The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult.Currently,some hydrologists employ the complete ensemble empirical mode decompos...The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult.Currently,some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method,a new time-frequency analysis method based on the empirical mode decomposition(EMD)algorithm,to decompose non-stationary raw data in order to obtain relatively stationary components for further study.However,the endpoint effect in CEEMDAN is often neglected,which can lead to decomposition errors that reduce the accuracy of the research results.In this study,we processed an original runoff sequence using the radial basis function neural network(RBFNN)technique to obtain the extension sequence before utilizing CEEMDAN decomposition.Then,we compared the decomposition results of the original sequence,RBFNN extension sequence,and standard sequence to investigate the influence of the endpoint effect and RBFNN extension on the CEEMDAN method.The results indicated that the RBFNN extension technique effectively reduced the error of medium and low frequency components caused by the endpoint effect.At both ends of the components,the extension sequence more accurately reflected the true fluctuation characteristics and variation trends.These advances are of great significance to the subsequent study of hydrology.Therefore,the CEEMDAN method,combined with an appropriate extension of the original runoff series,can more precisely determine multi-time scale characteristics,and provide a credible basis for the analysis of hydrologic time series and hydrological forecasting.展开更多
In this paper,a new concept of an optimal complete multipartite decomposition of type 1 (type 2) of a complete n-partite graph Q n is proposed and another new concept of a normal complete multipartite decomposition o...In this paper,a new concept of an optimal complete multipartite decomposition of type 1 (type 2) of a complete n-partite graph Q n is proposed and another new concept of a normal complete multipartite decomposition of K n is introduced.It is showed that an optimal complete multipartite decomposition of type 1 of K n is a normal complete multipartite decomposition.As for any complete multipartite decomposition of K n,there is a derived complete multipartite decomposition for Q n.It is also showed that any optimal complete multipartite decomposition of type 1 of Q n is a derived decomposition of an optimal complete multipartite decomposition of type 1 of K n.Besides,some structural properties of an optimal complete multipartite decomposition of type 1 of K n are given.展开更多
For the sake of guiding parameter setting of the hydraulic lifting pipeline system for cutter-suction mining of natural gas hydrates(“hydrates”for short)on the seabed,the decomposition characteristics of hydrates in...For the sake of guiding parameter setting of the hydraulic lifting pipeline system for cutter-suction mining of natural gas hydrates(“hydrates”for short)on the seabed,the decomposition characteristics of hydrates in hydraulic lifting pipelines and the effects of flow parameters on decomposition characteristics were studied in this paper.A temperature–pressure model for the hydrate hydraulic lifting pipeline,a hydrate decomposition mass transfer model and a pipeline multiphase flow model were established using mathematical modeling method according to thermodynamics and fluid mechanics.Then,the relationships of the temperature and pressure of pipeline fluid,the amount of hydrate particulate matter and the decomposition surface vs.the underwater depth under the effect of different influencing factors during the transformation from solid–liquid two-phase flow to solid–liquid–gas three-phase flow were analyzed.And the following research results were obtained.First,the decomposition of hydrate slows down and the decomposition surface moves upward slightly with the increase of flow rate in the pipeline.Second,particle size basically has no effects on the temperature and pressure of pipeline fluid,the hydrate phase equilibrium pressure and hydrate decomposition surface.However,only the hydrate particles whose diameter is smaller than 0.2 mm can be completely decomposed in the pipeline while the decomposition of those whose particles size is greater than 2.0 mm is negligible.Third,if the back pressure at the outlet is positive,the decomposition surface moves upward and the decomposition of hydrate slows down with the increase of the back pressure.And if the back pressure at the outlet is negative,the decomposition surface moves downward and the decomposition of hydrate speeds up with the increase of the back pressure.Fourth,the decomposition of hydrate slows down and the decomposition surface moves upward with the increase of mineral depth.However,the decomposition rate and decomposition surface are basically unchanged when the mineral depth is below 1500 m under water.Fifth,the experimental results are basically consistent with the numerical simulation results,and it is indicated that the newly established models are of high reliability.In conclusion,decomposition surface height and decomposition rate can be adjusted by controlling flow rate and outlet back pressure rationally during the cutter-suction mining of hydrates while the influences of particle diameter and mining depth on gas production need not be taken into consideration.展开更多
Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past par...Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past parking data or the plethora of variables that influence parking prediction,which not only makes the data more complicated and costs more time to run but can also lead to poor model fits.To solve this problem,a hybrid parking prediction model combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and gated recurrent unit(GRU)model is proposed to predict the number of parking spaces.In this model,CEEMDAN has the ability to gradually break down time series fluctuations or trends at various scales,producing a sequence of intrinsic mode functions(IMF)with various characteristic scales.Then,by keeping the majority of the original data’s content,removing superfluous information,and enhancing predicted response time,principal component analysis(PCA)decreases the dimensionality of the IMF series.Subsequently,the high-level abstract characteristics are entered into the GRU network,and the network is built,tested,and predicted based on the deep learning framework Keras.The validity of the presented model is verified by making use of real parking datasets from two three-dimensional parking lots.The test results reveal that the model outperforms the baseline model’s predictive accuracy,i.e.,a lower testing error.The real parking time series are most closely modeled by the CEEMDAN-PCA-GRU model.As a result,the method is superior to existing models for parking prediction.展开更多
As one of the key issues in China's sustainable development,rapid urbanization and continuous economic growth are accompanied by a steady increase of water consump-tion and a severe urban water crisis.A better und...As one of the key issues in China's sustainable development,rapid urbanization and continuous economic growth are accompanied by a steady increase of water consump-tion and a severe urban water crisis.A better understanding of the relationship among ur-banization,economic growth and water use change is necessary for Chinese decision mak-ers at various levels to address the positive and negative effects of urbanization.Thus,we established a complete decomposition model to quantify the driving effects of urbanization on economic growth and water use change for China and its 31 provincial administrative regions during the period of 1997-2011.The results show that,(1)China's urbanization only contrib-uted about 30%of the economic growth.Therefore,such idea as urbanization is the major driving force of economic growth may be weakened.(2)China's urbanization increased 2352×10^8 m3 of water use by increasing the economic aggregate.However,it decreased 4530×10^8 m3 of water use by optimizing the industrial structure and improving the water use efficiency.Therefore,such idea as urbanization is the major driving force of water demand growth may be reacquainted.(3)Urbanization usually made greater contribution to economic and water use growth in the provincial administrative regions in east and central China,which had larger population and economic aggregate and stepped into the accelerating period of urbanization.However,it also made greater contribution to industrial structure optimization and water use efficiency improvement,and then largely decreased total water use.In total,urbanization had negative effects on water use growth in most provincial administrative re-gions in China,and the spatiotemporal differences among them were lessened on the whole.(4)Though urbanization helps to decrease water use for China and most provincial adminis-trative regions,it may cause water crisis in urban built-up areas or urban agglomerations.Therefore,China should construct the water transfer and compensation mechanisms be-tween urban and rural areas,or low and high density urban areas as soon as possible.展开更多
Automatic conversion from a computer-aided design(CAD) model to Monte Carlo geometry is one of the most effective methods for large-scale and detailed Monte Carlo modeling. The CAD to Monte Carlo geometry converter(CM...Automatic conversion from a computer-aided design(CAD) model to Monte Carlo geometry is one of the most effective methods for large-scale and detailed Monte Carlo modeling. The CAD to Monte Carlo geometry converter(CMGC) is a newly developed conversion code based on the boundary representation to constructive solid geometry(BRep→CSG) conversion method. The goal of the conversion process in the CMGC is to generate an appropriate CSG representation to achieve highly efficient Monte Carlo simulations. We designed a complete solid decomposition scheme to split a complex solid into as few nonoverlapping simple sub-solids as possible. In the complete solid decomposition scheme, the complex solid is successively split by so-called direct, indirect, and auxiliary splitting surfaces. We defined the splitting edge and designed a method for determining the direct splitting surface based on the splitting edge, then provided a method for determining indirect and auxiliary splitting surfaces based on solid vertices. Only the sub-solids that contain concave boundary faces need to be supplemented with auxiliary surfaces because the solid is completely decomposed, which will reduce the redundancy in the CSG expression. After decomposition, these sub-solids are located on only one side of their natural and auxiliary surfaces;thus, each sub-solid can be described by the intersections of a series of half-spaces or geometrical primitives. The CMGC has a friendly graphical user interface and can convert a CAD model into geometry input files for several Monte Carlo codes. The reliability of the CMGC was evaluated by converting several complex models and calculating the relative volume errors. Moreover, JMCT was used to test the efficiency of the Monte Carlo simulation. The results showed that the converted models performed well in particle transport calculations.展开更多
To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive s...To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.展开更多
Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete en...Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.展开更多
Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors...Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.展开更多
Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely.On the other hand,estimating it is extremely challenging due to the non-stati...Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely.On the other hand,estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components.In this research,a novel hybrid forecasting model,namely complete ensemble empirical mode decomposition with adaptive noise-Gaussian process regression(CEEMDAN-GPR),has been developed for daily global solar radiation prediction.The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets.After that,the GPR model uses these subsets as inputs to perform its prediction.According to the results of this research,the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting,namely wavelet-GPR and wavelet packet-GPR,in terms of mean square error,root mean square error,coefficient of determination and relative root mean square error values,which reached 3.23 MJ/m^(2)/day,1.80 MJ/m^(2)/day,95.56%,and 8.80%,respectively(for one-step forward forecasting).The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.展开更多
Higher-order singular value decomposition （HOSVD） is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of the...Higher-order singular value decomposition （HOSVD） is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of them involve incomplete data. To obtain HOSVD of the data with missing values, one can first impute the missing entries through a certain tensor completion method and then perform HOSVD to the reconstructed data. However, the two-step procedure can be inefficient and does not make reliable decomposition. In this paper, we formulate an incomplete HOSVD problem and combine the two steps into solving a single optimization problem, which simultaneously achieves imputation of missing values and also tensor decomposition. We also present one algorithm for solving the problem based on block coordinate update （BCU）. Global convergence of the algorithm is shown under mild assumptions and implies that of the popular higher-order orthogonality iteration （HOOI） method, and thus we, for the first time, give global convergence of HOOI. In addition, we compare the proposed method to state-of-the-art ones for solving incom- plete HOSVD and also low-rank tensor completion problems and demonstrate the superior performance of our method over other compared ones. Furthermore, we apply it to face recognition and MRI image reconstruction to show its practical performance.展开更多
Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environme...Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.展开更多
Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed du...Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed due to its variable and random characteristics.For the objective to enhance the performance of forecasting short-term wind speed,this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit(GRU).The time series decomposition algorithm combines the following two parts:(1)the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),and(2)wavelet packet decomposition(WPD).Firstly,the normalized wind speed time series(WSTS)are handled by CEEMDAN to gain pure fixed-frequency components and a residual signal.The WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw WSTS.Finally,GRU networks are established for all the relevant components of the signals,and the predicted wind speeds are obtained by superimposing the prediction of each component.Results from two case studies,adopting wind data from laboratory and wind farm,respectively,suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm,and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.展开更多
In this paper, we introduce the complex completely positive tensor, which has a symmetric complex decomposition with all real and imaginary parts of the decomposition vectors being non-negative. Some properties of the...In this paper, we introduce the complex completely positive tensor, which has a symmetric complex decomposition with all real and imaginary parts of the decomposition vectors being non-negative. Some properties of the complex completely positive tensor are given. A semidefinite algorithm is also proposed for checking whether a complex tensor is complex completely positive or not. If a tensor is not complex completely positive, a certificate for it can be obtained;if it is complex completely positive, a complex completely positive decomposition can be obtained.展开更多
Land use change and its eco-environmental responses are foci in geographical research. As a region with uneven economic development, land use change and eco-environmental responses across Jiangsu Province are relevant...Land use change and its eco-environmental responses are foci in geographical research. As a region with uneven economic development, land use change and eco-environmental responses across Jiangsu Province are relevant to China＇s overall development pattern. The external function of regional land use changes during different stages of economic development. In this study, we proposed a novel classification system based on the dominant function of land use according to ＂production-ecology-life＂, and then analyzed land use change and regional eco-environmental responses from a functional perspective of regional development. The results showed that from 1985 to 2008, land use change features in Jiangsu were that productive land area decreased and eco- logical and living land areas increased. Land use changes in southern Jiangsu were the most dramatic. In southern and central parts of Jiangsu the agricultural production function weakened and urban life service function strengthened; in northern Jiangsu, the mining production function＇s comparative advantage highlighted that the rural life service function was weakening. Ecological environmental quality decreased slightly in Jiangsu and its three regions. The maximum contribution rate to ecological environmental change occurred in southern Jiangsu and the minimum rate was located in the north. Eco-environmental quality deteriorated in southern and central Jiangsu, related to expanding construction land in urban and rural areas. Ecological environmental quality deterioration in northern Jiangsu is probably due to land development and consolidation. The main reason for improvements in regional ecological environments is that agricultural production land was converted to water ecological land across Jiangsu.展开更多
基金supported by the National Key R&D Program of China(Grant No.2018YFC0406501)Outstanding Young Talent Research Fund of Zhengzhou Uni-versity(Grant No.1521323002)+2 种基金Program for Innovative Talents(in Science and Technology)at University of Henan Province(Grant No.18HASTIT014)State Key Laboratory of Hydraulic Engineering Simulation and Safety,Tianjin University(Grant No.HESS-1717)Foundation for University Youth Key Teacher of Henan Province(Grant No.2017GGJS006).
文摘The complex nonlinear and non-stationary features exhibited in hydrologic sequences make hydrological analysis and forecasting difficult.Currently,some hydrologists employ the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method,a new time-frequency analysis method based on the empirical mode decomposition(EMD)algorithm,to decompose non-stationary raw data in order to obtain relatively stationary components for further study.However,the endpoint effect in CEEMDAN is often neglected,which can lead to decomposition errors that reduce the accuracy of the research results.In this study,we processed an original runoff sequence using the radial basis function neural network(RBFNN)technique to obtain the extension sequence before utilizing CEEMDAN decomposition.Then,we compared the decomposition results of the original sequence,RBFNN extension sequence,and standard sequence to investigate the influence of the endpoint effect and RBFNN extension on the CEEMDAN method.The results indicated that the RBFNN extension technique effectively reduced the error of medium and low frequency components caused by the endpoint effect.At both ends of the components,the extension sequence more accurately reflected the true fluctuation characteristics and variation trends.These advances are of great significance to the subsequent study of hydrology.Therefore,the CEEMDAN method,combined with an appropriate extension of the original runoff series,can more precisely determine multi-time scale characteristics,and provide a credible basis for the analysis of hydrologic time series and hydrological forecasting.
基金Supported by the National Natural Science Foundation of China( 1 0 2 71 1 1 0 )
文摘In this paper,a new concept of an optimal complete multipartite decomposition of type 1 (type 2) of a complete n-partite graph Q n is proposed and another new concept of a normal complete multipartite decomposition of K n is introduced.It is showed that an optimal complete multipartite decomposition of type 1 of K n is a normal complete multipartite decomposition.As for any complete multipartite decomposition of K n,there is a derived complete multipartite decomposition for Q n.It is also showed that any optimal complete multipartite decomposition of type 1 of Q n is a derived decomposition of an optimal complete multipartite decomposition of type 1 of K n.Besides,some structural properties of an optimal complete multipartite decomposition of type 1 of K n are given.
基金supported by the National Natural Science Foundation of China“Study on Hydraulic Transport Mechanism and System Design Method of Seabed Gas Hydrate Cutter-Suction Mining”(No.51775561)the Hunan Natural Science Foundation“Working Mechanism and design Method of Hydraulic Lifting Equipment for Deep Sea Mining Valve Controlled Clean Water Pump”(No.2018JJ2522)。
文摘For the sake of guiding parameter setting of the hydraulic lifting pipeline system for cutter-suction mining of natural gas hydrates(“hydrates”for short)on the seabed,the decomposition characteristics of hydrates in hydraulic lifting pipelines and the effects of flow parameters on decomposition characteristics were studied in this paper.A temperature–pressure model for the hydrate hydraulic lifting pipeline,a hydrate decomposition mass transfer model and a pipeline multiphase flow model were established using mathematical modeling method according to thermodynamics and fluid mechanics.Then,the relationships of the temperature and pressure of pipeline fluid,the amount of hydrate particulate matter and the decomposition surface vs.the underwater depth under the effect of different influencing factors during the transformation from solid–liquid two-phase flow to solid–liquid–gas three-phase flow were analyzed.And the following research results were obtained.First,the decomposition of hydrate slows down and the decomposition surface moves upward slightly with the increase of flow rate in the pipeline.Second,particle size basically has no effects on the temperature and pressure of pipeline fluid,the hydrate phase equilibrium pressure and hydrate decomposition surface.However,only the hydrate particles whose diameter is smaller than 0.2 mm can be completely decomposed in the pipeline while the decomposition of those whose particles size is greater than 2.0 mm is negligible.Third,if the back pressure at the outlet is positive,the decomposition surface moves upward and the decomposition of hydrate slows down with the increase of the back pressure.And if the back pressure at the outlet is negative,the decomposition surface moves downward and the decomposition of hydrate speeds up with the increase of the back pressure.Fourth,the decomposition of hydrate slows down and the decomposition surface moves upward with the increase of mineral depth.However,the decomposition rate and decomposition surface are basically unchanged when the mineral depth is below 1500 m under water.Fifth,the experimental results are basically consistent with the numerical simulation results,and it is indicated that the newly established models are of high reliability.In conclusion,decomposition surface height and decomposition rate can be adjusted by controlling flow rate and outlet back pressure rationally during the cutter-suction mining of hydrates while the influences of particle diameter and mining depth on gas production need not be taken into consideration.
基金the National Natural Science Foundation of China(No.52062027)the Key Research and Development Project of Gansu Province(No.22YF7GA142)+3 种基金the Soft Science Special Project of Gansu Basic Research Plan(No.22JR4ZA035)the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(Nos.22ZD6GA010 and 21ZD3GA002)the Natural Science Foundation of Gansu Province(No.22JR5RA343)the Gansu Provincial Education Technology Innovation Project(No.2023CXZX-582)。
文摘Accurate prediction of parking spaces plays a crucial role in maximizing the efficiency of parking resources and optimizing traffic conditions.However,the majority of earlier research has used models based on past parking data or the plethora of variables that influence parking prediction,which not only makes the data more complicated and costs more time to run but can also lead to poor model fits.To solve this problem,a hybrid parking prediction model combining complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and gated recurrent unit(GRU)model is proposed to predict the number of parking spaces.In this model,CEEMDAN has the ability to gradually break down time series fluctuations or trends at various scales,producing a sequence of intrinsic mode functions(IMF)with various characteristic scales.Then,by keeping the majority of the original data’s content,removing superfluous information,and enhancing predicted response time,principal component analysis(PCA)decreases the dimensionality of the IMF series.Subsequently,the high-level abstract characteristics are entered into the GRU network,and the network is built,tested,and predicted based on the deep learning framework Keras.The validity of the presented model is verified by making use of real parking datasets from two three-dimensional parking lots.The test results reveal that the model outperforms the baseline model’s predictive accuracy,i.e.,a lower testing error.The real parking time series are most closely modeled by the CEEMDAN-PCA-GRU model.As a result,the method is superior to existing models for parking prediction.
基金National Social Science Foundation of China,No.13&ZD027National Natural Science Foundation of China,No.41101538
文摘As one of the key issues in China's sustainable development,rapid urbanization and continuous economic growth are accompanied by a steady increase of water consump-tion and a severe urban water crisis.A better understanding of the relationship among ur-banization,economic growth and water use change is necessary for Chinese decision mak-ers at various levels to address the positive and negative effects of urbanization.Thus,we established a complete decomposition model to quantify the driving effects of urbanization on economic growth and water use change for China and its 31 provincial administrative regions during the period of 1997-2011.The results show that,(1)China's urbanization only contrib-uted about 30%of the economic growth.Therefore,such idea as urbanization is the major driving force of economic growth may be weakened.(2)China's urbanization increased 2352×10^8 m3 of water use by increasing the economic aggregate.However,it decreased 4530×10^8 m3 of water use by optimizing the industrial structure and improving the water use efficiency.Therefore,such idea as urbanization is the major driving force of water demand growth may be reacquainted.(3)Urbanization usually made greater contribution to economic and water use growth in the provincial administrative regions in east and central China,which had larger population and economic aggregate and stepped into the accelerating period of urbanization.However,it also made greater contribution to industrial structure optimization and water use efficiency improvement,and then largely decreased total water use.In total,urbanization had negative effects on water use growth in most provincial administrative re-gions in China,and the spatiotemporal differences among them were lessened on the whole.(4)Though urbanization helps to decrease water use for China and most provincial adminis-trative regions,it may cause water crisis in urban built-up areas or urban agglomerations.Therefore,China should construct the water transfer and compensation mechanisms be-tween urban and rural areas,or low and high density urban areas as soon as possible.
基金the National Natural Science Foundation of China(No.11805017)。
文摘Automatic conversion from a computer-aided design(CAD) model to Monte Carlo geometry is one of the most effective methods for large-scale and detailed Monte Carlo modeling. The CAD to Monte Carlo geometry converter(CMGC) is a newly developed conversion code based on the boundary representation to constructive solid geometry(BRep→CSG) conversion method. The goal of the conversion process in the CMGC is to generate an appropriate CSG representation to achieve highly efficient Monte Carlo simulations. We designed a complete solid decomposition scheme to split a complex solid into as few nonoverlapping simple sub-solids as possible. In the complete solid decomposition scheme, the complex solid is successively split by so-called direct, indirect, and auxiliary splitting surfaces. We defined the splitting edge and designed a method for determining the direct splitting surface based on the splitting edge, then provided a method for determining indirect and auxiliary splitting surfaces based on solid vertices. Only the sub-solids that contain concave boundary faces need to be supplemented with auxiliary surfaces because the solid is completely decomposed, which will reduce the redundancy in the CSG expression. After decomposition, these sub-solids are located on only one side of their natural and auxiliary surfaces;thus, each sub-solid can be described by the intersections of a series of half-spaces or geometrical primitives. The CMGC has a friendly graphical user interface and can convert a CAD model into geometry input files for several Monte Carlo codes. The reliability of the CMGC was evaluated by converting several complex models and calculating the relative volume errors. Moreover, JMCT was used to test the efficiency of the Monte Carlo simulation. The results showed that the converted models performed well in particle transport calculations.
基金supported by the National Natural Science Foundation of China under Grant 51709228。
文摘To improve the feature extraction of ship-radiated noise in a complex ocean environment,a novel feature extraction method for ship-radiated noise based on complete ensemble empirical mode decomposition with adaptive selective noise(CEEMDASN) and refined composite multiscale fluctuation-based dispersion entropy(RCMFDE) is proposed.CEEMDASN is proposed in this paper which takes into account the high frequency intermittent components when decomposing the signal.In addition,RCMFDE is also proposed in this paper which refines the preprocessing process of the original signal based on composite multi-scale theory.Firstly,the original signal is decomposed into several intrinsic mode functions(IMFs)by CEEMDASN.Energy distribution ratio(EDR) and average energy distribution ratio(AEDR) of all IMF components are calculated.Then,the IMF with the minimum difference between EDR and AEDR(MEDR)is selected as characteristic IMF.The RCMFDE of characteristic IMF is estimated as the feature vectors of ship-radiated noise.Finally,these feature vectors are sent to self-organizing map(SOM) for classifying and identifying.The proposed method is applied to the feature extraction of ship-radiated noise.The result shows its effectiveness and universality.
基金Project supported by the National High-Tech R&D Program(863)of China(No.2014AA041501)
文摘Targeting the mode-mixing problem of intrinsic time-scale decomposition （ITD） and the parameter optimization problem of least-square support vector machine （LSSVM）, we propose a novel approach based on complete ensemble intrinsic time-scale decomposition （CEITD） and LSSVM optimized by the hybrid differential evolution and particle swarm optimization （HDEPSO） algorithm for the identification of the fault in a diesel engine. The approach consists mainly of three stages. First, to solve the mode-mixing problem of ITD, a novel CEITD method is proposed. Then the CEITD method is used to decompose the nonstationary vibration signal into a set of stationary proper rotation components （PRCs） and a residual signal. Second, three typical types of time-frequency features, namely singular values, PRCs energy and energy entropy, and AR model parameters, are extracted from the first several PRCs and used as the fault feature vectors. Finally, a HDEPSO algorithm is proposed for the parameter optimization of LSSVM, and the fault diagnosis results can be obtained by inputting the fault feature vectors into the HDEPSO-LSSVM classifier. Simulation and experimental results demonstrate that the proposed fault diagnosis approach can overcome the mode-mixing problem of ITD and accurately identify the fault patterns of diesel engines.
基金We gratefully acknowledge the support of National Natural Science Foundation of China(NSFC)(Grant No.51977133&Grant No.U2066209).
文摘Randomness and fluctuations in wind power output may cause changes in important parameters(e.g.,grid frequency and voltage),which in turn affect the stable operation of a power system.However,owing to external factors(such as weather),there are often various anomalies in wind power data,such as missing numerical values and unreasonable data.This significantly affects the accuracy of wind power generation predictions and operational decisions.Therefore,developing and applying reliable wind power interpolation methods is important for promoting the sustainable development of the wind power industry.In this study,the causes of abnormal data in wind power generation were first analyzed from a practical perspective.Second,an improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)method with a generative adversarial interpolation network(GAIN)network was proposed to preprocess wind power generation and interpolate missing wind power generation sub-components.Finally,a complete wind power generation time series was reconstructed.Compared to traditional methods,the proposed ICEEMDAN-GAIN combination interpolation model has a higher interpolation accuracy and can effectively reduce the error impact caused by wind power generation sequence fluctuations.
文摘Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely.On the other hand,estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components.In this research,a novel hybrid forecasting model,namely complete ensemble empirical mode decomposition with adaptive noise-Gaussian process regression(CEEMDAN-GPR),has been developed for daily global solar radiation prediction.The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets.After that,the GPR model uses these subsets as inputs to perform its prediction.According to the results of this research,the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting,namely wavelet-GPR and wavelet packet-GPR,in terms of mean square error,root mean square error,coefficient of determination and relative root mean square error values,which reached 3.23 MJ/m^(2)/day,1.80 MJ/m^(2)/day,95.56%,and 8.80%,respectively(for one-step forward forecasting).The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.
文摘Higher-order singular value decomposition （HOSVD） is an efficient way for data reduction and also eliciting intrinsic structure of multi-dimensional array data. It has been used in many applications, and some of them involve incomplete data. To obtain HOSVD of the data with missing values, one can first impute the missing entries through a certain tensor completion method and then perform HOSVD to the reconstructed data. However, the two-step procedure can be inefficient and does not make reliable decomposition. In this paper, we formulate an incomplete HOSVD problem and combine the two steps into solving a single optimization problem, which simultaneously achieves imputation of missing values and also tensor decomposition. We also present one algorithm for solving the problem based on block coordinate update （BCU）. Global convergence of the algorithm is shown under mild assumptions and implies that of the popular higher-order orthogonality iteration （HOOI） method, and thus we, for the first time, give global convergence of HOOI. In addition, we compare the proposed method to state-of-the-art ones for solving incom- plete HOSVD and also low-rank tensor completion problems and demonstrate the superior performance of our method over other compared ones. Furthermore, we apply it to face recognition and MRI image reconstruction to show its practical performance.
基金The National Natural Science Foundation of China under contract No.62275228the S&T Program of Hebei under contract Nos 19273901D and 20373301Dthe Hebei Natural Science Foundation under contract No.F2020203066.
文摘Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.
基金This work was supported in part by the National Key Research and Development Project of China(No.2019YFE0105300)the National Natural Science Foundation of China(No.61972443)the Hunan Provincial Key Research and Development Project of China(No.2022WK2006).
文摘Accurate wind speed prediction has been becoming an indispensable technology in system security,wind energy utilization,and power grid dispatching in recent years.However,it is an arduous task to predict wind speed due to its variable and random characteristics.For the objective to enhance the performance of forecasting short-term wind speed,this work puts forward a hybrid deep learning model mixing time series decomposition algorithm and gated recurrent unit(GRU).The time series decomposition algorithm combines the following two parts:(1)the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN),and(2)wavelet packet decomposition(WPD).Firstly,the normalized wind speed time series(WSTS)are handled by CEEMDAN to gain pure fixed-frequency components and a residual signal.The WPD algorithm conducts the second-order decomposition to the first component that contains complex and high frequency signal of raw WSTS.Finally,GRU networks are established for all the relevant components of the signals,and the predicted wind speeds are obtained by superimposing the prediction of each component.Results from two case studies,adopting wind data from laboratory and wind farm,respectively,suggest that the related trend of the WSTS can be separated effectively by the proposed time series decomposition algorithm,and the accuracy of short-time wind speed prediction can be heightened significantly mixing the time series decomposition algorithm and GRU networks.
基金National Natural Science Foundation of China (Grant No. 11701356)supported by National Natural Science Foundation of China (Grant No. 11571234)+2 种基金supported by National Natural Science Foundation of China (Grant No. 11571220)National Postdoctoral Program for Innovative Talents (Grant No. BX201600097)China Postdoctoral Science Foundation (Grant No. 2016M601562)。
文摘In this paper, we introduce the complex completely positive tensor, which has a symmetric complex decomposition with all real and imaginary parts of the decomposition vectors being non-negative. Some properties of the complex completely positive tensor are given. A semidefinite algorithm is also proposed for checking whether a complex tensor is complex completely positive or not. If a tensor is not complex completely positive, a certificate for it can be obtained;if it is complex completely positive, a complex completely positive decomposition can be obtained.
基金National Natural Science Foundation of China(71503117,41301651)A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘Land use change and its eco-environmental responses are foci in geographical research. As a region with uneven economic development, land use change and eco-environmental responses across Jiangsu Province are relevant to China＇s overall development pattern. The external function of regional land use changes during different stages of economic development. In this study, we proposed a novel classification system based on the dominant function of land use according to ＂production-ecology-life＂, and then analyzed land use change and regional eco-environmental responses from a functional perspective of regional development. The results showed that from 1985 to 2008, land use change features in Jiangsu were that productive land area decreased and eco- logical and living land areas increased. Land use changes in southern Jiangsu were the most dramatic. In southern and central parts of Jiangsu the agricultural production function weakened and urban life service function strengthened; in northern Jiangsu, the mining production function＇s comparative advantage highlighted that the rural life service function was weakening. Ecological environmental quality decreased slightly in Jiangsu and its three regions. The maximum contribution rate to ecological environmental change occurred in southern Jiangsu and the minimum rate was located in the north. Eco-environmental quality deteriorated in southern and central Jiangsu, related to expanding construction land in urban and rural areas. Ecological environmental quality deterioration in northern Jiangsu is probably due to land development and consolidation. The main reason for improvements in regional ecological environments is that agricultural production land was converted to water ecological land across Jiangsu.
