短期风力发电负荷预测的新方法

以陆上风力发电负荷数据作为研究对象,将注意力机制引入双向长短期记忆与卷积神经网络（CNN）的混合模型来预测短期电力负荷．结果显示:1）注意力机制通过对不同时步的输入进行加权,能够显著提升双向长短期记忆网络的预测性能;2）双向长短期记忆网络-CNN结构比CNN-双向长短期记忆网络结构更适用于短期负荷预测,前者相较后者能够充分利用时序信息,不会在输入初期就丢失关键信息;3）基于注意力机制的双向长短期记忆网络-CNN混合模型的均方根误差（RMSE）、平均绝对百分比误差（MAPE）分别达到了575.35和7.02%,比次佳模型（基于注意力机制的双向长短期记忆网络-CNN混合模型）分别降低了2.75%和9.65%,其在风电短期负荷预测方面有很好的应用前景．      

基于改进Bi-LSTM和XGBoost的电力负荷组合预测方法

电力负荷预测在平衡能源分配、经济性和电力系统安全可靠运行方面发挥着重要作用,精准的负荷预测可以降低电力运行的成本和风险,提高电网环境效益和经济效益.首先根据加权灰色关联投影算法对数据进行预处理,然后应用注意力(Attention)机制来改进双向长短期记忆(Bi-LSTM)模型,并结合极端梯度提升(XGBoost)模型构...     

基于Attention机制和ResNet的CNN-BiLSTM短期电力负荷预测模型研究

短期电力负荷预测有利于电力系统的高效运行,对电力市场实现有效调度有重要意义。短期电力负荷受多种因素影响,波动性大、随机性强,使得其预测准确率低。双向长短期记忆网络和卷积神经网络难以在短期负荷序列中提取足够多的信息,本文提出了一种结合注意力机制和残差网络的卷积神经网络-双向长短期记忆网络短期负荷预测方法。首先利用基准模型卷积神经网络-双向长短期记忆网络对输入特征进行信息提取,然后利用注意力机制突出提取到的关键信息,最后通过残差网络创建残差层以充分学习时序特征。通过某公开数据集进行实验,结果表明该方法的平均绝对百分比误差达到2.80%,均方根误差达到2.15,并与常用的五种模型预测结果对比,验证了所提模型的准确性及有效性。     

考虑侧向车换道影响的理论和数据组合驱动的车辆跟驰模型

为分析受侧向车辆换道影响下的目标车辆跟驰行为，结合多速度差理论跟驰模型和深度学习方法，提出了一种理论-数据组合驱动跟驰模型。首先考虑了跟驰车辆对于前向车辆和侧向车辆保持安全车距和受车辆速度差影响的特性，提出了双车道多速度差跟驰模型（FS-MAVD模型），并利用差分进化算法进行模型参数标定。构建了CNN-Bi-LSTM-Attention数据驱动车辆跟驰模型，利用卷积神经网络层（CNN）充分提取前向和侧向车辆交通特征，双向长短期记忆网络层（Bi-LSTM）考虑驾驶员记忆效应，注意力机制层（Attention）用于分配模型权重，并基于数据进行驾驶员记忆时长、模型训练批次和训练轮数参数的训练。考虑理论模型广泛适用性和数据驱动模型接近真实值且平滑的特点，采用最优加权法将两种模型进行组合预测。利用无人机拍摄的快速路车辆轨迹数据，建立跟驰行为样本集，对所建模型进行训练和测试，并与LSTM模型、Bi-LSTM模型、CNN-Bi-LSTM-Attention模型、FS-MAVD理论模型的预测效果进行对比，并分别比对不同模型对不同车辆的预测精度和误差。结果表明，本研究构建的组合模型在加速度预测精度达到9...     

基于注意力机制和双向长短期记忆网络的电能质量扰动识别

提出一种基于深度学习的电能质量扰动信号分类新方法。该方法应用注意力机制和双向长短期记忆网络构建分类模型,并利用Matlab仿真产生训练数据集与验证数据集。考虑了7种常见的复合扰动信号,并将其作为序列数据直接输入到网络中进行训练和验证。实验结果表明,本方法能准确地识别不同的扰动信号,在验证集上模型的识别准确率可以达到99.7%。通过对比实验发现,应用注意力机制和双向长短期记忆网络的模型的识别能力要优于支持向量机和多层感知机等传统机器学习算法。     

施工组织设计文档智慧辅助审查中的文本分类问题研究

施工组织设计是指导工程建设全过程活动的技术、经济和组织的综合性文件,随着自然语言处理(NLP)等人工智能技术的发展,本文针对施工组织设计文档智慧辅助审查中基础性工作之一-文本分类问题开展研究。为实现施工组织设计文本的自动分类,本文运用Word2vec词嵌入技术对文本进行向量化表示,基于Bi-LSTM捕捉文本上下文序列信息,融入Attention机制,提取文本有效信息,采用softmax激活函数分类。Attention Bi-LSTM在房建数据集上达到了0.97的准确率、召回率以及F1值,整体分类效果在正确率、宏平均、加权平均上均优于其他模型。融入Attention机制的Bi-LSTM文本分类模型通过双向捕获文本的特征并利用Attention机制提取有效信息,达到了联合优化的作用,提高了模型的分类性能。     

基于时间关系的Bi-LSTM+GCN因果关系抽取

针对传统时间关系只应用在机器学习方向关系抽取的问题, 提出一种基于序列标注实体识别的关系抽取方法. 先构建双向长短期记忆网络(Bi-LSTM)模型进行特征提取, 再输入时间关系作为特征矩阵进行图卷积. 实验结果表明: 时间关系能提高因果关系抽取效果, 并且包含时间关系的Bi-LSTM+GCN模型能有效抽取因果事件; 带有时间关系的Bi-LSTM+GCN模型获得因果关系的抽取结果优于传统方法因果关系的抽取结果.     

基于双向长短期记忆神经网络的配网电压异常数据检测

受自然环境、计量仪器等影响,量测数据会出现异常,导致调度人员错误决策,威胁电力系统安全稳定运行。为保障电力系统安全稳定运行,提出了一种基于双向长短期记忆(bidirectional long short-term memory, Bi-LSTM)神经网络的配网电压无监督异常数据检测方法。利用Bi-LSTM神经网络处理时序数据的双向特性,建立时序预测模型,通过对比预测值和实际值的误差检测异常数据。最后,基于某实际配网电压数据进行仿真验证,仿真结果表明:所提方法在准确率、F1分数等指标方面均优于决策树、K近邻、支持向量机、长短期记忆(long short-term memory, LSTM)神经网络。     

基于双向长短期记忆神经网络的岩相预测方法

介绍一种基于双向长短期记忆神经网络(Bi-directional long short-term memory,Bi-LSTM)的岩相预测方法,综合利用测井和地震数据进行高效准确的岩相预测。通过合成地震记录,进行井震数据的时深匹配,以地震吸收衰减数据、纵波阻抗、密度和伽马拟声波阻抗作为输入,以岩相作为标签,通过Bi-LSTM模型训练建立输入数据与岩相的非线性映射关系。将该方法应用于四川某浅层河道砂体勘探区岩相预测,结果表明,基于Bi-LSTM构建的岩相预测方法优于普通循环神经网络和普通LSTM,能够快速确定地下岩相,有效指示河道。基于Bi-LSTM的岩相预测方法能有效提取输入数据与岩相信息的非线性映射关系,对少井地区的岩相预测工作有较高的实用价值。     

基于组合神经网络的中文事件抽取

中文事件抽取通常使用循环神经网络(recurrent neural network, RNN)来进行事件和事件要素的抽取, 但 RNN 在处理长度较长的词语时容易丢失重要信息, 为此提出一种组合卷积神经网络(convolutional neural network, CNN)与双向长短期记忆(bidirectional long short-term memory, Bi-LSTM)网络的中文事件抽取模型 CNN-Bi-LSTM-CRF, 其中 CRF (conditional random field) 为条件随机场. 采用基于注意力机制和语义特征生成的字词联合向量, 使用 CNN 和 Bi-LSTM 模型对字词联合向量进行处理, 以获取其隐含表示, 最后通过 CRF 得出预测结果. 实验结果表明, 所提出的方法与其他现有的中文事件抽取方法相比, 准确率有明显提升.     

Characterization of Ordovician carbonate reservoirs, southeastern Saskatchewan, Canada

The discovery of the prolific Ordovician Red River reservoirs in 1995 in southeastern Saskatchewan was the catalyst for extensive exploration activity which resulted in the discovery of more than 15 new Red River pools. The best yields of Red River production to date have been from dolomite reservoirs. Understanding the processes of dolomitization is, therefore, crucial for the prediction of the connectivity, spatial distribution and heterogeneity of dolomite reservoirs.The Red River reservoirs in the Midale area consist of 3～4 thin dolomitized zones, with a total thickness of about 20 m, which occur at the top of the Yeoman Formation. Two types of replacement dolomite were recognized in the Red River reservoir: dolomitized burrow infills and dolomitized host matrix. The spatial distribution of dolomite suggests that burrowing organisms played an important role in facilitating the fluid flow in the backfilled sediments. This resulted in penecontemporaneous dolomitization of burrow infills by normal seawater. The dolomite in the host matrix is interpreted as having occurred at shallow burial by evaporitic seawater during precipitation of Lake Almar anhydrite that immediately overlies the Yeoman Formation. However, the low δ18O values of dolomited burrow infills (-5.9‰～ -7.8‰, PDB) and matrix dolomites (-6.6‰～ -8.1‰, avg. -7.4‰ PDB) compared to the estimated values for the late Ordovician marine dolomite could be attributed to modification and alteration of dolomite at higher temperatures during deeper burial, which could also be responsible for its 87Sr/86Sr ratios (0.7084～0.7088) that are higher than suggested for the late Ordovician seawaters (0.7078～0.7080). The trace amounts of saddle dolomite cement in the Red River carbonates are probably related to "cannibalization" of earlier replacement dolomite during the chemical compaction.     

An Index mechanism for multi-scale view in spatial databases

There are numerous geometric objects stored in the spatial databases. An importance function in a spatial database is that users can browse the geometric objects as a map efficiently. Thus the spatial database should display the geometric objects users concern about swiftly onto the display window. This process includes two operations:retrieve data from database and then draw them onto screen. Accordingly, to improve the efficiency, we should try to reduce time of both retrieving object and displaying them. The former can be achieved with the aid of spatial index such as R-tree, the latter require to simplify the objects. Simplification means that objects are shown with sufficient but not with unnecessary detail which depend on the scale of browse. So the major problem is how to retrieve data at different detail level efficiently. This paper introduces the implementation of a multi-scale index in the spatial database SISP (Spatial Information Shared Platform) which is generalized from R-tree. The difference between the generalization and the R-tree lies on two facets: One is that every node and geometric object in the generalization is assigned with a importance value which denote the importance of them, and every vertex in the objects are assigned with a importance value,too. The importance value can be use to decide which data should be retrieve from disk in a query. The other difference is that geometric objects in the generalization are divided into one or more sub-blocks, and vertexes are total ordered by their importance value. With the help of the generalized R-tree, one can easily retrieve data at different detail levels.Some experiments are performed on real-life data to evaluate the performance of solutions that separately use normal spatial index and multi-scale spatial index. The results show that the solution using multi-scale index in SISP is satisfying.     

A computer generator for randomly layered structures

AcomputergeneratorforrandomlylayeredstructuresYUJia shun1,2,HEZhen hua2(1.TheInstituteofGeologicalandNuclearSciences,NewZealand;2.StateKeyLaboratoryofOilandGasReservoirGeologyandExploitation,ChengduUniversityofTechnology,China)Abstract:Analgorithmisintrod…     

海南岛地体及其毗邻陆缘晚中生代—新生代古地磁研究和构造演化

本文叙述了对海南岛及其毗邻大陆边缘白垩纪到第四纪地层岩石进行古地磁研究的全部工作过程。通过分析岩石中剩余磁矢量的磁偏角及磁倾角的变化,提出海南岛白垩纪以来经历的构造演化模式如下:早期伴随顺时针旋转而向南迁移,后期伴随逆时针转动并向北运移。联系该地区及邻区的地质、地球物理资料,对海南岛上述的构造地体运动提出以下认识:北部湾内早期有一拉张作用,主要是该作用使湾内地壳显著伸长减薄,形成北部湾盆地。从而导致了海南岛的早期构造运动,而海南岛后期的构造运动则主要是受南海海底扩张的影响。海南地体运动规律的阐明对于了解北部湾油气盆地的形成演化有重要的理论和实际意义。     

Exciton Seebeck in Molecular Systems

Various applications relevant to the exciton dynamics,such as the organic solar cell,the large-area organic light-emitting diodes and the thermoelectricity,are operating under temperature gradient.The potential abnormal behavior of the exicton dynamics driven by the temperature difference may affect the efficiency and performance of the corresponding devices.In the above situations,the exciton dynamics under temperature difference is mixed with     

Achieving Unusual States of Matter Under High Pressure

The elongation method,originally proposed by Imamura was further developed for many years in our group.As a method towards O(N)with high efficiency and high accuracy for any dimensional systems.This treatment designed for one-dimensional(ID)polymers is now available for three-dimensional(3D)systems,but geometry optimization is now possible only for 1D-systems.As an approach toward post-Hartree-Fock,it was also extended to     

《Chinese Science Bulletin》SUBJECT INDEX TO VOLUME 59(2014)

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An on-line scaling method for improving scalability of a database cluster

The explosive growth of the Internet and database applications has driven database to be more scalable and available, and able to support on-line scaling without interrupting service. To support more client's queries without downtime and degrading the response time, more nodes have to be scaled up while the database is running. This paper presents the overview of scalable and available database that satisfies the above characteristics. And we propose a novel on-line scaling method. Our method improves the existing on-line scaling method for fast response time and higher throughputs. Our proposed method reduces unnecessary network use, i.e. , we decrease the number of data copy by reusing the backup data. Also, our on-line scaling operation can be processed parallel by selecting adequate nodes as new node. Our performance study shows that our method results in significant reduction in data copy time.     

An improved R-tree based on childnode''s probability

R-Tree is a good structure for spatial searching. But in this indexing structure,either the sequence of nodes in the same level or sequence of traveling these nodes when queries are made is random. Since the possibility that the object appears in different MBR which have the same parents node is different, if we make the subnode who has the most possibility be traveled first, the time cost will be decreased in most of the cases. In some case, the possibility of a point belong to a rectangle will shows direct proportion with the size of the rectangle. But this conclusion is based on an assumption that the objects are symmetrically distributing in the area and this assumption is not always coming into existence. Now we found a more direct parameter to scale the possibility and made a little change on the structure of R-tree, to increase the possibility of founding the satisfying answer in the front sub trees. We names this structure probability based arranged R-tree (PBAR-tree).     

Integrating GIS Web services based on mediating architecture

The geographic information service is enabled by the advancements in general Web service technology and the focused efforts of the OGC in defining XML-based Web GIS service. Based on these models, this paper addresses the issue of services chaining,the process of combining or pipelining results from several interoperable GIS Web Services to create a customized solution. This paper presents a mediated chaining architecture in which a specific service takes responsibility for performing the process that describes a service chain. We designed the Spatial Information Process Language (SIPL) for dynamic modeling and describing the service chain, also a prototype of the Spatial Information Process Execution Engine (SIPEE) is implemented for executing processes written in SIPL. Discussion of measures to improve the functionality and performance of such system will be included.