A New Method for Discharge State Prediction of Micro-EDM Using Empirical Mode Decomposition

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Zhenyuan Jia ◽  
Lingxuan Zhang ◽  
Fuji Wang ◽  
Wei Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Linear Prediction ◽  
Electrical Discharge Machining ◽  
Model Identification ◽  
Prediction Method ◽  
Ar Model ◽  
Micro Edm ◽  
Multisensor Data Fusion ◽  
State Prediction ◽  
Mode Decomposition

The property of high frequency in micro-EDM (electrical discharge machining) causes the discharge states to vary much faster than in conventional EDM, and discharge states of micro-EDM have the characteristics of nonstationarity, nonlinearity, and internal coupling, all of this makes it very difficult to carry out stable control. Thus empirical mode decomposition is adopted to conduct the prediction of the discharge states obtained through multisensor data fusion and fuzzy logic in micro-EDM. Combined with the autoregressive (AR) model identification and linear prediction, the mathematical model for EDM discharge state prediction using empirical mode decomposition is established and the corresponding prediction method is presented. Experiments demonstrate that the new prediction method with short identification data is highly accurate and operates quickly. Even using short model identification data, the accuracy of empirical mode decomposition prediction can stably reach a correlation of 74%, which satisfies statistical expectations. Additionally, the new process can also effectively eliminate the lag of conventional prediction methods to improve the efficiency of micro-EDM, and it provides a good basis to enhance the stability of the control system.

scholarly journals Identification of Visual Imagery by Electroencephalography Based on Empirical Mode Decomposition and an Auto-Regressive Model

2021 ◽  
Author(s):  
Yunfa Fu ◽  
Anmin Gong ◽  
Qian Qian ◽  
Wei Zhang ◽  
Lei Zhao
Keyword(s):  
Empirical Mode Decomposition ◽  
Visual Imagery ◽  
Ar Model ◽  
Support Vector ◽  
Imagery Task ◽  
Hilbert Huang Transform ◽  
Computer Interfaces ◽  
Mode Decomposition ◽  
Auto Regressive ◽  
Ar Models

Abstract The traditional imagery task for brain−computer interfaces (BCIs) consists of motor imagery (MI) in which subjects are instructed to imagine moving a certain part of their body. This kind of imagery task is difficult for subjects. In this study, we used a less studied yet more easily performed type of mental imagery—visual imagery (VI)—in which subjects are instructed to visualize a picture in their brain to implement a BCI. In this study, 18 subjects were recruited and instructed to observe one of two visual-cued pictures (one was static, while the other was moving), and then imagine the cued picture in each trial. Simultaneously, electroencephalography (EEG) signals were collected. Hilbert-Huang Transform (HHT), auto-regressive (AR) models and the combination of empirical mode decomposition (EMD) and AR were used to extract features, respectively. A support vector machine (SVM) was used to classify the two kinds of VI tasks. The average, highest, and lowest classification accuracies of HHT were 68.143.06%, 78.33%, and 53.3%, respectively. The values of the AR model were 56.292.73%, 71.67%, and 30%, respectively. The values obtained by the combination of EMD and the AR model were 78.402.07%, 87%, and 48.33%, respectively. The results indicate that multiple VI tasks were separable based on EEG, and that the combination of EMD and an AR model used in VI feature extraction was better than that of an HHT or AR model alone. Our work may provide ideas for the construction of a new online VI-BCI.

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