@Article{cmc.2020.011108,
AUTHOR = {Xiaorui Shao, Chang Soo Kim, Dae Geun Kim},
TITLE = {Accurate Multi-Scale Feature Fusion CNN for Time Series  Classification in Smart Factory},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {543--561},
URL = {http://www.techscience.com/cmc/v65n1/39582},
ISSN = {1546-2226},
ABSTRACT = {Time series classification (TSC) has attracted various attention in the 
community of machine learning and data mining and has many successful applications 
such as fault detection and product identification in the process of building a smart 
factory. However, it is still challenging for the efficiency and accuracy of classification 
due to complexity, multi-dimension of time series. This paper presents a new approach 
for time series classification based on convolutional neural networks (CNN). The 
proposed method contains three parts: short-time gap feature extraction, multi-scale local 
feature learning, and global feature learning. In the process of short-time gap feature 
extraction, large kernel filters are employed to extract the features within the short-time 
gap from the raw time series. Then, a multi-scale feature extraction technique is applied 
in the process of multi-scale local feature learning to obtain detailed representations. The 
global convolution operation with giant stride is to obtain a robust and global feature 
representation. The comprehension features used for classifying are a fusion of short time
gap feature representations, local multi-scale feature representations, and global feature 
representations. To test the efficiency of the proposed method named multi-scale feature 
fusion convolutional neural networks (MSFFCNN), we designed, trained MSFFCNN on 
some public sensors, device, and simulated control time series data sets. The comparative 
studies indicate our proposed MSFFCNN outperforms other alternatives, and we also 
provided a detailed analysis of the proposed MSFFCNN.},
DOI = {10.32604/cmc.2020.011108}
}