Prediction of a maximum pull-out load of anchor bolts using an optimal combination model

The mixed model of improved exponential and power function and unequal interval gray GM(1,1)model have poor accuracy in predicting the maximum pull-out load of anchor bolts.An optimal combination model was derived using the optimally weighted combination theory and the minimum sum of logarithmic squared errors as the objective function.Two typical anchor bolt pull-out engineering cases were selected to compare the performance of the proposed model with those of existing ones.Results showed that the optimal combination model was suitable not only for the slow P-s curve but also for the steep P-s curve.Its accuracy and stable reliability,as well as its prediction capability classification,were better than those of the other prediction models.Therefore,the optimal combination model is an effective processing method for predicting the maximum pull-out load of anchor bolts according to measured data.

OPTIMAL MODEL OF COMBINED PHASE CENTER FOR HORN FEEDS AND ITS COMPUTATIONAL METHOD

A definition of combined phase center for horn feeds is given.Formulas of E-planeand H-plane combined phase center for conical horns and the corresponding Optimal model arepresented,and a fast optimization method for solving this model is described.By using thismethod,the phase center of corrugated horn is discussed and calculated,and the variation of thephase center with distance and operating frequency is given.

Combined model based on optimized multi-variable grey model and multiple linear regression

The construction method of background value is improved in the original multi-variable grey model （MGM（1,m）） from its source of construction errors. The MGM（1,m） with optimized background value is used to eliminate the random fluctuations or errors of the observational data of all variables, and the combined prediction model together with the multiple linear regression is established in order to improve the simulation and prediction accuracy of the combined model. Finally, a combined model of the MGM（1,2） with optimized background value and the binary linear regression is constructed by an example. The results show that the model has good effects for simulation and prediction.

Control effect and optimization scheme of combined rockboltecable support for a tunnel in horizontally layered limestone:A case study

This study focused on the mechanical behavior of a deep-buried tunnel constructed in horizontally layered limestone,and investigated the effect of a new combined rockboltecable support system on the tunnel response.The Yujingshan Tunnel,excavated through a giant karst cave,was used as a case study.Firstly,a multi-objective optimization model for the rockboltecable support was proposed by using fuzzy mathematics and multi-objective comprehensive decision-making principles.Subsequently,the parameters of the surrounding rock were calibrated by comparing the simulation results obtained by the discrete element method(DEM)with the field monitoring data to obtain an optimized support scheme based on the optimization model.Finally,the optimization scheme was applied to the karst cave section,which was divided into the B-and C-shaped sections.The distribution range of the rockboltecable support in the C-shaped section was larger than that in the B-shaped section.The field monitoring results,including tunnel crown settlement,horizontal convergence,and axial force of the rockboltecable system,were analyzed to assess the effectiveness of the optimization scheme.The maximum crown settlement and horizontal convergence were measured to be 25.9 mm and 35 mm,accounting for 0.1%and 0.2%of the tunnel height and span,respectively.Although the C-shaped section had poorer rock properties than the B-shaped section,the crown settlement and horizontal convergence in the C-shaped section ranged from 46%to 97%of those observed in the B-shaped section.The cable axial force in the Bshaped section was approximately 60%of that in the C-shaped section.The axial force in the crown rockbolt was much smaller than that in the sidewall rockbolt.Field monitoring results demonstrated that the optimized scheme effectively controlled the deformation of the layered surrounding rock,ensuring that it remained within a safe range.These results provide valuable references for the design of support systems in deep-buried tunnels situated in layered rock masses.

THE TOPOLOGICAL OPTIMIZATION FOR TRUSS STRUCTURES WITH STRESS CONSTRAINTS BASED ON THE EXIST-NULL COMBINED MODEL

A new exist-null combined model is proposed for the structural topology optimization. The model is applied to the topology optimization of the truss with stress constraints. Satisfactory computational result can be obtained with more rapid and more stable convergence as compared with the cross-sectional optimization. This work also shows that the presence of independent and continuous topological variable motivates the research of structural topology optimization.

Optimization principle of combined surface and underground mining and its applications

The pit limit optimization is discussed, which is one of the most important problems in the combined min-ing method, on the basis of the economic model of ore-blocks. A new principle of the limit optimization is put for-ward through analyzing the limitations of moving cone method under such conditions. With a view to recovering asmuch mineral resource as possible and making the maximum profit from the whole deposit, the new principle is tomaximize the sum of gain from both open-pit and underground mining. The mathematical models along the horizon-tal and vertical directions and modules for software package (DM&MCAD) have been developed and tested inTonglushan Copper Mine. It has been proved to be rather effective in the mining practice.

Global optimal control for regional sewer systems

In order to control combined system overflow (CSO) pollution of regional sewer systems in Shanghai,a global optimal control (GOC) is presented in this study.The GOC is based on the analysis of current situation and can maximize the utilization of the free storage of each sub systems and decrease the frequencies and durations of CSOs and flooding.A representative regional sewer system,which is located in the northwest of Shanghai and composed of sub systems of Zhenguang,Zhenru and Tongchuan,was taken as an example to demonstrate the efficiency of GOC with hydraulic model simulation test in the two representative scenarios (Scenario Ⅰ and Ⅱ).The results indicated that a great improvement in CSO emission is obtained by using the GOC in the two scenarios,and the CSO volume of three sub systems,Zhenru,Tongchuan and Zhenguang decreases to about 37.0%,38.3% and 35.7% in Scenario Ⅰ and 47.5%,51.8% and 63.5% in Scenario Ⅱ respectively.

Prediction and Optimization Performance Models for Poor Information Sample Prediction Problems

The prediction process often runs with small samples and under-sufficient information.To target this problem,we propose a performance comparison study that combines prediction and optimization algorithms based on experimental data analysis.Through a large number of prediction and optimization experiments,the accuracy and stability of the prediction method and the correction ability of the optimization method are studied.First,five traditional single-item prediction methods are used to process small samples with under-sufficient information,and the standard deviation method is used to assign weights on the five methods for combined forecasting.The accuracy of the prediction results is ranked.The mean and variance of the rankings reflect the accuracy and stability of the prediction method.Second,the error elimination prediction optimization method is proposed.To make,the prediction results are corrected by error elimination optimization method(EEOM),Markov optimization and two-layer optimization separately to obtain more accurate prediction results.The degree improvement and decline are used to reflect the correction ability of the optimization method.The results show that the accuracy and stability of combined prediction are the best in the prediction methods,and the correction ability of error elimination optimization is the best in the optimization methods.The combination of the two methods can well solve the problem of prediction with small samples and under-sufficient information.Finally,the accuracy of the combination of the combined prediction and the error elimination optimization is verified by predicting the number of unsafe events in civil aviation in a certain year.

Coupling model and optimal combination scheme of water,fertilizer,dissolved oxygen and temperature in greenhouse tomato under drip irrigation

Water-fertilizer coupling technology has been widely used in the world.Poor soil aeration,low temperature or high temperature can affect the rate of nutrient uptake by crop roots.Aiming at the interaction between water,fertilizer,dissolved oxygen and temperature(WFOT)coupling model and irrigation flux of tomato in greenhouse,using these four factors with a five-level uniform-precision rotatable central composite design,a mathematical model was established among the four factors affecting tomato yield in a greenhouse,and the optimal combination scheme of WFOT was obtained.Within the test range,tomato yields increased with increasing irrigation quotas(X_(1)),fertilization amount(X_(2)),dissolved oxygen(X_(3))and geothermal pipe water temperature(X_(4)).The magnitude of the effect of each factor of WFOT on tomato yield was in the following order:X_(1),X_(2),X_(4),X_(3)(spring and summer),and X_(1),X_(3),X_(2),X_(4)(autumn and winter).The interaction between high water-low heat and low water-high heat were beneficial for yield increase(spring and summer),the high fertilizer-low heat and low fertilizer-high heat interactions were beneficial to yield increase(autumn and winter).If WFOT agronomic measures were adopted according to the 95%confidence interval,there was a 95%probability that the spring-summer tomato yield will be higher than 89902 kg/hm^(2).The WFOT coupling scheme was X_(1)of 4808-5091 m3/hm^(2),X_(2)(N-P_(2)O_(5)-K_(2)O)of 171-57-84 to 186-62-89 kg/hm^(2),X_(3)of 7.9-8.2 mg/L,and X_(4)of 34.9°C-37.0°C.There was a 95%probability of tomato yield higher than 85209 kg/hm^(2)in autumn and winter,and the WFOT coupling scheme was X_(1)of 5270-5416 m3/hm^(2),X_(2)(N-P_(2)O_(5)-K_(2)O)of 151-50-76 to 167-56-82 kg/hm^(2),X_(3)of 8.0-8.2 mg/L,and X_(4)of 34.1°C-36.2°C.Overall,and the model had a very good simulation effect,with application value.The relative error between spring-summer and autumn-winter yields ranged from 1.12%to 25.34%.The results of the study can provide a theoretical basis for improving the quality and efficiency of greenhouse tomatoes.

Wind power prediction based on variational mode decomposition multi-frequency combinations

Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition(EMD) or ensemble empirical mode decomposition(EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition(VMD). We use a back propagation neural network(BPNN),autoregressive moving average(ARMA)model, and least square support vector machine(LS-SVM) to predict high, intermediate,and low frequency components,respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value.Finally,the prediction performance of the single prediction models(ARMA,BPNN and LS-SVM)and the decomposition prediction models(EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error,and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.

Grey series time-delay predicting model in state estimation for power distribution networks

A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.

基于VMD-改进最优加权法的短期负荷变权组合预测策略

为提升短期电力负荷预测精度,提出了一种变权组合预测策略。首先,为了降低负荷数据的不平稳度,使用变分模态分解(variational mode decomposition,VMD)将负荷数据分解成了高频、低频、残差3种特征模态分量。其次,充分计及负荷数据的时序特点,参考指数加权法原理设计自适应误差重要性量化函数,并结合组合模型在时间窗口内的历史负荷数据的均方预测误差设计改进最优加权法的目标函数和约束条件,以完成子模型的准确变权。最后,针对波动较强的高频分量选定极端梯度提升(XGBoost)和卷积神经网络-长短期记忆(CNN-LSTM)模型并使用改进最优加权法进行组合预测、低频分量使用多元线性回归(MLR)模型预测、残差分量使用LSTM模型预测,叠加各模态分量的预测结果,实现了短期负荷数据的准确预测。实验结果表明,使用策略组合模型的平均绝对百分比误差为4.18%。与使用传统组合策略的组合模型相比,平均绝对百分比预测误差平均降低了0.87%。

基于改进金豺算法的短期负荷预测

针对电力负荷序列波动性和预测精度不高的问题,提出一种基于变分模态分解、排列熵和改进金豺算法优化双向长短期记忆网络的预测模型。首先,利用变分模态分解重构原始负荷序列,再采用排列熵理论对分解后的子序列进行熵值重组;然后,利用改进金豺算法对双向长短期记忆网络的参数进行优化,并对每个子序列建立预测模型;最后,组合各模型结果得到最终预测值。实验结果表明,本文模型预测精度更高,与真实值拟合度更好。

高含水油田“二三结合”转换时机优化

针对现有转换时机研究方法无法满足高含水期老油田“二三结合”转换时机优化设计需要的问题,在综合分析三次采油转换时机和技术经济评价方法的基础上,考虑工程技术可行性和经济效益可行性,提出综合考虑技术、经济、社会价值的“二三结合”全生命周期开发价值链理念,建立高含水期老油田“二三结合”全生命周期效益稳产开发价值链模型,并实例分析“二三结合”转换时机对油藏最终采收率和生命周期的敏感性。结果表明,稳产、效益、生命周期是“二三结合”全生命周期效益稳产开发的主要考虑因素,转换时机对效益和稳产的影响是相对立的。

灵活性驱动下的热电联产机组多目标协同控制策略

灵活性提升会显著影响热电联产机组的热舒适性及运行经济性等性能,为实现机组大范围快速变负荷过程中的综合性能优化,提出了一种基于预测控制的机组多目标协同控制策略。首先,建立了一套考虑灵活性、热舒适性、经济性及平稳性的机组多目标评价指标体系,为实现多目标协同,设计了一种基于预测控制的机组电热协调控制策略,在滚动优化中引入煤耗指标实现经济优化,并给出了多目标控制律的求解方法;然后,以多目标评价指标作为粒子群优化的目标函数,基于仿真模型离线寻优,获得了控制策略中的最佳参数设定,实现了机组综合性能的优化;最后,以某300 MW机组为例进行了仿真测试。结果表明:该策略可提升热电联产机组的运行灵活性,并在较大程度上保证机组的热舒适性和运行经济性,为热电联产机组的多目标优化控制提供了依据。

基于SARIMA和SVR组合模型的转向架系统寿命评估

随着地铁运营时间和里程的增加,地铁车辆逐渐接近其理论寿命,为确保车辆运行安全性,需对其重要子系统进行健康状态及剩余寿命评估。文中选取车辆转向架系统作为研究对象,提出了一种基于协方差优选法的季节性回归移动平均(SARIMA)和支持向量回归(SVR)的组合模型对转向架寿命进行评估。首先,将车辆转向架系统历史故障率转化为健康指数,然后基于协方差优选法将SARIMA和SVR进行赋权组合,根据转向架系统历史健康指数进行预测,最后建立历史和预测的健康指数与运行时间的数学模型,分析得到转向架系统的剩余寿命。以某地铁车辆转向架系统为例进行算例分析及验证,结果表明组合模型可更准确地预测其健康状态,为有关维修部门开展维修维护策略提供理论依据,估计得出其剩余寿命,为车辆寿命后期退役及延寿决策提供理论数据分析支撑。

配电网负荷预测中信号分解和预测模型组合的双层优化策略

负荷时间序列的波动性和非线性特征的加剧对负荷预测方法提出了更高的要求,而常规组合预测方法针对海量负荷数据存在应用局限性问题。为此,提出了配电网负荷预测中时序分解方法和预测模型组合的双层优化策略。首先针对某一负荷预测数据,在时序信号分解层配置权重,以负荷均方根误差最小寻优各分解方法的权重系数,进而获得各时序信号分解方法的最优组合;在此基础上,在预测模型层进行组合方案寻优,通过配置权重系数以获得各预测模型的最优组合,进一步提升负荷预测的精度。仿真结果表明,所提策略可根据预测对象的特征优化组合各信号分解方法和预测模型,降低了配电网负荷序列的非平稳性对预测精度的影响。

基于网络搜索数据的GDP组合预测研究

网络搜索数据(Web Search Data, WSD)是研究宏观经济现象的重要微观信息依据。从需求、供给与政策环境等方面选取和筛选关键词来合成网络搜索指数,采用金枪鱼群(Tuna Swarm Optimization, TSO)算法优化的最小二乘支持向量回归(Least Squares Support Vector Regression, LSSVR)模型,对GDP进行预测。结果表明,网络搜索指数与GDP具有强相关性,合成的网络搜索指数能较好地反映GDP的波动走势;网络搜索数据的加入使得预测结果呈现出强时效性,预测效果及预测精度都取决于对最优模型的选择,引入参数智能优化算法可提高模型的预测性能。提出的TSO-LSSVR&WSD模型充分利用网络搜索数据及组合预测优势,提高了GDP的预测精度和时效性,可应用于宏观经济指标预测中。

土壤Cr含量高光谱反演模型组合优化研究

土壤重金属污染高光谱反演的特征波段提取方法和反演模型的选择是影响反演精度的关键;二者如何优化组合,提高反演精度是目前亟需解决的难题。在华南典型铬(Cr)污染区,采集了92组土壤样品,使用电感耦合等离子体质谱(inductively coupled plasma mass spectrometry,ICP-MS)检测Cr含量,并使用ASD Field Spec4地物光谱仪在实验室收集其高光谱信息。光谱信息预处理采用平滑滤波(SG)+标准正态化(SNV)+二阶微分(SD)变换组合,减弱土壤散射和噪声的影响。选择竞争性自适应重加权采样(CARS)、逐步投影算法(SPA)、无信息变量消除(UVE)、遗传算法(GA)四种算法提取特征波段。选择多元线性回归(MLR)、偏最小二乘法(PLSR)、支持向量回归(SVR)和人工神经网络(ANN)四种反演模型建立特征波段与Cr含量之间的关系。通过对比不同特征波段提取方法和反演模型组合对土壤Cr含量反演的结果发现:采用CARS和UVE特征波段提取方法可以显著提高PLSR、MLR和SVR模型的预测效果;SPA方法能够提高ANN模型的预测效果;通过SG+SNV+SD+CARS+PLSR组合方式,提取位于800~1000、1400~1700以及2100~2450 nm之间的98个特征波段,建模后模型验证,决定系数R2为0.97,均方根误差RMSE为5.25 mg·kg^(-1),平均绝对误差MAE为4.35 mg·kg^(-1),相对分析误差RPD为3.94,表明该模型在预测土壤Cr含量具有优异的性能。以土壤Cr污染高光谱反演为例,通过比较不同特征波段提取方法与反演模型组合的反演精度,确定最优模型,为小样本土壤重金属污染反演的建模提供了思路。

机载悬臂体结构的主被动联合抑振

为解决机载悬臂体结构在长悬臂梁挂载状态下的全频域减振问题,提出了一种基于耦合级联的主被动联合抑振模型及控制优化方法。通过在远端头体连接处加装机械手进行抑振。机械手采用主动抑振减小低频正弦振动,其上串联的被动减振器降低高频随机振动。在对头体连接处进行负载振动等效的基础上,提出了基于减振因子和总均方根加速度的主被动综合减振率的计算方法,并根据高低频切换频率(10 Hz)和综合减振率(40%)等指标得到机械手主被动减振参数。通过耦合级联的方法解决了机械手在主动抑振过程中悬臂体、机械手及其被动减振器的串联耦合问题,从而构建串联耦合作用下的机械手全伺服系统模型。最后采用伺服优化、抑振前馈和极点配置等综合优化算法提升机械手的动态性能。根据仿真结果,机械手的主动抑振带宽和综合减振率均满足抑振需求,为全频域环境载荷作用下机载挂载的减振问题提供了一种新的方法。