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.

Seismic-inversion method for nonlinear mapping multilevel well–seismic matching based on bidirectional long short-term memory networks

In this paper,the recurrent neural network structure of a bidirectional long shortterm memory network(Bi-LSTM)with special memory cells that store information is used to characterize the deep features of the variation pattern between logging and seismic data.A mapping relationship model between high-frequency logging data and low-frequency seismic data is established via nonlinear mapping.The seismic waveform is infinitely approximated using the logging curve in the low-frequency band to obtain a nonlinear mapping model of this scale,which then stepwise approach the logging curve in the high-frequency band.Finally,a seismic-inversion method of nonlinear mapping multilevel well–seismic matching based on the Bi-LSTM network is developed.The characteristic of this method is that by applying the multilevel well–seismic matching process,the seismic data are stepwise matched to the scale range that is consistent with the logging curve.Further,the matching operator at each level can be stably obtained to effectively overcome the problems that occur in the well–seismic matching process,such as the inconsistency in the scale of two types of data,accuracy in extracting the seismic wavelet of the well-side seismic traces,and multiplicity of solutions.Model test and practical application demonstrate that this method improves the vertical resolution of inversion results,and at the same time,the boundary and the lateral characteristics of the sand body are well maintained to improve the accuracy of thin-layer sand body prediction and achieve an improved practical application effect.

A forecasting model for wave heights based on a long short-term memory neural network

To explore new operational forecasting methods of waves,a forecasting model for wave heights at three stations in the Bohai Sea has been developed.This model is based on long short-term memory(LSTM)neural network with sea surface wind and wave heights as training samples.The prediction performance of the model is evaluated,and the error analysis shows that when using the same set of numerically predicted sea surface wind as input,the prediction error produced by the proposed LSTM model at Sta.N01 is 20%,18%and 23%lower than the conventional numerical wave models in terms of the total root mean square error(RMSE),scatter index(SI)and mean absolute error(MAE),respectively.Particularly,for significant wave height in the range of 3–5 m,the prediction accuracy of the LSTM model is improved the most remarkably,with RMSE,SI and MAE all decreasing by 24%.It is also evident that the numbers of hidden neurons,the numbers of buoys used and the time length of training samples all have impact on the prediction accuracy.However,the prediction does not necessary improve with the increase of number of hidden neurons or number of buoys used.The experiment trained by data with the longest time length is found to perform the best overall compared to other experiments with a shorter time length for training.Overall,long short-term memory neural network was proved to be a very promising method for future development and applications in wave forecasting.

Seasonal Short-Term Load Forecasting for Power Systems Based onModal Decomposition and Feature-FusionMulti-Algorithm Hybrid Neural NetworkModel

To enhance the refinement of load decomposition in power systems and fully leverage seasonal change information to further improve prediction performance,this paper proposes a seasonal short-termload combination prediction model based on modal decomposition and a feature-fusion multi-algorithm hybrid neural network model.Specifically,the characteristics of load components are analyzed for different seasons,and the corresponding models are established.First,the improved complete ensemble empirical modal decomposition with adaptive noise(ICEEMDAN)method is employed to decompose the system load for all four seasons,and the new sequence is obtained through reconstruction based on the refined composite multiscale fuzzy entropy of each decomposition component.Second,the correlation between different decomposition components and different features is measured through the max-relevance and min-redundancy method to filter out the subset of features with strong correlation and low redundancy.Finally,different components of the load in different seasons are predicted separately using a bidirectional long-short-term memory network model based on a Bayesian optimization algorithm,with a prediction resolution of 15 min,and the predicted values are accumulated to obtain the final results.According to the experimental findings,the proposed method can successfully balance prediction accuracy and prediction time while offering a higher level of prediction accuracy than the current prediction methods.The results demonstrate that the proposedmethod can effectively address the load power variation induced by seasonal differences in different regions.

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.

Dynamic Hand Gesture Recognition Based on Short-Term Sampling Neural Networks

Hand gestures are a natural way for human-robot interaction.Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications.This paper presents a novel deep learning network for hand gesture recognition.The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation.To learn short-term features,each video input is segmented into a fixed number of frame groups.A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot.These two entities are fused and fed into a convolutional neural network(Conv Net)for feature extraction.The Conv Nets for all groups share parameters.To learn longterm features,outputs from all Conv Nets are fed into a long short-term memory(LSTM)network,by which a final classification result is predicted.The new model has been tested with two popular hand gesture datasets,namely the Jester dataset and Nvidia dataset.Comparing with other models,our model produced very competitive results.The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.

融合BiLSTM与CNN的推特黑灰产分类模型

当前推特等国外社交平台,已成为从事网络黑灰产犯罪不可或缺的工具,对推特上黑灰产账号进行发现、检测和分类对于打击网络犯罪、维护社会稳定具有重大意义。现有的推文分类模型双向长短时记忆网络(bi-directional long short-term memory,BiLSTM)可以学习推文的上下文信息,却无法学习局部关键信息,卷积神经网络(convolution neural network,CNN)模型可以学习推文的局部关键信息,却无法学习推文的上下文信息。结合BiLSTM与CNN两种模型的优势,提出了BiLSTM-CNN推文分类模型,该模型将推文进行向量化后,输入BiLSTM模型学习推文的上下文信息,再在BiLSTM模型后引入CNN层,进行局部特征的提取,最后使用全连接层将经过池化的特征连接在一起,并应用softmax函数进行四分类。模型在自主构建的中文推特黑灰产推文数据集上进行实验,并使用TextCNN、TextRNN、TextRCNN三种分类模型作为对比实验,实验结果显示,所提的BiLSTM-CNN推文分类模型在对四类推文进行分类的宏准确率为98.32%,明显高于TextCNN、TextRNN和TextRCNN三种模型的准确率。

Optimizing the Clinical Decision Support System (CDSS) by Using Recurrent Neural Network (RNN) Language Models for Real-Time Medical Query Processing

This research aims to enhance Clinical Decision Support Systems(CDSS)within Wireless Body Area Networks(WBANs)by leveraging advanced machine learning techniques.Specifically,we target the challenges of accurate diagnosis in medical imaging and sequential data analysis using Recurrent Neural Networks(RNNs)with Long Short-Term Memory(LSTM)layers and echo state cells.These models are tailored to improve diagnostic precision,particularly for conditions like rotator cuff tears in osteoporosis patients and gastrointestinal diseases.Traditional diagnostic methods and existing CDSS frameworks often fall short in managing complex,sequential medical data,struggling with long-term dependencies and data imbalances,resulting in suboptimal accuracy and delayed decisions.Our goal is to develop Artificial Intelligence(AI)models that address these shortcomings,offering robust,real-time diagnostic support.We propose a hybrid RNN model that integrates SimpleRNN,LSTM layers,and echo state cells to manage long-term dependencies effectively.Additionally,we introduce CG-Net,a novel Convolutional Neural Network(CNN)framework for gastrointestinal disease classification,which outperforms traditional CNN models.We further enhance model performance through data augmentation and transfer learning,improving generalization and robustness against data scarcity and imbalance.Comprehensive validation,including 5-fold cross-validation and metrics such as accuracy,precision,recall,F1-score,and Area Under the Curve(AUC),confirms the models’reliability.Moreover,SHapley Additive exPlanations(SHAP)and Local Interpretable Model-agnostic Explanations(LIME)are employed to improve model interpretability.Our findings show that the proposed models significantly enhance diagnostic accuracy and efficiency,offering substantial advancements in WBANs and CDSS.

Audiovisual speech recognition based on a deep convolutional neural network

Audiovisual speech recognition is an emerging research topic.Lipreading is the recognition of what someone is saying using visual information,primarily lip movements.In this study,we created a custom dataset for Indian English linguistics and categorized it into three main categories:(1)audio recognition,(2)visual feature extraction,and(3)combined audio and visual recognition.Audio features were extracted using the mel-frequency cepstral coefficient,and classification was performed using a one-dimension convolutional neural network.Visual feature extraction uses Dlib and then classifies visual speech using a long short-term memory type of recurrent neural networks.Finally,integration was performed using a deep convolutional network.The audio speech of Indian English was successfully recognized with accuracies of 93.67%and 91.53%,respectively,using testing data from 200 epochs.The training accuracy for visual speech recognition using the Indian English dataset was 77.48%and the test accuracy was 76.19%using 60 epochs.After integration,the accuracies of audiovisual speech recognition using the Indian English dataset for training and testing were 94.67%and 91.75%,respectively.

基于BiLSTM-XGBoost混合模型的储层岩性识别

储层岩性分类是地质研究基础,基于数据驱动的机器学习模型虽然能较好地识别储层岩性,但由于测井数据是特殊的序列数据,模型很难有效提取数据的空间相关性,造成模型对储层识别仍存在不足.针对此问题,本文结合双向长短期循环神经网络(bidirectional long short-term memory,BiLSTM)和极端梯度提升决策树(extreme gradient boosting decision tree,XGBoost),提出双向记忆极端梯度提升(BiLSTM-XGBoost,BiXGB)模型预测储层岩性.该模型在传统XGBoost基础上融入了BiLSTM,大大增强了模型对测井数据的特征提取能力.BiXGB模型使用BiLSTM对测井数据进行特征提取,将提取到的特征传递给XGBoost分类模型进行训练和预测.将BiXGB模型应用于储层岩性数据集时,模型预测的总体精度达到了91%.为了进一步验证模型的准确性和稳定性,将模型应用于UCI公开的Occupancy序列数据集,结果显示模型的预测总体精度也高达93%.相较于其他机器学习模型,BiXGB模型能准确地对序列数据进行分类,提高了储层岩性的识别精度,满足了油气勘探的实际需要,为储层岩性识别提供了新的方法.

GNSS拒止时基于并行CNN-BiLSTM回归和残差补偿的UAV导航误差校正方法

全球导航卫星系统(GNSS)拒止时,GNSS/惯性导航系统(INS)组合导航系统的性能严重下降,导致无人机集群导航误差快速发散.目前,利用神经网络预测位置与速度代替GNSS导航信息可校正无人机INS误差,但该方法仍存在定位误差较高且在轨迹突变时预测精度急剧下降的问题.因此,提出了一种基于卷积-双向长短时记忆网络联合残差补偿的位置与速度预测方法,用于提高位置与速度预测精度.首先,针对GNSS拒止后GNSS/INS组合导航系统定位误差较高的问题,提出卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)的融合模型,该模型可建立惯性测量单元(IMU)动力学测量数据与GNSS导航信息之间的关系,实现较准确的位置和速度预测.其次,针对轨迹突变时预测效果急剧下降的问题,提出并行CNNBiLSTM回归架构,在预测位置与速度的同时,挖掘IMU动力学测量数据、预测值与预测残差之间的关系,预测并补偿预测残差,增强模型在轨迹突变时的预测精度.仿真结果表明,所提模型在预测准确性、有效性和稳定性方面都优于CNN-LSTM、LSTM网络模型.

基于双流CNN-BiLSTM的毫米波雷达人体动作识别方法

目前基于雷达的人体动作识别方法,大多是先对人体动作的回波信号进行多维快速傅里叶变换(FFT)得到距离、多普勒和角度等信息,构造各种数据谱图后再输入到神经网络中进行分类识别,数据预处理过程较为复杂。提出了一种双流卷积神经网络(CNN)与双向长短时记忆网络(BiLSTM)串联的毫米波雷达人体动作识别方法。首先对原始的雷达回波信号复数采样数据(I/Q)进行帧差处理,以消除静态干扰,并将其转换为幅度/相位(A/P)的数据格式;然后将帧差后的I/Q和A/P数据分别输入单流的CNN-BiLSTM网络,提取人体动作的空间和时间特征,最后进行双流网络的融合以增强特征的交互性,提高识别准确率。实验结果表明,该方法数据预处理简单,并充分利用了动作数据的帧间相关性,模型收敛快,识别准确率可以达到99%,是一种快速有效的人体动作识别方法。

基于自注意力机制和改进的K-BiLSTM的水产养殖水体溶解氧含量预测模型

为精确预测水产养殖水体溶解氧含量,本研究提出一种基于自注意力机制(ATTN)和改进的K-means聚类-基于残差和批标准化(BN)的双向长短期记忆网络(BiLSTM)的水产养殖水体溶解氧含量预测模型。首先,根据环境数据的相似性,使用改进的K-means算法将数据划分成若干个类别;然后,在BiLSTM基础上构建残差连接和加入BN完成高层次特征提取,利用BiLSTM的长期记忆能力保存特征信息;最后,引入自注意力机制突出不同时间节点数据特征的重要性,进一步提升模型的性能。试验结果表明,本研究提出的基于自注意力机制和改进的K-BiLSTM模型的平均绝对误差为0.238、均方根误差为0.322、平均绝对百分比误差为0.035,与单一的BP模型、CNN-LSTM模型、传统的K-means-基于残差和BN的BiLSTM-ATTN等模型相比具有更优的预测性能和泛化能力。

基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测

为提高光伏功率的预测精度,提出一种变分模态分解(VMD)、模糊熵(FE)、卷积神经网络(CNN)和双向长短期记忆神经网络(BiLSTM)的光伏功率组合预测模型。该方法首先采用VMD将原始光伏序列数据分解成多个子序列,从而减少随机波动分量和噪声干扰对预测模型的影响,通过FE对每个子序列进行重组,使用一维CNN的局部连接及权值共享提取不同分量的特征,将CNN输出的特征融合并输入到BiLSTM模型中;利用BiLSTM模型建立历史数据之间的时间特征关系,得到光伏发电功率预测结果。与BiLSTM、CNN-BiLSTM、EEMD-CNN-BiLSTM、VMD-CNN-BiLSTM这4种模型进行比较,该文提出的VMD-FE-CNN-BiLSTM模型在光伏发电功率预测中具有较高的精确度和稳定性,满足光伏发电短期预测的要求。

融合CNN与BiLSTM模型的短期电能负荷预测

针对卷积神经网络(CNN)在捕捉预测序列间历史相关性方面的不足以及在变量复杂情况下出现的无法精准提取预测关键信息的问题,提出一种将双向长短期记忆网络(BiLSTM)与卷积神经网络结合的CNN-BiLSTM模型.首先,采用数据预处理方法保证数据的正确性和完整性,并对数据进行分析以探究多变量之间的相关性;其次,通过CNN与L1正则化对多维输入特征进行特征筛选,选取与预测相关的重要性特征向量;最后,使用BiLSTM对CNN输出的关键特征信息进行保存,形成向量与预测序列,并通过分析时序特征的潜在特点,提取用户的内在消费模式.实验比较了该模型与其他时序模型在不同时间分辨率下的预测效果,实验结果表明,CNN-BiLSTM模型在不同的回望时间间隔下表现出了最佳的预测性能,能够实现更好的短期负荷预测.

基于TCN-BiLSTM-Attention-ESN的光伏功率预测

针对光伏发电功率随机性强、难以准确预测的问题,提出一种基于时间卷积网络(TCN)、双向长短期记忆网络(BiLSTM)和回声状态网络(ESN)的组合预测方法。首先,使用自适应噪声完备集合经验模态分解(CEEMDAN)将功率数据分解为一系列相对平稳、不同波动模式的子功率序列;再将分解重构后的功率序列和其他特征序列输入到TCN-BiLSTM-Attention-ESN组合模型中,其中TCN-BiLSTM-Attention用于提取光伏序列波动特征并构建时空特征向量;最后,将所提取的时空特征向量输入ESN获得预测结果。采用新疆某光伏电站的光伏功率数据进行验证,结果表明与时下先进的预测方法相比,所提方法具有更高的预测精度,有助于提升光伏发电占比,保障电力系统平衡和运行安全。

基于BERT+CNN_BiLSTM的列控车载设备故障诊断

列控车载设备作为列车运行控制系统核心设备,在高速列车运行过程中发挥着重要作用。目前,其故障诊断仅依赖于现场作业人员经验,诊断效率相对较低。为了实现列控车载设备故障自动诊断并提高诊断效率,提出了BERT+CNN_BiLSTM故障诊断模型。首先,使用来自变换器的双向编码器表征量(Bidirectional encoder representations from transformers,BERT)模型将应用事件日志(Application event log,AElog)转换为计算机能够识别的可以挖掘语义信息的文本向量表示。其次,分别利用卷积神经网络(Convolutional neural network,CNN)和双向长短时记忆网络(Bidirectional long short-term memory,BiLSTM)提取故障特征并进行组合,从而增强空间和时序能力。最后,利用Softmax实现列控车载设备的故障分类与诊断。实验中,选取一列实际运行的列车为研究对象,以运行过程中产生的AElog日志作为实验数据来验证BERT+CNN_BiLSTM模型的性能。与传统机器学习算法、BERT+BiLSTM模型和BERT+CNN模型相比,BERT+CNN_BiLSTM模型的准确率、召回率和F1分别为92.27%、91.03%和91.64%,表明该模型在高速列车控制系统故障诊断中性能优良。

基于SSA-CNN-BiLSTM的航班延误预测

为了提高对机场航班延误时间的准确性,对预测模型进行了研究。采用麻雀搜索算法(SSA),结合卷积神经网络(CNN)和双向长短时记忆网络(BiLSTM),提出了一种基于SSA-CNN-BiLSTM的航班延误预测模型。使用美国亚特兰大机场的实际运行数据进行了验证,与BiLSTM,CNN-LSTM等基准模型进行了比较试验,并加入流量和时间双特征数据集验证模型性能。结果表明,SSA-CNN-BiLSTM模型在评价指标上表现最优,其平均绝对误差(MAE)为5.15,均方根误差(RMSE)为7.58,预测精度优于基准模型,具有良好的多特征处理能力。

Machine learning for pore-water pressure time-series prediction:Application of recurrent neural networks

Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.