scholarly journals Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory

Mathematics ◽  
2019 ◽  
Vol 7 (12) ◽  
pp. 1188 ◽  
Author(s):  
Yuanyuan Zhou ◽  
Min Zhou ◽  
Qing Xia ◽  
Wei-Chiang Hong
Keyword(s):  
Energy Savings ◽  
Electricity Consumption ◽  
Low Frequency ◽  
Support Vector ◽  
Forecasting Model ◽  
Management Approach ◽  
Intrinsic Mode Functions ◽  
Residual Term ◽  
Quantum Genetic Algorithm ◽  
Mode Decomposition

In the context of the nationwide call for “energy savings” in China, it is desirable to establish a more accurate forecasting model to manage the electricity consumption from the university dormitory, to provide a suitable management approach, and eventually, to achieve the “green campus” policy. This paper applies the empirical mode decomposition (EMD) method and the quantum genetic algorithm (QGA) hybridizing with the support vector regression (SVR) model to forecast the daily electricity consumption. Among the decomposed intrinsic mode functions (IMFs), define three meaningful items: item A contains the terms but the residual term; item B contains the terms but without the top two IMFs (with high randomness); and item C contains the terms without the first two IMFs and the residual term, where the first two terms imply the first two high-frequency part of the electricity consumption data, and the residual term is the low-frequency part. These three items are separately modeled by the employed SVR-QGA model, and the final forecasting values would be computed as A + B − C. Therefore, this paper proposes an effective electricity consumption forecasting model, namely EMD-SVR-QGA model, with these three items to forecast the electricity consumption of a university dormitory, China. The forecasting results indicate that the proposed model outperforms other compared models.

ENERGY FEATURE EXTRACTION AND SVM CLASSIFICATION OF MOTOR IMAGERY-INDUCED ELECTROENCEPHALOGRAMS

2012 ◽  
Vol 05 (02) ◽  
pp. 1250006 ◽  
Author(s):  
JIANING ZHENG ◽  
LIYU HUANG ◽  
JING ZHAO
Keyword(s):  
Motor Imagery ◽  
Low Frequency ◽  
New Combination ◽  
Support Vector ◽  
Frequency Noise ◽  
Intrinsic Mode Functions ◽  
Identification Rate ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Optimal Kernel

The precise classification for the electroencephalogram (EEG) in different mental tasks in the research on brain–computer interface (BCI) is the key for the design and clinical application of the system. In this paper, a new combination classification algorithm is presented and tested using the EEG data of right and left motor imagery experiments. First, to eliminate the low frequency noise in the original EEGs, the signals were decomposed by empirical mode decomposition (EMD) and then the optimal kernel parameters for support vector machine (SVM) were determined, the energy features of the first three intrinsic mode functions (IMFs) of every signal were extracted and used as input vectors of the employed SVM. The output of the SVM will be classification result for different mental task EEG signals. The study shows that mean identification rate of the proposed algorithm is 95%, which is much better than the present traditional algorithms.

scholarly journals Classification of sleep apnea using EMD-based features and PSO-trained neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sajjad Afrakhteh ◽  
Ahmad Ayatollahi ◽  
Fatemeh Soltani
Keyword(s):  
Neural Networks ◽  
Sleep Apnea ◽  
Back Propagation ◽  
Pso Algorithm ◽  
Bp Algorithm ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Linear Discriminant ◽  
Mode Decomposition ◽  
Obstructive Sleep

Abstract In this study, we propose a method for detecting obstructive sleep apnea (OSA) based on the features extracted from empirical mode decomposition (EMD) and the neural networks trained by particle swarm optimization (PSO) in the classification phase. After extracting the features from the intrinsic mode functions (IMF) of each heart rate variability (HRV) signal of each segment, these features were applied to the input of popular classifiers such as multi-layer perceptron neural networks (MLPNN), Naïve Bayes, linear discriminant analysis (LDA), k-nearest neighborhood (KNN), and support vector machines (SVM) were applied. The results show that the MLPNN learned with back propagation (BP) algorithm has a diagnostic accuracy of less than 90%, and this may be due to being derivative based property of the BP algorithm, which causes trapping in the local minima. For Improving MLPNN’s performance, we used the PSO algorithm instead of the BP method in training part. Therefore, the MLPNN’s accuracy improved from 89.36 to 97.66% after the application of the PSO algorithm. The proposed method has also reached to 97.78 and 97.96% in sensitivity and specificity, respectively. So, it can be concluded that the proposed method achieves better or comparable results when compared with the previous works in this field.

Internal leakage identification of hydraulic cylinder based on intrinsic mode functions with random forest

2019 ◽  
Vol 233 (15) ◽  
pp. 5532-5544 ◽  
Author(s):  
Lin Li ◽  
Yixiang Huang ◽  
Jianfeng Tao ◽  
Chengliang Liu
Keyword(s):  
Random Forest ◽  
Hydraulic Cylinder ◽  
Support Vector ◽  
Hydraulic Systems ◽  
Intrinsic Mode Functions ◽  
Random Forest Algorithm ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Hydraulic Cylinders ◽  
Experimental Pressure

Monitoring for internal leakage of hydraulic cylinders is vital to maintain the efficiency and safety of hydraulic systems. An intelligent classifier is proposed to automatically evaluate internal leakage levels based on the newly extracted features and random forest algorithm. The inlet and outlet pressures as well as the pressure differences of two chambers are chosen as the monitoring parameters for leakage identification. The empirical mode decomposition method is used to decompose the raw pressure signals into a series of intrinsic mode functions to obtain the essence in experimental signals. Then, the features extracted from intrinsic mode functions in terms of statistical analysis are formed the input vector to train the leakage detector. The classifier based on random forest is established to categorize internal leakage into proper levels. The accuracy of the internal leakage evaluator is verified by the experimental pressure signals. Moreover, an internal leakage evaluator is established based on the support vector machine algorithm, in which the wavelet transform is applied for feature extraction. The accuracy and efficiency of different classifiers are compared based on leakage experiments. The results show that the classifier trained by the intrinsic mode function features in terms of random forest algorithm may more effectively and accurately identify internal leakage levels of hydraulic cylinders. The leakage evaluator provides probability for online monitoring of the internal leakage of hydraulic cylinders based on the inherent sensors.

scholarly journals Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

scholarly journals Multiscale Combined Model Based on Run-Length-Judgment Method and Its Application in Oil Price Forecasting

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Wang Shu-ping ◽  
Hu Ai-mei ◽  
Wu Zhen-xin ◽  
Liu Ya-qing ◽  
Bai Xiao-wei
Keyword(s):  
Low Frequency ◽  
Oil Price ◽  
Support Vector ◽  
Combined Model ◽  
Run Length ◽  
Price Series ◽  
Mode Decomposition ◽  
Combined Models ◽  
Artificial Neural Network Ann ◽  
Combined Forecasting

Forecasting of oil price is an important area of energy market research. Based on the idea of decomposition-reconstruction-integration, this paper built a new multiscale combined forecasting model with the methods of empirical mode decomposition (EMD), artificial neural network (ANN), support vector machine (SVM), and time series methods. While building the model, we proposed a new idea to use run length judgment method to reconstruct the component sequences. Then this model was applied to analyze the fluctuation and trend of international oil price. Oil price series was decomposed and reconstructed into high frequency, medium frequency, low frequency, and trend sequences. Different features of fluctuation can be explained by irregular factors, season factors, major events, and long-term trend. Empirical analysis showed that the multiscale combined model obtained the best forecasting result compared with single models including ARIMA, Elman, SVM, and GARCH and combined models including ARIMA-SVM model and EMD-SVM-SVM method.

EMD fractal feature extraction technique in fingerprint of medicinal herbs

2015 ◽  
Vol 13 (04) ◽  
pp. 1550022
Author(s):  
Jianwei Du ◽  
Zhengguang Xu ◽  
Zhichun Mu ◽  
Yuan Yan Tang ◽  
Limin Cui ◽  
...  
Keyword(s):  
Recognition Rate ◽  
Low Frequency ◽  
Fractal Dimensions ◽  
Medicinal Herbs ◽  
The Novel ◽  
Intrinsic Mode Functions ◽  
Special Situation ◽  
Novel Approach ◽  
Mode Decomposition ◽  
Wavelet Features

This paper proposes the fractal features for glycyrrhiza fingerprint of medicinal herbs, to obtain the intrinsic mode functions (IMFs) from high to low frequency by using empirical mode decomposition (EMD). The EMD fractal features are extracted through computing the fractal dimensions of each IMF. The novel approach is applied to the recognition of the three types of glycyrrhiza fingerprints. Experiments show that EMD fractal features have better recognition rate than that of the traditional ones in the case of concentration-change, i.e. the number of peak and peak drift of sample which has slight changes. An existing method to extract the fractal features for fingerprint of medicinal herbs based on wavelet transform, which is called fractal-wavelet features, was presented. This method has anti-jamming property against the change of samples concentration. However, the recognition rate based on fractal-wavelet features is not satisfactory when fingerprint of medicinal herbs has some slight concentrations changes, the number of peak and peak drift of samples are processed in the special situation.

scholarly journals Detection of Startle-Type Epileptic Seizures using Machine Learning Technique

2018 ◽  
Vol 05 (02) ◽  
pp. 092-098
Author(s):  
Pushpa Balakrishnan ◽  
S. Hemalatha ◽  
Dinesh Nayak Shroff Keshav
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Brain Activity ◽  
Epileptic Seizures ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Absolute Deviation ◽  
Mode Decomposition ◽  
Life Threatening

Abstract Background Epilepsy is a common neurological disorder characterized by seizures and can lead to life-threatening consequences. The electroencephalogram (EEG) is a diagnostic test used to analyze brain activity in various neurological conditions including epilepsy and interpreted by the clinician for appropriate diagnosis. However, the process of EEG analysis for diagnosis can be automated using machine learning algorithms (MLAs) to aid the clinician. The objective of the study was to test different algorithms that could be used for the detection of seizures. Materials and Methods Video EEG (vEEG) was collected from subjects diagnosed to have episodes of seizures. The epilepsy dataset thus obtained was subjected to empirical mode decomposition (EMD) and the signal was decomposed into intrinsic mode functions (IMFs). The first five levels of decomposition were considered for analysis as per the established protocol. Statistical features such as interquartile range (IQR), entropy, and mean absolute deviation (MAD) were extracted from these IMFs. Results In this study, different MLAs such as nearest neighbor (NN), naïve Bayes (NB), and support vector machines (SVMs) were used to distinguish between normal (interictal) and abnormal (ictal) states. The demonstrated accuracy rates were 97.32% for NN, 99.02% for NB, and 93.75% for SVM. Conclusion Based on this accuracy and sensitivity, it may be posited that the NB classifier provides significantly better results for the detection of abnormal signals indicating that MLA can detect the seizure with better accuracy.

scholarly journals Mold Level Predict of Continuous Casting Using Hybrid EMD-SVR-GA Algorithm

Processes ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 177 ◽  
Author(s):  
Zhufeng Lei ◽  
Wenbin Su
Keyword(s):  
Continuous Casting ◽  
Production Control ◽  
Moving Average ◽  
Time Series Prediction ◽  
Casting Process ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Autoregressive Moving Average Model ◽  
Mode Decomposition ◽  
Moving Average Model

The prediction of mold level is a basic and key problem of continuous casting production control. Many current techniques fail to predict the mold level because of mold level is non-linear, non-stationary and does not have a normal distribution. A hybrid model, based on empirical mode decomposition (EMD) and support vector regression (SVR), is proposed to solve the mold level in this paper. Firstly, the EMD algorithm, with adaptive decomposition, is used to decompose the original mold level signal to many intrinsic mode functions (IMFs). Then, the SVR model optimized by genetic algorithm (GA) is used to predict the IMFs and residual sequences. Finally, the equalization of the predict results is reconstructed to obtain the predict result. Several hybrid predicting methods such as EMD and autoregressive moving average model (ARMA), EMD and SVR, wavelet transform (WT) and ARMA, WT and SVR are discussed and compared in this paper. These methods are applied to mold level prediction, the experimental results show that the proposed hybrid method based on EMD and SVR is a powerful tool for solving complex time series prediction. In view of the excellent generalization ability of the EMD, it is believed that the hybrid algorithm of EMD and SVR is the best model for mold level predict among the six methods, providing a new idea for guiding continuous casting process improvement.

scholarly journals Preterm-Term Birth Classification Using EMD-Based Time-Domain Features of Single-Channel Electrohysterogram Data

2021 ◽  
Author(s):  
Suparerk Janjarasjitt
Keyword(s):  
Preterm Birth ◽  
Time Domain ◽  
Single Channel ◽  
Support Vector ◽  
Computational Results ◽  
Intrinsic Mode Functions ◽  
Term Birth ◽  
Emg Signal ◽  
Mode Decomposition ◽  
The Time Domain

Abstract The preterm birth anticipation is a crucial task that can reduce the rate of preterm birth and also the complications of preterm birth. Electrohysterogram (EHG) or uterine electromyogram (EMG) data have been evidenced that they can provide an information useful for preterm birth anticipation. Four distinct time-domain features, i.e., mean absolute value, average amplitude change, difference absolute standard deviation value, and log detector, commonly applied to EMG signal processing are applied and investigated in this study. A single-channel of EHG data is decomposed into its constituent components, i.e., intrinsic mode functions, using empirical mode decomposition (EMD) before their time-domain features are extracted. The time-domain features of intrinsic mode functions of EHG data associated with preterm and term births are applied for preterm-term birth classification using support vector machine (SVM) with a radial basis function. The preterm-term classifications are validated using 10-fold cross validation. From the computational results, it is shown that the excellent preterm-term birth classification can be achieved using a single-channel of EHG data. The computational results further suggest that the best overall performance on preterm-term birth classification is obtained when thirteen (out of sixteen) EMD-based time-domain features are applied. The best accuracy, sensitivity, specificity, and F1-score achieved are, respectively, 0.9382, 0.9130, 0.9634, and 0.9366.

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