基于长短时记忆网络的Encoder-Decoder多步交通流预测模型

交通流序列多为单步预测.为实现交通流序列的多步预测,提出一种基于编码器解码器(encoder-decoder,ED)框架的长短期记忆网络(long short-term memory,LSTM)模型,即ED LSTM模型.将自回归滑动平均、支持向量回归机、XGBOOST、循环神经网络、卷积神经网络、LSTM作为对照组进行实验验证.实验结果表明,当预测时间步长增加时,ED框架能够减缓模型性能的下降趋势,LSTM能够充分挖掘时间序列中的非线性关系.除此之外,在单变量输入的情况下,在PEMS-04数据集上,当预测时间步长为t+1到t+12的12个时间步时,ED LSTM模型的均方根误差(root mean squard error,RMSE)及平均绝对误差(mean absolute error,MAE)分别下降0.210～5.422、0.061～0.191.相较于单因素输入,多因素输入的ED LSTM模型在12个预测时间步长下,RMSE、MAE分别下降0.840、0.136.实验证明了ED LSTM模型能够有效地用于交通流序列的多步及单因素、多因素预测任务.     

基于公交浮动车数据的城市主干道交通拥堵预测

交通拥堵预测是解决交通拥堵问题的前提。针对速度特性分析不全面的问题,基于公交浮动车数据,在速度时间相关性和空间相关性分析的基础上,加入了公交流量和时间占有率两个特征,提出了考虑时空特性和公交车流特性的改进粒子群优化的径向基函数神经网络(particle swarm optimization-radial basis function, PSO-RBF)速度预测模型。通过比较预测结果与速度阈值,得到城市主干道的交通拥堵情况。结果表明,与只考虑时空特性的预测结果相比,所提出的基于时空特性和公交车流特性的预测方法,可使模型预测的均方根误差(root mean square error, RMSE)、平均绝对误差(mean absolute error, MAE)分别降低13.58%、12.63%,决定系数达92.39%。同时,实例验证了改进的PSO-RBF神经网络模型的预测精度要优于标准的PSO-RBF神经网络。     

基于GCN-GRU组合模型的基坑周边管线沉降预测

为提高基坑变形预测结果的准确性，在传统的单点时间序列预测基础上，引入监测数据的空间特征对预测方法进行改进.基于图卷积神经网络(graph convolutional network, GCN)和门控循环单元(gate recurrent unit, GRU),构建一种能捕获数据时空关联性的变形预测模型GCN-GRU,并将其应用于上海某基坑周边管线沉降的变形预测.结果表明，相比于GRU时间序列预测模型，考虑了空间关联性的GCN-GRU模型在单步预测中的均方根误差(root mean square error, RMSE)和平均绝对百分比误差(mean absolute percentage error, MAPE)分别降低了27.3%和25.0%,多步预测中的RMSE和MAPE降低了37.2%和37.3%,预测结果准确性较高.该方法可为同类基坑工程周边管线沉降变形预测提供参考.     

基于Holt-Winters及长短期记忆的云资源组合预测模型

云资源的预测分析对于响应资源请求并及时做出决策非常重要,针对容器云资源的过度调配、供应不足的资源管理问题以及云资源预测精度低、数据波动性等问题,为使云资源的预测能够为工作负载的需求提前响应并做出合理分配,提出了一种基于Holt-Winters和长短期记忆神经网络(long short-term memory, LSTM)(HW-LSTM)的云资源组合预测模型,并以预测残差的变异系数赋权。对亚马逊CPU数据集的预测实验表明,提出的组合模型比Holt-Winters、LSTM及卷积神经网络(convolutional neural networks, CNN)模型预测性能及稳定性更好,均方根误差(root mean squared error, RMSE)、平均绝对误差(mean absolute error, MAE)、平均绝对百分比误差(mean absolute percentage error, MAPE)、平均绝对比例误差(mean absolute scaled error, MASE)、方差D(n)优化范围分别为0.065～1.026、0.023～0.269、0.001～0....     

毫米波雷达数据驱动的桥梁挠度预测

为监测公路桥梁健康状况从而保证车辆行驶桥面的安全性,基于毫米波雷达监测的桥梁挠度数据,结合深度学习理论,提出了一种基于卷积神经网络（convolutional neural network, CNN）与门控制循环单元（gate recurrent unit, GRU）组合的桥梁挠度预测模型。首先,获取高速公路大桥高精度挠度数据,通过数据预处理,在保留原始数据特征的基础上,修复部分噪声数据;其次,将处理后的样本数据、时间步长和特征数的三维数据,以桥梁挠度数据序列构造的输入矩阵作为输入层,经过CNN-GRU组合模型的密集连接层后,输出预测桥梁挠度值。最后,选取具有代表性的监测点数据,利用均方根误差 (root mean square error, RMSE)、平均绝对误差 (mean absolute error, MAE)、平均百分比误差 (mean absolute percentage error, MAPE)进行预测效果验证。结果表明,CNN-GRU模型的精度更高：较于传统LSTM（long short-term memory）模型在RMSE上提升了59.65%,MAE提升了61.30%;较于CNN-LSTM模型在RMSE上提升了2.48%,MAE提升了4.87%。其对于桥梁挠度极值及趋势的判断基本准确,可以作为桥梁健康状况预测的科学依据。     

基于关联规则及组合模型的面料需求预测

由于服装的面料组成具有复杂性,企业在不同时间对不同规格型号面料需求量不一致,传统的人工预测及单维度智能预测模型难以解决问题。针对服装企业面料需求非确定性、预测难的痛点,提出基于关联规则及组合模型的面料需求预测方法。文章首先构建Apriori面料型号关联模型,挖掘多批多类面料间的型号关联规则;然后构建Prophet时间序列模型与长短期记忆神经网络(long short-term memory,LSTM)的组合预测模型Prophet-LSTM,结合其在解决面料需求预测问题上的优势;最后将挖掘出的高关联面料型号历史需求数据作为输入,采用量子粒子群算法(quantum particle swarm optimization,QPSO)优化组合模型权值系数,进行关联面料需求量预测。使用RMES(root mean squared error,RMSE)及MAE(mean absolute error,MAE)作为评价指标设计对比实验,实验结果表明：采用量子粒子群优化的QPSOProphet-LSTM面料需求预测模型RMES较Prophet降低5.464,较LSTM降低1.184;MAE较Prophet降低4.261,较LSTM降低0.819,需求预测精度更高,支持服装企业面料柔性生产。     

基于数值与文本特征的LSTM股票价格预测研究

行为金融学理论指出，由社交媒体文本数据所折射出的投资者情绪在一定程度上影响着股票市场的波动。为了利用投资者情绪对股票市场作出更准确的预测，本文爬取2020年8月31日至2021年9月1日的沪深300指数股吧评论文本数据，使用基于融合基础情感词典和金融词典的分析方法将评论文本数据量化为投资者情绪指标，并利用长短期记忆（Long short-term memory,LSTM）神经网络构建了融合股票历史交易数据和基于股吧评论的投资者情绪指标的多特征预测模型。实验结果表明，基于数值和文本特征的LSTM股票价格预测模型的平均绝对误差（Mean absolute error,MAE）、均方根误差（Root mean squared error,RMSE）和均方误差（Mean square error,MSE）值较LSTM数值特征预测模型降低了18.84、15.79、1486.54。较GRU(Gated recurrent unit)数值文本特征模型，其MAE、RMSE、MSE值则降低了11.42、10.49、931.75。实验结果表明本文提出的方法可以有效预测股票价格指数。     

融合VMD和GRU模型的城市道路行程时间预测方法

城市道路交通环境复杂多变,城市道路行程时间具有较强的非线性与非稳定性,为提高城市道路行程时间的预测精度,提出了基于变分模态分解(variational mode decomposition,VMD)与门控循环单元(gated recurrent unit,GRU)相结合的组合预测模型。与传统分解算法相比,VMD拥有非递归求解和自主选择模态个数的优点。首先利用变分模态分解算法将原始行程时间序列分解为若干时间子序列,降低原始序列的非平稳性;然后对每个时间子序列建立GRU预测模型;最后将各个预测结果进行融合,得到行程时间序列预测的最终结果。实验结果表明,变分模态分解与门控循环单元结合的组合模型预测结果要比对照组的单一模型预测结果精准度高,均方根误差(root mean squared Error,RMSE)及下降约3.99～4.37,平均绝对误差(mean absolute error,MAE)下降约3.02～3.35;在组合预测模型中,门控循环单元(GRU)预测效果要比长短期记忆(long short-term memory,LSTM)预测效果表现更佳,均方根误差(root mean squared error,RMSE)下降0.34,平均绝对误差(mean absolute error,MAE)下降0.22。     

基于EMD-LSTM人工神经网络的云冈石窟环境参数预测

环境参数会直接影响石窟的风化过程,因此,预测环境参数是进行云冈石窟有效保护的重要内容.以云冈石窟第十窟为例,将壁温、环境湿度、环境温度的实测时序数据作为环境参数,使用经验模态分解(empirical model decomposition, EMD)对实测时序数据进行分解,研究了固有模态函数(intrinsic mode function, IMF)分量与实测时序数据的相关性,建立了基于EMD-长短期记忆(long short-term memory, LSTM)的人工神经网络(artificial neural network, ANN)组合模型.使用平均绝对误差(mean absolute error, MAE)、均方根误差(root mean square error, RMSE)、平均绝对百分比误差(mean absolute percentage error,MAPE)、决定系数(R²)作为评价指标,对比分析了使用组合模型与使用单一LSTM的ANN模型进行环境参数预测的效果.结果表明:IMF分量的变化速率越大,与实测时序数据的相关性就越强;对于组合模型中...     

基于麻雀搜索算法优化Elman残差自校正地面沉降预测模型

地面沉降是一种常见的地质灾害,严重阻碍当地居民的生产生活,如何对地面沉降进行准确预测已经成为相关专家学者讨论的热点话题,但常规数学模型难以对地面沉降量做出准确预测。提出了麻雀搜索算法(sparrow search algorithm, SSA)优化Elman的地面沉降量预测方法,同时根据组合模型原理提出了SSA-Elman残差自校正(SSA-Elman residual self-correction, SSA-Elman-RSC)模型的策略,通过残差校正的方式降低神经网络预测误差,成功地将地面沉降量预测模型应用于山西省大同市潇河产业园,将预测结果与未进行残差修正的模型预测结果进行比较分析。结果表明,对于均方根误差(root mean squared error, RMSE)、平均绝对误差(mean absolute error, MAE)、均方误差(mean square error, MSE)3个指标,SSA-Elman-RSC拥有更高的精度。该模型的提出为山西地区地面沉降量预测提供了一种新方法,并且组合模型的建立提供了一种新思路。     

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。     

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     

On the Thermodynamic Limit for Responses to Static Electromagnetic Fields

For molecular systems,the quantum-mechanical treatment of their responses to static electromagnetic fields usually employs a scalar-potential treatment of the electric field and a vector-potential treatment of the magnetic field.Although the potential for each field separately is associated with the choice of an(unphysical)origin,the precise choice of the origin for the electrostatic field has little consequences for the results.This is different for the     

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     

Journal of Donghua University ( English Edition) Contents of Volume 31

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