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Journal articles on the topic 'EMD - Neural networks'

Author: Grafiati

Published: 6 September 2023

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1

Zheng, Jing Wen, Shi Xiao Li, and Yang Kun. "A New Hybrid Model for Forecasting Crude Oil Price and the Techniques in the Model." Advanced Materials Research 974 (June 2014): 310–17. http://dx.doi.org/10.4028/www.scientific.net/amr.974.310.

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Being able to predict crude oil prices with a reputation of intransigence to analysis or the directions of changing in crude oil price is of increasing value. We seek a method to forecast oil prices with precise predictions. In this paper, a hybrid model was proposed, which firstly decomposes the crude oil prices into several time series with different frequencies,then predict these time series which are not white noises, and at last integrate the predictions as the final results. We use Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD) separately as the techn

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Saâdaoui, Foued, and Othman Ben Messaoud. "Multiscaled Neural Autoregressive Distributed Lag: A New Empirical Mode Decomposition Model for Nonlinear Time Series Forecasting." International Journal of Neural Systems 30, no. 08 (2020): 2050039. http://dx.doi.org/10.1142/s0129065720500392.

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Forecasting has always been the cornerstone of machine learning and statistics. Despite the great evolution of the time series theory, forecasters are still in the hunt for better models to make more accurate decisions. The huge advances in neural networks over the last years has led to the emergence of a new generation of effective models replacing classic econometric models. It is in this direction that we propose, in this paper, a new multiscaled Feedforward Neural Network (FNN), with the aim of forecasting multivariate time series. This new model, called Empirical Mode Decomposition (EMD)-

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Lei, Yu, Danning Zhao, and Hongbing Cai. "Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks." Artificial Satellites 51, no. 4 (2016): 149–61. http://dx.doi.org/10.1515/arsa-2016-0013.

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Abstract It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coor

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Ge, Yujia, Yurong Nan, and Lijun Bai. "A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks." Energies 12, no. 24 (2019): 4762. http://dx.doi.org/10.3390/en12244762.

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For power management in the energy harvesting wireless sensor networks (EH-WSNs), it is necessary to know in advance the collectable solar energy data of each node in the network. Our work aims to improve the accuracy of solar energy predictions. Therefore, several existing prediction algorithms in the literature are surveyed, and then this paper proposes a solar radiance prediction model based on a long short-term memory (LSTM) neural network in combination with the signal processing algorithm empirical mode decomposition (EMD). The EMD method is used to decompose the time sequence data into

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Jiang, Qi, Yuxin Cheng, Haozhe Le, Chunquan Li, and Peter X. Liu. "A Stacking Learning Model Based on Multiple Similar Days for Short-Term Load Forecasting." Mathematics 10, no. 14 (2022): 2446. http://dx.doi.org/10.3390/math10142446.

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It is challenging to obtain accurate and efficient predictions in short-term load forecasting (STLF) systems due to the complexity and nonlinearity of the electric load signals. To address these problems, we propose a hybrid predictive model that includes a sliding-window algorithm, a stacking ensemble neural network, and a similar-days predictive method. First, we leverage a sliding-window algorithm to process the time-series electric load data with high nonlinearity and non-stationarity. Second, we propose an ensemble learning scheme of stacking neural networks to improve forecasting perform

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Huang, Xiaoxin, and Xiuxiu Chen. "A Quantitative Model of International Trade Based on Deep Neural Network." Computational Intelligence and Neuroscience 2022 (May 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/9811358.

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This paper is an in-depth study of international trade quantification models based on deep neural networks. Based on an in-depth analysis of global trade characteristics, a summary of existing problems, and a comparative analysis of various prediction methods, this paper constructs the ARIMA model, BP neural network (BPNN) model, and deep neural network (DNN) model to make a comprehensive comparison of international trade quantification. The results show that the nonlinear model has a global trade quantification has some advantages over linear models, and the deep model shows better prediction

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Zhou, Shuyi, Brandon J. Bethel, Wenjin Sun, Yang Zhao, Wenhong Xie, and Changming Dong. "Improving Significant Wave Height Forecasts Using a Joint Empirical Mode Decomposition–Long Short-Term Memory Network." Journal of Marine Science and Engineering 9, no. 7 (2021): 744. http://dx.doi.org/10.3390/jmse9070744.

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Wave forecasts, though integral to ocean engineering activities, are often conducted using computationally expensive and time-consuming numerical models with accuracies that are blunted by numerical-model-inherent limitations. Additionally, artificial neural networks, though significantly computationally cheaper, faster, and effective, also experience difficulties with nonlinearities in the wave generation and evolution processes. To solve both problems, this study employs and couples empirical mode decomposition (EMD) and a long short-term memory (LSTM) network in a joint model for significan

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Zhang, Boning. "Foreign exchange rates forecasting with an EMD-LSTM neural networks model." Journal of Physics: Conference Series 1053 (July 2018): 012005. http://dx.doi.org/10.1088/1742-6596/1053/1/012005.

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Chengzhao, Zhang, Pan Heiping, and Zhou Ke. "Comparison of Back Propagation Neural Networks and EMD-Based Neural Networks in Forecasting the Three Major Asian Stock Markets." Journal of Applied Sciences 15, no. 1 (2014): 90–99. http://dx.doi.org/10.3923/jas.2015.90.99.

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Shu, Wangwei, and Qiang Gao. "Forecasting Stock Price Based on Frequency Components by EMD and Neural Networks." IEEE Access 8 (2020): 206388–95. http://dx.doi.org/10.1109/access.2020.3037681.

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11

Teng, Xian Bin, Jun Dong Zhang, Shi Hai Zhang, and Ran Ran Wang. "Fault Diagnosis of Diesel Engine Based on Wavelet Analysis, EMD and Neural Networks." Advanced Materials Research 211-212 (February 2011): 1031–35. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.1031.

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Based on the complexity of surface vibration of diesel engine, the wavelet denoising method is used to process the monitor signal Preliminary. And then several vibration modes are isolated based on EMD method. Finally take the energy of these vibration modes as the input parameters to create neural network for fault diagnosis of diesel engine valve. The method has accomplished the fault diagnosis of the diesel engine merging many methods.

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MEHBOOB, ZAREEN, and HUJUN YIN. "INFORMATION QUANTIFICATION OF EMPIRICAL MODE DECOMPOSITION AND APPLICATIONS TO FIELD POTENTIALS." International Journal of Neural Systems 21, no. 01 (2011): 49–63. http://dx.doi.org/10.1142/s012906571100264x.

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The empirical mode decomposition (EMD) method can adaptively decompose a non-stationary time series into a number of amplitude or frequency modulated functions known as intrinsic mode functions. This paper combines the EMD method with information analysis and presents a framework of information-preserving EMD. The enhanced EMD method has been exploited in the analysis of neural recordings. It decomposes a signal and extracts only the most informative oscillations contained in the non-stationary signal. Information analysis has shown that the extracted components retain the information content

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Lin, Hualing, and Qiubi Sun. "Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks." Energies 13, no. 7 (2020): 1543. http://dx.doi.org/10.3390/en13071543.

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Accurate prediction of crude oil prices is meaningful for reducing firm risks, stabilizing commodity prices and maintaining national financial security. Wrong crude oil price forecasts can bring huge losses to governments, enterprises, investors and even cause economic and social instability. Many classic econometrics and computational approaches show good performance for the ordinary time series prediction tasks, but not satisfactory in crude oil price predictions. They ignore the characteristics of non-linearity and non-stationarity of crude oil prices data, which hinder an accurate predicti

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Gui, Sibo, Meng Shi, Zhaolong Li, Haitao Wu, Quansheng Ren, and Jianye Zhao. "A Deep-Learning-Based Method for Optical Transmission Link Assessment Applied to Optical Clock Comparisons." Photonics 10, no. 8 (2023): 920. http://dx.doi.org/10.3390/photonics10080920.

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We apply the Empirical Mode Decomposition (EMD) algorithm and the Time Convolutional Network (TCN) structure, predicated on Convolutional Neural Networks, to successfully enable feature extraction within high-precision optical time-frequency signals, and provide effective identification and alerts for abnormal link states. Experimental validation confirms that the proposed method not only delivers an efficacy on par with traditional manual techniques, but also excels in swiftly identifying anomalies that typically elude conventional approaches. This investigation furnishes novel theoretical ba

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Hassard, Alan. "Investigaton of Eye Movement Desensitization in Pain Clinic Patients." Behavioural and Cognitive Psychotherapy 23, no. 2 (1995): 177–85. http://dx.doi.org/10.1017/s1352465800014429.

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Twenty-seven pain clinic patients referred for psychological treatment received Eye Movement Desensitization (EMD) as a major part of their treatment. Their progress was monitored using generalized measures with a three month follow-up. All patients responded to EMD in the session. Subsequently, nineteen completed treatment of whom twelve were successful and seven clear failures. Seven dropped out before completing treatment and one result was not clear. Overall the group showed a large decrease in some, but not all, psychological measures. There was some return of symptoms in the group over t

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HU, Niaoqing. "Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks." Journal of Mechanical Engineering 55, no. 7 (2019): 9. http://dx.doi.org/10.3901/jme.2019.07.009.

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Carmona, A. M., and G. Poveda. "Prediction of mean monthly river discharges in Colombia through Empirical Mode Decomposition." Proceedings of the International Association of Hydrological Sciences 366 (April 10, 2015): 172. http://dx.doi.org/10.5194/piahs-366-172-2015.

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Abstract. The hydro-climatology of Colombia exhibits strong natural variability at a broad range of time scales including: inter-decadal, decadal, inter-annual, annual, intra-annual, intra-seasonal, and diurnal. Diverse applied sectors rely on quantitative predictions of river discharges for operational purposes including hydropower generation, agriculture, human health, fluvial navigation, territorial planning and management, risk preparedness and mitigation, among others. Various methodologies have been used to predict monthly mean river discharges that are based on "Predictive Analytics", a

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Li, Chao, Quanjie Guo, Lei Shao, Ji Li, and Han Wu. "Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network." Electronics 11, no. 22 (2022): 3834. http://dx.doi.org/10.3390/electronics11223834.

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Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but the nonlinear load increases the complexity of forecasting. In order to solve the problem of modal aliasing in historical data, and fully explore the relationship between time series characteristics in load data, this paper proposes a gated cyclic network model (SSA–GRU) based on sparrow algorithm optimization. Firstly, the complementary sets and empirical mode decomposition (EMD) are used to decompose the original data to obtain the characteristic components. The SSA–GRU combined model is used to

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Centeno-Bautista, Manuel A., Angel H. Rangel-Rodriguez, Andrea V. Perez-Sanchez, Juan P. Amezquita-Sanchez, David Granados-Lieberman, and Martin Valtierra-Rodriguez. "Electrocardiogram Analysis by Means of Empirical Mode Decomposition-Based Methods and Convolutional Neural Networks for Sudden Cardiac Death Detection." Applied Sciences 13, no. 6 (2023): 3569. http://dx.doi.org/10.3390/app13063569.

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Sudden cardiac death (SCD) is a global health problem, which represents 15–20% of global deaths. This type of death can be due to different heart conditions, where ventricular fibrillation has been reported as the main one. These cardiac alterations can be seen in an electrocardiogram (ECG) record, where the heart’s electrical activity is altered. The present research uses these variations to be able to predict 30 min in advance when the SCD event will occur. In this regard, a methodology based on the complete ensemble empirical mode decomposition (CEEMD) method to decompose the cardiac signal

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Wu, Jian Hua, Zheng Qiang Yao, Y. Jin, H. B. Xie, Y. S. Zhao, and L. Ch Xu. "Application of Hilbert-Huang Transform to Predict Grinding Surface Quality On-Line." Key Engineering Materials 304-305 (February 2006): 227–31. http://dx.doi.org/10.4028/www.scientific.net/kem.304-305.227.

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Predicting the precision of grinding process, especially correlating surface functionality generation to grinding conditions, would be of great significance to improve grinding accuracy of the end precision product. Huang developed a very promising revolutionary spectral data analysis technique based on the Hilbert transform. The concrete methods of the EMD, the local Hilbert spectrum are introduced. An artificial neural network (ANN) based on back propagation is developed to predict surface roughness Ra. An accelerometer is employed as in-process surface recognition sensor during grinding pro

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Mbatha, Nkanyiso, and Hassan Bencherif. "Time Series Analysis and Forecasting Using a Novel Hybrid LSTM Data-Driven Model Based on Empirical Wavelet Transform Applied to Total Column of Ozone at Buenos Aires, Argentina (1966–2017)." Atmosphere 11, no. 5 (2020): 457. http://dx.doi.org/10.3390/atmos11050457.

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Total column of ozone (TCO) time series analysis and accurate forecasting is of great significance in monitoring the status of the Chapman Mechanism in the stratosphere, which prevents harmful UV radiation from reaching the Earth’s surface. In this study, we performed a detailed time series analysis of the TCO data measured in Buenos Aires, Argentina. Moreover, hybrid data-driven forecasting models, based on long short-term memory networks (LSTM) recurrent neural networks (RNNs), are developed. We extracted the updated trend of the TCO time series by utilizing the singular spectrum analysis (S

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22

Wang, Yijun, Peiqian Guo, Nan Ma, and Guowei Liu. "Robust Wavelet Transform Neural-Network-Based Short-Term Load Forecasting for Power Distribution Networks." Sustainability 15, no. 1 (2022): 296. http://dx.doi.org/10.3390/su15010296.

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A precise short-term load-forecasting model is vital for energy companies to create accurate supply plans to reduce carbon dioxide production, causing our lives to be more environmentally friendly. A variety of high-voltage-level load-forecasting approaches, such as linear regression (LR), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) models, have been proposed in recent decades. However, unlike load forecasting in high-voltage transmission systems, load forecasting at the distribution network level is more challenging since distribution networks are mor

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Feng, Zhijie, Po Hu, Shuiqing Li, and Dongxue Mo. "Prediction of Significant Wave Height in Offshore China Based on the Machine Learning Method." Journal of Marine Science and Engineering 10, no. 6 (2022): 836. http://dx.doi.org/10.3390/jmse10060836.

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Accurate wave prediction can help avoid disasters. In this study, the significant wave height (SWH) prediction performances of the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit network (GRU) were compared. The 10 m u-component of wind (U10), 10 m v-component of wind (V10), and SWH of the previous 24 h were used as input parameters to predict the SWHs of the future 1, 3, 6, 12, and 24 h. The SWH prediction model was established at three different sites located in the Bohai Sea, the East China Sea, and the South China Sea, separately. The experim

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Ahmed, Ammar, Youssef Serrestou, Kosai Raoof, and Jean-François Diouris. "Empirical Mode Decomposition-Based Feature Extraction for Environmental Sound Classification." Sensors 22, no. 20 (2022): 7717. http://dx.doi.org/10.3390/s22207717.

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In environment sound classification logs, Mel band energies (MBEs) are considered as the most successful and commonly used features for classification. The underlying algorithm, fast Fourier transform (FFT), is valid under certain restrictions. In this study, we address these limitations of Fourier transform and propose a new method to extract log Mel band energies using amplitude modulation and frequency modulation. We present a comparative study between traditionally used log Mel band energy features extracted by Fourier transform and log Mel band energy features extracted by our new approac

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Popa, Stefan Lucian, Teodora Surdea-Blaga, Dan Lucian Dumitrascu, et al. "Automatic Diagnosis of High-Resolution Esophageal Manometry Using Artificial Intelligence." Journal of Gastrointestinal and Liver Diseases 31, no. 4 (2022): 383–89. http://dx.doi.org/10.15403/jgld-4525.

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Background and Aims: High-resolution esophageal manometry (HREM) is the gold standard procedure used for the diagnosis of esophageal motility disorders (EMD). Artificial intelligence (AI) might provide an efficient solution for the automatic diagnosis of EMD by improving the subjective interpretation of HREM images. The aim of our study was to develop an AI-based system, using neural networks, for the automatic diagnosis of HREM images, based on one wet swallow raw image. Methods: In the first phase of the study, the manometry recordings of our patients were retrospectively analyzed by t

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Dryuchenko, M. A., and A. A. Sirota. "Image stegoanalysis using deep neural networks and heteroassociative integral transformations." Prikladnaya Diskretnaya Matematika, no. 55 (2022): 35–58. http://dx.doi.org/10.17223/20710410/55/3.

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The problem of steganalysis of digital images is considered. The proposed approach is based on the use of deep convolutional neural networks with a relatively simple architecture, distinguished by the use of additional layers of special processing. These networks are trained and used for steganalysis of small fragments of the original large images. For the analysis of full sized images, it is proposed to carry out secondary post-processing, which involves combining the obtained classification results in blocks as a sequence of binary features according to the scheme of a naive Bayesian classif

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Jiao, Xiaoxuan, Bo Jing, Yifeng Huang, Juan Li, and Guangyue Xu. "Research on fault diagnosis of airborne fuel pump based on EMD and probabilistic neural networks." Microelectronics Reliability 75 (August 2017): 296–308. http://dx.doi.org/10.1016/j.microrel.2017.03.007.

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Liu, Die, Yihao Bao, Yingying He, and Likai Zhang. "A Data Loss Recovery Technique Using EMD-BiGRU Algorithm for Structural Health Monitoring." Applied Sciences 11, no. 21 (2021): 10072. http://dx.doi.org/10.3390/app112110072.

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Missing data caused by sensor faults is a common problem in structural health monitoring systems. Due to negative effects, many methods that adopt measured data to infer missing data have been proposed to tackle this problem in previous studies. However, capturing complex correlations from measured data remains a significant challenge. In this study, empirical mode decomposition (EMD) combined with a bidirectional gated recurrent unit (BiGRU) is proposed for the recovery of the measured data. The proposed EMD-BiGRU converts the missing data task as predicted task of time sequence. The core of

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Kang, Aiqing, Qingxiong Tan, Xiaohui Yuan, Xiaohui Lei, and Yanbin Yuan. "Short-Term Wind Speed Prediction Using EEMD-LSSVM Model." Advances in Meteorology 2017 (2017): 1–22. http://dx.doi.org/10.1155/2017/6856139.

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Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to s

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Ma, Yu. "Two models for predicting stock prices in combination with LSTM." Highlights in Business, Economics and Management 5 (February 16, 2023): 664–73. http://dx.doi.org/10.54097/hbem.v5i.5256.

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In addition to its practical and theoretical significance, stock forecasting has long been a hot research topic for scholars domestically and abroad. Stock data are time-series in nature, and neural networks have achieved relatively good performance in dealing with time series problems, among which long-short-term memory neural networks are well suited to dealing with such time-series data with long-term dependence. However, the stock market is an environment that changes with the external environment, with high stochasticity and complex intrinsic nonlinear relationships between different phen

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Yu, Jing, Feng Ding, Chenghao Guo, and Yabin Wang. "System load trend prediction method based on IF-EMD-LSTM." International Journal of Distributed Sensor Networks 15, no. 8 (2019): 155014771986765. http://dx.doi.org/10.1177/1550147719867655.

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Accurately predicting the load change of the information system during operation has important guiding significance for ensuring that the system operation is not interrupted and resource scheduling is carried out in advance. For the information system monitoring time series data, this article proposes a load trend prediction method based on isolated forests-empirical modal decomposition-long-term (IF-EMD-LSTM). First, considering the problem of noise and abnormal points in the original data, the isolated forest algorithm is used to eliminate the abnormal points in the data. Second, in order to

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Guerrero-Sánchez, Alma E., Edgar A. Rivas-Araiza, Mariano Garduño-Aparicio, Saul Tovar-Arriaga, Juvenal Rodriguez-Resendiz, and Manuel Toledano-Ayala. "A Novel Methodology for Classifying Electrical Disturbances Using Deep Neural Networks." Technologies 11, no. 4 (2023): 82. http://dx.doi.org/10.3390/technologies11040082.

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Electrical power quality is one of the main elements in power generation systems. At the same time, it is one of the most significant challenges regarding stability and reliability. Due to different switching devices in this type of architecture, different kinds of power generators as well as non-linear loads are used for different industrial processes. A result of this is the need to classify and analyze Power Quality Disturbance (PQD) to prevent and analyze the degradation of the system reliability affected by the non-linear and non-stationary oscillatory nature. This paper presents a novel

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Cao, Zhiyong, Zhijuan Cao, Hongwei Zhao, et al. "Using Empirical Modal Decomposition to Improve the Daily Milk Yield Prediction of Cows." Wireless Communications and Mobile Computing 2022 (July 11, 2022): 1–7. http://dx.doi.org/10.1155/2022/1685841.

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In this study, the daily lactation data of Holstein dairy cows in one lactation period (305 days) were used as lactation time series data. Empirical mode decomposition (EMD) was used to decompose milk yield series. The nonstationary milk yield series with multiple oscillation modes was decomposed into several components. After eliminating the interference components, the interference components were superimposed. Remaining component reconstruction was used to get the denoising milk yield series. The denoising milk yield series retained the basic characteristics of the original milk yield serie

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Sadrawi, Muammar, Shou-Zen Fan, Maysam F. Abbod, Kuo-Kuang Jen, and Jiann-Shing Shieh. "Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks." BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/536863.

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This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, sy

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Jin, Zebin, Yixiao Jin, and Zhiyun Chen. "Empirical mode decomposition using deep learning model for financial market forecasting." PeerJ Computer Science 8 (September 14, 2022): e1076. http://dx.doi.org/10.7717/peerj-cs.1076.

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Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with

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Redwan, Sadi M., Md Rashed-Al-Mahfuz, and Md Ekramul Hamid. "Recognizing Command Words using Deep Recurrent Neural Network for Both Acoustic and Throat Speech." European Journal of Information Technologies and Computer Science 3, no. 2 (2023): 7–13. http://dx.doi.org/10.24018/compute.2023.3.2.88.

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The importance of speech command recognition in a human-machine interaction system is increased in recent years. In this study, we propose a deep neural network-based system for acoustic and throat command speech recognition. We apply a preprocessed pipeline to create the input of the deep learning model. Firstly, speech commands are decomposed into components using well-known signal decomposition techniques. The Mel-frequency cepstral coefficients (MFCC) feature extraction method is applied to each component of the speech commands to obtain the feature inputs for the recognition system. At th

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Camarena-Martinez, David, Martin Valtierra-Rodriguez, Arturo Garcia-Perez, Roque Alfredo Osornio-Rios, and Rene de Jesus Romero-Troncoso. "Empirical Mode Decomposition and Neural Networks on FPGA for Fault Diagnosis in Induction Motors." Scientific World Journal 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/908140.

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Nowadays, many industrial applications require online systems that combine several processing techniques in order to offer solutions to complex problems as the case of detection and classification of multiple faults in induction motors. In this work, a novel digital structure to implement the empirical mode decomposition (EMD) for processing nonstationary and nonlinear signals using the full spline-cubic function is presented; besides, it is combined with an adaptive linear network (ADALINE)-based frequency estimator and a feed forward neural network (FFNN)-based classifier to provide an intel

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Zheng, Huiting, Jiabin Yuan, and Long Chen. "Short-Term Load Forecasting Using EMD-LSTM Neural Networks with a Xgboost Algorithm for Feature Importance Evaluation." Energies 10, no. 8 (2017): 1168. http://dx.doi.org/10.3390/en10081168.

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Xu, Da-Chuan, Huai-Shu Hou, Cai-Xia Liu, and Chao-Fei Jiao. "Defect type identification of thin-walled stainless steel seamless pipe based on eddy current testing." Insight - Non-Destructive Testing and Condition Monitoring 63, no. 12 (2021): 697–703. http://dx.doi.org/10.1784/insi.2021.63.12.697.

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Aimed at eddy current detection of defects in thin-walled stainless steel seamless pipes, an effective detection method for identifying defect types is proposed. First, the empirical mode decomposition (EMD) method is used to process the collected eddy current signals and obtain the principal intrinsic mode function (IMF) components of different defects. The Hilbert-Huang transform (HHT) is used to extract the frequency-domain features of the principal IMF components, which are combined with the time-domain features to form an effective defect feature vector. Then, principal component analysis

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Rofii, Faqih, Agus Naba, Hari Arief Dharmawan, and Fachrudin Hunaini. "Development of empirical mode decomposition based neural network for power quality disturbances classification." EUREKA: Physics and Engineering, no. 2 (March 31, 2022): 28–44. http://dx.doi.org/10.21303/2461-4262.2022.002046.

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The complexity of the electric power network causes a lot of distortion, such as a decrease in power quality (PQ) in the form of voltage variations, harmonics, and frequency fluctuations. Monitoring the distortion source is important to ensure the availability of clean and quality electric power. Therefore, this study aims to classify power quality using a neural network with empirical mode decomposition-based feature extraction. The proposed method consists of 2 main steps, namely feature extraction, and classification. Empirical Mode Decomposition (EMD) was also applied to categorize the PQ

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Altuve, Miguel, Paula Lizarazo, and Javier Villamizar. "Human activity recognition using improved complete ensemble EMD with adaptive noise and long short-term memory neural networks." Biocybernetics and Biomedical Engineering 40, no. 3 (2020): 901–9. http://dx.doi.org/10.1016/j.bbe.2020.04.007.

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Asghar, Muhammad Adeel, Muhammad Jamil Khan, Muhammad Rizwan, Raja Majid Mehmood, and Sun-Hee Kim. "An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering." Sensors 20, no. 13 (2020): 3765. http://dx.doi.org/10.3390/s20133765.

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Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training ti

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Wang, Dongyu, Xiwen Cui, and Dongxiao Niu. "Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF." Sustainability 14, no. 12 (2022): 7307. http://dx.doi.org/10.3390/su14127307.

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Improving the accuracy of wind power forecasting can guarantee the stable dispatch and safe operation of the grid system. Here, we propose an EMD-PCA-RF-LSTM wind power forecasting model to solve problems in traditional wind power forecasting such as incomplete consideration of influencing factors, inaccurate feature identification, and complex space–time relationships between variables. The proposed model incorporates Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA), Random Forest (RF), and Long Short-Term Memory (LSTM) neural networks, And environmental factors are filt

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Gao, Hongbo, Shuang Qiu, Jun Fang, et al. "Short-Term Prediction of PV Power Based on Combined Modal Decomposition and NARX-LSTM-LightGBM." Sustainability 15, no. 10 (2023): 8266. http://dx.doi.org/10.3390/su15108266.

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Recently, solar energy has been gaining attention as one of the best promising renewable energy sources. Accurate PV power prediction models can solve the impact on the power system due to the non-linearity and randomness of PV power generation and play a crucial role in the operation and scheduling of power plants. This paper proposes a novel machine learning network framework to predict short-term PV power in a time-series manner. The combination of nonlinear auto-regressive neural networks with exogenous input (NARX), long short term memory (LSTM) neural network, and light gradient boosting

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Diez, Pablo F., Vicente A. Mut, Eric Laciar, Abel Torres, and Enrique M. Avila Perona. "FEATURES EXTRACTION METHOD FOR BRAIN-MACHINE COMMUNICATION BASED ON THE EMPIRICAL MODE DECOMPOSITION." Biomedical Engineering: Applications, Basis and Communications 25, no. 06 (2013): 1350058. http://dx.doi.org/10.4015/s1016237213500580.

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A brain-machine interface (BMI) is a communication system that translates human brain activity into commands, and then these commands are conveyed to a machine or a computer. It is proposes a technique for features extraction from electroencephalographic (EEG) signals and afterward, their classification on different mental tasks. The empirical mode decomposition (EMD) is a method capable of processing non-stationary and nonlinear signals, as the EEG. The EMD was applied on EEG signals of seven subjects performing five mental tasks. Six features were computed, namely, root mean square (RMS), va

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Jaramillo-Morán, Miguel A., Daniel Fernández-Martínez, Agustín García-García, and Diego Carmona-Fernández. "Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study." Energies 14, no. 23 (2021): 7845. http://dx.doi.org/10.3390/en14237845.

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European Union Allowances (EUAs) are rights to emit CO2 that may be sold or bought by enterprises. They were originally created to try to reduce greenhouse gas emissions, although they have become assets that may be used by financial intermediaries to seek for new business opportunities. Therefore, forecasting the time evolution of their price is very important for agents involved in their selling or buying. Neural Networks, an artificial intelligence paradigm, have been proved to be accurate and reliable tools for time series forecasting, and have been widely used to predict economic and ener

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Zeng, Wei, Mengqing Li, Chengzhi Yuan, Qinghui Wang, Fenglin Liu, and Ying Wang. "Classification of focal and non focal EEG signals using empirical mode decomposition (EMD), phase space reconstruction (PSR) and neural networks." Artificial Intelligence Review 52, no. 1 (2019): 625–47. http://dx.doi.org/10.1007/s10462-019-09698-4.

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Mohsenimanesh, Ahmad, Evgueniy Entchev, and Filip Bosnjak. "Hybrid Model Based on an SD Selection, CEEMDAN, and Deep Learning for Short-Term Load Forecasting of an Electric Vehicle Fleet." Applied Sciences 12, no. 18 (2022): 9288. http://dx.doi.org/10.3390/app12189288.

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Forecasting the aggregate charging load of a fleet of electric vehicles (EVs) plays an important role in the energy management of the future power system. Therefore, accurate charging load forecasting is necessary for reliable and efficient power system operation. A hybrid method that is a combination of the similar day (SD) selection, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and deep neural networks is proposed and explored in this paper. For the SD selection, an extreme gradient boosting (XGB)-based weighted k-means method is chosen and applied to evaluat

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Dang, Sanlei, Long Peng, Jingming Zhao, Jiajie Li, and Zhengmin Kong. "A Quantile Regression Random Forest-Based Short-Term Load Probabilistic Forecasting Method." Energies 15, no. 2 (2022): 663. http://dx.doi.org/10.3390/en15020663.

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In this paper, a novel short-term load forecasting method amalgamated with quantile regression random forest is proposed. Comprised with point forecasting, it is capable of quantifying the uncertainty of power load. Firstly, a bespoke 2D data preprocessing taking advantage of empirical mode decomposition (EMD) is presented. It can effectively assist subsequent point forecasting models to extract spatial features hidden in the 2D load matrix. Secondly, by exploiting multimodal deep neural networks (DNN), three short-term load point forecasting models are conceived. Furthermore, a tailor-made mu

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Zhang, Yixiang, Zenggui Gao, Jiachen Sun, and Lilan Liu. "Machine-Learning Algorithms for Process Condition Data-Based Inclusion Prediction in Continuous-Casting Process: A Case Study." Sensors 23, no. 15 (2023): 6719. http://dx.doi.org/10.3390/s23156719.

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Quality-related prediction in the continuous-casting process is important for the quality and process control of casting slabs. As intelligent manufacturing technologies continue to evolve, numerous data-driven techniques have been available for industrial applications. This case study was aimed at developing a machine-learning algorithm, capable of predicting slag inclusion defects in continuous-casting slabs, based on process condition sensor data. A large dataset consisting of sensor data from nearly 7300 casting samples has been analyzed, with the empirical mode decomposition (EMD) algorit

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