A hybrid dynamic time warping-deep neural network architecture for unsupervised acoustic modeling

2015 ◽  
Author(s):  
Roland Thiollière ◽  
Ewan Dunbar ◽  
Gabriel Synnaeve ◽  
Maarten Versteegh ◽  
Emmanuel Dupoux
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Dynamic Time Warping ◽  
Deep Neural Network ◽  
Acoustic Modeling ◽  
Time Warping ◽  
Neural Network Architecture ◽  
Dynamic Time

scholarly journals The Next Failure Time Prediction of Escalators via Deep Neural Network with Dynamic Time Warping Preprocessing

2020 ◽  
Vol 10 (16) ◽  
pp. 5622
Author(s):  
Zitong Zhou ◽  
Yanyang Zi ◽  
Jingsong Xie ◽  
Jinglong Chen ◽  
Tong An
Keyword(s):  
Neural Network ◽  
Dynamic Time Warping ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Failure Time ◽  
Low Complexity ◽  
Time Warping ◽  
Average Prediction Error ◽  
Time Prediction ◽  
Dynamic Time

The escalator is one of the most popular travel methods in public places, and the safe working of the escalator is significant. Accurately predicting the escalator failure time can provide scientific guidance for maintenance to avoid accidents. However, failure data have features of short length, non-uniform sampling, and random interference, which makes the data modeling difficult. Therefore, a strategy that combines data quality enhancement with deep neural networks is proposed for escalator failure time prediction in this paper. First, a comprehensive selection indicator (CSI) that can describe the stationarity and complexity of time series is established to select inherently excellent failure sequences. According to the CSI, failure sequences with high stationarity and low complexity are selected as the referenced sequences to enhance the quality of other failure sequences by using dynamic time warping preprocessing. Secondly, a deep neural network combining the advantages of a convolutional neural network and long short-term memory is built to train and predict quality-enhanced failure sequences. Finally, the failure-recall record of six escalators used for 6 years is analyzed by using the proposed method as a case study, and the results show that the proposed method can reduce the average prediction error of failure time to less than one month.

Study on Rapid Archival Technology of Bullets Based on Graph Convolutional Neural Network

2021 ◽  
Author(s):  
Shi-bo Pan ◽  
Di-lin Pan ◽  
Nan Pan ◽  
Xiao Ye ◽  
Miaohan Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Dynamic Time Warping ◽  
Large Scale ◽  
Line Graph ◽  
High Accuracy ◽  
Time Warping ◽  
Large Numbers ◽  
Dynamic Time

Traditional gun archiving methods are mostly carried out through bullets’ physics or photography, which are inefficient and difficult to trace, and cannot meet the needs of large-scale archiving. Aiming at such problems, a rapid archival technology of bullets based on graph convolutional neural network has been studied and developed. First, the spot laser is used to take the circle points of the bullet rifling traces. The obtained data is filtered and noise-reduced to make the corresponding line graph, and then the dynamic time warping (DTW) algorithm convolutional neural network model is used to perform the processing on the processed data. Not only is similarity matched, the rapid matching of the rifling of the bullet is also accomplished. Comparison of experimental results shows that this technology has the advantages of rapid archiving and high accuracy. Furthermore, it can be carried out in large numbers at the same time, and is more suitable for practical promotion and application.

scholarly journals Multi-Temporal Data Fusion in MS and SAR Images Using the Dynamic Time Warping Method for Paddy Rice Classification

Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 77
Author(s):  
Tsu Chiang Lei ◽  
Shiuan Wan ◽  
You Cheng Wu ◽  
Hsin-Ping Wang ◽  
Chia-Wen Hsieh
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Decision Tree ◽  
Data Fusion ◽  
Dynamic Time Warping ◽  
Image Data ◽  
Synthetic Aperture ◽  
Time Warping ◽  
Radar Images ◽  
Dynamic Time

This study employed a data fusion method to extract the high-similarity time series feature index of a dataset through the integration of MS (Multi-Spectrum) and SAR (Synthetic Aperture Radar) images. The farmlands are divided into small pieces that consider the different behaviors of farmers for their planting contents in Taiwan. Hence, the conventional image classification process cannot produce good outcomes. The crop phenological information will be a core factor to multi-period image data. Accordingly, the study intends to resolve the previous problem by using three different SPOT6 satellite images and nine Sentinel-1A synthetic aperture radar images, which were used to calculate features such as texture and indicator information, in 2019. Considering that a Dynamic Time Warping (DTW) index (i) can integrate different image data sources, (ii) can integrate data of different lengths, and (iii) can generate information with time characteristics, this type of index can resolve certain classification problems with long-term crop classification and monitoring. More specifically, this study used the time series data analysis of DTW to produce “multi-scale time series feature similarity indicators”. We used three approaches (Support Vector Machine, Neural Network, and Decision Tree) to classify paddy patches into two groups: (a) the first group did not apply a DTW index, and (b) the second group extracted conflict predicted data from (a) to apply a DTW index. The outcomes from the second group performed better than the first group in regard to overall accuracy (OA) and kappa. Among those classifiers, the Neural Network approach had the largest improvement of OA and kappa from 89.51, 0.66 to 92.63, 0.74, respectively. The rest of the two classifiers also showed progress. The best performance of classification results was obtained from the Decision Tree of 94.71, 0.81. Observing the outcomes, the interference effects of the image were resolved successfully by various image problems using the spectral image and radar image for paddy rice classification. The overall accuracy and kappa showed improvement, and the maximum kappa was enhanced by about 8%. The classification performance was improved by considering the DTW index.

scholarly journals USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

2020 ◽  
pp. 104-117
Author(s):  
O.S. Amosov ◽  
◽  
S.G. Amosova ◽  
D.S. Magola ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Neural Network Architecture ◽  
Computer Attacks ◽  
Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

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