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It is proposed to identify the beginning and end of the SARS-CoV-2 and subprime crises on the NASDAQ. The EEMD was used to decompose the index into consecutive series with the same number of components and their correlation coefficients were calculated, the power spectrum of the original series was also analyzed. Signals of instability associated with changes in both the components’ correlations and the NASDAQ spectrum were identified. It is recommended to apply the procedure on other series and other crises; likewise, the method is based on the detection of discrepancies, thus being a monitoring tool, but not one of quantitative forecasts. The originality of the work lies in the use of the modified EEMD for the decomposition of consecutive series in the same number of components, and the use of the correlation coefficient between components and the spectrum of the original series as measures of system stability. The approach proved to be useful for identifying and anticipating large changes in the behavior of a time series, Se propone identificar el inicio y terminación de las crisis por SARS-CoV-2 y subprime en el NASDAQ. Se utilizó el EEMD para la descomposición del índice en series consecutivas con el mismo número de componentes y se calcularon sus coeficientes de correlación, también se analizó el espectro de potencia de la serie original. Se identificaron señales de inestabilidad asociadas a cambios tanto en las correlaciones de los componentes como del espectro del NASDAQ. Se recomienda aplicar el procedimiento sobre otras series y otras crisis; asimismo, el método se basa en la detección de discrepancias, lo que implica ser una herramienta de monitoreo, mas no una de pronósticos cuantitativos. La originalidad del trabajo radica en el uso del EEMD modificado para la descomposición de series consecutivas en el mismo número de componentes, y la utilización del coeficiente de correlación entre componentes y el espectro de la serie original como medidas de estabilidad del sistema. El enfoque mostró ser útil para identificar y anticipar grandes cambios en el comportamiento de una serie de tiempo.

Detection and avoidance of regenerative chatter play a crucial role in ensuring the high quality and efficiency of machining operations. Predominant analytical approaches provide stability lobe diagrams for machining processes. Deep learning is a general term given to the most recent and successful group of machine learning methods that proved great promise in many areas of human life. This study purposes a novel transfer learning framework that combines analytical solutions and convolution neural network (CNN) under a novel transfer learning framework. Stability lobes and numerical time-domain solutions of analytical methods are used to train and label, arguably one of the most successful CNN architectures, AlexNet. This approach eliminates the need for a time-consuming and costly experimental data collection phase for training. Furthermore, an ensemble empirical mode decomposition based signal pre-processing method is developed. An IMF-based multi-band ensemble approach is proposed where only intrinsic mode functions relevant to each modal frequency of the system are selected based on their entropy increase and used in training multiple AlexNet instances. The measured data were collected during shoulder milling from a CNC-vertical milling machine. The results revealed considerable success in several scenarios ranging from 82 to 100%, without using any experimentally measured data in training. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 118M414, The authors are thankful to Prof. Dr. Yusuf Altıntaş for his support in the dynamic characterization of the CNC-machine tool. This project is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) 1001 program (No. 118M414).

Detection and avoidance of regenerative chatter play a crucial role in ensuring the high quality and efficiency of machining operations. Predominant analytical approaches provide stability lobe diagrams for machining processes. Deep learning is a general term given to the most recent and successful group of machine learning methods that proved great promise in many areas of human life. This study purposes a novel transfer learning framework that combines analytical solutions and convolution neural network (CNN) under a novel transfer learning framework. Stability lobes and numerical time-domain solutions of analytical methods are used to train and label, arguably one of the most successful CNN architectures, AlexNet. This approach eliminates the need for a time-consuming and costly experimental data collection phase for training. Furthermore, an ensemble empirical mode decomposition based signal pre-processing method is developed. An IMF-based multi-band ensemble approach is proposed where only intrinsic mode functions relevant to each modal frequency of the system are selected based on their entropy increase and used in training multiple AlexNet instances. The measured data were collected during shoulder milling from a CNC-vertical milling machine. The results revealed considerable success in several scenarios ranging from 82 to 100%, without using any experimentally measured data in training. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature., Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK: 118M414, The authors are thankful to Prof. Dr. Yusuf Altıntaş for his support in the dynamic characterization of the CNC-machine tool. This project is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) 1001 program (No. 118M414).

Nonlinear signals are often encountered in many applications, such as biomedical signal processing, fault diagnosis, and image processing. Ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithms have been proposed for the analysis of nonlinear and non-stationary signals. In this paper, we compare the performance of EEMD and CEEMDAN algorithms based on the Root Mean Square (RMS) statistical indicator for nonlinear signal processing. We evaluate the effectiveness of these algorithms using two synthetic signals and a real-world vibration signal from a gearbox. The results show that CEEMDAN provides a 50% improvement over EEMD in terms of RMS and the number of trials or computation time required. The study also shows that EEMD is prone to mode mixing and requires a large number of trials to achieve accurate results. On the other hand, CEEMDAN overcomes the mode mixing issue and provides more accurate results with fewer trials or computation time. Our findings suggest that CEEMDAN is a more efficient algorithm for nonlinear signal processing, particularly in real-world applications where computation time is a limiting factor.

In this work, it is a question of a monitoring of cutting tool wear in mechanical turning. This monitoring is carried out in three phases which correspond to the life of the tool. To achieve this objective of improving the monitoring of the cutting tool, we have proposed the EEMD processing algorithm which decomposes a large signal into small signals (IMFs) by comparing it to the EMD algorithm, which is an algorithm used in the analysis of non-linear and non-stationary signals. The phenomenon of mode mixing is one of the major drawbacks of EMD. The EEMD eliminates the mode mixing effect. The EEMD principle is to add extra white noise to the signal with a certain number of tries.

Nonlinear signals are often encountered in many applications, such as biomedical signal processing, fault diagnosis, and image processing. Ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithms have been proposed for the analysis of nonlinear and non-stationary signals. In this paper, we compare the performance of EEMD and CEEMDAN algorithms based on the Root Mean Square (RMS) statistical indicator for nonlinear signal processing. We evaluate the effectiveness of these algorithms using two synthetic signals and a real-world vibration signal from a gearbox. The results show that CEEMDAN provides a 50% improvement over EEMD in terms of RMS and the number of trials or computation time required. The study also shows that EEMD is prone to mode mixing and requires a large number of trials to achieve accurate results. On the other hand, CEEMDAN overcomes the mode mixing issue and provides more accurate results with fewer trials or computation time. Our findings suggest that CEEMDAN is a more efficient algorithm for nonlinear signal processing, particularly in real-world applications where computation time is a limiting factor.

In this work, it is a question of a monitoring of cutting tool wear in mechanical turning. This monitoring is carried out in three phases which correspond to the life of the tool. To achieve this objective of improving the monitoring of the cutting tool, we have proposed the EEMD processing algorithm which decomposes a large signal into small signals (IMFs) by comparing it to the EMD algorithm, which is an algorithm used in the analysis of non-linear and non-stationary signals. The phenomenon of mode mixing is one of the major drawbacks of EMD. The EEMD eliminates the mode mixing effect. The EEMD principle is to add extra white noise to the signal with a certain number of tries.

Nonlinear signals are often encountered in many applications, such as biomedical signal processing, fault diagnosis, and image processing. Ensemble empirical mode decomposition (EEMD) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithms have been proposed for the analysis of nonlinear and non-stationary signals. In this paper, we compare the performance of EEMD and CEEMDAN algorithms based on the Root Mean Square (RMS) statistical indicator for nonlinear signal processing. We evaluate the effectiveness of these algorithms using two synthetic signals and a real-world vibration signal from a gearbox. The results show that CEEMDAN provides a 50% improvement over EEMD in terms of RMS and the number of trials or computation time required. The study also shows that EEMD is prone to mode mixing and requires a large number of trials to achieve accurate results. On the other hand, CEEMDAN overcomes the mode mixing issue and provides more accurate results with fewer trials or computation time. Our findings suggest that CEEMDAN is a more efficient algorithm for nonlinear signal processing, particularly in real-world applications where computation time is a limiting factor.

In this work, it is a question of a monitoring of cutting tool wear in mechanical turning. This monitoring is carried out in three phases which correspond to the life of the tool. To achieve this objective of improving the monitoring of the cutting tool, we have proposed the EEMD processing algorithm which decomposes a large signal into small signals (IMFs) by comparing it to the EMD algorithm, which is an algorithm used in the analysis of non-linear and non-stationary signals. The phenomenon of mode mixing is one of the major drawbacks of EMD. The EEMD eliminates the mode mixing effect. The EEMD principle is to add extra white noise to the signal with a certain number of tries.

Herein, we propose a novel hybrid method for forecasting steel prices by modeling nonlinearity and time variations together to enhance forecasting adaptability. The multivariate empirical mode decomposition (MEMD)–ensemble-EMD (EEMD) approach was employed for preprocessing to separate the nonlinear and time variation components of a hot-rolled coil (HRC) price return series, and a particle swarm optimization (PSO)-based least squares support vector regression (LSSVR) approach and a generalized autoregressive conditional heteroskedasticity (GARCH) model were applied to capture the nonlinear and time variation characteristics of steel returns, respectively. The empirical results revealed that compared with the traditional models, the proposed hybrid method yields superior forecasting performance for HRC returns. The evidence also suggested that in capturing the price dynamics of HRC during the COVID-19 pandemic period, the asymmetric GARCH model with MEMD–LSSVR outperformed not only standard GARCH models but also the EEMD-LSSVR models. The proposed MEMD–LSSVR–GARCH model for steel price forecasting provides a useful decision support tool for steelmakers and consumers to evaluate steel price trends.

Detection and avoidance of regenerative chatter play a crucial role in ensuring the high quality and efficiency of machining operations. Predominant analytical approaches provide stability lobe diagrams for machining processes. Deep learning is a general term given to the most recent and successful group of machine learning methods that proved great promise in many areas of human life. This study purposes a novel transfer learning framework that combines analytical solutions and convolution neural network (CNN) under a novel transfer learning framework. Stability lobes and numerical time-domain solutions of analytical methods are used to train and label, arguably one of the most successful CNN architectures, AlexNet. This approach eliminates the need for a time-consuming and costly experimental data collection phase for training. Furthermore, an ensemble empirical mode decomposition based signal pre-processing method is developed. An IMF-based multi-band ensemble approach is proposed where only intrinsic mode functions relevant to each modal frequency of the system are selected based on their entropy increase and used in training multiple AlexNet instances. The measured data were collected during shoulder milling from a CNC-vertical milling machine. The results revealed considerable success in several scenarios ranging from 82 to 100%, without using any experimentally measured data in training., TUBITAK (The Scientific and Technological Research Council of Turkey) 1001 programTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [118M414], The authors are thankful to Prof. Dr. Yusuf Altnta for his support in the dynamic characterization of the CNC-machine tool. This project is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) 1001 program (No. 118M414).

Detection and avoidance of regenerative chatter play a crucial role in ensuring the high quality and efficiency of machining operations. Predominant analytical approaches provide stability lobe diagrams for machining processes. Deep learning is a general term given to the most recent and successful group of machine learning methods that proved great promise in many areas of human life. This study purposes a novel transfer learning framework that combines analytical solutions and convolution neural network (CNN) under a novel transfer learning framework. Stability lobes and numerical time-domain solutions of analytical methods are used to train and label, arguably one of the most successful CNN architectures, AlexNet. This approach eliminates the need for a time-consuming and costly experimental data collection phase for training. Furthermore, an ensemble empirical mode decomposition based signal pre-processing method is developed. An IMF-based multi-band ensemble approach is proposed where only intrinsic mode functions relevant to each modal frequency of the system are selected based on their entropy increase and used in training multiple AlexNet instances. The measured data were collected during shoulder milling from a CNC-vertical milling machine. The results revealed considerable success in several scenarios ranging from 82 to 100%, without using any experimentally measured data in training., TUBITAK (The Scientific and Technological Research Council of Turkey) 1001 programTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [118M414], The authors are thankful to Prof. Dr. Yusuf Altnta for his support in the dynamic characterization of the CNC-machine tool. This project is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) 1001 program (No. 118M414).

Herein, we propose a novel hybrid method for forecasting steel prices by modeling nonlinearity and time variations together to enhance forecasting adaptability. The multivariate empirical mode decomposition (MEMD)–ensemble-EMD (EEMD) approach was employed for preprocessing to separate the nonlinear and time variation components of a hot-rolled coil (HRC) price return series, and a particle swarm optimization (PSO)-based least squares support vector regression (LSSVR) approach and a generalized autoregressive conditional heteroskedasticity (GARCH) model were applied to capture the nonlinear and time variation characteristics of steel returns, respectively. The empirical results revealed that compared with the traditional models, the proposed hybrid method yields superior forecasting performance for HRC returns. The evidence also suggested that in capturing the price dynamics of HRC during the COVID-19 pandemic period, the asymmetric GARCH model with MEMD–LSSVR outperformed not only standard GARCH models but also the EEMD-LSSVR models. The proposed MEMD–LSSVR–GARCH model for steel price forecasting provides a useful decision support tool for steelmakers and consumers to evaluate steel price trends.

Herein, we propose a novel hybrid method for forecasting steel prices by modeling nonlinearity and time variations together to enhance forecasting adaptability. The multivariate empirical mode decomposition (MEMD)–ensemble-EMD (EEMD) approach was employed for preprocessing to separate the nonlinear and time variation components of a hot-rolled coil (HRC) price return series, and a particle swarm optimization (PSO)-based least squares support vector regression (LSSVR) approach and a generalized autoregressive conditional heteroskedasticity (GARCH) model were applied to capture the nonlinear and time variation characteristics of steel returns, respectively. The empirical results revealed that compared with the traditional models, the proposed hybrid method yields superior forecasting performance for HRC returns. The evidence also suggested that in capturing the price dynamics of HRC during the COVID-19 pandemic period, the asymmetric GARCH model with MEMD–LSSVR outperformed not only standard GARCH models but also the EEMD-LSSVR models. The proposed MEMD–LSSVR–GARCH model for steel price forecasting provides a useful decision support tool for steelmakers and consumers to evaluate steel price trends.

In the paper, the pursued objective is to take advantage of two main relevant cascade methods, namely Ensemble Empirical Mode Decomposition (EEMD) and Discrete Wavelet Transform (DWT), for the improvement of the sensitivity of scalar indicators such as Kurtosis (Kurt) and Crest Factor (CF) within the application of condition monitoring by vibration analysis on electric machines. The measurements were possible thanks to the piezoelectric sensors, where the signals were recorded from the machine's critical and judiciously chosen points. The paper demonstrates that when the motor runs under faulty conditions, it is possible to notice the appearance of spallings, which cause the signal to be disturbed and consequently modify the distribution (which is of Gaussian kind in a flawless situation). Nevertheless, those impulse excitations can have a tremendous effect on the values of time-domain indicators. The paper proposes two powerful denoising methods, discussed in-depth the effectiveness of each technique. The conclusion drawn from the analysis shows that the approach improves the sensitivity of selected indicators and therefore increases their reliability for fault presence detection. © 2021 IEEE., Export Date: 20 September 2021; Conference Paper; CODEN: CRIIE

[EN] Nowadays air quality data can be easily accumulated by sensors around the world. Analysis on air quality data is very useful for society decision. Among five major air pollutants which are calculated for AQI (Air Quality Index), PM2.5 data is the most concerned by the people. PM2.5 data is also cross-impacted with the other factors in the air and which has properties of non-linear non-stationary including high noise level and outlier. Traditional methods cannot solve the problem of PM2.5 data clustering very well because of their inherent characteristics. In this paper, a novel model-based feature extraction method is proposed to address this issue. The EPLS model includes: (1) Mode Decomposition, in which EEMD algorithm is applied to the aggregation dataset; (2) Dimension Reduction, which is carried out for a more significant set of vectors; (3) Least Squares Projection, in which all testing data are projected to the obtained vectors. Synthetic dataset and air quality dataset are applied to different clustering methods and similarity measures. Experimental results demonstrate that EPLS is efficient in dealing with high noise level and outlier air quality clustering problems, and which can also be adapted to various clustering techniques and distance measures. (C) 2016 Elsevier B.V. All rights reserved.

Aiming at the problems of low accuracy of non-stationary signal spectrum analysis in rotating machinery vibration, this paper puts forward a kind of rotating mechanical signal analysis method based on EEMD and spectrum correction. Firstly, ensemble empirical mode decomposition (EEMD) is used to obtain the intrinsic mode functions (IMF) of the original signal; secondly, do correlation analysis for each IMF component and the original signal separately, and find out the IMF component with the largest correlation coefficient and calculate the frequency spectrum of the IMF; finally, spectrum correction algorithm is employed to get accurate spectrum for quantitative analysis. A practical vibration signal of rotor vibration platform is applied to testing the method of this paper, the EMD method and wavelet analysis method separately. The results show that the proposed new method can improve the precision of spectrum analysis for rotating mechanical signal significantly; therefore, it has a good application prospect.

Aiming at the problems of low accuracy of non-stationary signal spectrum analysis in rotating machinery vibration, this paper puts forward a kind of rotating mechanical signal analysis method based on EEMD and spectrum correction. Firstly, ensemble empirical mode decomposition (EEMD) is used to obtain the intrinsic mode functions (IMF) of the original signal; secondly, do correlation analysis for each IMF component and the original signal separately, and find out the IMF component with the largest correlation coefficient and calculate the frequency spectrum of the IMF; finally, spectrum correction algorithm is employed to get accurate spectrum for quantitative analysis. A practical vibration signal of rotor vibration platform is applied to testing the method of this paper, the EMD method and wavelet analysis method separately. The results show that the proposed new method can improve the precision of spectrum analysis for rotating mechanical signal significantly; therefore, it has a good application prospect.

Aiming at the problems of low accuracy of non-stationary signal spectrum analysis in rotating machinery vibration, this paper puts forward a kind of rotating mechanical signal analysis method based on EEMD and spectrum correction. Firstly, ensemble empirical mode decomposition (EEMD) is used to obtain the intrinsic mode functions (IMF) of the original signal; secondly, do correlation analysis for each IMF component and the original signal separately, and find out the IMF component with the largest correlation coefficient and calculate the frequency spectrum of the IMF; finally, spectrum correction algorithm is employed to get accurate spectrum for quantitative analysis. A practical vibration signal of rotor vibration platform is applied to testing the method of this paper, the EMD method and wavelet analysis method separately. The results show that the proposed new method can improve the precision of spectrum analysis for rotating mechanical signal significantly; therefore, it has a good application prospect.

Aiming at the problems of low accuracy of non-stationary signal spectrum analysis in rotating machinery vibration, this paper puts forward a kind of rotating mechanical signal analysis method based on EEMD and spectrum correction. Firstly, ensemble empirical mode decomposition (EEMD) is used to obtain the intrinsic mode functions (IMF) of the original signal; secondly, do correlation analysis for each IMF component and the original signal separately, and find out the IMF component with the largest correlation coefficient and calculate the frequency spectrum of the IMF; finally, spectrum correction algorithm is employed to get accurate spectrum for quantitative analysis. A practical vibration signal of rotor vibration platform is applied to testing the method of this paper, the EMD method and wavelet analysis method separately. The results show that the proposed new method can improve the precision of spectrum analysis for rotating mechanical signal significantly; therefore, it has a good application prospect.

Aiming at the problems of low accuracy of non-stationary signal spectrum analysis in rotating machinery vibration, this paper puts forward a kind of rotating mechanical signal analysis method based on EEMD and spectrum correction. Firstly, ensemble empirical mode decomposition (EEMD) is used to obtain the intrinsic mode functions (IMF) of the original signal; secondly, do correlation analysis for each IMF component and the original signal separately, and find out the IMF component with the largest correlation coefficient and calculate the frequency spectrum of the IMF; finally, spectrum correction algorithm is employed to get accurate spectrum for quantitative analysis. A practical vibration signal of rotor vibration platform is applied to testing the method of this paper, the EMD method and wavelet analysis method separately. The results show that the proposed new method can improve the precision of spectrum analysis for rotating mechanical signal significantly; therefore, it has a good application prospect.

Nowadays wind turbines are wide employed as a clean energy resource. However, their use implies a wide number of eolic structures which also requires a demanding monitoring and maintenance. The most technique employed for those kind of tasksis vibration analysis inasmuch as it is non-destructive technique and presents a high performance for this task. Among vibration analysis, Components identification turns important because it allows programming the machine maintenance in a proper time. In addition, the accessibility for installing several mechanical vibration transducers into the machine is limited either by the physical space or the high cost of sensor networks. Therefore, it is useful and needed to perform an analysis using just one sensor. In that sense, the accelerometers are commonly utilized since these sensors allow extracting the velocity and displacement, signals which have additional information about the machine, performing a double digital integration. Nonetheless, digital integration evolves several difficulties such as biased errors, leakages in the signal, and strong instability at low and high frequencies.This paper proposes a new methodology based on the ensemble empirical mode decomposition (EEMD) for extracting low frequency components of a wind turbine structure from a single-channel vibration measurement and double integration in time to improve the interpretability of the components.The methodology overcomes the lack of multiple vibration measurements using pseudo-sources and it is especially suitable forsignals with high frequency behavior.

Nowadays wind turbines are wide employed as a clean energy resource. However, their use implies a wide number of eolic structures which also requires a demanding monitoring and maintenance. The most technique employed for those kind of tasksis vibration analysis inasmuch as it is non-destructive technique and presents a high performance for this task. Among vibration analysis, Components identification turns important because it allows programming the machine maintenance in a proper time. In addition, the accessibility for installing several mechanical vibration transducers into the machine is limited either by the physical space or the high cost of sensor networks. Therefore, it is useful and needed to perform an analysis using just one sensor. In that sense, the accelerometers are commonly utilized since these sensors allow extracting the velocity and displacement, signals which have additional information about the machine, performing a double digital integration. Nonetheless, digital integration evolves several difficulties such as biased errors, leakages in the signal, and strong instability at low and high frequencies.This paper proposes a new methodology based on the ensemble empirical mode decomposition (EEMD) for extracting low frequency components of a wind turbine structure from a single-channel vibration measurement and double integration in time to improve the interpretability of the components.The methodology overcomes the lack of multiple vibration measurements using pseudo-sources and it is especially suitable forsignals with high frequency behavior.

Nowadays wind turbines are wide employed as a clean energy resource. However, their use implies a wide number of eolic structures which also requires a demanding monitoring and maintenance. The most technique employed for those kind of tasksis vibration analysis inasmuch as it is non-destructive technique and presents a high performance for this task. Among vibration analysis, Components identification turns important because it allows programming the machine maintenance in a proper time. In addition, the accessibility for installing several mechanical vibration transducers into the machine is limited either by the physical space or the high cost of sensor networks. Therefore, it is useful and needed to perform an analysis using just one sensor. In that sense, the accelerometers are commonly utilized since these sensors allow extracting the velocity and displacement, signals which have additional information about the machine, performing a double digital integration. Nonetheless, digital integration evolves several difficulties such as biased errors, leakages in the signal, and strong instability at low and high frequencies.This paper proposes a new methodology based on the ensemble empirical mode decomposition (EEMD) for extracting low frequency components of a wind turbine structure from a single-channel vibration measurement and double integration in time to improve the interpretability of the components.The methodology overcomes the lack of multiple vibration measurements using pseudo-sources and it is especially suitable forsignals with high frequency behavior.

Nowadays wind turbines are wide employed as a clean energy resource. However, their use implies a wide number of eolic structures which also requires a demanding monitoring and maintenance. The most technique employed for those kind of tasksis vibration analysis inasmuch as it is non-destructive technique and presents a high performance for this task. Among vibration analysis, Components identification turns important because it allows programming the machine maintenance in a proper time. In addition, the accessibility for installing several mechanical vibration transducers into the machine is limited either by the physical space or the high cost of sensor networks. Therefore, it is useful and needed to perform an analysis using just one sensor. In that sense, the accelerometers are commonly utilized since these sensors allow extracting the velocity and displacement, signals which have additional information about the machine, performing a double digital integration. Nonetheless, digital integration evolves several difficulties such as biased errors, leakages in the signal, and strong instability at low and high frequencies.This paper proposes a new methodology based on the ensemble empirical mode decomposition (EEMD) for extracting low frequency components of a wind turbine structure from a single-channel vibration measurement and double integration in time to improve the interpretability of the components.The methodology overcomes the lack of multiple vibration measurements using pseudo-sources and it is especially suitable forsignals with high frequency behavior.

Increasing demand on energy has accelerated research on improving the reliability of wind turbines. As a critical component in wind turbine drivetrains, the majority of gearbox failures have shown to initiate from bearing failures. The low signal-to-noise ratio and transient nature of bearing signals pose significant difficulty for bearing defect diagnosis at the incipient stage. For improved bearing diagnosis, this paper presents a new method that integrates ensemble empirical mode decomposition (EEMD) with independent component analysis (ICA) to effectively separate bearing and gear meshing signals, without requiring a priori information on rotating speeds or bandwidth. The method first decomposes sensor measurement into a series of intrinsic mode functions (IMFs) as pseudo multi-channel signals, by means of EEMD, to satisfy the requirement by ICA for redundant information. ICA is performed on the IMFs to separate defective bearing components from gear meshing signal. Enveloping spectrum analysis is then performed to identify bearing structural defects. Both numerical and experimental studies have demonstrated the merit of the developed new method in improving gearbox diagnosis., CC BY-NC-ND 3.0Edited by Roberto Teti

Increasing demand on energy has accelerated research on improving the reliability of wind turbines. As a critical component in wind turbine drivetrains, the majority of gearbox failures have shown to initiate from bearing failures. The low signal-to-noise ratio and transient nature of bearing signals pose significant difficulty for bearing defect diagnosis at the incipient stage. For improved bearing diagnosis, this paper presents a new method that integrates ensemble empirical mode decomposition (EEMD) with independent component analysis (ICA) to effectively separate bearing and gear meshing signals, without requiring a priori information on rotating speeds or bandwidth. The method first decomposes sensor measurement into a series of intrinsic mode functions (IMFs) as pseudo multi-channel signals, by means of EEMD, to satisfy the requirement by ICA for redundant information. ICA is performed on the IMFs to separate defective bearing components from gear meshing signal. Enveloping spectrum analysis is then performed to identify bearing structural defects. Both numerical and experimental studies have demonstrated the merit of the developed new method in improving gearbox diagnosis., CC BY-NC-ND 3.0Edited by Roberto Teti

Increasing demand on energy has accelerated research on improving the reliability of wind turbines. As a critical component in wind turbine drivetrains, the majority of gearbox failures have shown to initiate from bearing failures. The low signal-to-noise ratio and transient nature of bearing signals pose significant difficulty for bearing defect diagnosis at the incipient stage. For improved bearing diagnosis, this paper presents a new method that integrates ensemble empirical mode decomposition (EEMD) with independent component analysis (ICA) to effectively separate bearing and gear meshing signals, without requiring a priori information on rotating speeds or bandwidth. The method first decomposes sensor measurement into a series of intrinsic mode functions (IMFs) as pseudo multi-channel signals, by means of EEMD, to satisfy the requirement by ICA for redundant information. ICA is performed on the IMFs to separate defective bearing components from gear meshing signal. Enveloping spectrum analysis is then performed to identify bearing structural defects. Both numerical and experimental studies have demonstrated the merit of the developed new method in improving gearbox diagnosis., CC BY-NC-ND 3.0Edited by Roberto Teti

Increasing demand on energy has accelerated research on improving the reliability of wind turbines. As a critical component in wind turbine drivetrains, the majority of gearbox failures have shown to initiate from bearing failures. The low signal-to-noise ratio and transient nature of bearing signals pose significant difficulty for bearing defect diagnosis at the incipient stage. For improved bearing diagnosis, this paper presents a new method that integrates ensemble empirical mode decomposition (EEMD) with independent component analysis (ICA) to effectively separate bearing and gear meshing signals, without requiring a priori information on rotating speeds or bandwidth. The method first decomposes sensor measurement into a series of intrinsic mode functions (IMFs) as pseudo multi-channel signals, by means of EEMD, to satisfy the requirement by ICA for redundant information. ICA is performed on the IMFs to separate defective bearing components from gear meshing signal. Enveloping spectrum analysis is then performed to identify bearing structural defects. Both numerical and experimental studies have demonstrated the merit of the developed new method in improving gearbox diagnosis., CC BY-NC-ND 3.0Edited by Roberto Teti

Increasing demand on energy has accelerated research on improving the reliability of wind turbines. As a critical component in wind turbine drivetrains, the majority of gearbox failures have shown to initiate from bearing failures. The low signal-to-noise ratio and transient nature of bearing signals pose significant difficulty for bearing defect diagnosis at the incipient stage. For improved bearing diagnosis, this paper presents a new method that integrates ensemble empirical mode decomposition (EEMD) with independent component analysis (ICA) to effectively separate bearing and gear meshing signals, without requiring a priori information on rotating speeds or bandwidth. The method first decomposes sensor measurement into a series of intrinsic mode functions (IMFs) as pseudo multi-channel signals, by means of EEMD, to satisfy the requirement by ICA for redundant information. ICA is performed on the IMFs to separate defective bearing components from gear meshing signal. Enveloping spectrum analysis is then performed to identify bearing structural defects. Both numerical and experimental studies have demonstrated the merit of the developed new method in improving gearbox diagnosis., CC BY-NC-ND 3.0Edited by Roberto Teti

Cílem této práce je seznámit se s principem metody empirického rozkladu (EMD) signálu a možnostmi jejího využití pro filtraci kolísání izoelektrické linie EKG signálů. Práce popisuje hlavní komponenty EKG signálu, dále výběr možných typů rušení signálu a jejich vlastnosti a principiální popis vybraných metod pro filtraci EKG signálu. Na závěr je v práci popsáno zhodnocení účinnosti metody empirického rozkladu při filtraci kolísání izoelektrické linie ve srovnání s lineární filtrací. Funkčnost použitých algoritmů byla ověřena na signálech standardní knihovny CSE., The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.

Cílem této práce je seznámit se s principem metody empirického rozkladu (EMD) signálu a možnostmi jejího využití pro filtraci kolísání izoelektrické linie EKG signálů. Práce popisuje hlavní komponenty EKG signálu, dále výběr možných typů rušení signálu a jejich vlastnosti a principiální popis vybraných metod pro filtraci EKG signálu. Na závěr je v práci popsáno zhodnocení účinnosti metody empirického rozkladu při filtraci kolísání izoelektrické linie ve srovnání s lineární filtrací. Funkčnost použitých algoritmů byla ověřena na signálech standardní knihovny CSE., The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.

金融市場瞬息萬變，幾乎所有商品價格都是非線性的動態過程，如何預測價格一直都是倍受討論和研究的議題。隨著電腦科技的不斷進步,許多財務學者以市場上的歷史交易資料作為研究對象，希望能夠預測出有效的結果。本研究利用 EEMD 法拆解原始加權指數訊號，建立類神經網路模型，並預測出未來市場之價格後，利用 FK 值當作交易門檻，帶回台指選擇權做交易測試並計算報酬。由於不同神經元個數會配適出不同的預測結果，本研究希望能夠找到較適合使用在指數預測的網路架構。, The financial market forecasting is characterized by data intensity, noise, non-stationary, high degree of uncertainty, and hidden relationships. Investors are concerned about the forecasting market price. Throughout the development of computational technology, researchers have been involved in data mining on historical trading enabling them to have a more accurate data. This research uses Ensemble Empirical Mode Decomposition-based Artificial Neural Networks (ANNs) learning paradigm to provide different ways to analyze the stock market. In our research, we used the ANN method to obtain our prediction of the stock price. First, the previous day’s stock price needs to be decomposed in order to see the various variables, that is, the numerous IMFs seen on the graphs. Acquiring the information, it is inserted into the ANN method to get a prediction. Following that, the prediction can then be transformed into a simpler result via the Forward Calculator % K indicator. As a result, the FK value can display a signal if to buy or sell, and confirm trading time, and make buy or sell Call-Put decisions on TAIEX options. In summary,we found different neuron numbers in the hidden layers that may affect the result of prediction.

Cílem této práce je seznámit se s principem metody empirického rozkladu (EMD) signálu a možnostmi jejího využití pro filtraci kolísání izoelektrické linie EKG signálů. Práce popisuje hlavní komponenty EKG signálu, dále výběr možných typů rušení signálu a jejich vlastnosti a principiální popis vybraných metod pro filtraci EKG signálu. Na závěr je v práci popsáno zhodnocení účinnosti metody empirického rozkladu při filtraci kolísání izoelektrické linie ve srovnání s lineární filtrací. Funkčnost použitých algoritmů byla ověřena na signálech standardní knihovny CSE., The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.

Cílem této práce je seznámit se s principem metody empirického rozkladu (EMD) signálu a možnostmi jejího využití pro filtraci kolísání izoelektrické linie EKG signálů. Práce popisuje hlavní komponenty EKG signálu, dále výběr možných typů rušení signálu a jejich vlastnosti a principiální popis vybraných metod pro filtraci EKG signálu. Na závěr je v práci popsáno zhodnocení účinnosti metody empirického rozkladu při filtraci kolísání izoelektrické linie ve srovnání s lineární filtrací. Funkčnost použitých algoritmů byla ověřena na signálech standardní knihovny CSE., The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.

Cílem této práce je seznámit se s principem metody empirického rozkladu (EMD) signálu a možnostmi jejího využití pro filtraci kolísání izoelektrické linie EKG signálů. Práce popisuje hlavní komponenty EKG signálu, dále výběr možných typů rušení signálu a jejich vlastnosti a principiální popis vybraných metod pro filtraci EKG signálu. Na závěr je v práci popsáno zhodnocení účinnosti metody empirického rozkladu při filtraci kolísání izoelektrické linie ve srovnání s lineární filtrací. Funkčnost použitých algoritmů byla ověřena na signálech standardní knihovny CSE., The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.

Cílem této práce je seznámit se s principem metody empirického rozkladu (EMD) signálu a možnostmi jejího využití pro filtraci kolísání izoelektrické linie EKG signálů. Práce popisuje hlavní komponenty EKG signálu, dále výběr možných typů rušení signálu a jejich vlastnosti a principiální popis vybraných metod pro filtraci EKG signálu. Na závěr je v práci popsáno zhodnocení účinnosti metody empirického rozkladu při filtraci kolísání izoelektrické linie ve srovnání s lineární filtrací. Funkčnost použitých algoritmů byla ověřena na signálech standardní knihovny CSE., The aim of this thesis is to introduce with principle of Empirical Mode Decomposition method and possibility use for correction of baseline wander in ECG signals. The thesis describes the main components of the ECG signal, a selection of possible types of signal noise, its property and principles of chosen methods for filtration of ECG signals. In conclusion the evaluation of the effectiveness of the EMD method for filtering a baseline wander and it comparing with effectiveness of the linear filtration. Functionality of used algorithms has been tested on signals of CSE standard library.