Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction

On fractal dimensions of soil radon gas time series

Automatic detection of absence seizures with compressive sensing EEG

Since the mine microseismic and blasting signals are non-linear and non-stationary, the traditional linear analysis methods cannot effectively classify the microseismic and blasting signals. In this study, a method for feature extraction and classification of mine microseismic signals based on CEEMDAN_SE (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise_Sample Entropy) is proposed. Firstly, the microseismic of rock mass and blasting vibration signals are decomposed by CEEMDAN. Then five sensitive IMF components of IMF4~IMF8 (microseismic) and IMF1~IMF5 (blasting) are screened out by using correlation coefficient. Next, the sample entropy values of the sensitive IMF components are calculated. Finally, the sample entropy is used as the eigenvector and input into ELM (extreme learning machine) to classify and identify the microseismic and blasting signals. The results show that there is a significant difference between the sample entropy values of microseismic and blasting signals. Combining CEEMDAN_SE with ELM, the accuracy of classification, recognition of microseismic and blasting signals is 91.5%. Compared with EEMD_SE_ELM and EMD_SE_ELM, the classification, recognition accuracy of CEEMDAN_ SE_ELM is improved by 2.5% and 5%, respectively. Therefore, this method provides a new idea for the classification and recognition of mine microseismic signals.

To develop a manipulator system based on spontaneous EEG signal control, this paper proposes a common spatial pattern method combined with the Bhattacharyya distance of time-frequency signal (TB-CSP) for EEG signal feature extraction. Firstly, for different subjects, the original EEG signal is divided by a specific time window and different frequency band; Secondly, the average Bhattacharyya distance of different frequency bands in a specific time window is calculated to select the optimal time-frequency signal and input it to CSP for feature extraction; Finally, the extracted features are input into SVM and LDA to classify and complete the control of the manipulator. The proposed method achieves 82.6% accuracy on the public dataset BCI IVa and 79.48% accuracy on the self-collected dataset.

The smart grid is vulnerable to network attacks, thus requiring a high detection rate and fast detection speed for intrusion detection systems. With a fast training speed and a strong model generalization ability, the extreme learning machine (ELM) perfectly meets the needs of intrusion detection of the smart grid. In this paper, the ELM is applied to the field of smart grid intrusion detection. Aiming at the problem that the randomness of input weights and hidden layer bias in the ELM cannot guarantee the optimal performance of the ELM intrusion detection model, a genetic algorithm (GA)-ELM algorithm based on a genetic algorithm (GA) is proposed. GA is used to optimize the input weight and hidden layer bias of the ELM. Firstly, the input weight and hidden layer bias of the ELM are mapped to the chromosome vector of a GA, and the test error of the ELM model is set as the fitness function of the GA. Then, the parameters of the ELM intrusion detection model are optimized by genetic operation; the input weight and bias, corresponding to the minimum test error, are selected to improve the performance of the ELM model. Compared with the ELM and online sequential extreme learning machine (OS-ELM), the GA-ELM effectively improves the accuracy, detection rate and precision of intrusion detection and reduces the false positive rate and missing report rate.

Bearing defect identification by swarm decomposition considering permutation entropy measure and opposition-based slime mould algorithm

Epilepsy is one of the frequent brain disorder that may consequence in the brain dysfunction and cognitive disorders. Epileptic seizures can occur due to transient and unexpected electrical interruptions of brain. EEG (ElectroEncephaloGram) is one of the non-invasive methods for analyzing the human brain dynamics that affords a direct evaluation of cortical behavior. Seizures are featured by short and episodic neuronal synchronous discharges with considerably enlarged amplitude. This uneven synchrony may happen in the brain accordingly i.e., partial seizures visible only in few channels of the EEG signal or generalized seizures, which are seen in every channel of the EEG signal involving the whole brain. Existing analysis of epilepsy depends on difficult visual screening by extremely trained clinicians. Data recordings create very lengthy data, and therefore the inspection and identification of epilepsy takes more time for diagnosis. At present time computerized systems are usually established to make the diagnosis simpler. This paper discusses an implementation of automated epileptic EEG detection system using neural networks. In this paper, a statistical parameter regarded as Sample Entropy (SampEn), is used as a method for feature extraction for performing the task of classifying EEG signals, which are normal, ictal and interictal. Here the value of SampleEn plays a vital role in the diagnosis of Epileptic seizures. It is been observed that the value of SampEn falls suddenly during an Epileptic Seizure, and hence this information delineates the exact occurrence of Epilepsy. Classification strategies are based on models like Back-propagation Neural Network (BPNN),Support Vector Machine(SVM) and Extreme Learning Machine (ELM). Hybrid ELM (HELM) is the classification method devised in the current paper and is the first of its kind to be implemented for EEG classification. This classification method uses the Analytical Hierarchy Process (AHP) method to select the input weights and hidden biases, the ELM algorithm to analytically determine the output weights and the Levenberg-Marquardt (LM) algorithm to learn the network. Experimental results show that automatic epilepsy detection using sample entropy (SampEn) and Hybrid ELM achieves better accuracy in lesser time.

Welding parameters play a significant role in determining the bead geometry and hence the quality of the weld joint. To further improve prediction accuracy and optimization quality of laser brazing, the published experiment results are utilized to build a prediction model of top and bottom width of weld bead and further establish an optimization model by a novel hybrid extreme learning machine and genetic algorithm method. Firstly, about two-third experiment results are used to train the extreme learning machine network for suitable weights, and then the rest one-third experiments are selected as a test set to illustrate the validity and reliability of the prediction network. Then, by regarding the prediction values from the extreme learning machine as the fitness values of genetic algorithm, optimal results of the inputs and outputs are obtained through the procedure of selection, crossover, and mutation. Eventually, the final optimization results are confirmed by verification experiments. Meanwhile, the hybrid model is more accurate and stable than that using back propagation neural network and genetic algorithm. In the whole, the proposed method in this paper is an effective and accurate way for optimizing laser brazing welding parameters and guiding the actual welding process.

Sewage treatment process has the following characteristics: nonlinear, delay etc, and is very complicated to establish the model for its control process. A reasonable model is set up for elaborate prediction effluent quality, which can satisfy the standard of the effluent water and requirements of energy saving simultaneously. Extreme learning machine (ELM), i.e the machine learning method that new lately developed has high accuracy, reliability and outstanding performance in prediction. To get higher prediction effect, in this paper, there are two ways are proposed to improve the ELM, (1) Optimizing the parameters. The ELM whose input weights and bias threshold are optimized by particle swarm optimization algorithm (PSO) and genetic algorithm (GA), respectively; (2) Changing learning mode. To develop an online sequential learning algorithm (OS) for the ELM with additive or radial basis function (RBF) hidden nodes in a unified framework. Therefore, the several comparison approaches refer to optimize the ELM, e.g., PSO-ELM, GA-ELM, OS-ELM are applied to effluent quality prediction, and chemical oxygen demand (COD) is taken as examples in this paper. The results show that PSO-ELM model has remarkably superior performance on effluent quality prediction than peer models in terms of mean absolute error, mean absolute percentage error, root mean square error, and coefficient of determination.

Effect of background noise characteristics on early warning indicators of thermoacoustic instability

The shallow seawater depth inversion based on remote sensing technology is important for water depth detection, which is of considerable significance to marine engineering, shipping, and marine military security. In this study, we took the Taiping Island and its adjacent waters in the South China Sea as a test bed and developed a water depth inversion model on the basis of extreme learning machine (ELM) and extreme learning machine optimized by genetic algorithm (GA-ELM). In GA-ELM, the input weights and the hidden layer biases were optimized by genetic algorithm. The two models allowed the evaluation of nonlinear relationships between the reflectance of high-resolution imagery from WorldView-2 and actual water depth obtained from the S-57 sea chart. The eight bands of the high-resolution image and the actual water depth were used as the input layer and the output layer, and the sigmoid function was introduced as activation function. Finally, the model accuracy was evaluated by using mean relative error (MRE), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and the regression analysis between the retrieved water depth and the actual data. The simulation results showed that the two models had better stability than the second-order polynomial regression, BP neural network, and RBF neural network. Furthermore, GA-ELM had a more compact network structure and better generalization ability than ELM. Thus, we concluded that GA-ELM had higher precision and could achieve a better inversion result in the experimental area.

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

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This paper presents a hybrid algorithm for the creation of heterogeneous single layer neural networks (SLNNs). The proposed self-adaptive heterogeneous hybrid extreme learning machine (SA-He-HyELM) trains a series of SLNNs with different neuron types in the hidden layer utilizing the extreme learning machine (ELM) algorithm. These networks are evolved into heterogeneous networks (networks having different combinations of hidden neurons) with the help of a modified genetic algorithm (GA). The algorithm is able to handle two architecturally different neuron types: traditional low order (linear) units and higher order units with different transfer functions. The GA is fully self-adaptive and uses one novel hybrid crossover operator along with a self-adaptive mutation operator in order to retain ELM’s simplicity and minimize the number of parameters need tuning. The experimental part of the current paper involves testing SA-He-HyELM with traditional ELM and other three ELM-based methods. The experimental part utilized a series of regression and classification experiments on relatively large datasets. In all cases the proposed method managed to get lower MSE or higher classification accuracy when compared to the aforementioned methods.

Developing a hybrid CEEMDAN-PE-HE-SWT method to remove the noise of measured carbon dioxide blast wave