scholarly journals Real Time Emulation of Parametric Guitar Tube Amplifier with Long Short Term Memory Neural Network

2018 ◽  
Author(s):  
Thomas Schmitz ◽  
Jean-Jacques Embrechts
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

Real-Time Crash Risk Prediction using Long Short-Term Memory Recurrent Neural Network

2019 ◽  
Vol 2673 (4) ◽  
pp. 314-326 ◽  
Author(s):  
Jinghui Yuan ◽  
Mohamed Abdel-Aty ◽  
Yaobang Gong ◽  
Qing Cai
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Risk Prediction ◽  
Logistic Model ◽  
Short Term Memory ◽  
Crash Risk ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Conditional Logistic Model

With the help of traffic detectors widely deployed along arterial roads and intersections, real-time traffic data are collected and updated in a very short time period, which makes it possible to conduct real-time analysis at signalized intersections. Among them, real-time crash risk prediction is one of the most promising and challenging research topics. This study attempts to predict real-time crash risk by considering time series dependency with the employment of a long short-term memory recurrent neural network (LSTM-RNN) algorithm. Also, the synthetic minority over-sampling technique (SMOTE) was utilized in this study to generate a balanced training dataset for algorithm training. In comparison, a conditional logistic model was developed based on matched case control design. Both models were evaluated based on the real-world unbalanced test dataset rather than an artificially balanced dataset. The comparison results indicate that the LSTM-RNN with SMOTE outperforms the conditional logistic model. The methods and findings of this study attempt to verify the feasibility of real-time crash risk prediction by using LSTM-RNN with over-sampled dataset (SMOTE).

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer

2021 ◽  
pp. 001872082199500
Author(s):  
Ziyang Xie ◽  
Li Li ◽  
Xu Xu
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Driver Distraction ◽  
Distracted Driving ◽  
Measurement Unit ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Objective We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). Background Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. Methods Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver’s right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. Results The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. Conclusion The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.

Real Time Electrocardiogram Annotation with a Long Short Term Memory Neural Network

2019 ◽  
Author(s):  
Federico Corradi ◽  
Jeroen Buil ◽  
Helene De Canniere ◽  
Willemijn Groenendaal ◽  
Pieter Vandervoort
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Real-time fault diagnosis using deep fusion of features extracted by parallel long short-term memory with peephole and convolutional neural network

2020 ◽  
pp. 095965182094829
Author(s):  
Funa Zhou ◽  
Zhiqiang Zhang ◽  
Danmin Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Short Term Memory ◽  
Two Dimensional ◽  
Short Term ◽  
Term Memory ◽  
One Dimensional ◽  
Long Short Term Memory

Analysis of one-dimensional vibration signals is the most common method used for safety analysis and health monitoring of rotary machines. How to effectively extract features involved in one-dimensional sequence data is crucial for the accuracy of real-time fault diagnosis. This article aims to develop more effective means of extracting useful features potentially involved in one-dimensional vibration signals. First, an improved parallel long short-term memory called parallel long short-term memory with peephole is designed by adding a peephole connection before each forget gate to prevent useless information transferring in the cell. It can not only solve the memory bottleneck problem of traditional long short-term memory for long sequence but also can make full use of all possible information helpful for feature extraction. Second, a fusion network with new training mechanism is designed to fuse features extracted from parallel long short-term memory with peephole and convolutional neural network, respectively. The fusion network can incorporate two-dimensional screenshot image into comprehensive feature extraction. It can provide more accurate fault diagnosis result since two-dimensional screenshot image is another form of expression for one-dimensional vibration sequence involving additional trend and locality information. Finally, real-time two-dimensional screenshot image is fed into convolutional neural network to secure a real-time online diagnosis which is the primary requirement of the engineers in health monitoring. Validity of the proposed method is verified by fault diagnosis for rolling bearing and gearbox.

scholarly journals Real Time Prediction of Sclera Force with LSTM Neural Networks in Robot-Assisted Retinal Surgery

2020 ◽  
Vol 896 ◽  
pp. 183-194
Author(s):  
Chang Yan He ◽  
Niravkumar Patel ◽  
Marin Kobilarov ◽  
Iulian Iordachita
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Contact Force ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
User Behavior ◽  
Short Term ◽  
Insertion Depth ◽  
Term Memory ◽  
Long Short Term Memory

Retinal microsurgery is one of the most technically demanding surgeries, during which the surgical tool needs to be inserted into the eyeball and is constantly constrained by the sclerotomy port. During the surgery, any unexpected manipulation could cause extreme tool-sclera contact force leading to sclera damage. Although, a robot assistant could reduce hand tremor and improve the tool positioning accuracy, it cannot prevent or alarm the surgeon about the upcoming danger caused by surgeon’s misoperations, i.e., applying excessive force on the sclera. In this paper, we present a new method based on a Long Short Term Memory recurrent neural network for predicting the user behavior, i.e., the contact force between the tool and sclera (sclera force) and the insertion depth of the tool from sclera contact point (insertion depth) in real time (40Hz). The predicted force information is provided to the user through auditory feedback to alarm any unexpected sclera force. The user behavior data is collected in a mock retinal surgical operation on a dry eye phantom with Steady Hand Eye Robot and a novel multi-function sensing tool. The Long Short Term Memory recurrent neural network is trained on the collected time series of sclera force and insertion depth. The network can predict the sclera force and insertion depth 100 milliseconds in the future with 95.29% and 96.57% accuracy, respectively, and can help reduce the fraction of unsafe sclera forces from 40.19% to 15.43%.

scholarly journals Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0251701
Author(s):  
Anca Loredana Udriștoiu ◽  
Irina Mihaela Cazacu ◽  
Lucian Gheorghe Gruionu ◽  
Gabriel Gruionu ◽  
Andreea Valentina Iacob ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Short Term Memory ◽  
Color Doppler ◽  
Short Term ◽  
Term Memory ◽  
Pancreatic Masses ◽  
Computer Aided ◽  
Long Short Term Memory ◽  
Aided Diagnosis

Differential diagnosis of focal pancreatic masses is based on endoscopic ultrasound (EUS) guided fine needle aspiration biopsy (EUS-FNA/FNB). Several imaging techniques (i.e. gray-scale, color Doppler, contrast-enhancement and elastography) are used for differential diagnosis. However, diagnosis remains highly operator dependent. To address this problem, machine learning algorithms (MLA) can generate an automatic computer-aided diagnosis (CAD) by analyzing a large number of clinical images in real-time. We aimed to develop a MLA to characterize focal pancreatic masses during the EUS procedure. The study included 65 patients with focal pancreatic masses, with 20 EUS images selected from each patient (grayscale, color Doppler, arterial and venous phase contrast-enhancement and elastography). Images were classified based on cytopathology exam as: chronic pseudotumoral pancreatitis (CPP), neuroendocrine tumor (PNET) and ductal adenocarcinoma (PDAC). The MLA is based on a deep learning method which combines convolutional (CNN) and long short-term memory (LSTM) neural networks. 2688 images were used for training and 672 images for testing the deep learning models. The CNN was developed to identify the discriminative features of images, while a LSTM neural network was used to extract the dependencies between images. The model predicted the clinical diagnosis with an area under curve index of 0.98 and an overall accuracy of 98.26%. The negative (NPV) and positive (PPV) predictive values and the corresponding 95% confidential intervals (CI) are 96.7%, [94.5, 98.9] and 98.1%, [96.81, 99.4] for PDAC, 96.5%, [94.1, 98.8], and 99.7%, [99.3, 100] for CPP, and 98.9%, [97.5, 100] and 98.3%, [97.1, 99.4] for PNET. Following further validation on a independent test cohort, this method could become an efficient CAD tool to differentiate focal pancreatic masses in real-time.

Sign in / Sign up

Export Citation Format

Share Document