scholarly journals Emotion Analysis Model of MOOC Course Review Based on BiLSTM

2021 ◽  
Vol 16 (08) ◽  
pp. 93
Author(s):  
Shen Ji ◽  
Tan Fangbi
Keyword(s):  
Short Term Memory ◽  
Training Data ◽  
Computer Application ◽  
Online Course ◽  
Analysis Model ◽  
Short Term ◽  
Data Set ◽  
Emotional Evaluation ◽  
Memory Network ◽  
Long Short Term Memory

Online course review can objectively reflect the emotional tendency of learners towards the learning effect. This paper proposes a deep neural network based sentiment analysis model for MOOC course reviews. The model uses Bidirectional Long Short-Term Memory Network (BiLSTM) to analyze Chinese semantic. In order to deal with the imbalance of training data set, this paper introduces two methods to balance it and adds dropout mechanism to prevent the over fitting of the model. The model is then applied to the emotional evaluation of MOOC course of “Fundamentals of College Computer Application”. The application results show that the model has achieved good accuracy and can well realize the emotional orientation analysis of online course reviews so as to provide valuable reference for Course Builders.

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 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.

scholarly journals Outpatient Text Classification Using Attention-Based Bidirectional LSTM for Robot-Assisted Servicing in Hospital

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 106 ◽  
Author(s):  
Che-Wen Chen ◽  
Shih-Pang Tseng ◽  
Ta-Wen Kuan ◽  
Jhing-Fa Wang
Keyword(s):  
Text Classification ◽  
Short Term Memory ◽  
Family Doctor ◽  
Training Data ◽  
Service Robots ◽  
Service Robot ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

In general, patients who are unwell do not know with which outpatient department they should register, and can only get advice after they are diagnosed by a family doctor. This may cause a waste of time and medical resources. In this paper, we propose an attention-based bidirectional long short-term memory (Att-BiLSTM) model for service robots, which has the ability to classify outpatient categories according to textual content. With the outpatient text classification system, users can talk about their situation to a service robot and the robot can tell them which clinic they should register with. In the implementation of the proposed method, dialog text of users in the Taiwan E Hospital were collected as the training data set. Through natural language processing (NLP), the information in the dialog text was extracted, sorted, and converted to train the long-short term memory (LSTM) deep learning model. Experimental results verify the ability of the robot to respond to questions autonomously through acquired casual knowledge.

scholarly journals Airfoil buffet aerodynamics at plunge and pitch excitation based on long short-term memory neural network prediction

2021 ◽  
Author(s):  
R. Zahn ◽  
C. Breitsamter
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Training Data ◽  
Good Prediction ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Identification Approach ◽  
Long Short Term Memory ◽  
Transonic Buffet

AbstractIn the present study, a nonlinear system identification approach based on a long short-term memory (LSTM) neural network is applied for the prediction of transonic buffet aerodynamics. The identification approach is applied as a reduced-order modeling (ROM) technique for an efficient computation of time-varying integral quantities such as aerodynamic force and moment coefficients. Therefore, the nonlinear identification procedure as well as the generalization of the ROM are presented. The training data set for the LSTM–ROM is provided by performing forced-motion unsteady Reynolds-averaged Navier–Stokes simulations. Subsequent to the training process, the ROM is applied for the computation of the aerodynamic integral quantities associated with transonic buffet. The performance of the trained ROM is demonstrated by computing the aerodynamic loads of the NACA0012 airfoil investigated at transonic freestream conditions. In contrast to previous studies considering only a pitching excitation, both the pitch and plunge degrees of freedom of the airfoil are individually and simultaneously excited by means of an user-defined training signal. Therefore, strong nonlinear effects are considered for the training of the ROM. By comparing the results with a full-order computational fluid dynamics solution, a good prediction capability of the presented ROM method is indicated. However, compared to the results of previous studies including only the airfoil pitching excitation, a slightly reduced prediction performance is shown.

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.

Sign in / Sign up

Export Citation Format

Share Document