)is introduced to tackle above problem.It is integrated with the Collaborative Optimization(CO)method to solve RBMDO problems.Furthermore,the formula and procedure of RBMDO using GMM-SOMVSA-Based CO(GMM-SOMVSA-CO)are proposed.Finally,an engineering example is given to show the application of the GMM-SOMVSA-CO method.

Improved Stiffness Modeling for An Exechon‑Like Parallel Kinematic Machine(PKM)and Its Application

Hole drilling or contour milling for the large and complex workpieces such as automobile panels and aircraft fuselages makes a high combined demand on machining accuracy,stiffness and workspace of machining equipment.Therefore,a 5-DOF(degrees of freedom)parallel kinematic machine(PKM)with redundant constraints is proposed.Based on the kinematics analysis of the parallel mechanism using intermediate variables,the kinematics problems of the PKM are solved through equivalent kinematics model.The structural stiffness matrix method is adopted to model the stiffness of the parallel mechanism of the PKM,where the stiffness of each joint and branch component is obtained by stiffness formula and finite element analysis.And the stiffness model of the parallel mechanism is improved by correction coefficient matrix,each element of which is constructed as a polynomial function of three independent end variables of the parallel mechanism.The terminal stiffness matrices obtained by simulation result are used to determine the coefficients of polynomial function by least square fitting to describe the correction coefficient over the workspace of the parallel mechanism quantitatively.The experiment results prove that the modification method can greatly improve the stiffness model of the parallel mechanism.To enhance the machining accuracy of the PKM,the proposed kinematics model and the improved stiffness model are utilized to optimize the working stiffness of parallel machine by searching the best relative position of parallel machine and workpiece.A plate workpiece taken as example is examined in the case study section,which demonstrates the effectiveness of optimization method.

Multi-objectives nonlinear structure optimization for actuator in trajectory correction fuze subject to high impact loadings

This paper presents an actuator used for the trajectory correction fuze,which is subject to high impact loadings during launch.A simulation method is carried out to obtain the peak-peak stress value of each component,from which the ball bearings are possible failures according to the results.Subsequently,three schemes against impact loadings,full-element deep groove ball bearing and integrated raceway,needle roller thrust bearing assembly,and gaskets are utilized for redesigning the actuator to effectively reduce the bearings’stress.However,multi-objectives optimization still needs to be conducted for the gaskets to decrease the stress value further to the yield stress.Four gasket’s structure parameters and three bearings’peak-peak stress are served as the four optimization variables and three objectives,respectively.Optimized Latin hypercube design is used for generating sample points,and Kriging model selected according to estimation result can establish the relationship between the variables and objectives,representing the simulation which is time-consuming.Accordingly,two optimization algorithms work out the Pareto solutions,from which the best solutions are selected,and verified by the simulation to determine the gaskets optimized structure parameters.It can be concluded that the simulation and optimization method based on these components is effective and efficient.

Self-correcting wavelet neural network control of continuous rotary electro-hydraulic servo motor

In allusion to the problem of friction,leakage,vibration and noise existing in continuous rotary motor electro-hydraulic servo system,highly nonlinearity and uncertainties affecting the system performance,based on the transfer function of electro-hydraulic servo system,a kind of Pol-Ind friction model is proposed.The parameters of Pol-Ind friction model are identified and the accurate mathematical model of friction torque is obtained by experiment.The self-correcting wavelet neural network(WNN)controller is proposed,and Adam optimization algorithm is used to perform gradient optimization on scale factor and displacement factor in wavelet basis function,so as to improve the speed and precision of parameter optimization.Through comparative simulation analysis,it is clearly that the self-correcting WNN controller can effectively improve the frequency response and tracking accuracy of continuous rotary motor electro-hydraulic servo system.

Discrete-Time Nonlinear Stochastic Optimal Control Problem Based on Stochastic Approximation Approach

In this paper, a computational approach is proposed for solving the discrete-time nonlinear optimal control problem, which is disturbed by a sequence of random noises. Because of the exact solution of such optimal control problem is impossible to be obtained, estimating the state dynamics is currently required. Here, it is assumed that the output can be measured from the real plant process. In our approach, the state mean propagation is applied in order to construct a linear model-based optimal control problem, where the model output is measureable. On this basis, an output error, which takes into account the differences between the real output and the model output, is defined. Then, this output error is minimized by applying the stochastic approximation approach. During the computation procedure, the stochastic gradient is established, so as the optimal solution of the model used can be updated iteratively. Once the convergence is achieved, the iterative solution approximates to the true optimal solution of the original optimal control problem, in spite of model-reality differences. For illustration, an example on a continuous stirred-tank reactor problem is studied, and the result obtained shows the applicability of the approach proposed. Hence, the efficiency of the approach proposed is highly recommended.

基于多目标优化的箱梁渡槽结构设计研究

针对箱梁渡槽所受水荷载远大于同等跨径箱梁桥所受汽车荷载导致槽身纵向受力突出的问题,在渡槽结构设计中统筹考虑箱梁纵向应力储备和造价,构建了以截面尺寸、钢筋用量等6个参数为设计变量,以渡槽边缘正应力和造价为目标函数,以槽身挠度、法向应力等9个验算指标为约束条件的多目标优化模型,并基于支持向量机近似模型和NSGA2遗传算法求解多目标优化模型的Pareto最优解集,然后综合专家打分、COWA算子、博弈论综合赋权与TOPSIS法从Pareto最优解集中选出最佳方案。结果表明:与初始方案相比,最佳方案的渡槽边缘正应力储备提高了17.55%,渡槽上部结构总造价降低8.84%;与有限元分析相比,该方法在保持较高计算精度的同时节省了65.42%的计算时间成本。

木勺生产线机械臂的动静特性及结构优化

为实现木勺生产线上下料功能,同时节约成本,采用建模软件与有限元分析软件的协同仿真技术,对木勺生产线的机械臂进行动静性能分析,运用Parameters Correlation方法筛选出对生产线机械臂动静性能影响较大的设计变量。在此基础上,采用拉丁超立方抽样法对筛选的设计变量进行采样,对比分析Kriging模型与NPR模型构建的生产线机械臂的近似模型,并采用MOGA法对设计的目标函数进行求解。对优化后的木勺生产线机械臂进行建模分析,对比得出,优化后的模型Y方向最大振幅为1.1137 mm,可实现设想功能,动静性能得到提升。研究成果可为后续生产线机械臂结构设计提供参考。

基于MPC的光电热联合系统建模与控制优化

为解决我国北方地区冬季采暖产生的能源消耗与环境污染问题,改善光资源丰富、较丰富地区光能利用率不足的现状,充分利用谷电和日照优势,针对太阳能利用与建筑采暖相结合的分布式能源系统性问题,提出一种基于TRNSYS动态建模与数值建模相结合的光电热联合供暖系统。综合考虑小范围内供暖温度的时滞性以及系统各设备的出力情况,联合Matlab搭建模型预测控制器(MPC),提出一种基于MPC的误差实时校正优化控制策略。分析表明:采用MPC的控制优化,在热负荷跟踪方面,最大误差降低4.16%,平均误差降低2.79%;在室内温度控制方面,最大偏差降低1.2℃,平均偏差降低0.2℃;在太阳能利用占比方面,太阳辐射强度趋近于800 W/m^(2)时,太阳能利用占比差距达最大8.9%。分析结果说明该系统可以更快速、更准确地跟踪建筑热负荷波动,并且有效抑制室内温度波动,提高清洁能源的利用率。

泡沫铝填充薄壁金属圆管吸能特性的预测及优化方法

为提高泡沫铝填充薄壁金属圆管的吸能特性并进行高效率结构设计,基于均匀拉丁超立方试验设计方法和径向基函数神经网络,构建轴向冲击下泡沫铝填充铝管吸能特性的近似模型,并将近似模型嵌入遗传算法中实现构件的结构优化。研究结果表明:基于径向基函数神经网络构建的近似模型的拟合优度大于0.99、均方根误差小于0.08,且近似模型的计算耗时仅为数值计算的0.56%;近似模型在保证较高精度的同时可大幅提高计算效率;对泡沫铝填充铝管的结构优化发现,通过增大圆管半径和壁厚、减小高度可使泡沫铝填充铝管的平均压缩力最大化,反之,可使泡沫铝填充铝管的峰值压缩力最小化;基于遗传算法的多目标优化显著提高了泡沫铝填充铝管的吸能特性;该研究成果可为泡沫铝填充薄壁金属圆管的快速设计和优化提供参考。

基于COA-GRU的低成本气体传感器数据修正方法

针对低成本气体传感器在受到温度、湿度、压力、气体交叉干扰等影响时检测精度低的问题,提出了一种长鼻浣熊-门控循环单元神经网络(COA-GRU)的修正模型,用于提高传感器检测精度。首先,根据低成本传感器的非线性特性构建了GRU修正模型;其次,利用COA算法解决修正模型的多局部极值以及参数组合寻优问题;最后,利用低成本传感器组以及H200D气体检测装置的实测数据对该方法进行了仿真实验。结果表明,使用COA-GRU修正模型后,SO_(2)、CO、NO_(2)、CO_(2)传感器的平均绝对误差分别降低了72.0%、28.4%、29.6%、13.5%,能够有效提高低成本传感器的检测精度。

融合随机傅里叶特征的混合神经网络模型

深度神经网络模型作为目前最成功的学习模型之一,在模型训练时涉及大量的参数学习,特别对于较大规模的样本存在特征处理困难、模型结构复杂等问题.线性或亚线性时间复杂度的核近似算法具有计算开销小、易于扩展到大规模数据的优点.因此,利用线性核近似算法可以进行特征处理并降低深度神经网络模型的结构复杂程度.本文提出一种基于傅里叶特征空间的混合神经网络模型优化方法,并由此构建一个混合深度神经网络模型.该模型将基于随机傅里叶特征变换的浅层网络与卷积神经网络相结合,傅里叶层进行数据特征处理与提取的同时,降低混合网络模型的结构复杂程度,优化深度神经网络模型结构.实验结果表明本文提出的模型拥有较少的参数量和较低的浮点计算量,同时模型可保持较高的测试准确率以及更快的收敛效率.