Discharge Voltage Time Series Classification of Lithium-ion Cells Based on Deep Neural Networks

2018 ◽  
Author(s):  
Chengbao Liu ◽  
Xuelei Wang ◽  
Jie Tan ◽  
Lianjing Wang ◽  
Wei Sun ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Lithium Ion ◽  
Discharge Voltage ◽  
Time Series Classification ◽  
Lithium Ion Cells

scholarly journals Adversarial Attacks on Deep Neural Networks for Time Series Classification

2019 ◽  
Author(s):  
Hassan Ismail Fawaz ◽  
Germain Forestier ◽  
Jonathan Weber ◽  
Lhassane Idoumghar ◽  
Pierre-Alain Muller
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Time Series Classification

scholarly journals Regularizing Fully Convolutional Networks for Time Series Classification by Decorrelating Filters

2019 ◽  
Vol 33 ◽  
pp. 10003-10004 ◽  
Author(s):  
Kaushal Paneri ◽  
Vishnu TV ◽  
Pankaj Malhotra ◽  
Lovekesh Vig ◽  
Gautam Shroff
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Deep Neural Networks ◽  
Training Data ◽  
Time Series Classification ◽  
Strong Correlations ◽  
Average Correlation ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Classification Tasks

Deep neural networks are prone to overfitting, especially in small training data regimes. Often, these networks are overparameterized and the resulting learned weights tend to have strong correlations. However, convolutional networks in general, and fully convolution neural networks (FCNs) in particular, have been shown to be relatively parameter efficient, and have recently been successfully applied to time series classification tasks. In this paper, we investigate the application of different regularizers on the correlation between the learned convolutional filters in FCNs using Batch Normalization (BN) as a regularizer for time series classification (TSC) tasks. Results demonstrate that despite orthogonal initialization of the filters, the average correlation across filters (especially for filters in higher layers) tends to increase as training proceeds, indicating redundancy of filters. To mitigate this redundancy, we propose a strong regularizer, using simple yet effective filter decorrelation. Our proposed method yields significant gains in classification accuracy for 44 diverse time series datasets from the UCR TSC benchmark repository.

scholarly journals Deep Neural Networks for the Classification of Pure and Impure Strawberry Purees

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1223 ◽  
Author(s):  
Zhong Zheng ◽  
Xin Zhang ◽  
Jinxing Yu ◽  
Rui Guo ◽  
Lili Zhangzhong
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Classification Accuracy ◽  
Deep Neural Networks ◽  
Short Term Memory ◽  
Convolutional Network ◽  
Different Types ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

In this paper, a comparative study of the effectiveness of deep neural networks (DNNs) in the classification of pure and impure purees is conducted. Three different types of deep neural networks (DNNs)—the Gated Recurrent Unit (GRU), the Long Short Term Memory (LSTM), and the temporal convolutional network (TCN)—are employed for the detection of adulteration of strawberry purees. The Strawberry dataset, a time series spectroscopy dataset from the UCR time series classification repository, is utilized to evaluate the performance of different DNNs. Experimental results demonstrate that the TCN is able to obtain a higher classification accuracy than the GRU and LSTM. Moreover, the TCN achieves a new state-of-the-art classification accuracy on the Strawberry dataset. These results indicates the great potential of using the TCN for the detection of adulteration of fruit purees in the future.

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

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