scholarly journals An LSTM Network for Apnea and Hypopnea Episodes Detection in Respiratory Signals

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5858
Author(s):  
Jakub Drzazga ◽  
Bogusław Cyganek
Keyword(s):  
Sleep Disordered Breathing ◽  
Short Term Memory ◽  
Classification Error ◽  
Normal Breathing ◽  
Proposed Model ◽  
Respiratory Event ◽  
Shallow Breathing ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Thoracic And Abdominal

One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves polysomnography examination, which is expensive. Alternatively, diagnostic doctors can be supported with recordings from the in-home polygraphy sensors. Furthermore, numerous attempts for providing an automated apnea episodes annotation algorithm have been made. Most of them, however, do not distinguish between apnea and hypopnea episodes. In this work, a novel solution for epoch-based annotation problem is presented. Utilizing an architecture based on the long short-term memory (LSTM) networks, the proposed model provides locations of sleep disordered breathing episodes and identifies them as either apnea or hypopnea. To achieve this, special pre- and postprocessing steps have been designed. The obtained labels can be then used for calculation of the respiratory event index (REI), which serves as a disease severity indicator. The input for the model consists of the oronasal airflow along with the thoracic and abdominal respiratory effort signals. Performance of the proposed architecture was verified on the SHHS-1 and PhysioNet Sleep databases, obtaining mean REI classification error of 9.24/10.52 with standard deviation of 11.61/7.92 (SHHS-1/PhysioNet). Normal breathing, hypopnea and apnea differentiation accuracy is assessed on both databases, resulting in the correctly classified samples percentage of 86.42%/84.35%, 49.30%/58.28% and 68.20%/69.50% for normal breathing, hypopnea and apnea classes, respectively. Overall accuracies are 80.66%/82.04%. Additionally, the effect of wake periods is investigated. The results show that the proposed model can be successfully used for both episode classification and REI estimation tasks.

scholarly journals The Data-Driven Modeling of Pressure Loss in Multi-Batch Refined Oil Pipelines with Drag Reducer Using Long Short-Term Memory (LSTM) Network

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5871
Author(s):  
Shengshi Wang ◽  
Lianyong Zuo ◽  
Miao Li ◽  
Qiao Wang ◽  
Xizhen Xue ◽  
...  
Keyword(s):  
Pressure Loss ◽  
Short Term Memory ◽  
Data Driven ◽  
Refined Oil ◽  
Proposed Model ◽  
Oil Pipelines ◽  
Transportation Process ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Data Driven Modeling

Due to the addition of the drag reducer in refined oil pipelines for increasing the pipeline throughput as well as reducing energy consumption, the classical method based on the Darcy-Weisbach Formula for precise pressure loss calculation presents a large error. Additionally, the way to accurately calculate the pressure loss of the refined oil pipeline with the drag reducer is in urgent need. The accurate pressure loss value can be used as the input parameter of pump scheduling or batch scheduling models of refined oil pipelines, which can ensure the safe operation of the pipeline system, achieving the goal of energy-saving and cost reduction. This paper proposes the data-driven modeling of pressure loss for multi-batch refined oil pipelines with the drag reducer in high accuracy. The multi-batch sequential transportation process and the differences in the physical properties between different kinds of refined oil in the pipelines are taken into account. By analyzing the changes of the drag reduction rate over time and the autocorrelation of the pressure loss sequence data, the sequential time effect of the drag reducer on calculating pressure loss is considered and therefore, the long short-term memory (LSTM) network is utilized. The neural network structure with two LSTM layers is designed. Moreover, the input features of the proposed model are naturally inherited from the Darcy-Weisbach Formula and on adaptation to the multi-batch sequential transportation process in refined oil pipelines, using the particle swarm optimization (PSO) algorithm for network hyperparameter tuning. Case studies show that the proposed data-driven model based on the LSTM network is valid and capable of considering the multi-batch sequential transportation process. Furthermore, the proposed model outperforms the models based on the Darcy-Weisbach Formula and multilayer perceptron (MLP) from previous studies in accuracy. The MAPEs of the proposed model of pipelines with the drag reducer are all less than 4.7% and the best performance on the testing data is 1.3627%, which can provide the calculation results of pressure loss in high accuracy. The results also indicate that the model’s capturing sequential effect of the drag reducer from the input data set contributed to improving the calculation accuracy and generalization ability.

scholarly journals IoT-Based Wearable Sensors and Bidirectional LSTM Network for Action Recognition of Aerobics Athletes

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Jing Ye ◽  
Hui Wang ◽  
MeiJie Li ◽  
Ning Wang
Keyword(s):  
Short Term Memory ◽  
Inertial Sensors ◽  
Inertial Sensor ◽  
Wearable Sensors ◽  
Motion Prediction ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
The Times ◽  
The Internet Of Things

Aerobics is the fusion of gymnastics, dance, and music; it is a body of a sports project, along with the development of the society. The growing demand for aerobics inevitably increases the demand for aerobics coach and teacher and has opened elective aerobics class which is an effective way of cultivating professional talents relevant to aerobics. Aerobics has extended fixed teaching mode and cannot conform to the development of the times. The motion prediction of aerobics athletes is a new set of teaching aid. In this paper, a motion prediction model of aerobics athletes is built based on the wearable inertial sensor of the Internet of Things and the bidirectional long short term memory (BiLSTM) network. Firstly, a wireless sensor network based on ZigBee was designed and implemented to collect the posture data of aerobics athletes. The inertial sensors were used for data collection and transmission of the data to the cloud platform through Ethernet. Then, the movement of aerobics athletes is recognized and predicted by the BiLSTM network. Based on the BiLSTM network and the attention mechanism, this paper proposes to solve the problem of low classification accuracy caused by the traditional method of directly summing and averaging the updated output vectors corresponding to each moment of the BiLSTM layer. The simulation experiment is also carried out in this paper. The experimental results show that the proposed model can recognize aerobics effectively.

scholarly journals BJBN： BERT-JOIN-BiLSTM Networks for Medical Auxiliary Diagnostic

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Chuanjie Xu ◽  
Feng Yuan ◽  
Shouqiang Chen
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
Local Features ◽  
Global Information ◽  
Short Term ◽  
Baseline Model ◽  
Proposed Model ◽  
Global Representation ◽  
Long Short Term Memory ◽  
Lstm Network

This study proposed a medicine auxiliary diagnosis model based on neural network. The model combines a bidirectional long short-term memory（Bi-LSTM）network and bidirectional encoder representations from transformers (BERT), which can well complete the extraction of local features of Chinese medicine texts. BERT can learn the global information of the text, so use BERT to get the global representation of medical text and then use Bi-LSTM to extract local features. We conducted a large number of comparative experiments on datasets. The results show that the proposed model has significant advantages over the state-of-the-art baseline model. The accuracy of the proposed model is 0.75.

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 A Combined Method for MEMS Gyroscope Error Compensation Using a Long Short-Term Memory Network and Kalman Filter in Random Vibration Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1181
Author(s):  
Chenhao Zhu ◽  
Sheng Cai ◽  
Yifan Yang ◽  
Wei Xu ◽  
Honghai Shen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Standard Deviation ◽  
Error Compensation ◽  
Random Vibration ◽  
Short Term Memory ◽  
Combined Method ◽  
Short Term ◽  
Mems Gyroscope ◽  
Long Short Term Memory ◽  
Lstm Network

In applications such as carrier attitude control and mobile device navigation, a micro-electro-mechanical-system (MEMS) gyroscope will inevitably be affected by random vibration, which significantly affects the performance of the MEMS gyroscope. In order to solve the degradation of MEMS gyroscope performance in random vibration environments, in this paper, a combined method of a long short-term memory (LSTM) network and Kalman filter (KF) is proposed for error compensation, where Kalman filter parameters are iteratively optimized using the Kalman smoother and expectation-maximization (EM) algorithm. In order to verify the effectiveness of the proposed method, we performed a linear random vibration test to acquire MEMS gyroscope data. Subsequently, an analysis of the effects of input data step size and network topology on gyroscope error compensation performance is presented. Furthermore, the autoregressive moving average-Kalman filter (ARMA-KF) model, which is commonly used in gyroscope error compensation, was also combined with the LSTM network as a comparison method. The results show that, for the x-axis data, the proposed combined method reduces the standard deviation (STD) by 51.58% and 31.92% compared to the bidirectional LSTM (BiLSTM) network, and EM-KF method, respectively. For the z-axis data, the proposed combined method reduces the standard deviation by 29.19% and 12.75% compared to the BiLSTM network and EM-KF method, respectively. Furthermore, for x-axis data and z-axis data, the proposed combined method reduces the standard deviation by 46.54% and 22.30% compared to the BiLSTM-ARMA-KF method, respectively, and the output is smoother, proving the effectiveness of the proposed method.

scholarly journals Air pollution forecasting application based on deep learning model and optimization algorithm

2021 ◽  
Author(s):  
Azim Heydari ◽  
Meysam Majidi Nezhad ◽  
Davide Astiaso Garcia ◽  
Farshid Keynia ◽  
Livio De Santoli
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Power Plant ◽  
Air Temperature ◽  
Short Term Memory ◽  
Combined Cycle ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

scholarly journals Extraction of local and global features by a convolutional neural network–long short-term memory network for diagnosing bearing faults

2021 ◽  
pp. 095440622110165
Author(s):  
Zhang Chao ◽  
Wang Wei-zhi ◽  
Zhang Chen ◽  
Fan Bin ◽  
Wang Jian-guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Short Term Memory ◽  
Vibration Signal ◽  
Short Term ◽  
Global Features ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Lstm Network

Accurate and reliable fault diagnosis is one of the key and difficult issues in mechanical condition monitoring. In recent years, Convolutional Neural Network (CNN) has been widely used in mechanical condition monitoring, which is also a great breakthrough in the field of bearing fault diagnosis. However, CNN can only extract local features of signals. The model accuracy and generalization of the original vibration signals are very low in the process of vibration signal processing only by CNN. Based on the above problems, this paper improves the traditional convolution layer of CNN, and builds the learning module (local feature learning block, LFLB) of the local characteristics. At the same time, the Long Short-Term Memory (LSTM) is introduced into the network, which is used to extract the global features. This paper proposes the new neural network—improved CNN-LSTM network. The extracted deep feature is used for fault classification. The improved CNN-LSTM network is applied to the processing of the vibration signal of the faulty bearing collected by the bearing failure laboratory of Inner Mongolia University of science and technology. The results show that the accuracy of the improved CNN-LSTM network on the same batch test set is 98.75%, which is about 24% higher than that of the traditional CNN. The proposed network is applied to the bearing data collection of Western Reserve University under the condition that the network parameters remain unchanged. The experiment shows that the improved CNN-LSTM network has better generalization than the traditional CNN.

Sentence similarity evaluation using Sent2Vec and siamese neural network with parallel structure

2021 ◽  
pp. 1-10
Author(s):  
Hye-Jeong Song ◽  
Tak-Sung Heo ◽  
Jong-Dae Kim ◽  
Chan-Young Park ◽  
Yu-Seop Kim
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Parallel Structure ◽  
Short Term ◽  
Similarity Estimation ◽  
Accurate Judgment ◽  
Proposed Model ◽  
Sentence Similarity ◽  
Long Short Term Memory

Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. The vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. The vector is input to softmax layer, and finally, the similarity between the two sentences is determined. The proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models.

An Intelligent Approach to Detect Fake News Using Artificial Intelligence Technique

2021 ◽  
Vol 13 (2) ◽  
pp. 1-12
Author(s):  
Sumit Das ◽  
Manas Kumar Sanyal ◽  
Sarbajyoti Mallik
Keyword(s):  
Artificial Intelligence ◽  
Web Application ◽  
Short Term Memory ◽  
Real Data ◽  
Fake News ◽  
Artificial Intelligence Technique ◽  
News Reports ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Intelligent Approach

There is a lot of fake news roaming around various mediums, which misleads people. It is a big issue in this advanced intelligent era, and there is a need to find some solution to this kind of situation. This article proposes an approach that analyzes fake and real news. This analysis is focused on sentiment, significance, and novelty, which are a few characteristics of this news. The ability to manipulate daily information mathematically and statistically is allowed by expressing news reports as numbers and metadata. The objective of this article is to analyze and filter out the fake news that makes trouble. The proposed model is amalgamated with the web application; users can get real data and fake data by using this application. The authors have used the AI (artificial intelligence) algorithms, specifically logistic regression and LSTM (long short-term memory), so that the application works well. The results of the proposed model are compared with existing models.

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