An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

Breast cancer is a significant threat to the global population,affecting not only women but also a threat to the entire population.With recent advancements in digital pathology,Eosin and hematoxylin images provide enhanced clarity in examiningmicroscopic features of breast tissues based on their staining properties.Early cancer detection facilitates the quickening of the therapeutic process,thereby increasing survival rates.The analysis made by medical professionals,especially pathologists,is time-consuming and challenging,and there arises a need for automated breast cancer detection systems.The upcoming artificial intelligence platforms,especially deep learning models,play an important role in image diagnosis and prediction.Initially,the histopathology biopsy images are taken from standard data sources.Further,the gathered images are given as input to the Multi-Scale Dilated Vision Transformer,where the essential features are acquired.Subsequently,the features are subjected to the Bidirectional Long Short-Term Memory(Bi-LSTM)for classifying the breast cancer disorder.The efficacy of the model is evaluated using divergent metrics.When compared with other methods,the proposed work reveals that it offers impressive results for detection.

Slope stability prediction based on a long short-term memory neural network:comparisons with convolutional neural networks,support vector machines and random forest models

The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.

Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis

A correct and timely fault diagnosis is important for improving the safety and reliability of chemical processes. With the advancement of big data technology, data-driven fault diagnosis methods are being extensively used and still have considerable potential. In recent years, methods based on deep neural networks have made significant breakthroughs, and fault diagnosis methods for industrial processes based on deep learning have attracted considerable research attention. Therefore, we propose a fusion deeplearning algorithm based on a fully convolutional neural network(FCN) to extract features and build models to correctly diagnose all types of faults. We use long short-term memory(LSTM) units to expand our proposed FCN so that our proposed deep learning model can better extract the time-domain features of chemical process data. We also introduce the attention mechanism into the model, aimed at highlighting the importance of features, which is significant for the fault diagnosis of chemical processes with many features. When applied to the benchmark Tennessee Eastman process, our proposed model exhibits impressive performance, demonstrating the effectiveness of the attention-based LSTM FCN in chemical process fault diagnosis.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

Landslide displacement prediction based on optimized empirical mode decomposition and deep bidirectional long short-term memory network

There are two technical challenges in predicting slope deformation.The first one is the random displacement,which could not be decomposed and predicted by numerically resolving the observed accumulated displacement and time series of a landslide.The second one is the dynamic evolution of a landslide,which could not be feasibly simulated simply by traditional prediction models.In this paper,a dynamic model of displacement prediction is introduced for composite landslides based on a combination of empirical mode decomposition with soft screening stop criteria(SSSC-EMD)and deep bidirectional long short-term memory(DBi-LSTM)neural network.In the proposed model,the time series analysis and SSSC-EMD are used to decompose the observed accumulated displacements of a slope into three components,viz.trend displacement,periodic displacement,and random displacement.Then,by analyzing the evolution pattern of a landslide and its key factors triggering landslides,appropriate influencing factors are selected for each displacement component,and DBi-LSTM neural network to carry out multi-datadriven dynamic prediction for each displacement component.An accumulated displacement prediction has been obtained by a summation of each component.For accuracy verification and engineering practicability of the model,field observations from two known landslides in China,the Xintan landslide and the Bazimen landslide were collected for comparison and evaluation.The case study verified that the model proposed in this paper can better characterize the"stepwise"deformation characteristics of a slope.As compared with long short-term memory(LSTM)neural network,support vector machine(SVM),and autoregressive integrated moving average(ARIMA)model,DBi-LSTM neural network has higher accuracy in predicting the periodic displacement of slope deformation,with the mean absolute percentage error reduced by 3.063%,14.913%,and 13.960%respectively,and the root mean square error reduced by 1.951 mm,8.954 mm and 7.790 mm respectively.Conclusively,this model not only has high prediction accuracy but also is more stable,which can provide new insight for practical landslide prevention and control engineering.

Device Anomaly Detection Algorithm Based on Enhanced Long Short-Term Memory Network

The problems in equipment fault detection include data dimension explosion,computational complexity,low detection accuracy,etc.To solve these problems,a device anomaly detection algorithm based on enhanced long short-term memory(LSTM)is proposed.The algorithm first reduces the dimensionality of the device sensor data by principal component analysis(PCA),extracts the strongly correlated variable data among the multidimensional sensor data with the lowest possible information loss,and then uses the enhanced stacked LSTM to predict the extracted temporal data,thus improving the accuracy of anomaly detection.To improve the efficiency of the anomaly detection,a genetic algorithm(GA)is used to adjust the magnitude of the enhancements made by the LSTM model.The validation of the actual data from the pumps shows that the algorithm has significantly improved the recall rate and the detection speed of device anomaly detection,with the recall rate of 97.07%,which indicates that the algorithm is effective and efficient for device anomaly detection in the actual production environment.

基于注意力机制的CNN-BiLSTM的IGBT剩余使用寿命预测

针对绝缘栅双极型晶体管(IGBT)可靠性问题,提出了一种融合卷积神经网络(CNN)、双向长短期记忆(BiLSTM)网络和注意力机制的剩余使用寿命(RUL)预测模型,可用于IGBT的寿命预测。模型中使用CNN提取特征参数,BiLSTM提取时序信息,注意力机制加权处理特征参数。使用IGBT加速老化数据集对提出的模型进行验证。结果表明,对比自回归差分移动平均(ARIMA)、长短期记忆(LSTM)、多层LSTM(Multi-LSTM)、 BiLSTM预测模型,在均方根误差和决定系数等评价指标方面该模型的性能最优。验证了提出的寿命预测模型对IGBT失效预测是有效的。

Prophet-LSTM组合模型在运输航空征候预测中的应用

为准确预测中国运输航空征候万时率,提出了一种将时间序列模型和神经网络模型组合的预测方法。首先,利用2008年1月—2020年12月的运输航空征候万时率数据建立Prophet模型,使用RStudio软件进行模型拟合,获取运输航空征候万时率的线性部分;其次,利用长短期记忆网络(Long Short-Term Memory,LSTM)建模,获取运输航空征候万时率的非线性部分;最后,利用方差倒数法建立Prophet-LSTM组合模型,使用建立的组合模型对2021年1—12月运输航空征候万时率进行预测,将预测结果与实际值进行对比验证。结果表明,Prophet-LSTM组合模型的EMA、EMAP、ERMS分别为0.0973、16.1285%、0.1287。相较于已有的自回归移动平均(Auto Regression Integrated Moving Average,ARIMA)+反向传播神经网络(Back Propagation Neural Network,BPNN)组合模型和GM(1,1)+ARIMA+LSTM组合模型,Prophet-LSTM组合模型的EMA、EMAP、ERMS分别减小了0.0259、10.4874百分点、0.0143和0.0128、2.0599百分点、0.0086,验证了Prophet-LSTM组合模型的预测精度更高,性能更优良。

基于GA-LSTM自适应卡尔曼滤波的路面不平度识别

准确、快速地识别出车辆当前行驶的路面激励信息,是实现智能底盘控制进而保证车辆平顺性的关键。针对传统路面不平度识别算法准确率低、自适应性差等问题,提出了基于遗传算法(genetic algorithm,GA)优化长短期记忆神经网络(long short-term memory networks,LSTM)自适应卡尔曼滤波的路面不平度识别算法。基于2自由度车辆悬架模型,通过灰色关联法选择LSTM神经网络的特征输入变量,并采用GA优化LSTM神经网络的模型参数以准确识别路面等级,并据此实时更新卡尔曼滤波器算法中的噪声矩阵,实现了在复杂路况下对路面不平度的自适应识别。仿真和试验研究表明,所提出的基于GA-LSTM自适应卡尔曼滤波算法能够快速准确的识别路面不平度与路面等级,与传统卡尔曼滤波算法相比,相关系数、均方根误差和最大绝对误差分别提高3.11%、37.5%和51.2%,表明所提算法对复杂工况具有很好的自适应能力。

基于多源信息融合和WOA-CNN-LSTM的外脚手架隐患分类预警研究

面对施工现场外脚手架隐患信息的多样性,传统的基于传感器监测的单一信号预警研究存在容错力不佳、含有信息有限等问题。针对施工现场外脚手架“图像+监测”数据,提出一种基于数据层和特征层信息融合的脚手架隐患分类预警方法。首先,利用Revit三维建模软件建立外脚手架实体模型,对不同初始隐患下的外脚手架进行有限元分析,划分隐患预警等级;其次,利用无迹卡尔曼滤波算法(Unscented Kalman Filter,UKF)及卷积长短时记忆网络(Convolutional Neural Network-Long Short Term Memory Network,CNN-LSTM)实现脚手架同类信息数据层融合及异类信息特征层融合;最后,通过实时收集西安市某在建项目落地式双排扣件式钢管脚手架隐患信息,对其进行分类预警,并使用鲸鱼优化算法(Whale Optimization Algorithm,WOA)对CNN-LSTM网络进行参数优化,发现隐藏节点个数为30、学习率为0.0072、正则化系数为1×10^(-4)时分类效果最佳,优化后预警精度达到了91.4526%。通过可视化WOA-CNN-LSTM、CNN-LSTM、CNN-SVM(Support Vector Machine,支持向量机)及CNN-GRU(Gate Recurrent Unit,门控循环单元)分类预警结果,证实了优化后的CNN-LSTM网络在脚手架分类预警方面的优越性。

基于CNN-LSTM和卷复制方法的高可用系统设计方法

针对单机服务器存在的单点故障问题,以及主备双机中存在的逻辑故障导致数据丢失的问题,设计了一种基于卷积和长短期记忆神经网络(CNN-LSTM)和卷复制方法的HA(High Availability)系统。系统至少包含两个节点,一个主节点以及一个或多个备用节点,主节点和备节点之间支持主备切换。每个服务器节点上包含4个模块,分别是负责接收配置信息与读写请求的代理模块;进行磁盘读写操作和重定向读写的磁盘I/O(输入输出)模块;负责主备节点间备份快照、映射表、数据块复制的卷复制模块以及基于CNN-LSTM进行状态检测的高可用模块。实验表明,该系统不仅可以解决单点故障问题,也可以解决主备双机集群中无法解决的逻辑错误问题;同时基于CNN-LSTM方法,自动针对服务器的运行健康状态进行分析和预测,可以根据预测结果自动通知管理员进行处理或自动进行主备切换。

基于多维可预知特征的TCN-LSTM城轨短期客流预测

地铁客流量波动受众多因素影响,准确的客流预测数据有利于制定更高效的行车控制方案和客流管控方案。为提高客流预测精度,提出一种基于多维可预知特征的时序卷积神经网络-长短期记忆神经网络模型(TCNLSTM)地铁短期客流预测方法。考虑外部因素的影响,引入Spearman相关系数分析并提取日期、天气等可预知特征及其状态集,以提升预测精度,缩小特征空间,克服了冗余特征数据导致的模型过于复杂问题;通过融合时序卷积神经网络(TCN)提取的客流时间序列特征和可预知特征状态集构建了长短期记忆神经网络(LSTM)层输入,组合模型学习客流与外部影响因素的长短期依赖,从而实现常规日、节假日、不同天气等多场景下的短期客流预测。基于某西南城市地铁刷卡交易数据,对比差分整合移动平均自回归模型(ARIMA)、TCN、LSTM及TCN-LSTM模型的短期客流预测结果,得出组合模型的总体平均绝对误差(MAE)值比其他方法低27%~48%,均方误差(MSE)值低13%~35%,平均绝对百分比误差(MAPE)值低2.8%~6.7%,上述3项指标均表明TCN-LSTM模型的客流预测效果更好。此外,对比实验表明通过融入提取的可预知特征数据,TCN-LSTM模型在测试集上的预测误差评价指标明显降低,所提方法能有效提高地铁短期客流预测精度。

基于K均值聚类算法和LSTM神经网络的管道腐蚀阶段预测方法

针对声发射检测获得的管道腐蚀信号,提出了一种基于K均值(K-means)聚类算法和长短期记忆(LSTM)神经网络的管道腐蚀阶段预测方法。首先,利用K-means聚类算法将腐蚀信号分类,再构建LSTM神经网络模型,并采取了无监督学习的方式,以声发射波形为出发点,对模型进行参数优化,最后进行管道腐蚀阶段预测,并根据评价指标对模型进行评价。研究表明:对LSTM神经网络模型适当增加隐藏层,可以使得模型更加稳定,鲁棒性更好;与现有故障诊断模型相比,LSTM神经网络模型的精度更高。

基于VMD-SSA-LSTM的架空输电导线覆冰预测模型

针对输电导线覆冰过程间断性强且波动性大而导致的现有模型预测精度不高的问题,从覆冰厚度数据的时序信息和气象信息出发,提出一种基于变分模态分解(variational mode decomposition,VMD)的麻雀搜索算法(sparrow search algorithm,SSA)优化长短期记忆网络(long short-term memory network,LSTM)的覆冰组合预测模型.该方法首先使用VMD分解覆冰厚度数据,降低了原始序列的不稳定性,得到具有不同中心频率的本征模态分量;其次,采用SSA算法对LSTM中的3个参数进行寻优;最后,对模态分量分别建立LSTM预测模型,将各个模态分量的预测值叠加为覆冰厚度的总预测值.通过实例仿真,对所提预测模型进行验证.结果表明:VMD-SSA-LSTM组合模型与其他模型相比,其预测精度有进一步提高,验证了所提方法的有效性.

基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型研究

传统的切削过程功率获取需要基于复杂的切削功率模型且很少考虑刀具磨损的影响,针对此设计了一种基于变分模态分解(VMD)、麻雀搜索算法(SSA)、长短时记忆(LSTM)神经网络的考虑刀具磨损的数控铣床切削功率预测模型,该模型无需解构数控铣床运行过程的能耗机理,基于一次性的历史实验数据即可实现数控铣床切削过程功率的高精度预测。首先,采用人工智能机器视觉技术对刀具磨损图片进行分析处理,获取刀具磨损图像的数字化特征,从而得到刀具最大磨损量;然后,建立基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型,利用VMD对数控铣床运行数据进行分解,采用SSA算法对LSTM神经网络超参数进行寻优,并将分解出的铣床运行数据分量输入到LSTM神经网络中,接着将每个分量的预测值相加,得到切削功率预测值;最后以面铣加工为例,将所提出的预测模型与BP神经网络、LSTM神经网络和传统模型进行对比分析,验证了所提模型的有效性和优越性。

SBAS-InSAR技术融合CNN-LSTM模型的矿区开采沉陷监测与预测

针对传统矿区开采沉陷监测方法耗费人力财力和预测预警模型较少的问题,研究提出一种基于短基线集合成孔径雷达干涉测量(Small Baseline Subset-Interferometry Synthetic Aperture Radar,SBAS-InSAR)技术和卷积神经网络(Convolutional Neural Networks,CNN)与长短期记忆网络(Long Short-Term Memory,LSTM)相结合的矿区开采沉陷监测预测方法。首先,利用SBAS-InSAR技术对建新煤矿进行矿区开采沉陷监测,获取了该矿区的年平均沉降速率和累计沉降值。用GNSS监测数据与SBAS-InSAR结果进行对比验证,其拟合效果较好。其次,在此基础上利用CNN-LSTM模型预测后6期沉降数据,其结果与CNN和LSTM预测结果进行对比。研究显示,CNN-LSTM模型的平均绝对误差(S_(MAE))和均方根误差(S_(RMSE))比单一的CNN和LSTM分别至少降低了44.8%和40.6%,其决定系数均高于98%。最后,进一步预测前6期和中6期沉降数据,验证了CNN-LSTM预测模型在时间上的一致性。因此,SBAS-InSAR融合CNN-LSTM模型在类似矿山开采沉陷监测和预测中有较好的应用前景。

基于AM-LSTM的飞行区航空器滑行轨迹预测与冲突识别

为解决航空器点源定位难以有效预测而引发冲突风险愈来愈多的问题,构建基于注意力机制(AM)和长短期记忆网络(LSTM)的时间序列轨迹预测模型AM-LSTM,预测未来短时间内飞行区航空器的瞬时点源位置;在此基础上,根据航空器型号和滑行航向对其进行轮廓扩展,以航空器速度作为安全距离权重,通过射线法实现轮廓冲突的判定;并以乌鲁木齐地窝堡机场为例进行验证,利用训练完成的轨迹预测模型预测飞行区航空器滑行轨迹,以识别航空器轮廓间的滑行冲突。结果表明:AM-LSTM预测模型能够准确预测飞行区航空器运动轨迹。未来3 s内轨迹位置预测的平均位移误差为1.05 m,轨迹点位置预测精准性可达94.37%,故能在轨迹预测的基础上精确识别滑行冲突风险,有利于保障飞行区的安全运行。

基于LSTM-AE的民机空调热交换器性能异常检测方法

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基于Transformer-LSTM的闽南语唇语识别

针对端到端句子级闽南语唇语识别的问题,提出一种基于Transformer和长短时记忆网络(LSTM)的编解码模型.编码器采用时空卷积神经网络及Transformer编码器用于提取唇读序列时空特征,解码器采用长短时记忆网络并结合交叉注意力机制用于文本序列预测.最后,在自建闽南语唇语数据集上进行实验.实验结果表明:模型能有效地提高唇语识别的准确率.