scholarly journals Spiking Echo State Convolutional Neural Network for Robust Time Series Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 4927-4935 ◽  
Author(s):  
Anguo Zhang ◽  
Wei Zhu ◽  
Juanyu Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Time Series Classification

scholarly journals ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification

2019 ◽  
Author(s):  
Kathan Kashiparekh ◽  
Jyoti Narwariya ◽  
Pankaj Malhotra ◽  
Lovekesh Vig ◽  
Gautam Shroff
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Time Series Classification

scholarly journals Low-dimensional Convolutional Neural Network for Solar Flares GOES Time-series Classification

2022 ◽  
Vol 258 (1) ◽  
pp. 12
Author(s):  
Vlad Landa ◽  
Yuval Reuveni
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Solar Flare ◽  
Convolutional Neural Network ◽  
Solar Flares ◽  
Time Series Data ◽  
Series Data ◽  
Time Series Classification ◽  
X Ray ◽  
Low Dimensional

Abstract Space weather phenomena such as solar flares have a massive destructive power when they reach a certain magnitude. Here, we explore the deep-learning approach in order to build a solar flare-forecasting model, while examining its limitations and feature-extraction ability based on the available Geostationary Operational Environmental Satellite (GOES) X-ray time-series data. We present a multilayer 1D convolutional neural network to forecast the solar flare event probability occurrence of M- and X-class flares at 1, 3, 6, 12, 24, 48, 72, and 96 hr time frames. The forecasting models were trained and evaluated in two different scenarios: (1) random selection and (2) chronological selection, which were compared afterward in terms of common score metrics. Additionally, we also compared our results to state-of-the-art flare-forecasting models. The results indicates that (1) when X-ray time-series data are used alone, the suggested model achieves higher score results for X-class flares and similar scores for M-class as in previous studies. (2) The two different scenarios obtain opposite results for the X- and M-class flares. (3) The suggested model combined with solely X-ray time-series fails to distinguish between M- and X-class magnitude solar flare events. Furthermore, based on the suggested method, the achieved scores, obtained solely from X-ray time-series measurements, indicate that substantial information regarding the solar activity and physical processes are encapsulated in the data, and augmenting additional data sets, both spatial and temporal, may lead to better predictions, while gaining a comprehensive physical interpretation regarding solar activity. All source codes are available at https://github.com/vladlanda.

scholarly journals Merging Subject Matter Expertise and Deep Convolutional Neural Network for State-Based Online Machine-Part Interaction Classification

2021 ◽  
Author(s):  
Hao Wang ◽  
Yassine Qamsane ◽  
James Moyne ◽  
Kira Barton
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Subject Matter ◽  
Machine Part ◽  
Deep Convolutional Neural Network ◽  
Time Series Classification ◽  
Testing Dataset ◽  
Subject Matter Expertise ◽  
Point Detection

Abstract Machine-part interaction classification is a key capability required by Cyber-Physical Systems (CPS), a pivotal enabler of Smart Manufacturing (SM). While previous relevant studies on the subject have primarily focused on time series classification, change point detection is equally important because it provides temporal information on changes in behavior of the machine. In this work, we address point detection and time series classification for machine-part interactions with a deep Convolutional Neural Network (CNN) based framework. The CNN in this framework utilizes a two-stage encoder-classifier structure for efficient feature representation and convenient deployment customization for CPS. Though data-driven, the design and optimization of the framework are Subject Matter Expertise (SME) guided. An SME defined Finite State Machine (FSM) is incorporated into the framework to prohibit intermittent misclassifications. In the case study, we implement the framework to perform machine-part interaction classification on a milling machine, and the performance is evaluated using a testing dataset and deployment simulations. The implementation achieved an average F1-Score of 0.946 across classes on the testing dataset and an average delay of 0.24 seconds on the deployment simulations.

scholarly journals Dynamic Multi-Scale Convolutional Neural Network for Time Series Classification

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 109732-109746 ◽  
Author(s):  
Bin Qian ◽  
Yong Xiao ◽  
Zhenjing Zheng ◽  
Mi Zhou ◽  
Wanqing Zhuang ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Time Series Classification ◽  
Multi Scale

Time Series Classification With Multivariate Convolutional Neural Network

2019 ◽  
Vol 66 (6) ◽  
pp. 4788-4797 ◽  
Author(s):  
Chien-Liang Liu ◽  
Wen-Hoar Hsaio ◽  
Yao-Chung Tu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Convolutional Neural Network ◽  
Time Series Classification
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