Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness(KIC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression(LMR) and gene expression programming(GEP)methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength(UCS), Brazilian tensile strength(BTS), and elastic modulus(E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets.Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of KIC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination(R2),root mean square error(RMSE), and mean absolute error(MAE) were utilized herein. In the case of testing datasets, the values of R2, RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156,respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R2value and lower errors.

Identifying the dependency pattern of daily rainfall of Dhaka station in Bangladesh using Markov chain and logistic regression model

Bangladesh is a subtropical monsoon climate characterized by wide seasonal variations in rainfall, moderately warm temperatures, and high humidity. Rainfall is the main source of irrigation water everywhere in the Bangladesh where the inhabitants derive their income primarily from farming. Stochastic rainfall models were concerned with the occurrence of wet day and depth of rainfall for different regions to model the daily occurrence of rainfall and achieved satisfactory results around the world. In connection to the Markov chain of different order, logistic regression is conducted to visualize the dependence of current rainfall upon the rainfall of previous two-time period. It had been shown that wet day of the previous two time period compared to the dry day of previous two time period influences positively the wet day of current time period, that is the dependency of dry-wet spell for the occurrence of rain in the rainy season from April to September in the study area. Daily data are collected from meteorological department of about 26 years on rainfall of Dhaka station during the period January 1985-August 2011 to conduct the study. The test result shows that the occurrence of rainfall follows a second order Markov chain and logistic regression also tells that dry followed by dry and wet followed by wet is more likely for the rainfall of Dhaka station and also the model could perform adequately for many applications of rainfall data satisfactorily.

Revisiting ENSO impacts on the Indian Ocean SST based on a combined linear regression method

The El Nino-Southern Oscillation(ENSO)has great impacts on the Indian Ocean sea surface temperature(SST).In fact,two major modes of the Indian Ocean SST namely the Indian Ocean Basin(IOB)and the Indian Ocean Dipole(IOD)modes,exerting strong influences on the Indian Ocean rim countries,are both influenced by the ENSO.Based on a combined linear regression method,this study quantifies the ENSO impacts on the IOB and the IOD during ENSO concurrent,developing,and decaying stages.After removing the ENSO impacts,the spring peak of the IOB disappears along with significant decrease in number of events,while the number of events is only slightly reduced and the autumn peak remains for the IOD.By isolating the ENSO impacts during each stage,this study reveals that the leading impacts of ENSO contribute to the IOD development,while the delayed impacts facilitate the IOD phase switch and prompt the IOB development.Besides,the decadal variations of ENSO impacts are various during each stage and over different regions.These imply that merely removing the concurrent ENSO impacts would not be sufficient to investigate intrinsic climate variability of the Indian Ocean,and the present method may be useful to study climate variabilities independent of ENSO.

Control method based on DRFNN sliding mode for multifunctional flexible multistate switch

To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.

基于ADS-B与Mode-SEHS联合观测的民航空域风场重建方法

准确实时的风场数据对保障民航飞行安全有着重要作用,针对风场的精确重构问题,提出了一种基于飞行器监测数据的风场重建方法。旨在利用广播式自动相关监视和S模式增强型监视联合观测数据计算空域内的风观测值,并结合机器学习中的高斯过程回归模型,利用时间和空间上离散的风观测值进行模型训练,完整重建目标空域风场。实验结果表明,重建的风场风速的平均绝对误差为2.72m/s,相对误差为8.21%,风向的平均绝对误差为3.66°,证明了方法能够快速地完成准确实时的风场重建。

Design of Wind Turbine Torque Controller with Second‑Order Integral Sliding Mode Based on VGWO Algorithm

A robust control strategy using the second-order integral sliding mode control(SOISMC)based on the variable speed grey wolf optimization(VGWO)is proposed.The aim is to maximize the wind power extraction of wind turbine.Firstly,according to the uncertainty model of wind turbine,a SOISMC torque controller with fast convergence speed,strong robustness and effective chattering reduction is designed,which ensures that the torque controller can effectively track the reference speed.Secondly,given the strong local search ability of the grey wolf optimization(GWO)and the fast convergence speed and strong global search ability of the particle swarm optimization(PSO),the speed component of PSO is introduced into GWO,and VGWO with fast convergence speed,high solution accuracy and strong global search ability is used to optimize the parameters of wind turbine torque controller.Finally,the simulation is implemented based on Simulink/SimPowerSystem.The results demonstrate the effectiveness of the proposed strategy under both external disturbance and model uncertainty.

A Freight Mode Choice Analysis Using a Binary Logit Model and GIS: The Case of Cereal Grains Transportation in the United States

Mode choice is important in shipping commodities efficiently. This paper develops a binary logit model and a regression model to study the cereal grains movement by truck and rail in the United States using the publically available Freight Analysis Framework (FAF2.2) database and U.S. highway and networks and TransCAD, a geographic information system with strong transportation modeling capabilities. The binary logit model and the regression model both use the same set of generic variables, including mode split probability, commodity weight, value, network travel time, and fuel cost. The results show that both the binary logit and regression models perform well for cereal grains transportation in the United States, with the binary logit model yielding overall better estimates with respect to the observed truck and rail mode splits. The two models can be used to study other commodities between two modes and may produce better results if more mode specific variables are used.

Electricity Demand Time Series Forecasting Based on Empirical Mode Decomposition and Long Short-Term Memory

Load forecasting is critical for a variety of applications in modern energy systems.Nonetheless,forecasting is a difficult task because electricity load profiles are tied with uncertain,non-linear,and non-stationary signals.To address these issues,long short-term memory(LSTM),a machine learning algorithm capable of learning temporal dependencies,has been extensively integrated into load forecasting in recent years.To further increase the effectiveness of using LSTM for demand forecasting,this paper proposes a hybrid prediction model that incorporates LSTM with empirical mode decomposition(EMD).EMD algorithm breaks down a load time-series data into several sub-series called intrinsic mode functions(IMFs).For each of the derived IMFs,a different LSTM model is trained.Finally,the outputs of all the individual LSTM learners are fed to a meta-learner to provide an aggregated output for the energy demand prediction.The suggested methodology is applied to the California ISO dataset to demonstrate its applicability.Additionally,we compare the output of the proposed algorithm to a single LSTM and two state-of-the-art data-driven models,specifically XGBoost,and logistic regression(LR).The proposed hybrid model outperforms single LSTM,LR,and XGBoost by,35.19%,54%,and 49.25%for short-term,and 36.3%,34.04%,32%for longterm prediction in mean absolute percentage error,respectively.

HP nonlinear guidance law design based on smooth sliding mode control

To eliminate the perturbation of interceptor detection induced by aerodynamic heating,the head pursuit (HP) guidance law for three-dimensional interception was presented. The guidance law positioned the interceptor ahead of the target on its flight trajectory,and the speed of interceptor was required to be lower than that of the target. On the basis of a novel head pursuit three-dimensional guidance model,a nonlinear guidance law was developed based on smooth sliding mode control theory. At the same time,a special observer was designed to estimate the target acceleration,and a numerical example on maneuvering ballistic target interception verified the effectiveness of the presented guidance law.

基于参数自适应SVR和VMD-TCN的水电机组劣化趋势预测

针对水电机组难以利用实时监测数据对机组劣化状态进行有效评估,以及水电机组不同运行工况对运行状态指标趋势预测模型参数影响显著的问题,提出一种基于参数自适应支持向量回归机(SVR)、变分模态分解(VMD)和时间卷积网络(TCN)的水电机组劣化趋势预测方法;首先按照功率和水头将机组运行工况细化为若干典型工况,在此基础上采用改进天鹰算法建立SVR模型,对各个工况下的预测参数进行寻优,建立起工况与最优参数的数据;再通过神经网络对工况和最优预测参数进行拟合,构建出映射两者复杂关系的非线性函数,然后将构建出的映射关系加入到传统的SVR中,实现适应于水电机组工况变化的自适应SVR健康模型;其次,根据健康模型输出的标准值和监测数据,计算出劣化趋势序列;最后,考虑到劣化趋势序列的非线性因素,建立了一个基于VMD-TCN的时间序列预测模型,以实现对劣化趋势的准确预测。并设计多组对比实验,验证所提出模型的精度更高,时间更快。

基于雨课堂混合式教学模式的工程数学教学质量评价研究

该文基于雨课堂混合式教学模式的工程数学教学数据,构建教学质量评价体系。首先,从雨课堂教学数据中提取出对教学质量有重大影响的五个因素,即课件预习率、到课率、课堂答题得分率、课后作业和期中考试成绩。其次,以期末考试成绩作为课程教学质量的目标,建立单因素线性回归模型和多因素线性回归模型。结果表明每个因素对期末考试成绩的影响都显著,但在多因素分析中,预习率、课后作业对期末考试成绩的影响显著,其中课后作业占比最大,贡献最高。最后,利用统计回归模型预测期末考试成绩,对学生实施精准教学。该方法可为工程数学高效、准确的教学质量评价提供借鉴和参考,推动智能化教学质量评价体系的建立。

基于EMD-BiLSTM-ANFIS的负荷区间预测

考虑到新型电力负荷随机性增强,传统的准确预测方法已无法满足要求,提出一种EMD-BiLSTM-ANFIS(Empirical Mode Decomposition-Bi-directional Long Short-Term Memory-Adaptive Network-based Fuzzy Inference System)分位数预测负荷概率密度的方法,使用负荷预测区间取代点预测的准确数值,能为电力系统分析与决策提供更多数据,增强预测的可靠性。首先将原始负荷序列通过EMD(Empirical Mode Decomposition)分解成若干分量,并通过计算样本熵分为3类分量。然后将重构后的3类分量与由相关性筛选的外界因素特征采用BiLSTM、ANFIS模型进行训练和分位数回归(QR:Quantile Regression),并将分量的预测区间结果累加得到最终负荷的预测区间。最后利用核密度估计输出任意时刻用户负荷概率密度预测结果。通过与CNN-BiLSTM(Convolutional Neural Network-Bidirectional Long Short-Term Memory)、LSTM(Long Short-Term Memory)模型对比点预测及区间预测结果,证明了该方法的有效性。

基于新闻情感分析和区间分解的汇率预测研究

汇率序列具有非线性和连续变化等特点,其细节波动是一系列事件和新闻综合影响的结果.然而,现有区间预测模型难以量化重大事件和公众情绪的影响,导致其缺乏广泛的适用性,且传统区间分解方法存在上下界混叠的缺陷.因此,论文从新冠疫情冲击出发,提出一种基于新闻情感分析和区间分解的汇率波动实时预测模型.首先,基于Snownlp情感词典对外汇新闻文本进行情感分析,获得相应的情感分数.另外,构建全球恐惧指数(the global fear index,简称GFI)以量化新冠疫情的影响,并将其与芝加哥期权交易所波动率(the Chicago board options exchange volatility index,简称VIX指数)相结合作为汇率的影响因素.然后,提出一种新的区间经验模态分解(interval empirical mode decomposition,简称IEMD)方法对区间汇率序列进行多尺度分解,并根据样本熵重构得到高、中、低频区间序列和残差项.其次,利用极限学习机(extreme learning machine,简称ELM)、多层感知机(multi-layer perceptron,简称MLP)、随机森林(random forest,简称RF)和二次曲面支持向量回归(quadric surface support vector regression,简称QSSVR)分别对不同特征的子序列进行组合预测,以提高预测结果的准确性和稳定性.最后,利用论文方法对美元兑人民币、澳元兑人民币和瑞士法郎兑人民币3种汇率进行实证预测分析,结果表明,论文模型适用于重大事件影响下的汇率区间波动预测,与现有方法相比具有较高的预测精度.

基于功率重构和时序特性约束的长预见期光伏集群功率预测

光伏装机容量的逐渐增大为大规模的光伏并网带来了巨大挑战,突破更长预见期的光伏功率预测有助于电力系统的安全稳定运行。现有研究及应用最长预见期为7 d,为将预见期延长至8~15 d,提出了一种基于功率重构和时序特性约束的长预见期光伏集群功率预测方法。首先,采用近似积分计算日电量和辐照能;其次,基于麻雀搜索算法优化变分模态分解以分解电量及辐照能序列,并采用多元线性回归模型对不同频率的分量进行预测叠加得到电量预测结果;然后,根据出力特性建立约束过程,将电量预测结果重构为光伏功率;最后,将所提方法应用于中国甘肃省某光伏集群,模型在不同季节典型月功率预测的均方根误差平均降低2.55%,验证了方法的有效性。

航空旅客出行需求强度异质性研究

为了研究旅客出行需求强度问题,采用问卷调查的方式采集数据,对回收的问卷进行数据整理,构建了潜在类别模型,并用软件mplus对出行人群的出行需求强度进行分类,得到结果如下:可通过旅客属性特征调查,将旅客分为4类人群,分别是高需求人群、中高需求人群、中低需求人群、低需求人群。对分类结果采用多元logistic回归分析,结果表示需求强度人群划分影响因素主要为职业、出行目的、票价提高是否出行等。根据回归结果分析可以将4类需求强度异质性做具体的分析。

基于变分模态分解和IGJO-SVR的网络舆情预测

网络舆情演化趋势预测在当今的网络环境中对相关政府部门监管舆情发展和维持社会舆论稳定具有十分重要的现实意义。本文针对网络舆情数据的特殊性以及考虑模型预测结果的精确性,使用变分模态分解(VMD)和改进后的金豺优化支持向量回归(IGJO-SVR)构建网络舆情演化趋势预测模型,并以“北溪”事件相关舆情数据为案例进行实证研究,对比结果表明,本文所构建的预测模型精度显著优于其余模型。基于变分模态分解VMD和IGJO-SVR的网络舆情热度预测模型具有较为优秀的预测精度,在实际工作中可为相关政府部门提供切实有效的舆情态势研判和决策帮助。

耕地“非粮化”影响因素空间效应研究——以珠三角为例

为科学管控耕地非粮化,本研究以珠三角县级行政区为研究单元,在揭示耕地非粮化空间分异特征基础上,采用空间杜宾模型和地理加权回归模型探究耕地非粮化及其空间效应。结果表明:珠三角2019年各县平均非粮化率为47.8%,高于全国平均水平。从非粮化率来看,耕地非粮化集聚于珠三角周边县域及部分中部县域,以低-低和高-高集聚为主;从非粮化面积来看,耕地非粮化集聚于研究区东北部,以高-高集聚为主。珠三角非粮化存在空间依赖性。从直接效应看,第一产业GDP占比、到市中心的距离与非粮化呈负相关,劳均耕地面积、有效耕地灌溉面积与非粮化呈正相关;从溢出效应看,人均GDP与非粮化呈正相关。第一产业GDP占比和有效耕地灌溉面积对非粮化的影响均呈现中部高、周边低的空间异质性特征。研究表明,经济发展水平较高区域更易产生“非粮化”,非粮化治理应当因地制宜、分级整治,坚决落实“非粮化”管理政策,提高种粮收益和粮食综合生产力,促进粮农降本增效。

基于CEEMD-BiLSTM-RFR的短期光伏功率预测

由于短期光伏预测中气象因素的时间尺度不同,直接分析其对光伏功率的相关性,易忽略时间尺度的影响,进而导致预测模型误差。为提高光伏功率预测精度,构建了预测模型。首先,利用互补集合经验模态分解(complementary empirical mode decomposition,CEEMD)将光伏序列进行分解,得到在不同时间尺度上的光伏分量;然后,通过Pearson相关系数分析各光伏分量与空气温度、太阳辐射度、风速、风向和空气湿度的关系,对于强相关分量建立关于气象因素的随机森林回归(random forest regression,RFR)预测模型,弱相关分量直接通过双向长短期记忆网络(bidirectional long short-term memory neural network,BiLSTM)进行预测;并将预测求和输出。通过安徽省蚌埠市光伏电站7月实测数据进行验证,实验结果表明,所提预测模型CEEMD-BiLSTM-RFR相比传统预测模型有较好的预测精度。

Second-order terminal sliding mode control for hypersonic vehicle in cruising flight with sliding mode disturbance observer

This paper focuses on the design of nonlinear robust controller and disturbance observer for the longitudinal dynamics of a hypersonic vehicle （HSV） in the presence of parameter uncertainties and external disturbances. First, by combining terminal sliding mode control （TSMC） and second-order sliding mode control （SOSMC） approach, the second- order terminal sliding control （2TSMC） is proposed for the velocity and altitude tracking control of the HSV. The 2TSMC possesses the merits of both TSMC and SOSMC, which can provide fast convergence, continuous control law and high- tracking precision. Then, in order to increase the robustness of the control system and improve the control performance, the sliding mode disturbance observer （SMDO） is presented. The closed-loop stability is analyzed using the Lyapunov technique. Finally, simulation results illustrate the effectiveness of the proposed method, as well as the improved overall performance over the conventional sliding mode control （SMC）.

众数自适应Lasso回归的统计推断

本文给出了自适应Lasso的众数回归模型,用来对众数回归模型的变量进行选择.对比传统的均值回归模型和中位数回归模型,众数回归在解决重尾、多峰分布问题时更加稳健.众数回归模型的主要估计方法是核估计方法,当自变量的数目较大时,该方法会产生难以忽略的计算误差.本文在核估计方法的众数回归模型基础上添加惩罚项,并通过自适应Lasso方法进行参数估计,有效的剔除了贡献率低的自变量,同时提高了计算的准确性.本文详细阐述了该计算方法,并在一些正则条件下,给出了模型的参数的估计方法和估计值的渐近正态性.模拟实验和实证分析研究了所提方法在有限样本下的性质.对比均值回归模型和传统的众数回归模型,添加自适应Lasso惩罚项的众数回归模型极大地提高了参数估计的准确性.