A Hybrid Approach to Pattern Recognition of A Nonstationary Signal Using Variable Window STFT and Complex Kalman Filter

2007 ◽  
Author(s):  
B. N. Biswal ◽  
P. K. Dash ◽  
G. Panda ◽  
P. K. Tripathy
Keyword(s):  
Pattern Recognition ◽  
Kalman Filter ◽  
Hybrid Approach ◽  
Nonstationary Signal ◽  
Variable Window

scholarly journals A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1596 ◽  
Author(s):  
Xin Zhao ◽  
Haikun Wei ◽  
Chenxi Li ◽  
Kanjian Zhang
Keyword(s):  
Kalman Filter ◽  
Wind Speed ◽  
State Space ◽  
State Space Model ◽  
Hybrid Approach ◽  
Wind Farms ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Space Model ◽  
Wind Speeds

The ability to predict wind speeds is very important for the security and stability of wind farms and power system operations. Wind speeds typically vary slowly over time, which makes them difficult to forecast. In this study, a hybrid nonlinear estimation approach combining Gaussian process (GP) and unscented Kalman filter (UKF) is proposed to predict dynamic changes of wind speed and improve forecasting accuracy. The proposed approach can provide both point and interval predictions for wind speed. Firstly, the GP method is established as the nonlinear transition function of a state space model, and the covariance obtained from the GP predictive model is used as the process noise. Secondly, UKF is used to solve the state space model and update the initial prediction of short-term wind speed. The proposed hybrid approach can adjust dynamically in conjunction with the distribution changes. In order to evaluate the performance of the proposed hybrid approach, the persistence model, GP model, autoregressive (AR) model, and AR integrated with Kalman filter (KF) model are used to predict the results for comparison. Taking two wind farms in China and the National Renewable Energy Laboratory (NREL) database as the experimental data, the results show that the proposed hybrid approach is suitable for wind speed predictions, and that it can increase forecasting accuracy.

scholarly journals Machine Learning Techniques and Syntactic Pattern Recognition Based Heart Disease Prediction for Smart Health

2021 ◽  
Author(s):  
Samir Bandyopadhyay ◽  
Shawni Dutta
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Heart Disease ◽  
Hybrid Approach ◽  
Machine Learning Techniques ◽  
Syntactic Pattern Recognition ◽  
Minimal Processing ◽  
Learning Stage ◽  
Syntactic Pattern ◽  
Pattern Recognition Approach

Cardiovascular disease (CVD) may sometimes unexpected loss of life. It affects the heart and blood vessels of body. CVD plays an important factor of life since it may cause death of human. It is necessary to detect early of this disease for securing patients life. In this chpter two exclusively different methods are proposed for detection of heart disease. The first one is Pattern Recognition Approach with grammatical concept and the second one is machine learning approach. In the syntactic pattern recognition approach initially ECG wave from different leads is decomposed into pattern primitive based on diagnostic criteria. These primitives are then used as terminals of the proposed grammar. Pattern primitives are then input to the grammar. The parsing table is created in a tabular form. It finally indicates the patient with any disease or normal. Here five diseases beside normal are considered. Different Machine Learning (ML) approaches may be used for detecting patients with CVD and assisting health care systems also. These are useful for learning and utilizing the patterns discovered from large databases. It applies to a set of information in order to recognize underlying relationship patterns from the information set. It is basically a learning stage. Unknown incoming set of patterns can be tested using these methods. Due to its self-adaptive structure Deep Learning (DL) can process information with minimal processing time. DL exemplifies the use of neural network. A predictive model follows DL techniques for analyzing and assessing patients with heart disease. A hybrid approach based on Convolutional Layer and Gated-Recurrent Unit (GRU) are used in the paper for diagnosing the heart disease.

Fault Diagnosis of Gearbox in Wind Turbine Based on Wavelet Transform and Support Vector Machine

2014 ◽  
Vol 536-537 ◽  
pp. 18-21 ◽  
Author(s):  
Peng Fei Li ◽  
Jia Wei Xiang
Keyword(s):  
Pattern Recognition ◽  
Wavelet Transform ◽  
Wind Turbine ◽  
Hybrid Approach ◽  
Vibration Signal ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Gear Fault ◽  
Energy Ratios ◽  
Robust To Noise

To deal with the lack of effective experimental data under the current condition for gearbox fault pattern recognition, the Wind Turbine Drivetrain Diagnostics Simulator (WTDS) was used for experimental investigation and gained large number of gear fault samples. The wavelet transform is employed to decompose the vibration signal to obtain the energy ratio in each frequency band. Taking energy ratios as feature vectors, the pattern recognition results are obtained by the support vector classification (SVC). The experimental results show that the hybrid approach is robust to noise and has high classification accuracy.

scholarly journals Nonstationary Signal Analysis with Kernel Machines

2010 ◽  
pp. 223-241
Author(s):  
Paul Honeine ◽  
Cédric Richard ◽  
Patrick Flandrin
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Signal Analysis ◽  
Selection Criterion ◽  
Reproducing Kernels ◽  
Classification Problem ◽  
Optimal Time ◽  
Nonstationary Signal ◽  
Time Frequency ◽  
Recent Developments

This chapter introduces machine learning for nonstationary signal analysis and classification. It argues that machine learning based on the theory of reproducing kernels can be extended to nonstationary signal analysis and classification. The authors show that some specific reproducing kernels allow pattern recognition algorithm to operate in the time-frequency domain. Furthermore, the authors study the selection of the reproducing kernel for a nonstationary signal classification problem. For this purpose, the kernel-target alignment as a selection criterion is investigated, yielding the optimal time-frequency representation for a given classification problem. These links offer new perspectives in the field of nonstationary signal analysis, which can benefit from recent developments of statistical learning theory and pattern recognition.

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