聚丙烯复合材料老化数据集成学习

聚丙烯复合材料老化实验周期长,且单次实验采集的数据样本少,使用传统机器学习方法进行预测的准确度较低.为了解决聚丙烯复合材料老化数据样本少与预测准确性低的问题,提出了一种虚拟样本生成(virtual sample generation,VSG)的集成学习预测方法.首先,对聚丙烯复合材料老化数据使用高斯混合模型(Gaussian mixed model,GMM)虚拟样本生成方法平滑生成验证有效的虚拟样本;然后,使用生成后的数据集建立集成学习预测模型,该模型包含随机森林(random forest,RF)、极端梯度提升(extreme gradient boosting,XGBoost)算法、轻量级梯度提升机(light gradient boosting machine,LightGBM)算法以及分类梯度提升(categorical boosting,CatBoost)算法.实验表明:集成学习模型的LightGBM算法与CatBoost算法性能最优,在测试数据上均方误差为0.001 3与0.0001,比RF算法与XGBoost算法分别高出0.4与0.2.聚丙烯复合材料老化虚拟样本生成与集成...     

聚丙烯复合材料老化数据集成学习

聚丙烯复合材料老化实验周期长, 且单次实验采集的数据样本少, 使用传统机器学习方法进行预测的准确度较低. 为了解决聚丙烯复合材料老化数据样本少与预测准确性低的问题, 提出了一种虚拟样本生成(virtual sample generation, VSG)的集成学习预测方法. 首先, 对聚丙烯复合材料老化数据使用高斯混合模型(Gaussian mixed model, GMM)虚拟样本生成方法平滑生成验证有效的虚拟样本; 然后, 使用生成后的数据集建立集成学习预测模型, 该模型包含随机森林(random forest, RF)、极端梯度提升(extreme gradient boosting, XGBoost)算法、轻量级梯度提升机(light gradient boosting machine, LightGBM)算法以及分类梯度提升(categorical boosting, CatBoost)算法. 实验表明: 集成学习模型的 LightGBM 算法与 CatBoost 算法性能最优, 在测试数据上均方误差为 0.001 3 与 0.000 1, 比 RF 算法与 XGBoost 算法分别高出 0.4 与 0.2. 聚丙烯复合材料老化虚拟样本生成与集成学习方法可以有效解决实验周期长、单次实验采集的数据样本少的问题, 并可取得比单一机器学习算法更优的性能.     

基于贝叶斯优化注意力机制LSTNet模型的短期电力负荷预测

为更充分挖掘多元负荷序列间的有效信息,从而提高预测精度,提出了一种集成贝叶斯超参数优化算法、注意力机制的长期和短期时间序列网络（long and short-term time-series network with attention,LSTNet-attention）以及误差修正的短期负荷预测模型。首先,构建基于贝叶斯优化的LSTNet-attention模型进行初步预测,利用贝叶斯算法优化模型多个结构参数,降低人工设置参数的随机性,并通过注意力机制合理分配特征权重;然后,通过基于贝叶斯参数优化的极端梯度提升算法(extreme gradient boosting,XGBoost)误差修正模型来挖掘初步预测误差序列中潜在、未被利用的有效信息,进行误差预测和修正,进而得到最终的预测结果。通过使用澳大利亚某地真实负荷数据进行实证分析,实验结果表明,所提预测模型相较于其它模型具有更好的预测效果,可为负荷预测等工作提供一定参考。     

基于XGBoost的短时交通流预测模型

为提高路段短时交通流的预测精度,选取路段平均旅行时间作为预测指标,建立了一种基于极端样度上升(extrem gradient boosting,XGBoost)的短时交通流预测模型。首先通过对交通流数据的分析,在考虑交通流时空特性的基础上,分别构建目标路段时间序列训练集、测试集以及时空序列训练集、测试集,然后基于XGBoost模型以及构建的训练样本集建立时间序列预测模型以及时空序列预测模型,并利用训练好的模型进行预测,最后将模型预测结果与线性回归模型、神经网络模型预测结果进行比较。实验结果表明:基于XGBoost的短时交通流预测模型能够对路段未来时段平均旅行时间进行比较准确的预测,其中时间序列预测模型均方根误差为5. 32,时空序列预测模型均方根误差为4. 82,均低于线性回归模型和神经网络模型,且相比于仅考虑时间因素的短时交通流预测模型,同时考虑时空因素的预测模型得到的误差更低,预测效果更好。     

基于LSTM-LGB模型的光伏电站辐照强度预测

为提高光伏电站辐照强度的预测精度,提出一种基于长短时记忆网络(long short term memory network,简称LSTM)和轻梯度提升机(light gradient boosting machine,简称LightGBM)的组合模型.以LightGBM模型的预测结果作为LSTM模型的一个特征输入,然后采用误差倒数法对以上两种模型的数据进行加权组合,得到组合模型的预测值.算例分析结果表明:与其他3种模型比较,该文组合模型的预测精度最高.     

基于机器学习的飞机起落架着陆载荷预测模型

随着物联网、大数据技术的深入发展,一型装备交付部队的同时,往往需同步提供数字孪生模型以优化视情维护过程。论文基于某型号飞机试飞数据,提出一种将机器学习技术用于飞机起落架着陆载荷预测模型构建的方法。以某型号飞机飞行参数为输入,以传感器实测的左起落架垂向载荷为输出,经数据清洗和特征降维后,分别建立极端梯度提升（extreme gradient boosting, XGBoost）、随机森林（Random Forest）和多层前馈（back propagation, BP）神经网络模型,并对所建模型进行调优。经对比和评估,XGBoost模型具有最高的预测精度,对起落架载荷绝大多数样本的预测误差均保持在6%以内,同时建模时间少,泛化能力强,为起落架载荷预测最优模型。     

基于LightGBM的船舶航速预测模型

针对现有基于机器学习算法的船舶航速预测模型无法兼顾计算精度高、泛化能力强及计算速度快的问题，提出基于LightGBM的船舶航速预测模型，并以一艘安装有能效监测系统的内河船舶为研究对象，运用LightGBM算法建立以实时风速、风向、水深、水流速度、尾轴转速、轴功率和主机油耗为输入的船舶航速预测模型，并同时与RR、SVR、DT、BPNN、RF、GBDT和XGBoost七种机器学习算法的航速预测结果进行比较。结果表明：基于LightGBM建立的船舶航速预测模型的精度、泛化能力、运算速度均排名第二，综合性能最好，可在保证较高预测精度和较强泛化能力前提下，实现对船舶航速的快速预测。     

基于LSTM-XGboost组合的超短期风电功率预测方法

受数值天气预报信息影响,风电功率变化具有较强的随机波动性,传统单一预测模型精度较低,难以满足现实预测需求。为此,提出基于LSTM-XGboost组合的超短期风电功率预测方法。首先,基于风电场的气象数据,采用皮尔逊相关系数法筛选与风电功率强相关的气象数据,建立风电功率预测模型数据集;然后,将归一化处理的数据集作为LSTM(long short-term memory)和XGboost (extreme gradient boosting)的模型输入,分别构建LSTM和XGboost的超短期风电预测模型,在此基础上,采用误差倒数法对LSTM和XGboost的预测数据进行加权构建组合预测模型;最后,以张家口某示范工程风电场实际运行数据验证组合模型的有效性。结果表明,相较于其他4种单一预测模型,组合模型具有更高的预测精度。     

基于可解释机器学习模型的南宁市野火灾害易发性研究

野火易发性评价对野火灾害的前期预防以及灾害管理决策的制定至关重要。目前野火易发性的研究主要集中于提高模型的预测精度,而往往忽略对模型的内部决策机制进行解释分析。为此,构建了一种基于可解释机器学习的野火易发性模型,并详细分析了各因子对野火易发性预测结果的影响。以南宁市历史野火样本为基础,综合考虑样本的空间分布特征,选取高程、归一化植被指数(normalized difference vegetation index, NDVI)、年均降雨和平均气温等18项评价因子,利用分类和回归树(calssification and regression tree, CART)、随机森林(random forest, RF)、轻量的梯度提升机(light gradient boosting machine, LGBM)和极致梯度提升(extreme gradient boosting, XGBoost)4种机器学习模型构建野火易发性预测模型。基于性能最优的易发性模型,运用沙普利加和解释(shapley additive explanations, SHAP)方法完成特征全局性解释、依赖性分析和典型样本...     

基于多机器学习模型的逐小时PM2.5浓度预测对比

【目的】比较分析XGBoost模型、LightGBM模型、随机森林模型(RF)、K最近邻模型(KNN)、长短期记忆神经网络(LSTM)、决策树模型(DT)共6个PM_2.5浓度预测模型,以准确、及时预测环境PM_2.5浓度。【方法】基于重庆市合川区2020年全年空气质量监测数据和气象数据,通过最大相关最小冗余算法(MRMR)进行数据降维选择最优特征子集,作为模型的输入,逐一进行PM_2.5浓度预测;考虑到不同季节PM_2.5浓度差异较大,故分季节预测了PM_2.5浓度;为了探究各模型预测性能,计算了各模型运行时间和内存占用,并基于PM_2.5与特征变量的相关性和特征变量的重要性探讨了模型预测性能季节性差异原因。【结果】模型总体预测精度从高到低排序为 XGBoost、RF、LightGBM、LSTM、KNN、DT模型;预测性能方面,6个模型均表现为秋冬季节预测精度高于春夏季节;LightGBM模型可在保证模型精度的情况下,大幅减少模型训练时间和内存占用;特征重要性显示PM₁₀浓度、气温和气压的重要性高,O₃浓度、风向和NO₂浓度重要性相对较弱。【结论】采取MRMR方法进行数据降维选取的最优特征子集能较好地预测PM_2.5浓度;相比较而言,XGBoost、RF、LightGBM、LSTM模型在PM_2.5浓度预测上具有较优性能,其中综合性能较好的为LightGBM模型。     

Markedness and its Interpretation in Language Use

Language markedness is a common phenomenon in languages, and is reflected from hearing, vision and sense, i.e. the variation in the three aspects such as phonology, morphology and semantics. This paper focuses on the interpretation of markedness in language use following the three perspectives, i.e. pragmatic interpretation, psychological interpretation and cognitive interpretation, with an aim to define the function of markedness.     

Features of Women's language

Language is a means of verbal communication. People use language to communicate with each other. In the society, no two speakers are exactly alike in the way of speaking. Some differences are due to age, gender, statue and personality. Above all, gender is one of the obvious reasons. The writer of this paper tries to describe the features of women＇s language from these perspectives： pronunciation, intonation, diction, subjects, grammar and discourse. From the discussion of the features of women＇s language, more attention should be paid to language use in social context. What＇s more, the linguistic phenomena in a speaking community can be understood more thoroughly.     

低渗透非均质砂岩油藏启动压力梯度研究

理论推导与室内实验相结合,建立了低渗透非均质砂岩油藏启动压力梯度确定方法。首先借助油藏流场与电场相似的原理,推导了非均质砂岩油藏启动压力梯度计算公式。其次基于稳定流实验方法,建立了非均质砂岩油藏启动压力梯度测试方法。结果表明:低渗透非均质砂岩油藏的启动压力梯度确定遵循两个等效原则。平面非均质油藏的启动压力梯度等于各级渗透率段的启动压力梯度关于长度的加权平均;纵向非均质油藏的启动压力梯度等于各渗透率层的启动压力梯度关于渗透率与渗流面积乘积的加权平均。研究成果可用于有效指导低渗透非均质砂岩油藏的合理井距确定,促进该类油藏的高效开发。     

An Analysis of Catherine＇s Character——Wuthering Heights

Wuthering Heights, Emily Bronte＇s only novel, was published in December of 1847 under the pseudonym Ellis Bell. The book did not gain immediate success, but it is now thought one of the finest novels in the English language. Catherine is the key character of this masterpiece, because everybody and everything center on her though she had a short life. We can understand this masterpiece better if we know Catherine well.     

Lower Paleozoic oil relationships within Williston Basin, Canada

The Williston Basin is a significant petroleum province, containing oil production zones that include the Middle Cambrian to Lower Ordovician, Upper Ordovician, Middle Devonian, Upper Devonian and Mississippian and within the Jurassic and Cretaceous. The oils of the Williston Basin exhibit a wide range of geochemical characteristics defined as "oil families", although the geochemical signature of the Cambrian Deadwood Formation and Lower Ordovician Winnipeg reservoired oils does not match any "oil family". Despite their close stratigraphic proximity, it is evident that the oils of the Lower Palaeozoic within the Williston Basin are distinct. This suggests the presence of a new "oil family" within the Williston Basin. Diagnostic geochemical signatures occur in the gasoline range chromatograms, within saturate fraction gas chromatograms and biomarker fingerprints. However, some of the established criteria and cross-plots that are currently used to segregate oils into distinct genetic families within the basin do not always meet with success, particularly when applied to the Lower Palaeozoic oils of the Deadwood and Winnipeg Formation.     

On Tragic Consciousness In The Works By Ernest Hemingway

As an American modern novelist who were famous in the literary world, Hemingway was not a person who always followed the trend but a sharp observer. At the same time, he was a tragedy maestro, he paid great attention on existence, fate and end-result. The dramatis personae's tragedy of his works was an extreme limit by all means tragedy on the meaning of fearless challenge that failed. The beauty of tragedy was not produced on the destruction of life, but now this kind of value was in the impact activity. They performed for the reader about the tragedy on challenging for the limit and the death.     

Achieving Unusual States of Matter Under High Pressure

正The periodicity of the elements and the non-reactivity of the inner-shell electrons are two related principles of chemistry,rooted in the atomic shell structure.Within compounds,Group I elements,for example,invariably assume the+1 oxidation state,and their chemical properties differ completely from those of the p-block elements.These general rules govern our understanding of chemical structures and reactions.Using first principles calcula-     

Exchange-Correlation and Electronic Excitation Energies from Pairing Matrix Fluctuations

We have developed an adiabatic connection to formulate the ground-state exchange-correlation energy in terms of pairing matrix linear fluctuations.This formulation of the exchange-correlation energy opens a new channel for density functional approximations based on the many-body perturbation theory.We illustrate the potential of such approaches with an approximation based on the particle-particle Random Phase Approximation(pp-RPA).This re-     

Density Functional Theory for the Response of Periodic Systems to Electric Fields Based on the Vector Potential Approach

正The electronic and nuclear(structural/vibrational)response of 1D-3D nanoscale systems to electric fields gives rise to a host of optical,mechanical,spectral,etc.properties that are of high theoretical and applied interest.Due to the computational difficulty of treating such large systems it is convenient to model them as infinite and periodic(at least,in first approximation).The fundamental theoretical/computational problem in doing so is that     

Call for Papers

<正>"The Journal of Shanghai Normal University:Mathematics"is published by Shanghai Normal University as regular issues of The Journal of Shanghai Normal University each year from 2014 in English.The editors-in-chief of the issues are professors Yuhao Cong and Maoan Han.The Journal of Shanghai Normal University was started in 1958 with