考虑数据分解和进化捕食策略的BiLSTM短期光伏发电功率预测

提出一种考虑数据分解和进化捕食策略的双向长短期记忆网络(BiLSTM)短期光伏发电功率预测模型。首先,针对大量高频分量且频率成分复杂的原始光伏发电功率,通过数据分解理论,提出互补集合经验模态分解(CEEMD)与矩阵运算的奇异值分解(SVD)融合的(SVD-CEEMD-SVD,SCS)方法,实现光伏发电功率数据的二次降噪。然后,建立进化捕食策略(EPPS)和BiLSTM的组合预测模型,以更好地挖掘模型的内在特征,提升功率预测精度。最后,以山东某地区实际光伏电站为例,验证模型在滤除光伏发电功率噪声和提升预测精度方面的有效性。

行为异常检测技术在零信任访问控制中的应用

零信任有效解决了网络边界模糊的问题,在多种访问控制方法中得到广泛应用.针对大部分零信任访问控制方法仅使用简单统计方法计算信任评分、防范未知风险能力较差、缺乏对不同用户的自适应能力的问题,提出了一种引入行为异常检测的零信任访问控制方法.该方法设计了一种结合行为异常检测策略的信任引擎,通过自编码器和双向长短期记忆神经网络的建模能力,表征用户的行为模式,利用均方误差损失函数计算异常行为表征值,同时融合其他要素计算信任评分.该方法利用异常行为表征值设定信任阈值,以自适应调整用户访问策略.实验结果表明,所提方法对用户行为间的关联敏感,能够识别用户的异常行为并阻止授权,实现持续评估、细粒度的访问控制.

基于多特征融合的航空发动机剩余寿命预测

针对航空发动机剩余可用寿命(RUL)预测任务中代表性特征提取不充分导致RUL预测精度较低等问题,提出了一种基于多特征融合的航空发动机RUL预测方法.利用指数平滑法(ES)降低原始数据中的噪声干扰,得到相对平稳的特征数据.使用双向长短期记忆网络(Bi-LSTM)提取特征数据的时序特征,利用多头注意力机制(Multi-attention)为时序特征赋予权重;设计卷积长短期记忆网络(Conv-LSTM)提取特征数据的时空特征;提取特征数据的手工特征并使用Softmax函数计算权重.设计一个特征融合框架将上述特征进行融合,然后通过全连接网络回归实现最终RUL预测.使用C-MAPSS数据集对模型进行仿真验证,与Bi-LSTM等模型进行对比,模型RUL预测精度更高,适应性更好.

一种基于双向长短期记忆结构与多尺度卷积结构融合的轴承智能故障诊断方法

轴承作为旋转机械中最易损耗的核心基础部件之一,是机械装备的重点监测对象。针对现有轴承智能故障诊断模型存在的对数据信息挖掘片面性及利用率低等问题,构建了一种基于双向长短期记忆(Bidirectional Long Short-term Memory,BLSTM)结构与多尺度卷积结构融合的深度学习网络模型。为了增强模型的分类性能以及提高模型对实际工程环境的贴合度,数据集中各类故障数据的数据量为非等量;然后将数据集通过BLSTM结构来获取具有对称性的数据特征,从而减少模型对前后故障信息记忆的紊乱、增强信息利用率,接着通过多尺度卷积结构对数据特征进行多角度理解与交流,防止特征提取片面化,同时还能增强模型的抗噪性能;最后通过全连接网络实现智能分类。将所提模型分别对深沟球轴承与圆柱滚子轴承故障数据进行处理分析,结果表明该智能模型具有较高的准确度与实用性。

基于双向长短期记忆深度学习模型的短期风功率预测方法研究

风功率的准确预测对电力系统的规划、调度运行等方面均具有重要意义。该文以风功率预测误差最小为目标,提出了一种基于双向长短期记忆深度学习模型的短期风功率预测方法,包括3层(输入层、隐含层和输出层)网络结构的详细设计以及网络训练过程。输入层负责对原始数据进行预处理以满足网络输入要求,隐含层采用双向长短期记忆单元构建以提取输入数据的非线性特征,输出层提供预测结果,网络训练采用Adam优化方法。在此基础上,基于实际风电场采集数据为算例,对该文所提出模型进行训练与测试,验证了该文所提方法的可行性与优越性。

A multi-attention RNN-based relation linking approach for question answering over knowledge base

Aiming at the relation linking task for question answering over knowledge base,especially the multi relation linking task for complex questions,a relation linking approach based on the multi-attention recurrent neural network(RNN)model is proposed,which works for both simple and complex questions.First,the vector representations of questions are learned by the bidirectional long short-term memory(Bi-LSTM)model at the word and character levels,and named entities in questions are labeled by the conditional random field(CRF)model.Candidate entities are generated based on a dictionary,the disambiguation of candidate entities is realized based on predefined rules,and named entities mentioned in questions are linked to entities in knowledge base.Next,questions are classified into simple or complex questions by the machine learning method.Starting from the identified entities,for simple questions,one-hop relations are collected in the knowledge base as candidate relations;for complex questions,two-hop relations are collected as candidates.Finally,the multi-attention Bi-LSTM model is used to encode questions and candidate relations,compare their similarity,and return the candidate relation with the highest similarity as the result of relation linking.It is worth noting that the Bi-LSTM model with one attentions is adopted for simple questions,and the Bi-LSTM model with two attentions is adopted for complex questions.The experimental results show that,based on the effective entity linking method,the Bi-LSTM model with the attention mechanism improves the relation linking effectiveness of both simple and complex questions,which outperforms the existing relation linking methods based on graph algorithm or linguistics understanding.

基于混合分布注意力机制与混合神经网络的语音情绪识别方法

针对现有语音情绪识别中存在无关特征多和准确率较差的问题,提出一种基于混合分布注意力机制与混合神经网络的语音情绪识别方法。该方法在2个通道内,分别使用卷积神经网络和双向长短时记忆网络进行语音的空间特征和时序特征提取,然后将2个网络的输出同时作为多头注意力机制的输入矩阵。同时,考虑到现有多头注意力机制存在的低秩分布问题,在注意力机制计算方式上进行改进,将低秩分布与2个神经网络的输出特征的相似性做混合分布叠加,再经过归一化操作后将所有子空间结果进行拼接,最后经过全连接层进行分类输出。实验结果表明,基于混合分布注意力机制与混合神经网络的语音情绪识别方法比现有其他方法的准确率更高,验证了所提方法的有效性。

基于多任务BiLSTM的配送人员活动识别

当前物流业中,对于配送人员的薪酬计算大都基于配送距离和物品重量等因素,其缺乏对配送人员具体活动类型及能量消耗的考虑,难以对薪酬进行高效合理分配。基于此,在数据层面,通过与某大型物流公司合作,为25名配送人员穿戴相应设备,采集其在配送过程中的加速度计和陀螺仪等真实数据。算法层面,提出了一种基于多任务双向长短时记忆(BiLSTM)的深度网络结构,通过大量实验表明,BiL⁃STM模型在活动识别和能量消耗分级上的分类准确率分别达到92.8%和94.2%,结果皆优于基准多任务LSTM算法和其他代表性学习算法。

基于优化A-BiLSTM的滚动轴承故障诊断

为提高超参数设置的效率及其与模型的适配性,改善人工设置模型参数的高成本和低效率问题,提出一种基于蜜獾算法(Honey badger algorithm,HBA)优化注意力双向长短时记忆网络(HBA-A-BiLSTM)的滚动轴承故障诊断方法。首先,通过HBA对A-BiLSTM模型进行最优超参数组合搜寻,然后基于最优超参数下的A-BiLSTM模型进行故障诊断性能测试。最后,基于不同工况的数据集进行模型泛化能力测试。采用CWRU数据集对所提方法的故障诊断效果进行验证,利用诊断精度以及混淆矩阵进行评价。实验结果表明,与其他群智能优化算法相比,蜜獾算法搜索全局性能好,收敛速度快,优化后的最终模型的故障诊断准确率达到了99.5%,具有良好的效果,且在不同工况下能够实现稳定、准确的故障诊断性能,泛化能力强。

基于EMD-BiLSTM的短期电力负荷预测研究

短期电力负荷预测能够评估某地区整体的电力负荷变化情况,对电力系统安全稳定运行至关重要,而气象因素也具有深刻的影响。作者提出一种融合了经验模态分解(EMD)和双向长短期记忆网络(BiLSTM)的模型,首先结合气象因素分析其与电力系统用电量的相关性,选取有相关性的气象因素和历史负荷数据一起作为输入特征集,利用EMD算法将随机性强的历史电力负荷数据分解为有限个特征互异的固有模态函数分量和趋势分量。然后和气象因素一起输入到BiLSTM中深度挖掘历史数据并训练模型。最后对各分量数据分别预测并叠加输出预测值。以某地电力负荷数据为实际算例,算例结果表明,采用该方法预测模型拟合度能达到97%,具有较好的预测效果。相较于LSTM网络和BiLSTM网络的预测结果,其预测曲线更贴近于历史负荷数据,特别是对于电力负荷趋势的突然变化,其预测精度得到有效提升。

Deep Broad Learning for Emotion Classification in Textual Conversations

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual conversations.It is becoming one of the most important tasks for natural language processing in recent years.However,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual context.To address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely DBL.It aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad learning.Extensive experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion classification.In addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

Ultra-short-term Interval Prediction of Wind Power Based on Graph Neural Network and Improved Bootstrap Technique

Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and optimization of power systems.However,the volatility and intermittence of wind power pose uncertainties to traditional point prediction,resulting in an increased risk of power system operation.To represent the uncertainty of wind power,this paper proposes a new method for ultra-short-term interval prediction of wind power based on a graph neural network(GNN)and an improved Bootstrap technique.Specifically,adjacent wind farms and local meteorological factors are modeled as the new form of a graph from the graph-theoretic perspective.Then,the graph convolutional network(GCN)and bi-directional long short-term memory(Bi-LSTM)are proposed to capture spatiotemporal features between nodes in the graph.To obtain highquality prediction intervals(PIs),an improved Bootstrap technique is designed to increase coverage percentage and narrow PIs effectively.Numerical simulations demonstrate that the proposed method can capture the spatiotemporal correlations from the graph,and the prediction results outperform popular baselines on two real-world datasets,which implies a high potential for practical applications in power systems.

Action-Aware Encoder-Decoder Network for Pedestrian Trajectory Prediction

Accurate pedestrian trajectory predictions are critical in self-driving systems,as they are fundamental to the response-and decision-making of ego vehicles.In this study,we focus on the problem of predicting the future trajectory of pedestrians from a first-person perspective.Most existing trajectory prediction methods from the first-person view copy the bird’s-eye view,neglecting the differences between the two.To this end,we clarify the differences between the two views and highlight the importance of action-aware trajectory prediction in the first-person view.We propose a new action-aware network based on an encoder-decoder framework with an action prediction and a goal estimation branch at the end of the encoder.In the decoder part,bidirectional long short-term memory(Bi-LSTM)blocks are adopted to generate the ultimate prediction of pedestrians’future trajectories.Our method was evaluated on a public dataset and achieved a competitive performance,compared with other approaches.An ablation study demonstrates the effectiveness of the action prediction branch.

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

Sentence Boundary Disambiguation(SBD)is a preprocessing step for natural language processing.Segmenting text into sentences is essential for Deep Learning(DL)and pretraining language models.Tibetan punctuation marks may involve ambiguity about the sentences’beginnings and endings.Hence,the ambiguous punctuation marks must be distinguished,and the sentence structure must be correctly encoded in language models.This study proposed a component-level Tibetan SBD approach based on the DL model.The models can reduce the error amplification caused by word segmentation and part-of-speech tagging.Although most SBD methods have only considered text on the left side of punctuation marks,this study considers the text on both sides.In this study,465669 Tibetan sentences are adopted,and a Bidirectional Long Short-Term Memory(Bi-LSTM)model is used to perform SBD.The experimental results show that the F1-score of the Bi-LSTM model reached 96%,the most efficient among the six models.Experiments are performed on low-resource languages such as Turkish and Romanian,and high-resource languages such as English and German,to verify the models’generalization.

结合注意力机制的循环神经网络复述识别模型

传统基于深度学习的复述识别模型通常以关注文本表示为核心,忽略了对多粒度交互特征的挖掘与匹配.为此,建模文本交互空间,分别利用双向长短时记忆网络对两个候选复述句按条件编码,基于迭代隐状态的输出,通过逐词软对齐的方式从词、短语、句子等多个粒度层次推理并获取句子对的语义表示,最后综合不同视角的语义表达利用softmax实现二元分类.为解决复述标注训练语料不足,在超过580000句子对的数据集上利用语言建模任务对模型参数无监督预训练,再使用预训练好的参数在标准数据集上有监督微调.与先前最佳的神经网络模型相比,所提出模型在标准数据集MSRP上准确率提高2.96%,F1值改善2%.所提出模型综合文本全局和局部匹配信息,多粒度、多视角地描述文本交互匹配模式,能够降低对人工特征工程的需求,具有良好的实用性.

基于BLSTM-RNN的语音驱动逼真面部动画合成

双向长短时记忆(bidirectional lorg short term memory,BLSTM)是一种特殊的递归神经网络(recurrent neural network,RNN),能够有效地对语音的长时上下文进行建模。该文提出一种基于深度BLSTM的语音驱动面部动画合成方法,利用说话人的音视频双模态信息训练BLSTM-RNN神经网络,采用主动外观模型(active appearance model,AAM)对人脸图像进行建模,将AAM模型参数作为网络输出,研究网络结构和不同语音特征输入对动画合成效果的影响。基于LIPS2008标准评测库的实验结果表明:具有BLSTM层的网络效果明显优于前向网络的,基于BLSTM-前向-BLSTM 256节点(BFB256)的三层模型结构的效果最佳,FBank、基频和能量组合可以进一步提升动画合成效果。

基于BLSTM-AM模型的TBM稳定段掘进参数预测

隧道掘进机(TBM)掘进数据的上升段为实时岩体条件感知和掘进性能参数预测提供了丰富的信息。提出一种融合注意力机制的双向长短时记忆网络,来实现TBM掘进稳定段的性能参数预测。在所提出的模型中,4个主要参数的时间序列数据作为主要输入来提取岩机作用关系,稳定段的推进速度和刀盘转速作为辅助输入来考虑主司机的控制行为,模型输出推力和扭矩预测值。不同于传统的预测模型,所提出的模型不依赖于地质参数,通过自动学习上升段的特征来建立控制参数与预测性能参数之间的映射关系。模型建立过程,采用多项数据处理技术来修正异常值、过滤噪声及归一化等,并提出了基于扭矩时序曲线来识别TBM上升段和稳定段的方法。依托于吉林引松供水隧洞工程,验证了该模型的有效性和准确性。结果表明,所建模型有较好的预测效果,可辅助于类似地质条件的TBM智能化施工。