An intelligent strategy for the protection of AC microgrids is presented in this paper. This method was halving to an initial signal processing step and a machine learning-based forecasting step. The initial stage investigates currents and voltages with a window-based approach based on the Dynamic Decomposition method (DDM) and then involves the norms of the signals to the resultant DDM data. The results of the currents and voltages norms are applied as features for a topology data analysis algorithm for fault type classifying in the AC microgrid for fault location purposes. The Algorithm was tested on a microgrid that operates with precision equal to 100% in fault classification and a mean error lower than 20 m when forecasting the fault location. The proposed method robustly operates in sampling frequency, fault resistance variation, and noisy and high impedance fault conditions.

Since the most important issue in the production of digital currencies is energy consumption, the ‎use of illegal electricity in mining farms has become very popular. Illegal mining is particularly ‎important in countries such as Iran where the price of electrical energy is extremely low. This issue ‎has caused numerous problems such as frequent blackouts, large losses for industries and even ‎daily power cuts in several large cities. Previous machine learning approaches for miner detection ‎are mostly supervised methods which rely on labeled data. Due to the fact that the number of ‎labeled data is very limited in reality, we propose unsupervised methods in this paper. A real data ‎set from Markazi Province Distribution Company in Iran has been employed to produce the results. ‎The classification process consists of two stages: in the first stage, Dynamic Mode Decomposition ‎‎(DMD) has been used to extract new features which compose the set of features along with certain ‎factors from the Advanced Metering Infrastructure (AMI). These features are selected for 58 ‎subscribers with positive and negative labels. In the second stage, a number of unsupervised Models ‎are built from the results of the first stage. The highest accuracy of classification obtained is 74% ‎from unsupervised algorithms and 85% for supervised algorithms, which is very significant ‎considering the fact that unsupervised algorithms do not need labeled data‎.‎

Simulation and numerical analysis of physical phenomena, especially for the unsteady problems due to the dependency of the numerical algorithms on the computer hardware and the large number of computational nodes, are the most important problems. For these reasons, the number of computations and computational costs increase. The order reduction method is the one that has been widely used in recent years to reduce computational time. In this way, by reducing the constraints of the system without changing the inherent features of the problem, the computational efficiency will dramatically increase. In this study, using the basic concepts of Dynamical systems, the thermal diffusion problem is investigated using the Dynamic Modes Decomposition method. Then, a reduced order Model is established for the related governing equation of this phenomenon. Accordingly, based on the projection of the governing equation in the vector space of Modes, by using Dynamic Modes, a reduced order Model is obtained with respect to the properties of Dynamic Modes. The obtained Model to simulate the time evolution and parametric variations can be properly replaced with the original equation and predict the behavior of the system with very good accuracy. A comparison between the results of the present reduced order Models and the simulations of the exact solution shows high computation accuracy.

‎Forecasting price trends in financial markets is of particular importance for traders because price trends are inherently Dynamic and forecasting these trends is complicated‎. In this study‎, ‎we present a new hybrid method based on combination of the Dynamic Mode Decomposition method and long short-term memory method for forecasting financial markets‎. This new method is in this way that we first extract the dominant and coherent data using the Dynamic Mode Decomposition method and then predict financial market trends with the help of these data and the long short-term memory method‎.‎ To demonstrate the efficacy of this method‎, ‎we present three practical examples‎: ‎closing price of US Dollar to Iranian Rial‎, ‎closing prices of zob roy Isfahan stock‎, ‎and also closing prices of siman shargh stock‎. ‎These examples exhibit bullish‎, ‎bearish‎, ‎and neutral behaviors‎, ‎respectively‎.‎ It seems that the proposed new method works better in predicting the financial market than the existing long-short-term memory method‎.

Potential field anomalies are usually superposed large-scale structures and small-scale structures anomalies. Separation of these two categories of anomalies is the most important step in the data interpretation. Different methods have been introduced for these types of works, but most of them are the semi-automatic methods. In this paper, empirical Mode Decomposition method is used to differentiate regional and residual anomalies. This automatic method is based on extraction of the intrinsic oscillatory Modes of data. Efficiency of this method is investigated on both synthetic and real data acquired on Tromspberg area of South Africa. Different results show that this technique have higher accuracy than conventional methods like as polynomial fitting.

Bearings are the most important and most used components in different industries. Early bearing fault diagnosis can prevent human and financial losses. One of the best methods for fault diagnosis of these elements is via vibration analysis. In this paper Empirical Mode Decomposition (EMD) which is a fairly new signal processing method of nonlinear and nonstationary signals is used for analyzing vibration signals extracted from bearings. This method was proposed by Huang in 1998. In this research, extracted signal from healthy and faulty bearings are decomposed in to empirical Modes. By analyzing different empirical Modes from 8 derived empirical Modes for healthy and faulty bearings under different load conditions from zero to three horsepower, the first Mode has the most information to classify bearing condition. From the first empirical Mode six features in time domain were calculated for healthy bearing, bearing with inner race fault, bearing with outer race fault and bearing with ball fault. These eight features were used as input vector to a designed ANFIS network for bearing condition classification. The ANFIS network was able to detect different condition of bearing with 100% precession.

Background: Electrocardiogram (ECG) plays a vital role in the analysis of heart activity. It can be used to analyze the different heart diseases and mental stress assessment also. Various noises, such as baseline wandering, muscle artifacts and power line interface disturbs the information within the ECG signal. To acquire correct information from ECG signal, these noises should be removed. Methods: In the proposed work, the improved variational Mode Decomposition (IVMD) method for the removal of noise in ECG signals is used. In the proposed method, the weighted signal amplitude integrated over the timeframe of the ECG signal varies the window size during Decomposition. Raw ECG data are extracted from 10 subjects and ECG data are also taken from the MIT BIH database for the proposed method. Results: The performance comparison of traditional variational Mode Decomposition (VMD) and the proposed technique is also calculated using mean square error, percentage root mean square difference, signal to noise ratio and correlation coefficient. The extracted highest signal to noise ratio (SNR) value of acquired ECG signals using traditional VMD is 42db whereas highest value of signal to noise ratio (SNR) using improved VMD (IVMD) is 83db. Conclusion: The proposed IVMD technique represented better performance than traditional VMD for denoising of ECG signals.

Since the analytical methods have a low accuracy and numerical algorithms are time-consuming with hardware limitations, therefore researchers are interested to develop Models with high speed and efficiency. The reduced order Model is the method that could be an alternative approach for simulating Dynamical systems. These Models are mainly developed based on the calculation of the Dynamical systems' effective structures. The Dynamic Mode Decomposition method is one of the methods for calculating these basic structures. In this study, using this Model and based on the principles of Dynamical systems, a reduced order Model has been developed for the Burgers equation. The results show that if the Reynolds number increases then the effects of the viscous term in the governing equation are decreased, accordingly the required dissipation of the system to stabilize the numerical solution is reduced. Also, due to the incompleteness of the Modes which are selected in the order reduction procedure, the dissipation level of the surrogate Model is reduced more. Therefore, by creating an artificial dissipation called the eddy viscosity approach, the stability of the Model is enhanced. Finally, by comparing the results obtained from the reduced order Model and direct numerical simulation, the accuracy of this Model is proven.

The quality of seismic data varies tremendously, from areas where excellent reflections (or refractions) are obtained to areas in which the most Modern equipment, complex field techniques, and sophisticated data processing do not yield usable data. Between these extremes, lie most areas in which useful results can be obtained. Seismic records are generally affected by various types of noise, such as ground rolls, multiples, random noise, and reflection and reflected refraction from near surface structures. Random noise resultiing from random oscillation during data acquisition is one of the most important and harmful noises that exists in seismic data over all times and frequencies. Many efforts have been made to remove this type of noise from seismic data. The predictive filter is an ordinary method commonly used for random noise attenuation from seismic data. This filter can be used in various domains, such as the f-x domain (Haris and White, 1997) and the discrete Cosine domain (Lu and Liu, 2007). Jones and Levy (1987) removed events which were not coherent trace-to-trace events by means of the Karhunen-Loeve transform.The empirical Mode Decomposition (EMD) method is an algorithm for the analysis of multicomponent signals that breaks them down into a number of amplitude and frequency modulated zero-mean signals, termed intrinsic Mode functions (IMFs). An IMF must fulfill two requirements: (1) the number of extrema and the number of zero crossings are either equal or differ at most by one; (2) at any point, the mean value of the envelope defined by the local maxima and the envelope defined by the local minima is zero.In contrast to conventional Decomposition methods such as wavelets, which perform the analysis by projecting the signal under consideration onto a number of predefined basis vectors, EMD expresses the signal as an expansion of basic functions that are signal-dependent and estimated via an iterative procedure called sifting. Apart from the specific applications of EMD, a more generalized task in which EMD can prove useful is signal denoising (Kopsinis and McLaughlin, 2009). When EMD is used for denoising, the problem is to identify properly which IMFs contain noise characteristics. Certain Modes will consist mainly of noise, whereas other Modes will contain both signal and noise characteristics. In the case of white Gaussian noise, the noise-only energy of the Modes decreases logarithmically. The first Mode, carrying the highest amount of noise energy, will consist mainly of noise, and the effect of noise should gradually weaken with higher Modes.In this paper, a new signal denoising method based on the empirical Mode Decomposition framework is used to suppress random noises in seismic data. A Noisy signal is decomposed into oscillatory components (IMFs). The empirical Mode Decomposition denoising method involves filtering each intrinsic Mode function and reconstructing s the estimated signal using the processed intrinsic Mode functions. The direct application of wavelet-like thresholding to the Decomposition Modes is, in principle, wrong and can have catastrophic consequences regarding the continuity of the reconstructed signal. This arises as a result of the special attributes of IMFs; namely, they resemble an AM/FM modulated sinusoid with zero mean. Consequently, we used the interval thresholding method instead of direct theresholding method to denoise seismic signal.the efficiency of the proposed method was tested on both synthetic and real seismic data. In every case, results show that the denoising algorithm can suppress random noise significantly.