scholarly journals Compilation of Load Spectrum for 5MN Metal Extruder Based on Long Short-Term Memory Network

2021 ◽  
Vol 11 (20) ◽  
pp. 9708
Author(s):  
Xiaole Cheng ◽  
Te Han ◽  
Peilin Yang ◽  
Xugang Zhang
Keyword(s):  
Short Term Memory ◽  
Extrapolation Method ◽  
Load Prediction ◽  
Load Spectrum ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Lstm Network

As an important condition for fatigue analysis and life prediction, load spectrum is widely used in various engineering fields. The extrapolation of load samples is an important step in compiling load spectrum. It is of great significance to select an appropriate load extrapolation method. This paper proposes a load extrapolation method based on long short-term memory (LSTM) network, introduces the basic principle of the extrapolation method, and applies the method to the data set collected under the working state of 5MN metal extruder. The comparison between the extrapolated load data and the actual load shows that the trend of the extrapolated load data is basically consistent with the original tendency. In addition, this method is compared with the rain flow extrapolation method based on statistical distribution. Through the comparison of the short-term load spectrum compiled by the two extrapolation methods, it is found that the load spectrum extrapolation method based on LSTM network can better realize load prediction and optimize the compilation of load spectrum.

A novel long short-term memory network for lithium-ion battery health diagnosis using charging curve

2021 ◽  
pp. 014233122110409
Author(s):  
Mingqiang Lin ◽  
Denggao Wu ◽  
Gengfeng Zheng ◽  
Ji Wu
Keyword(s):  
Lithium Ion Batteries ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Battery Degradation ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Input Gate

Lithium-ion batteries are widely used as the power source in electric vehicles. The state of health (SOH) diagnosis is very important for the safety and storage capacity of lithium-ion batteries. In order to accurately and robustly estimate lithium-ion battery SOH, a novel long short-term memory network (LSTM) based on the charging curve is proposed for SOH estimation in this work. Firstly, aging features that reflect the battery degradation phenomenon are extracted from the charging curves. Then, considering capture the long-term tendency of battery degradation, some improvements are made in the proposed LSTM model. The connection between the input gate and the output gate is added to better control output information of the memory cell. Meanwhile, the forget gate and input gate are coupled into a single update gate for selectively forgetting before the accumulation of information. To achieve more reliability and robustness of the SOH estimation method, the improved LSTM network is adaptively trained online by using a particle filter. Furthermore, to verify the effectiveness of the proposed method, we compare the proposed method with two data-driven methods on the public battery data set and the commercial battery data set. Experimental results demonstrate the proposed method can obtain the highest SOH accuracy.

Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)

2021 ◽  
pp. 016555152110239
Author(s):  
Wei Du ◽  
Guanran Jiang ◽  
Wei Xu ◽  
Jian Ma
Keyword(s):  
Short Term Memory ◽  
Rapid Development ◽  
The United States ◽  
Sequential Pattern ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Business Interests ◽  
Memory Network ◽  
Long Short Term Memory

With the rapid development of the patent marketplace, patent trading recommendation is required to mitigate the technology searching cost of patent buyers. Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company’s business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation. KBiLSTM uses knowledge graph embeddings to profile patents with rich patent information. It introduces bidirectional long short-term memory network (BiLSTM) to capture the sequential pattern in a company’s historical records. In addition, to address a company’s diverse technology interests, we design an attention mechanism to aggregate the company’s historical patents given a candidate patent. Experimental results on the United States Patent and Trademark Office (USPTO) data set show that KBiLSTM outperforms state-of-the-art baselines for patent trading recommendation in terms of F1 and normalised discounted cumulative gain (nDCG). The attention visualisation of randomly selected company intuitively demonstrates the recommendation effectiveness.

scholarly journals A Long Short-Term Memory (LSTM) Network for Hourly Estimation of PM2.5 Concentration in Two Cities of South Korea

2020 ◽  
Vol 10 (11) ◽  
pp. 3984 ◽  
Author(s):  
Khaula Qadeer ◽  
Wajih Ur Rehman ◽  
Ahmad Muqeem Sheri ◽  
Inyoung Park ◽  
Hong Kook Kim ◽  
...  
Keyword(s):  
Air Pollution ◽  
South Korea ◽  
Short Term Memory ◽  
Short Term ◽  
Deterministic Models ◽  
Term Memory ◽  
Pm 2.5 ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Lstm Network

Air pollution not only damages the environment but also leads to various illnesses such as respiratory tract and cardiovascular diseases. Nowadays, estimating air pollutants concentration is becoming very important so that people can prepare themselves for the hazardous impact of air pollution beforehand. Various deterministic models have been used to forecast air pollution. In this study, along with various pollutants and meteorological parameters, we also use the concentration of the pollutants predicted by the community multiscale air quality (CMAQ) model which are strongly related to PM 2.5 concentration. After combining these parameters, we implement various machine learning models to predict the hourly forecast of PM 2.5 concentration in two big cities of South Korea and compare their results. It has been shown that Long Short Term Memory network outperforms other well-known gradient tree boosting models, recurrent, and convolutional neural networks.

scholarly journals Dynamic Displacement Forecasting of Dashuitian Landslide in China Using Variational Mode Decomposition and Stack Long Short-Term Memory Network

2019 ◽  
Vol 9 (15) ◽  
pp. 2951 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen ◽  
Kanglin Cong ◽  
Shaolin Zhu ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Forecast Accuracy ◽  
Short Term ◽  
Dynamic Displacement ◽  
Term Memory ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Lstm Network

In recent decades, landslide displacement forecasting has received increasing attention due to its ability to reduce landslide hazards. To improve the forecast accuracy of landslide displacement, a dynamic forecasting model based on variational mode decomposition (VMD) and a stack long short-term memory network (SLSTM) is proposed. VMD is used to decompose landslide displacement into different displacement subsequences, and the SLSTM network is used to forecast each displacement subsequence. Then, the forecast values of landslide displacement are obtained by reconstructing the forecast values of all displacement subsequences. On the other hand, the SLSTM networks are updated by adding the forecast values into the training set, realizing the dynamic displacement forecasting. The proposed model was verified on the Dashuitian landslide in China. The results show that compared with the two advanced forecasting models, long short-term memory (LSTM) network, and empirical mode decomposition (EMD)–LSTM network, the proposed model has higher forecast accuracy.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

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