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.

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.

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.

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.

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。

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.

基于1DCNN-BiLSTM的端到端滚动轴承故障诊断方法

针对滚动轴承早期故障诊断时时频域特征选取主观性强、时序特征信息利用不足等问题,提出一种基于卷积神经网络和双向长短时记忆神经网络的滚动轴承早期故障诊断方法。采用卷积神经网络提取原始振动信号特征,并在卷积层后引入批正则化层,以消除数据的不规则性对权重优化的影响,并通过扩展首层卷积层和调整步长以提高特征提取效率。引入双向长短时记忆神经网络提升卷积神经网络对时序特征的提取能力,通过批正则化层和Dropout层增强模型的鲁棒性和减少神经元与神经元之间的依赖关系。最后,通过滚动轴承试验数据对文中方法进行验证。结果表明:与传统方法相比,文中方法不仅训练速度更快,而且故障诊断准确率也大幅提高。

基于时序分析及CNN-BiLSTM-AM的阶跃型滑坡位移预测

传统基于递归神经网络的模型对阶跃型滑坡位移预测能力不足,为解决这一问题,提出一种基于时序分析及卷积神经网络-双向长短期记忆-注意力机制(CNN-BiLSTM-AM)的滑坡位移动态预测模型。首先使用变分模态分解方法(VMD)将序列分解为趋势项、周期项和随机项。采用二次指数平滑法拟合趋势项位移,然后引入最大互信息系数法(MIC)计算各类影响因子与周期项位移相关性,对于周期项和随机项位移采用CNN-BiLSTM-AM混合模型进行多因素和单因素预测,最终累加各分量预测值得到累积位移预测结果。实验结果表明,所提方法在最终累计位移预测结果中拟合系数R~2达0.984和0.987,平均绝对误差(MAE)分别为5.334和3.947,均方根误差(RMSE)分别为6.196和4.941,相比卷积神经网络-长短期记忆(CNN-LSTM)、麻雀搜索算法-核极限学习机(SSA-KELM)和NARX方法,所提方法能够更好的捕捉监测数据的时间相关性,预测精度显著提高,可为阶跃型滑坡预警及防治工作提供参考。

Neural Network-Based State of Charge Estimation Method for Lithium-ion Batteries Based on Temperature

Lithium-ion batteries are commonly used in electric vehicles,mobile phones,and laptops.These batteries demonstrate several advantages,such as environmental friendliness,high energy density,and long life.However,battery overcharging and overdischarging may occur if the batteries are not monitored continuously.Overcharging causesfire and explosion casualties,and overdischar-ging causes a reduction in the battery capacity and life.In addition,the internal resistance of such batteries varies depending on their external temperature,elec-trolyte,cathode material,and other factors;the capacity of the batteries decreases with temperature.In this study,we develop a method for estimating the state of charge(SOC)using a neural network model that is best suited to the external tem-perature of such batteries based on their characteristics.During our simulation,we acquired data at temperatures of 25°C,30°C,35°C,and 40°C.Based on the tem-perature parameters,the voltage,current,and time parameters were obtained,and six cycles of the parameters based on the temperature were used for the experi-ment.Experimental data to verify the proposed method were obtained through a discharge experiment conducted using a vehicle driving simulator.The experi-mental data were provided as inputs to three types of neural network models:mul-tilayer neural network(MNN),long short-term memory(LSTM),and gated recurrent unit(GRU).The neural network models were trained and optimized for the specific temperatures measured during the experiment,and the SOC was estimated by selecting the most suitable model for each temperature.The experimental results revealed that the mean absolute errors of the MNN,LSTM,and GRU using the proposed method were 2.17%,2.19%,and 2.15%,respec-tively,which are better than those of the conventional method(4.47%,4.60%,and 4.40%).Finally,SOC estimation based on GRU using the proposed method was found to be 2.15%,which was the most accurate.

Prediction of Leakage from an Axial Piston Pump Slipper with Circular Dimples Using Deep Neural Networks

Oil leakage between the slipper and swash plate of an axial piston pump has a significant effect on the efficiency of the pump.Therefore,it is extremely important that any leakage can be predicted.This study investigates the leakage,oil film thickness,and pocket pressure values of a slipper with circular dimples under different working conditions.The results reveal that flat slippers suffer less leakage than those with textured surfaces.Also,a deep learning-based framework is proposed for modeling the slipper behavior.This framework is a long short-term memory-based deep neural network,which has been extremely successful in predicting time series.The model is compared with four conventional machine learning methods.In addition,statistical analyses and comparisons confirm the superiority of the proposed model.

基于奇异谱分析的CNN-BiLSTM短期空调负荷预测模型

空调负荷的精准预测对建筑空调系统优化控制具有重要意义。为提高空调负荷预测精度,提出了一种基于奇异谱分析(SSA,Singular Spectrum Analysis)的卷积神经网络(CNN,Convolutional Neural Network)和双向长短时记忆网络(BiLSTM,Bidirectional Long Short Term Memory)短期空调负荷预测模型。使用皮尔森相关系数选取与空调负荷高相关性特征。针对空调负荷的波动性和随机性,采用SSA将空调负荷分解为多个分量,同时将各个分量带入CNN-BiLSTM模型进行预测,该模型利用了CNN的特征提取和BiLSTM的双向学习能力,并将各个分量预测结果进行重构。通过不同建筑类型的空调数据对该模型进行验证分析,发现所提出模型在预测办公建筑空调负荷中RMSE、MAPE和MAE为19.47RT、14.72RT和2.33%,在预测商业建筑空调负荷中RMSE、MAPE和MAE为82.5RT、34.21RT和0.87%。结果表明,所提出的模型具有普适性且精度较高,可进行推广应用。

自样本特征构造的1DCNN-BiLSTM网侧光伏功率预测

为解决电网难以获取NWP数据和无法建立光伏功率预测模型的问题,提出一种自样本特征构造的一维卷积双向长短期记忆神经网络光伏发电功率预测方法。通过K均值聚类和功率骤减事件检测的特征工程获取细粒度的天气状态标签,实现基于自身样本的特征构造,以解决样本特征缺少问题;采用卷积和长短期记忆网络结合的模型结构,解决局部特征提取和长期依赖的问题。算例验证结果表明,所提方法改善整体的预测性能,降低多特征数据存在的数据匮乏和数据稳定性风险,为模型输入特征较少的网侧光伏功率短期预测提供一种有效途径。

SDN环境下基于CNN-BiLSTM的入侵检测研究

软件定义网络(SDN)是一种将控制层和数据层分离的新型网络架构,在实现网络集中管理和可编程性的同时也面临易受到入侵攻击的问题。针对此问题设计了检测防御机制。利用深度学习算法,对数据集进行处理后,融合卷积神经网络(CNN)和双向长短期记忆网络(BiLSTM),设计了CNN-BiLSTM模型检测攻击,利用SDN可编程性设计了防御机制,搭建基于SDN的网络平台进行仿真实验。实验结果表明,所设计方法相较传统检测方法可更准确检测出入侵流量,并在检测出后有效实现了防御功能。

Synthetic well logs generation via Recurrent Neural Networks

To supplement missing logging information without increasing economic cost, a machine learning method to generate synthetic well logs from the existing log data was presented, and the experimental verification and application effect analysis were carried out. Since the traditional Fully Connected Neural Network(FCNN) is incapable of preserving spatial dependency, the Long Short-Term Memory(LSTM) network, which is a kind of Recurrent Neural Network(RNN), was utilized to establish a method for log reconstruction. By this method, synthetic logs can be generated from series of input log data with consideration of variation trend and context information with depth. Besides, a cascaded LSTM was proposed by combining the standard LSTM with a cascade system. Testing through real well log data shows that: the results from the LSTM are of higher accuracy than the traditional FCNN; the cascaded LSTM is more suitable for the problem with multiple series data; the machine learning method proposed provides an accurate and cost effective way for synthetic well log generation.

基于太赫兹光谱及1DCNN-BiLSTM的黄蜀葵花金丝桃苷含量预测

太赫兹(THz)光谱具有信噪比高、光子能量低、穿透性强、安全和快速等优点,已广泛应用于食药检测.现有基于HPLC等方法的黄蜀葵花金丝桃苷含量检测耗时长、操作复杂.提出一种基于太赫兹光谱及1DCNN-BiLSTM的黄蜀葵花金丝桃苷含量预测方法:首先采集黄蜀葵花的太赫兹时域谱,并通过傅里叶等变换获取其7种光谱数据;然后利用主成分分析(PCA)对7种光谱数据降维,以获得维数一致的7元样本数据;接着将降维对齐后的7元样本数据输入设计好的1DCNN-BiLSTM网络,以获得金丝桃苷含量预测.与1DCNN、BiLSTM的对比实验结果表明,1DCNN-BiLSTM网络具有较高的预测精度,10次随机实验的平均决定系数达0.970 5.

Real-time UAV path planning based on LSTM network

To address the shortcomings of single-step decision making in the existing deep reinforcement learning based unmanned aerial vehicle(UAV)real-time path planning problem,a real-time UAV path planning algorithm based on long shortterm memory(RPP-LSTM)network is proposed,which combines the memory characteristics of recurrent neural network(RNN)and the deep reinforcement learning algorithm.LSTM networks are used in this algorithm as Q-value networks for the deep Q network(DQN)algorithm,which makes the decision of the Q-value network has some memory.Thanks to LSTM network,the Q-value network can use the previous environmental information and action information which effectively avoids the problem of single-step decision considering only the current environment.Besides,the algorithm proposes a hierarchical reward and punishment function for the specific problem of UAV real-time path planning,so that the UAV can more reasonably perform path planning.Simulation verification shows that compared with the traditional feed-forward neural network(FNN)based UAV autonomous path planning algorithm,the RPP-LSTM proposed in this paper can adapt to more complex environments and has significantly improved robustness and accuracy when performing UAV real-time path planning.

基于DBO-VMD和IWOA-BILSTM神经网络组合模型的短期电力负荷预测

新能源在现代电力系统中占比不断提高,其负荷不规律性、波动性远大于传统电力系统,这就导致负荷预测精度不高。针对这个问题,提出了蜣螂优化(dung beetle optimizer,DBO)算法优化变分模态分解(variational mode decomposition,VMD)与改进鲸鱼优化算法优化双向长短期记忆(improved whale optimization algorithm-bidirectional long short-term memory,IWOA-BILSTM)神经网络相结合的短期负荷预测模型。首先利用DBO优化VMD,分解时间序列数据,并根据最小包络熵对各种特征数据进行分类,增强了分解效果。通过对原始数据进行有效分解,降低了数据的波动性。然后使用非线性收敛因子、自适应权重策略与随机差分法变异策略增强鲸鱼优化算法的局部及全局搜索能力得到改进鲸鱼优化算法(improved whale optimization algorithm,IWOA),并用于优化双向长短期记忆(bidirectional long short-term memory,BILSTM)神经网络,增加了模型预测的精确度。最后将所提方法应用于某地真实的负荷数据,得到最终相对均方根误差、平均绝对误差和平均绝对百分比误差分别为0.0084、48.09、0.66%,证明了提出的模型对于短期负荷预测的有效性。

融合滞后极限学习机的IDBiLSTM短时交通流预测

深度学习短时交通流预测中,存在数据处理实时性较弱,以及算法对交通流数据的复用和修正能力不足导致预测性能较差的问题。针对这一问题,提出一种融合滞后极限学习机的深度双向长短时记忆神经网络短时交通流预测方法。首先,引入权值共享机制对双向长短时记忆网络模型进行结构优化,在模型训练过程中不断进行权重更新和偏置更新,从而充分利用逆序逆转数据增强数据的复用和修正能力;其次,为了进一步提高算法实时性,引入极限学习机模型,并在其神经元激活函数中嵌入生物神经系统中的滞后参数进行优化,加速了运算效率,提升算法的整体实时性。实验结果表明:提出的方法预测精度和算法实时性均有提升,与经典方法CNN-BiLSTM和多元集合CNN-LSTM相比,平均绝对误差分别减少了6.82、6.47,计算速度分别提高了12、19 s,具备良好的短时交通流预测能力和实时性。