1.主要工作是将机械设备的传感器数据,LSTM-encoder-decoder模型输入正常数据时间序列训练模型,重构时间序列,然后使用异常数据进行测试,产生较高的重构错误,表明时间序列数据为异常的。 ps:在encoder-decoder模型中有score机制,较高的异常分数是更可能为异常的。
decoder的初始状态--为了随机初始化(图2. ) SAE:1层lstm--encoder,1层lstm--decoder。 LSTM-SAE的学习包括两阶段:1.预训练。2.微调。 预处理阶段:用贪心逐层架构训练LSTM-SAE块,如图3. 分四步: 1. 训练堆栈中的第一个LSTM-AE,保留其encoder层作为第二个LSTM-AE块的输入。 2. 加载保存的encoder层,用...
State of chargeLong Short-Term MemoryGated Recurrent UnitEncoder-DecoderA lithium-ion battery is rechargeable and is widely used in portable devices and electric vehicles (EVs). State-of-Charge (SOC) estimation is vital function in a battery management system (BMS) since high-accuracy SOC ...
A hierarchical temporal attention-based LSTM encoder-decoder model for individual mobility prediction https://europepmc.org/article/pmc/pmc7252178
3, the LSTM-AE architecture consists of two LSTM layers; one runs as an encoder and the other runs as a decoder. The encoder layer takes the input sequence (\({x}_{1},{x}_{2},\,\mathrm{...,}\,{x}_{n}\)) and encodes it into a learned representation vector. Then, the ...
An attention-based encoder–decoder system is proposed, and our experiment proves the efficiency of the proposed approach. To the best of our knowledge, this is the first effort for Urdu language bidirectional transliteration toward neural machine translation....
Finally, the LSTM decoder receives all features and predicts the vehicle trajectory. The experimental results show that the proposed method demonstrates superior predictive performance by using the Next Generation Simulation (NGSIM) dataset. 展开
We propose a Long Short Term Memory based Encoder-Decoder (LSTM-ED) scheme to obtain an unsupervised health index (HI) for a system using multi-sensor time-series data. LSTM-ED is trained to reconstruct the time-series corresponding to healthy state of a system. The reconstruction error is ...
Attention-Model与Encoder-Decoder相结合的模型框架如图2所示。根据图2可知,每一个输出元素都有对应含有输入序列概率分布的语义编码C,因此可以通过如下公式来表示输出结果yi:yi=F(Ci,y1,y2,y3,…,yi-1)(3)上式中,Ci为输入序列X处于编码阶段时的历史状态,设S(xi)为输入xi在编码过程中的非线性函数处理结果,...
A mask-based long short-term memory (LSTM) network is employed for noise suppression and speech restoration is performed via spectral mapping with a convolutional encoder-decoder network (CED). The proposed method improves speech quality (PESQ) over state-of-the-art single-stage methods by about...