The mirror extending approach proposed by Zhao and Huang in EMD method is improved in this paper. Mirror extending manner of data is kept unchanged, but the approach for determining envelopes is changed. When the end ...The mirror extending approach proposed by Zhao and Huang in EMD method is improved in this paper. Mirror extending manner of data is kept unchanged, but the approach for determining envelopes is changed. When the end of data is obviously not extremum, the envelope is determined by the first inner extremum and the image value in the mirror, ignoring the value on the end. This improvement eliminates the frequency compression near the end and decreases the error. Meanwhile, tridiagonal equations are used and the calculation speed is much increased. The temporal process curve is more important in reflecting the real physical process and comparable with other phenomena. Frequency mixing in IMFs makes it impossible. A high frequency reconstruction (HFR) approach is proposed to eliminate common frequency mixing and reconstruct an IMF with all high frequency portions. By this approach, the IMFs without frequency mixing are obtained to express significative processes. The high frequency information restored in high frequency IMF can be extracted by general spectrum method. After obtaining IMFs by EMD method, some of the theoretical and technological issues still exist when using the IMFs. The consistency of IMFs with real physical process is discussed in detail. By virtue of the approach proposed in this paper, the EMD method can be widely used in various fields.展开更多
Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on f...Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.展开更多
In a wind turbine,the rolling bearing is the critical component.However,it has a high failure rate.Therefore,the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the hig...In a wind turbine,the rolling bearing is the critical component.However,it has a high failure rate.Therefore,the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment.In this study,the failure form and the corresponding reason for the failure are discussed firstly.Then,the natural frequency and the characteristic frequency are analyzed.The Empirical Mode Decomposition(EMD)algorithm is used to extract the characteristics of the vibration signal of the rolling bearing.Moreover,the eigenmode function is obtained and then filtered by the kurtosis criterion.Consequently,the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained.Then the fault analysis is performed.To enhance the accuracy of fault diagnosis,based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing,four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.展开更多
This paper proposes a hybrid short-term load forecasting method,which is based on the fuzzy combination weights as well as the empirical mode decomposition process(FCW-EMD),and support vector machine optimized via the...This paper proposes a hybrid short-term load forecasting method,which is based on the fuzzy combination weights as well as the empirical mode decomposition process(FCW-EMD),and support vector machine optimized via the Bat algorithm as well as the Kalman filtering process(KF-BA-SVM).The subjective weight is presented as a new theory and is applied to capture the inherent correlation effectively among hourly loads.Based on the proposed objective weights and subjective weights,the fuzzy combination weights theory(FCW)-a new similar day selection theory is presented,which improves the accuracy of the similar day selection,and correspondingly,makes the original data for EMD processing decrease dramatically.BA is introduced to optimize parameters of the SVM model for further improving the forecasting accuracy.Using the decomposed load series via empirical model decomposition(EMD)as inputs to SVM and further correcting the output of SVM via KF,a hybrid FCW-EMD and KF-BA-SVM short-term load forecasting method is established.Numerical case studies on the load forecasting of a transformer substation in south China show that the proposed hybrid forecasting model outperforms other forecasting methods and effectively improves the prediction accuracy.展开更多
The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credib...The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credible quantitative estimates of future climate change; however, the mismatches between the IPCC AR4 model ensembles and the observations, especially the multi-decadal variability (MDV), have cast shadows on the confidence of the model-based decadal projections of future cli mate. This paper reports an evaluation of many individual runs of AR4 models in the simulation of past global mean tempera ture. We find that most of the individual model runs fail to reproduce the MDV of past climate, which may have led to the overestimation of the projection of global warming for the next 40 years or so. Based on such an evaluation, we propose an al ternative approach, in which the MDV signal is taken into account, to project the global mean temperature for the next 40 years and obtain that the global warming during 2011–2050 could be much smaller than the AR4 projection.展开更多
The ring-width chronology of a Juniperus przewalskii tree from the middle of the Qilian Mountain was constructed to estimate the annual precipitation (from previous August to current July) since AD 1480.The reconstruc...The ring-width chronology of a Juniperus przewalskii tree from the middle of the Qilian Mountain was constructed to estimate the annual precipitation (from previous August to current July) since AD 1480.The reconstruction showed four major alternations of drying and wetting over the past 521 years.The rainy 16th century was followed by persistent drought in the 17th century.Moreover,relatively wet conditions persisted from the 18th to the beginning of 20th century until the recurrence of a drought during the 1920s and 1930s.Based on the Empirical Mode Decomposition method,eight Intrinsic Mode Functions (IMFs) were extracted,each representing unique fluctuations of the reconstructed precipitation in the time-frequency domain.The high amplitudes of IMFs on different timescales were often consistent with the high amount of precipitation,and vice versa.The IMF of the lowest frequency indicated that the precipitation has undergone a slow increasing trend over the past 521 years.The 2-3 year and 5-8 year time-scales reflected the characteristics of inter-annual variability in precipitation relevant to regional atmospheric circulation and the El Ni?o-Southern Oscillation (ENSO),respectively.The 10-13 year scale of IMF may be associated with changing solar activity.Specifically,an amalgamation of previous and present data showed that droughts were likely to be a historically persistent feature of the Earth's climate,whereas the probability of intensified rainfall events seemed to increase during the course of the 19th and 20th centuries.These changing characteristics in precipitation indicate an unprecedented alteration of the hydrological cycle,with unknown future amplitude.Our reconstruction complements existing information on past precipitation changes in the Qilian Mountain,and provides additional low-frequency information not previously available.展开更多
Aiming at the problem of gliding near space hypersonic vehicle(NSHV)trajectory prediction,a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition(EMD)is proposed.The method analyz...Aiming at the problem of gliding near space hypersonic vehicle(NSHV)trajectory prediction,a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition(EMD)is proposed.The method analyzes the motion characteristics of the skipping gliding NSHV and verifies that the aerodynamic acceleration of the target has a relatively stable rule.On this basis,EMD is used to extract the trend of aerodynamic acceleration into multiple sub-items,and aggregate sub-items with similar attributes.Then,a prior basis function is set according to the aerodynamic acceleration stability rule,and the aggregated data are fitted by the basis function to predict its future state.After that,the prediction data of the aerodynamic acceleration are used to drive the system to predict the target trajectory.Finally,experiments verify the effectiveness of the method.In addition,the distribution of prediction errors in space is discussed,and the reasons are analyzed.展开更多
Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity ...Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.展开更多
The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in ...The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.展开更多
In order to improve the accuracy and reliability of ammonia(NH3)concentration prediction,which can provides a support to the ventilation control strategy,so as to reduce the impact of NH3 on the health and productivit...In order to improve the accuracy and reliability of ammonia(NH3)concentration prediction,which can provides a support to the ventilation control strategy,so as to reduce the impact of NH3 on the health and productivity of swine,this paper proposed an NH3 concentration prediction method based on Empirical Mode Decomposition(EMD)and Elman neural network modelling.The NH3 concentration and other four environmental parameters including temperature,humidity,carbon dioxide and light intensity were decomposed into several different time-scale intrinsic mode functions(IMFs).Then,the Elman neural network prediction model was used to predict each IMF.The predicted NH3 was obtained by reconstructing all the IMFs by EMD.The results show that for the proposed method,the determination coefficient between the predicted and real measured value is 0.9856,the Mean Absolute Error is 0.7088 ppm,the Root Mean Square Error is 0.9096 ppm,and the Mean Absolute Percentage Error is 0.41%.Compared with the Elman neural network,the proposed method has a good improvement in the accuracy,and provide effective parameters for the environmental monitoring of the swine house and the regulation of the NH3 concentration.展开更多
Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price predictio...Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.展开更多
Electric load forecasting is an efficient tool for system planning, and consequently, building sustainable power systems. However, achieving desirable performance is difficult owing to the irregular, nonstationary, no...Electric load forecasting is an efficient tool for system planning, and consequently, building sustainable power systems. However, achieving desirable performance is difficult owing to the irregular, nonstationary, nonlinear, and noisy nature of the observed data. Therefore, a new attention-based encoderdecoder model is proposed, called empirical mode decomposition-attention-long short-term memory(EMD-Att-LSTM).EMD is a data-driven technique used for the decomposition of complex series into subsequent simpler series. It explores the inherent properties of data to obtain the components such as trend and seasonality. Neural network architecture driven by deep learning uses the idea of a fine-grained attention mechanism, that is, considering the hidden state instead of the hidden state vectors, which can help reflect the significance and contributions of each hidden state dimension. In addition, it is useful for locating and concentrating the relevant temporary data,leading to a distinctly interpretable network. To evaluate the proposed model, we use the repository dataset of Australian energy market operator(AEMO). The proposed architecture provides superior empirical results compared with other advanced models. It is explored using the indices of root mean square error(RMSE) and mean absolute percentage error(MAPE).展开更多
文摘The mirror extending approach proposed by Zhao and Huang in EMD method is improved in this paper. Mirror extending manner of data is kept unchanged, but the approach for determining envelopes is changed. When the end of data is obviously not extremum, the envelope is determined by the first inner extremum and the image value in the mirror, ignoring the value on the end. This improvement eliminates the frequency compression near the end and decreases the error. Meanwhile, tridiagonal equations are used and the calculation speed is much increased. The temporal process curve is more important in reflecting the real physical process and comparable with other phenomena. Frequency mixing in IMFs makes it impossible. A high frequency reconstruction (HFR) approach is proposed to eliminate common frequency mixing and reconstruct an IMF with all high frequency portions. By this approach, the IMFs without frequency mixing are obtained to express significative processes. The high frequency information restored in high frequency IMF can be extracted by general spectrum method. After obtaining IMFs by EMD method, some of the theoretical and technological issues still exist when using the IMFs. The consistency of IMFs with real physical process is discussed in detail. By virtue of the approach proposed in this paper, the EMD method can be widely used in various fields.
基金supported by the Major State Basic Research Development of China (Grant No. 2011CB706803)National Natural Science Foundation of China (Grant No. 50875098)Important National Science & Technology Specific Projects of China (Grant No. 2009ZX04014-024)
文摘Chatter often poses limiting factors on the achievable productivity and is very harmful to machining processes. In order to avoid effectively the harm of cutting chatter,a method of cutting state monitoring based on feed motor current signal is proposed for chatter identification before it has been fully developed. A new data analysis technique,the empirical mode decomposition(EMD),is used to decompose motor current signal into many intrinsic mode functions(IMF) . Some IMF's energy and kurtosis regularly change during the development of the chatter. These IMFs can reflect subtle mutations in current signal. Therefore,the energy index and kurtosis index are used for chatter detection based on those IMFs. Acceleration signal of tool as reference is used to compare with the results from current signal. A support vector machine(SVM) is designed for pattern classification based on the feature vector constituted by energy index and kurtosis index. The intelligent chatter detection system composed of the feature extraction and the SVM has an accuracy rate of above 95% for the identification of cutting state after being trained by experimental data. The results show that it is feasible to monitor and predict the emergence of chatter behavior in machining by using motor current signal.
基金the Guangdong Basic and Applied Basic Research Foundation(Grant No.2021A1515012070)the Sichuan Science and Technology Program(Grant Nos.2021YFS0336 and 2019YJ0712)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.ZYGX2019J035)the Sichuan Science and Technology Innovation Seedling Project Funding Project(Grant No.2020023)are gratefully acknowledged.
文摘In a wind turbine,the rolling bearing is the critical component.However,it has a high failure rate.Therefore,the failure analysis and fault diagnosis of wind power rolling bearings are very important to ensure the high reliability and safety of wind power equipment.In this study,the failure form and the corresponding reason for the failure are discussed firstly.Then,the natural frequency and the characteristic frequency are analyzed.The Empirical Mode Decomposition(EMD)algorithm is used to extract the characteristics of the vibration signal of the rolling bearing.Moreover,the eigenmode function is obtained and then filtered by the kurtosis criterion.Consequently,the relationship between the actual fault frequency spectrum and the theoretical fault frequency can be obtained.Then the fault analysis is performed.To enhance the accuracy of fault diagnosis,based on the previous feature extraction and the time-frequency domain feature extraction of the data after EMD decomposition processing,four different classifiers are added to diagnose and classify the fault status of rolling bearings and compare them with four different classifiers.
文摘This paper proposes a hybrid short-term load forecasting method,which is based on the fuzzy combination weights as well as the empirical mode decomposition process(FCW-EMD),and support vector machine optimized via the Bat algorithm as well as the Kalman filtering process(KF-BA-SVM).The subjective weight is presented as a new theory and is applied to capture the inherent correlation effectively among hourly loads.Based on the proposed objective weights and subjective weights,the fuzzy combination weights theory(FCW)-a new similar day selection theory is presented,which improves the accuracy of the similar day selection,and correspondingly,makes the original data for EMD processing decrease dramatically.BA is introduced to optimize parameters of the SVM model for further improving the forecasting accuracy.Using the decomposed load series via empirical model decomposition(EMD)as inputs to SVM and further correcting the output of SVM via KF,a hybrid FCW-EMD and KF-BA-SVM short-term load forecasting method is established.Numerical case studies on the load forecasting of a transformer substation in south China show that the proposed hybrid forecasting model outperforms other forecasting methods and effectively improves the prediction accuracy.
基金supported by the National Basic Research Program of Chi-na (Grant No. 2011CB952000)the National Natural Science Founda-tion of China (Grant No. 40810059003)+1 种基金Qian Cheng was partly supported by the "Strategic Priority Research Program" of the Chinese Academy of Sciences (Grant No. XDA05090103)Wu Zhaohua was supported by the Natural Science Foundation of USA (Grant No. ATM-0917743)
文摘The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credible quantitative estimates of future climate change; however, the mismatches between the IPCC AR4 model ensembles and the observations, especially the multi-decadal variability (MDV), have cast shadows on the confidence of the model-based decadal projections of future cli mate. This paper reports an evaluation of many individual runs of AR4 models in the simulation of past global mean tempera ture. We find that most of the individual model runs fail to reproduce the MDV of past climate, which may have led to the overestimation of the projection of global warming for the next 40 years or so. Based on such an evaluation, we propose an al ternative approach, in which the MDV signal is taken into account, to project the global mean temperature for the next 40 years and obtain that the global warming during 2011–2050 could be much smaller than the AR4 projection.
基金supported by the National Natural Science Foundation of China (Grant Nos. 41001058, 41001009, 40971119 and 40890052)the China Postdoctoral Science Foundation (Grant Nos. 201003194)
文摘The ring-width chronology of a Juniperus przewalskii tree from the middle of the Qilian Mountain was constructed to estimate the annual precipitation (from previous August to current July) since AD 1480.The reconstruction showed four major alternations of drying and wetting over the past 521 years.The rainy 16th century was followed by persistent drought in the 17th century.Moreover,relatively wet conditions persisted from the 18th to the beginning of 20th century until the recurrence of a drought during the 1920s and 1930s.Based on the Empirical Mode Decomposition method,eight Intrinsic Mode Functions (IMFs) were extracted,each representing unique fluctuations of the reconstructed precipitation in the time-frequency domain.The high amplitudes of IMFs on different timescales were often consistent with the high amount of precipitation,and vice versa.The IMF of the lowest frequency indicated that the precipitation has undergone a slow increasing trend over the past 521 years.The 2-3 year and 5-8 year time-scales reflected the characteristics of inter-annual variability in precipitation relevant to regional atmospheric circulation and the El Ni?o-Southern Oscillation (ENSO),respectively.The 10-13 year scale of IMF may be associated with changing solar activity.Specifically,an amalgamation of previous and present data showed that droughts were likely to be a historically persistent feature of the Earth's climate,whereas the probability of intensified rainfall events seemed to increase during the course of the 19th and 20th centuries.These changing characteristics in precipitation indicate an unprecedented alteration of the hydrological cycle,with unknown future amplitude.Our reconstruction complements existing information on past precipitation changes in the Qilian Mountain,and provides additional low-frequency information not previously available.
基金supported by the National High-Tech R&D Program of China(2015AA70560452015AA8017032P)the Postgraduate Funding Project(JW2018A039)。
文摘Aiming at the problem of gliding near space hypersonic vehicle(NSHV)trajectory prediction,a trajectory prediction method based on aerodynamic acceleration empirical mode decomposition(EMD)is proposed.The method analyzes the motion characteristics of the skipping gliding NSHV and verifies that the aerodynamic acceleration of the target has a relatively stable rule.On this basis,EMD is used to extract the trend of aerodynamic acceleration into multiple sub-items,and aggregate sub-items with similar attributes.Then,a prior basis function is set according to the aerodynamic acceleration stability rule,and the aggregated data are fitted by the basis function to predict its future state.After that,the prediction data of the aerodynamic acceleration are used to drive the system to predict the target trajectory.Finally,experiments verify the effectiveness of the method.In addition,the distribution of prediction errors in space is discussed,and the reasons are analyzed.
基金The authors gratefully acknowledge the support of the National Natural Science Foundation of China(No.11574250).
文摘Underwater acoustic signal processing is one of the research hotspots in underwater acoustics.Noise reduction of underwater acoustic signals is the key to underwater acoustic signal processing.Owing to the complexity of marine environment and the particularity of underwater acoustic channel,noise reduction of underwater acoustic signals has always been a difficult challenge in the field of underwater acoustic signal processing.In order to solve the dilemma,we proposed a novel noise reduction technique for underwater acoustic signals based on complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),minimum mean square variance criterion(MMSVC) and least mean square adaptive filter(LMSAF).This noise reduction technique,named CEEMDAN-MMSVC-LMSAF,has three main advantages:(i) as an improved algorithm of empirical mode decomposition(EMD) and ensemble EMD(EEMD),CEEMDAN can better suppress mode mixing,and can avoid selecting the number of decomposition in variational mode decomposition(VMD);(ii) MMSVC can identify noisy intrinsic mode function(IMF),and can avoid selecting thresholds of different permutation entropies;(iii) for noise reduction of noisy IMFs,LMSAF overcomes the selection of deco mposition number and basis function for wavelet noise reduction.Firstly,CEEMDAN decomposes the original signal into IMFs,which can be divided into noisy IMFs and real IMFs.Then,MMSVC and LMSAF are used to detect identify noisy IMFs and remove noise components from noisy IMFs.Finally,both denoised noisy IMFs and real IMFs are reconstructed and the final denoised signal is obtained.Compared with other noise reduction techniques,the validity of CEEMDAN-MMSVC-LMSAF can be proved by the analysis of simulation signals and real underwater acoustic signals,which has the better noise reduction effect and has practical application value.CEEMDAN-MMSVC-LMSAF also provides a reliable basis for the detection,feature extraction,classification and recognition of underwater acoustic signals.
基金supported financially by the National Natural Science Foundation(No.41174117)the Major National Science and Technology Projects(No.2011ZX05031–001)
文摘The frequency–space（f–x） empirical mode decomposition（EMD） denoising method has two limitations when applied to nonstationary seismic data. First, subtracting the first intrinsic mode function（IMF） results in signal damage and limited denoising. Second, decomposing the real and imaginary parts of complex data may lead to inconsistent decomposition numbers. Thus, we propose a new method named f–x spatial projection-based complex empirical mode decomposition（CEMD） prediction filtering. The proposed approach directly decomposes complex seismic data into a series of complex IMFs（CIMFs） using the spatial projection-based CEMD algorithm and then applies f–x predictive filtering to the stationary CIMFs to improve the signal-to-noise ratio. Synthetic and real data examples were used to demonstrate the performance of the new method in random noise attenuation and seismic signal preservation.
基金This research is financially supported by National Key Research and Development Program of China(2016YFD0700204-02)The“Young Talents”Project of Northeast Agricultural University(17QC20)+1 种基金Research on Attitude Fusion Zero Offset Correction and Decoupling Noise Reduction for Non-flat Production Flow Sensors,China Postdoctoral Fund(2016M601406)Central Guide to Local Science and Technology Development(ZY17C06)and The Earmarked Fund for China Agriculture Research System(No.CARS-35).The authors are grateful to anonymous reviewers for their comments.
文摘In order to improve the accuracy and reliability of ammonia(NH3)concentration prediction,which can provides a support to the ventilation control strategy,so as to reduce the impact of NH3 on the health and productivity of swine,this paper proposed an NH3 concentration prediction method based on Empirical Mode Decomposition(EMD)and Elman neural network modelling.The NH3 concentration and other four environmental parameters including temperature,humidity,carbon dioxide and light intensity were decomposed into several different time-scale intrinsic mode functions(IMFs).Then,the Elman neural network prediction model was used to predict each IMF.The predicted NH3 was obtained by reconstructing all the IMFs by EMD.The results show that for the proposed method,the determination coefficient between the predicted and real measured value is 0.9856,the Mean Absolute Error is 0.7088 ppm,the Root Mean Square Error is 0.9096 ppm,and the Mean Absolute Percentage Error is 0.41%.Compared with the Elman neural network,the proposed method has a good improvement in the accuracy,and provide effective parameters for the environmental monitoring of the swine house and the regulation of the NH3 concentration.
基金supported by the Sichuan Science and Technology Program under Grant 2020JDJQ0037 and 2020YFG0312.
文摘Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.
文摘Electric load forecasting is an efficient tool for system planning, and consequently, building sustainable power systems. However, achieving desirable performance is difficult owing to the irregular, nonstationary, nonlinear, and noisy nature of the observed data. Therefore, a new attention-based encoderdecoder model is proposed, called empirical mode decomposition-attention-long short-term memory(EMD-Att-LSTM).EMD is a data-driven technique used for the decomposition of complex series into subsequent simpler series. It explores the inherent properties of data to obtain the components such as trend and seasonality. Neural network architecture driven by deep learning uses the idea of a fine-grained attention mechanism, that is, considering the hidden state instead of the hidden state vectors, which can help reflect the significance and contributions of each hidden state dimension. In addition, it is useful for locating and concentrating the relevant temporary data,leading to a distinctly interpretable network. To evaluate the proposed model, we use the repository dataset of Australian energy market operator(AEMO). The proposed architecture provides superior empirical results compared with other advanced models. It is explored using the indices of root mean square error(RMSE) and mean absolute percentage error(MAPE).
