Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series

An accurate prediction of complex product quality parameters from process time series by an end-to-end learning approach remains a significant challenge in machine learning. A special difficulty is the application of industrial batch process data because many batch processes generate variable length time series. In the industrial application of such methods, explainability is often desired. In this study, a 1D convolutional neural network (CNN) algorithm with a masking layer is proposed to solve the problem for time series of variable length. In addition, a novel combination of 1D CNN and class activation mapping (CAM) technique is part of this study to better understand the model results and highlight some regions of interest in the time series. As a comparative state-of-the-art unsupervised machine learning method, the One-Nearest Neighbours (1NN) algorithm combined with dynamic time warping (DTW) was used. Both methods are investigated as end-to-end learning methods with balanced and unbalanced class distributions and with scaled and unscaled input data, respectively. The FastDTW and DTAIDistance algorithms were investigated for the DTW calculation. The data set is made up of sensor signals that was collected during the production of plastic parts. The objective was to predict a quality parameter of plastic parts during production. For this research, the quality parameter will be a difficult or only destructively measurable parameter and both methods will be investigated for their applicability to this prediction task. The application of the proposed approach to an industrial facility for producing plastic products shows a prediction accuracy of 83.7%. It can improve the reverence method by approximately 1.4%. In addition to the slight increase in accuracy, the CNN training time was significantly reduced compared to the DTW calculation.

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Gait Phase Recognition of Hip Exoskeleton System Based on CNN and HHO-SVM Model

Wang,

et al. 2024

Electronics

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Gait phase recognition is crucial for developing wearable lower-limb exoskeleton robots and is a prerequisite for the compliance control of lower-limb exoskeleton robots. Accurately estimating the gait phase is still a key challenge in exoskeleton control. To address these challenges, this study proposes a hybrid model that combines Convolutional Neural Networks (CNN) and Harris Hawks Optimization (HHO)—based Support Vector Machine (SVM). First, the collected sensor signals are preprocessed by normalization to reduce the differences in the data of the subjects. Then, a simplified CNN is used to automatically extract more discriminative features from the dataset. These features are classified using SVM instead of the softmax layer in CNN. In addition, an improved Harris hawk optimization (HHO) algorithm is used to optimize the SVM classification process. This model can accurately identify the heel strike (HS), flat foot (FF), heel off (HO), and swing (SW) phases of the gait cycle. The experimental results show that the CNN-HHO-SVM algorithm can achieve an average phase recognition accuracy of 96.03% for seven subjects in the self-built dataset, which is superior to the traditional method that relies on manually extracting time-frequency features. In addition, the F1-score and macro-recall of the CNN-HHO-SVM algorithm are better than those of other algorithms, which verifies the superiority of the algorithm.

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Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series

Cited by 3 publications

References 12 publications

PostAugment: Adversarial Data Augmentation with Hard Sample Suppression by Incorrect Class Likelihood

PostAugment: Adversarial Data Augmentation with Hard Sample Suppression by Incorrect Class Likelihood

An end-to-end machine learning approach with explanation for time series with varying lengths

Gait Phase Recognition of Hip Exoskeleton System Based on CNN and HHO-SVM Model

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