Dynamic response prediction of vehicle-bridge interaction system using feedforward neural network and deep long short-term memory network

Structures ◽  
2021 ◽  
Vol 34 ◽  
pp. 2415-2431
Author(s):  
Huile Li ◽  
Tianyu Wang ◽  
Gang Wu
Keyword(s):  
Neural Network ◽  
Dynamic Response ◽  
Short Term Memory ◽  
Response Prediction ◽  
Feedforward Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Interaction System ◽  
Memory Network ◽  
Long Short Term Memory

scholarly journals Pemanfaatan Asynchronous Advantage Actor-Critic Dalam Pembuatan AI Game Bot Pada Game Arcade

2019 ◽  
Vol 1 (2) ◽  
pp. 74-84
Author(s):  
Evan Kusuma Susanto ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Trial And Error ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Asynchronous Advantage Actor-Critic (A3C) adalah sebuah algoritma deep reinforcement learning yang dikembangkan oleh Google DeepMind. Algoritma ini dapat digunakan untuk menciptakan sebuah arsitektur artificial intelligence yang dapat menguasai berbagai jenis game yang berbeda melalui trial and error dengan mempelajari tempilan layar game dan skor yang diperoleh dari hasil tindakannya tanpa campur tangan manusia. Sebuah network A3C terdiri dari Convolutional Neural Network (CNN) di bagian depan, Long Short-Term Memory Network (LSTM) di tengah, dan sebuah Actor-Critic network di bagian belakang. CNN berguna sebagai perangkum dari citra output layar dengan mengekstrak fitur-fitur yang penting yang terdapat pada layar. LSTM berguna sebagai pengingat keadaan game sebelumnya. Actor-Critic Network berguna untuk menentukan tindakan terbaik untuk dilakukan ketika dihadapkan dengan suatu kondisi tertentu. Dari hasil percobaan yang dilakukan, metode ini cukup efektif dan dapat mengalahkan pemain pemula dalam memainkan 5 game yang digunakan sebagai bahan uji coba.

scholarly journals A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

2022 ◽  
Vol 12 (1) ◽  
pp. 721
Author(s):  
Ahmed Nasser ◽  
Huthaifa AL-Khazraji
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Predictive Maintenance ◽  
Fault Prediction ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.</p>

Downscaling rainfall using deep learning long short‐term memory and feedforward neural network

2019 ◽  
Vol 39 (10) ◽  
pp. 4170-4188 ◽  
Author(s):  
Duong Tran Anh ◽  
Song P. Van ◽  
Thanh D. Dang ◽  
Long P. Hoang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Feedforward Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

scholarly journals A Method Based on GA-CNN-LSTM for Daily Tourist Flow Prediction at Scenic Spots

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 261
Author(s):  
Wenxing Lu ◽  
Haidong Rui ◽  
Changyong Liang ◽  
Li Jiang ◽  
Shuping Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Feature Vectors ◽  
Flow Prediction ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Tourist Flow

Accurate tourist flow prediction is key to ensuring the normal operation of popular scenic spots. However, one single model cannot effectively grasp the characteristics of the data and make accurate predictions because of the strong nonlinear characteristics of daily tourist flow data. Accordingly, this study predicts daily tourist flow in Huangshan Scenic Spot in China. A prediction method (GA-CNN-LSTM) which combines convolutional neural network (CNN) and long-short-term memory network (LSTM) and optimized by genetic algorithm (GA) is established. First, network search data, meteorological data, and other data are constructed into continuous feature maps. Then, feature vectors are extracted by convolutional neural network (CNN). Finally, the feature vectors are input into long-short-term memory network (LSTM) in time series for prediction. Moreover, GA is used to scientifically select the number of neurons in the CNN-LSTM model. Data is preprocessed and normalized before prediction. The accuracy of GA-CNN-LSTM is evaluated using mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson correlation coefficient and index of agreement (IA). For a fair comparison, GA-CNN-LSTM model is compared with CNN-LSTM, LSTM, CNN and the back propagation neural network (BP). The experimental results show that GA-CNN-LSTM model is approximately 8.22% higher than CNN-LSTM on the performance of MAPE.

scholarly journals Computational Model for Therapy Optimization of Wearable Cardioverter Defibrillator: Shockable Rhythm Detection and Optimal Electrotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Oishee Mazumder ◽  
Rohan Banerjee ◽  
Dibyendu Roy ◽  
Ayan Mukherjee ◽  
Avik Ghose ◽  
...  
Keyword(s):  
Neural Network ◽  
Computational Model ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Cardioverter Defibrillator ◽  
Wearable Cardioverter Defibrillator ◽  
Memory Network ◽  
Long Short Term Memory

Wearable cardioverter defibrillator (WCD) is a life saving, wearable, noninvasive therapeutic device that prevents fatal ventricular arrhythmic propagation that leads to sudden cardiac death (SCD). WCD are frequently prescribed to patients deemed to be at high arrhythmic risk but the underlying pathology is potentially reversible or to those who are awaiting an implantable cardioverter-defibrillator. WCD is programmed to detect appropriate arrhythmic events and generate high energy shock capable of depolarizing the myocardium and thus re-initiating the sinus rhythm. WCD guidelines dictate very high reliability and accuracy to deliver timely and optimal therapy. Computational model-based process validation can verify device performance and benchmark the device setting to suit personalized requirements. In this article, we present a computational pipeline for WCD validation, both in terms of shock classification and shock optimization. For classification, we propose a convolutional neural network-“Long Short Term Memory network (LSTM) full form” (Convolutional neural network- Long short term memory network (CNN-LSTM)) based deep neural architecture for classifying shockable rhythms like Ventricular Fibrillation (VF), Ventricular Tachycardia (VT) vs. other kinds of non-shockable rhythms. The proposed architecture has been evaluated on two open access ECG databases and the classification accuracy achieved is in adherence to American Heart Association standards for WCD. The computational model developed to study optimal electrotherapy response is an in-silico cardiac model integrating cardiac hemodynamics functionality and a 3D volume conductor model encompassing biophysical simulation to compute the effect of shock voltage on myocardial potential distribution. Defibrillation efficacy is simulated for different shocking electrode configurations to assess the best defibrillator outcome with minimal myocardial damage. While the biophysical simulation provides the field distribution through Finite Element Modeling during defibrillation, the hemodynamic module captures the changes in left ventricle functionality during an arrhythmic event. The developed computational model, apart from acting as a device validation test-bed, can also be used for the design and development of personalized WCD vests depending on subject-specific anatomy and pathology.

scholarly journals Recurrent Neural Network based Models for Word Prediction

2019 ◽  
Vol 8 (4) ◽  
pp. 7433-7437
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
English Language ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
The Given ◽  
Current Word

Globally, people are spending a cumulative amount of time on their mobile device, laptop, tab, desktop, etc,. for messaging, sending emails, banking, interaction through social media, and all other activities. It is necessary to cut down the time spend on typing through these devices. It can be achieved when the device can provide the user more options for what the next word might be for the current typed word. It also increases the speed of typing. In this paper, we suggest and presented a comparative study on various models like Recurrent Neural Network, Stacked Recurrent Neural Network, Long Short Term Memory network (LSTM) and Bi-directional LSTM that gives solution for the above said problem. Our primary goal is to suggest the best model among the four models to predict the next word for the given current word in English Language. Our survey says that for predicting next word RNN provide accuracy 60% and loss 40%, Stacked RNN provide accuracy 62% and loss 38%, LSTM provide accuracy 64% and loss 36% and Bidirectional LSTM provide accuracy 72% and loss 28%.

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