When modeling the soil/atmosphere interaction,it is of paramount importance to determine the net radiation flux.There are two common calculation methods for this purpose.Method 1 relies on use of air temperature,while...When modeling the soil/atmosphere interaction,it is of paramount importance to determine the net radiation flux.There are two common calculation methods for this purpose.Method 1 relies on use of air temperature,while Method 2 relies on use of both air and soil temperatures.Nowadays,there has been no consensus on the application of these two methods.In this study,the half-hourly data of solar radiation recorded at an experimental embankment are used to calculate the net radiation and long-wave radiation at different time-scales(half-hourly,hourly,and daily) using the two methods.The results show that,compared with Method 2 which has been widely adopted in agronomical,geotechnical and geo-environmental applications.Method 1 is more feasible for its simplicity and accuracy at shorter time-scale.Moreover,in case of longer time-scale,daily for instance,less variations of net radiation and long-wave radiation are obtained,suggesting that no detailed soil temperature variations can be obtained.In other words,shorter time-scales are preferred in determining net radiation flux.展开更多
A new measurement-based admission control algorithm to support Quality of Service(QoS) demand is proposed for soft real-time applications. In the algorithm, admission test is performed across Multiple Time-Scales (MTS...A new measurement-based admission control algorithm to support Quality of Service(QoS) demand is proposed for soft real-time applications. In the algorithm, admission test is performed across Multiple Time-Scales (MTS) to accurately capture traffic fluctuation on various time-scales. By applying the QoS requirements directly to admission test, the MTS algorithm can properly meet the QoS target and maximize the bandwidth utilization.展开更多
Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term ...Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term memory(LSTM)and multiple load forecasting errors.This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month.This is obtained by combining the mean absolute percentage error(MAPE)curve of the load forecasting with the error restriction requirements of the dispatcher.Based on the day-ahead scheduling plan,the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES.This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling.This study uses the IES as an example,and it dynamically determines the time scale of the energy monthly.In addition,this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.展开更多
The Huolin River is one of the most important water sources for Xianghai wetland, Horqin wetland, and Chaganhu wetland in the western Songnen Plain of Northeast China. The annual runoff series of 46 years at Baiyunhus...The Huolin River is one of the most important water sources for Xianghai wetland, Horqin wetland, and Chaganhu wetland in the western Songnen Plain of Northeast China. The annual runoff series of 46 years at Baiyunhushuo Hydrologic Station, which is located in the middle reaches of the Huolin River, were analyzed by using wavelet analysis. Main objective was to discuss the periodic characteristics of the runoff, and examine the temporal patterns of the Huolin River recharging to the floodplain wetlands in the lower reaches of the river, and the corresponding effects of recharging variation on the environmental evolution of the wetlands. The results show that the annual runoff varied mainly at three time scales. The intensities of periodical signals at different time scales were strongly characterized by local distribution in its time frequency domain. The interdecadal variation at a scale of more than 30yr played a leading role in the temporal pattern ofrnnoffvariation, and at this scale, the runoffat Baiyunhushuo Hydrologic Station varied in turn of flood, draught and flood. Accordingly, the landscape of the floodplain wetlands presented periodic features, especially prominent before the 1990s. Compared with intense human activities, the runoff periodic pattern at middle （10-20yr） and small （1-10yr） scales, which has relatively low energy, exerted unobvious effects on the environmental evolution of the floodplain wetlands, especially after the 1990s.展开更多
According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object...According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98%and 99.05%,respectively,indicating the application value of the new model.展开更多
The variational calculus of time-scale non-shifted systems includes both the traditional continuous and traditional significant discrete variational calculus.Not only can the combination ofand∇derivatives be beneficia...The variational calculus of time-scale non-shifted systems includes both the traditional continuous and traditional significant discrete variational calculus.Not only can the combination ofand∇derivatives be beneficial to obtaining higher convergence order in numerical analysis,but also it prompts the timescale numerical computational scheme to have good properties,for instance,structure-preserving.In this letter,a structure-preserving algorithm for time-scale non-shifted Hamiltonian systems is proposed.By using the time-scale discrete variational method and calculus theory,and taking a discrete time scale in the variational principle of non-shifted Hamiltonian systems,the corresponding discrete Hamiltonian principle can be obtained.Furthermore,the time-scale discrete Hamilton difference equations,Noether theorem,and the symplectic scheme of discrete Hamiltonian systems are obtained.Finally,taking the Kepler problem and damped oscillator for time-scale non-shifted Hamiltonian systems as examples,they show that the time-scale discrete variational method is a structure-preserving algorithm.The new algorithm not only provides a numerical method for solving time-scale non-shifted dynamic equations but can be calculated with variable step sizes to improve the computational speed.展开更多
This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using A...This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.展开更多
S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact o...S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact of the backoff protocol cannot be adequately evaluated or a finite population model where the number of nodes is fixed. In this letter, a combination of both models is proposed using the time-scale decomposition technique. This methodology allows to study the system under more realistic conditions where the dynamics of users enter and leaving the system are reflected on the performance of the system as well as the impact of the backoff protocol. Also, it allows studying the system in non-saturation conditions. The proposed methodology divides the analysis in two parts： packet-level and connection-level. This analysis renders suitable results when the time scale of the packet level and connection level statistics is different. On the other hand, when these scales are similar, the proposed methodology is no longer suited.展开更多
The global uniform asymptotic stability of competitive neural networks with different time scales and delay is investigated. By the method of variation of parameters and the method of inequality analysis, the conditio...The global uniform asymptotic stability of competitive neural networks with different time scales and delay is investigated. By the method of variation of parameters and the method of inequality analysis, the condition for global uniformly asymptotically stable are given. A strict Lyapunov function for the flow of a competitive neural system with different time scales and delay is presented. Based on the function, the global uniform asymptotic stability of the equilibrium point can be proved.展开更多
Previous studies have shown evidence of atmospheric extratropical wave trains modulating sea ice area in the Weddell and Amundsen/Bellingshausen seas on intraseasonal time-scales(20–100 d). Here we investigate mechan...Previous studies have shown evidence of atmospheric extratropical wave trains modulating sea ice area in the Weddell and Amundsen/Bellingshausen seas on intraseasonal time-scales(20–100 d). Here we investigate mechanisms relating intraseasonal extreme sea ice extent and Ekman layer dynamics with emphasis on the Weddell Sea. This study extends from 1989 to 2013 and focuses on the winter season. Wind stress τ is calculated with winds from the Climate Forecast System reanalysis(CFSR) to evaluate momentum transfer between the atmosphere and the Ekman layer. Lag-composites of the anomalies of Ekman transport and the Ekman pumping indicate that divergence of mass in the Ekman layer and upwelling lead the occurrence of extreme sea ice contraction on intraseasonal time-scales in the Weddell Sea. Opposite conditions(i.e., convergence of the mass and downwelling) lead extreme sea ice expansion on intraseasonal time-scales. This study suggests that the Ekman pumping resulting from the anomalous wind stress on intraseasonal time-scales can transport these warmer waters to the surface contributing to sea ice melting. Additionally, high resolution sea ice fraction and ocean currents obtained from satellite and in situ data are used to investigate in detail mechanisms associated with persistent extreme sea ice expansion and contraction on intraseasonal time-scales. These case studies reveal that atmospheric circumpolar waves on intraseasonal time-scales can induce contrasting anomalies of about ±20% in sea ice concentration at the Weddell and western Antarctica Peninsula margins within less than 30 d. This study shows that extreme anomalies in sea ice may lag between 5–25 d(1–5 pentads) the ocean-atmospheric forcing on intraseasonal time-scales.展开更多
The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling sca...The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling scales are characterized by multiple time scales,making it challenging for the existing data-driven biodegradability evaluation methods to achieve feasible results.In this paper,an intelligent evaluation method is proposed based on multiple time-scale analyses to ensure realtime and accurate biodegradability evaluation of the petrochemical wastewater treatment process.Firstly,a multiple time-scale reconfiguration method is introduced to regularize the datasets consistently by regulating the time-series characteristics of the collected variables.Moreover,missing data for large time-scale variables are supplemented by linear interpolation.Secondly,a multi-scale feature extraction algorithm based on partial least squares is designed to obtain biodegradability feature variables and remove noise and redundant information.Thirdly,an intelligent evaluation model based on a dynamic fuzzy min-max neural network is established to realize the classification of biodegradability.Finally,the proposed evaluation method is applied to the practical petrochemical wastewater treatment process.The experimental results demonstrate that the proposed method can provide real-time and accurate evaluation of the petrochemical wastewater biodegradability.展开更多
Since the dynamical system and control system of the missile are typically nonlinear, an effective acceleration tracking autopilot is designed using the dynamic surface control(DSC)technique in order to make the missi...Since the dynamical system and control system of the missile are typically nonlinear, an effective acceleration tracking autopilot is designed using the dynamic surface control(DSC)technique in order to make the missile control system more robust despite the uncertainty of the dynamical parameters and the presence of disturbances. Firstly, the nonlinear mathematical model of the tail-controlled missile is decomposed into slow acceleration dynamics and fast pitch rate dynamics based on the naturally existing time scale separation. Secondly, the controller based on DSC is designed after obtaining the linear dynamics characteristics of the slow and fast subsystems. An extended state observer is used to detect the uncertainty of the system state variables and aerodynamic parameters to achieve the compensation of the control law. The closed-loop stability of the controller is derived and rigorously analyzed. Finally, the effectiveness and robustness of the design is verified by Monte Carlo simulation considering different initial conditions and parameter uptake. Simulation results illustrate that the missile autopilot based DSC controller achieves better performance and robustness than the other two well-known autopilots.The method proposed in this paper is applied to the design of a missile autopilot, and the results show that the acceleration tracking autopilot based on the DSC controller can ensure accurate tracking of the required commands and has better performance.展开更多
The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and...The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and ms.This beamline has been specifically designed to facilitate the investigation of a wide range of rapid phenomena,such as the deformation and failure of materials subjected to intense dynamic loads.In addition,it enables the study of high-pressure and high-speed fuel spray processes in automotive engines.The light source of this beamline is a cryogenic permanent magnet undulator(CPMU)that is cooled by liquid nitrogen.This CPMU can generate X-ray photons within an energy range of 8.7-30 keV.The beamline offers two modes of operation:monochromatic beam mode with a liquid nitrogen-cooled double-crystal monochromator(DCM)and pink beam mode with the first crystal of the DCM out of the beam path.Four X-ray imaging methods were implemented in BL16U2:single-pulse ultrafast X-ray imaging,microsecond-resolved X-ray dynamic imaging,millisecond-resolved X-ray dynamic micro-CT,and high-resolution quantitative micro-CT.Furthermore,BL16U2 is equipped with various in situ impact loading systems,such as a split Hopkinson bar system,light gas gun,and fuel spray chamber.Following the completion of the final commissioning in 2021 and subsequent trial operations in 2022,the beamline has been officially available to users from 2023.展开更多
In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essen...In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties.展开更多
Most of the classical self-similar traffic models are asymptotic in nature. Therefore, it is crucial for an appropriate buffer design of a switch and queuing based performance evaluation. In this paper, we investigate...Most of the classical self-similar traffic models are asymptotic in nature. Therefore, it is crucial for an appropriate buffer design of a switch and queuing based performance evaluation. In this paper, we investigate delay and loss behavior of the switch under self-similar fixed length packet traffic by modeling it as CMMPP/D/1 and CMMPP/D/1/K, respectively, where Circulant Markov Modulated Poisson Process (CMMPP) is fitted by equating the variance of CMMPP and that of self-similar traffic. CMMPP model is already the validated one to emulate the self-similar characteristics. We compare the analytical results with the simulation ones.展开更多
State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is...State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established.Then,a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline.Meanwhile,to accommodate the measuring resolutions of the integrated network,a hybrid SE approach is developed based on the two-stage iterative algorithm.Results show that,in both steady and dynamic processes,the two-stage estimator has good accuracy and convergence.The hybrid estimator has good performance on tracking the variation of the states in the heating network,even when the available measurements are limited.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
In recent years,subsynchronous control interaction(SSCI)has frequently taken place in renewable-connected power systems.To counter this issue,utilities have been seeking tools for fast and accurate identification of S...In recent years,subsynchronous control interaction(SSCI)has frequently taken place in renewable-connected power systems.To counter this issue,utilities have been seeking tools for fast and accurate identification of SSCI events.The main challenges of SSCI monitoring are the time-varying nature and uncertain modes of SSCI events.Accordingly,this paper presents a simple but effective method that takes advantage of intrinsic time-scale decomposition(ITD).The main purpose is to improve the accuracy and robustness of ITD by incorporating the least-squares method.Results show that the proposed method strikes a good balance between dynamic performance and estimation accuracy.More importantly,the method does not require any prior information,and its performance is therefore not affected by the frequency constitution of the SSCI.Comprehensive comparative studies are conducted to demonstrate the usefulness of the method through synthetic signals,electromagnetic temporary program(EMTP)simulations,and field-recorded SSCI data.Finally,real-time simulation tests are conducted to show the feasibility of the method for real-time monitoring.展开更多
This paper investigates the controllability of two time-scale systems using both the time-scale separation model and the slow-fast order reduction model. This work considers the effect of a singular perturbation param...This paper investigates the controllability of two time-scale systems using both the time-scale separation model and the slow-fast order reduction model. This work considers the effect of a singular perturbation parameter on the model transformations to improve the criterion precision. The Maclaurin expansion method and homotopy arithmetic are introduced to obtain t-dependent controllability criteria. Examples indicate that the s-dependent controllability criteria are more accurate and that the controllability of two time-scale systems does not change during model transformations with these more accurate forms.展开更多
基金support of the European Commission by the Marie Curie IRSES Project GREAT-Geotechnical and Geological Responses to Climate Change:Exchanging Approaches and Technologies on a World-wide Scale(FP7-PEOPLE2013-IRSES-612665)the China Scholarship Council(CSC)Ecole des Ponts Paris Tech for their financial supports
文摘When modeling the soil/atmosphere interaction,it is of paramount importance to determine the net radiation flux.There are two common calculation methods for this purpose.Method 1 relies on use of air temperature,while Method 2 relies on use of both air and soil temperatures.Nowadays,there has been no consensus on the application of these two methods.In this study,the half-hourly data of solar radiation recorded at an experimental embankment are used to calculate the net radiation and long-wave radiation at different time-scales(half-hourly,hourly,and daily) using the two methods.The results show that,compared with Method 2 which has been widely adopted in agronomical,geotechnical and geo-environmental applications.Method 1 is more feasible for its simplicity and accuracy at shorter time-scale.Moreover,in case of longer time-scale,daily for instance,less variations of net radiation and long-wave radiation are obtained,suggesting that no detailed soil temperature variations can be obtained.In other words,shorter time-scales are preferred in determining net radiation flux.
文摘A new measurement-based admission control algorithm to support Quality of Service(QoS) demand is proposed for soft real-time applications. In the algorithm, admission test is performed across Multiple Time-Scales (MTS) to accurately capture traffic fluctuation on various time-scales. By applying the QoS requirements directly to admission test, the MTS algorithm can properly meet the QoS target and maximize the bandwidth utilization.
基金supported by the Fundamental Research Funds for the Central Universities(No.2017MS093)
文摘Considering the complex coupling of multiple energies and the varying load forecasting errors for an integrated energy system(IES),this study proposes a dynamic time-scale scheduling strategy based on long short-term memory(LSTM)and multiple load forecasting errors.This strategy dynamically selects a hybrid timescale which is suitable for a variety of energies for each month.This is obtained by combining the mean absolute percentage error(MAPE)curve of the load forecasting with the error restriction requirements of the dispatcher.Based on the day-ahead scheduling plan,the output of the partial equipment is selectively adjusted at each time-scale to realize multi-energy collaborative optimization and gives full play to the comprehensive advantages of the IES.This is achieved by considering the differences in the response speed for each piece of equipment within the intra-day scheduling.This study uses the IES as an example,and it dynamically determines the time scale of the energy monthly.In addition,this investigation presents a detailed analysis of the output plan of the key equipment to demonstrate the necessity and the advantages of the strategy.
基金Under the auspices of Knowledge Innovation Program of Chinese Academy of Sciences (No. KZCX3-SW-332-01)
文摘The Huolin River is one of the most important water sources for Xianghai wetland, Horqin wetland, and Chaganhu wetland in the western Songnen Plain of Northeast China. The annual runoff series of 46 years at Baiyunhushuo Hydrologic Station, which is located in the middle reaches of the Huolin River, were analyzed by using wavelet analysis. Main objective was to discuss the periodic characteristics of the runoff, and examine the temporal patterns of the Huolin River recharging to the floodplain wetlands in the lower reaches of the river, and the corresponding effects of recharging variation on the environmental evolution of the wetlands. The results show that the annual runoff varied mainly at three time scales. The intensities of periodical signals at different time scales were strongly characterized by local distribution in its time frequency domain. The interdecadal variation at a scale of more than 30yr played a leading role in the temporal pattern ofrnnoffvariation, and at this scale, the runoffat Baiyunhushuo Hydrologic Station varied in turn of flood, draught and flood. Accordingly, the landscape of the floodplain wetlands presented periodic features, especially prominent before the 1990s. Compared with intense human activities, the runoff periodic pattern at middle （10-20yr） and small （1-10yr） scales, which has relatively low energy, exerted unobvious effects on the environmental evolution of the floodplain wetlands, especially after the 1990s.
基金Supported by National Natural Science Foundation of China(Grant Nos.11701144,11971149)Henan Province Key and Promotion Special(Science and Technology)Project(Grant No.212102310305).
文摘According to the World Health Organization,about 50 million people worldwide suffer from epilepsy.The detection and treatment of epilepsy face great challenges.Electroencephalogram(EEG)is a significant research object widely used in diagnosis and treatment of epilepsy.In this paper,an adaptive feature learning model for EEG signals is proposed,which combines Huber loss function with adaptive weight penalty term.Firstly,each EEG signal is decomposed by intrinsic time-scale decomposition.Secondly,the statistical index values are calculated from the instantaneous amplitude and frequency of every component and fed into the proposed model.Finally,the discriminative features learned by the proposed model are used to detect seizures.Our main innovation is to consider a highly flexible penalization based on Huber loss function,which can set different weights according to the influence of different features on epilepsy detection.Besides,the new model can be solved by proximal alternating direction multiplier method,which can effectively ensure the convergence of the algorithm.The performance of the proposed method is evaluated on three public EEG datasets provided by the Bonn University,Childrens Hospital Boston-Massachusetts Institute of Technology,and Neurological and Sleep Center at Hauz Khas,New Delhi(New Delhi Epilepsy data).The recognition accuracy on these two datasets is 98%and 99.05%,respectively,indicating the application value of the new model.
基金This work was supported by the National Natural Science Foundation of China(Nos.11972241,11572212)the Natural Science Foundation of Jiangsu Province(No.BK20191454)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX20_0251).
文摘The variational calculus of time-scale non-shifted systems includes both the traditional continuous and traditional significant discrete variational calculus.Not only can the combination ofand∇derivatives be beneficial to obtaining higher convergence order in numerical analysis,but also it prompts the timescale numerical computational scheme to have good properties,for instance,structure-preserving.In this letter,a structure-preserving algorithm for time-scale non-shifted Hamiltonian systems is proposed.By using the time-scale discrete variational method and calculus theory,and taking a discrete time scale in the variational principle of non-shifted Hamiltonian systems,the corresponding discrete Hamiltonian principle can be obtained.Furthermore,the time-scale discrete Hamilton difference equations,Noether theorem,and the symplectic scheme of discrete Hamiltonian systems are obtained.Finally,taking the Kepler problem and damped oscillator for time-scale non-shifted Hamiltonian systems as examples,they show that the time-scale discrete variational method is a structure-preserving algorithm.The new algorithm not only provides a numerical method for solving time-scale non-shifted dynamic equations but can be calculated with variable step sizes to improve the computational speed.
基金Supported by the National Natural Science Foundation of China (No. 60872105)the Program for Science & Technology Innovative Research Team of Qing Lan Project in Higher Educational Institutions of Jiangsuthe Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)
文摘This paper improves and presents an advanced method of the voice conversion system based on Gaussian Mixture Models(GMM) models by changing the time-scale of speech.The Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum(STRAIGHT) model is adopted to extract the spectrum features,and the GMM models are trained to generate the conversion function.The spectrum features of a source speech will be converted by the conversion function.The time-scale of speech is changed by extracting the converted features and adding to the spectrum.The conversion voice was evaluated by subjective and objective measurements.The results confirm that the transformed speech not only approximates the characteristics of the target speaker,but also more natural and more intelligible.
文摘S-ALOHA （Slotted ALOHA） random access protocol is a widely used protocol mainly for the transmission of short packets in wireless networks. Most papers consider either an infinite population model where the impact of the backoff protocol cannot be adequately evaluated or a finite population model where the number of nodes is fixed. In this letter, a combination of both models is proposed using the time-scale decomposition technique. This methodology allows to study the system under more realistic conditions where the dynamics of users enter and leaving the system are reflected on the performance of the system as well as the impact of the backoff protocol. Also, it allows studying the system in non-saturation conditions. The proposed methodology divides the analysis in two parts： packet-level and connection-level. This analysis renders suitable results when the time scale of the packet level and connection level statistics is different. On the other hand, when these scales are similar, the proposed methodology is no longer suited.
文摘The global uniform asymptotic stability of competitive neural networks with different time scales and delay is investigated. By the method of variation of parameters and the method of inequality analysis, the condition for global uniformly asymptotically stable are given. A strict Lyapunov function for the flow of a competitive neural system with different time scales and delay is presented. Based on the function, the global uniform asymptotic stability of the equilibrium point can be proved.
文摘Previous studies have shown evidence of atmospheric extratropical wave trains modulating sea ice area in the Weddell and Amundsen/Bellingshausen seas on intraseasonal time-scales(20–100 d). Here we investigate mechanisms relating intraseasonal extreme sea ice extent and Ekman layer dynamics with emphasis on the Weddell Sea. This study extends from 1989 to 2013 and focuses on the winter season. Wind stress τ is calculated with winds from the Climate Forecast System reanalysis(CFSR) to evaluate momentum transfer between the atmosphere and the Ekman layer. Lag-composites of the anomalies of Ekman transport and the Ekman pumping indicate that divergence of mass in the Ekman layer and upwelling lead the occurrence of extreme sea ice contraction on intraseasonal time-scales in the Weddell Sea. Opposite conditions(i.e., convergence of the mass and downwelling) lead extreme sea ice expansion on intraseasonal time-scales. This study suggests that the Ekman pumping resulting from the anomalous wind stress on intraseasonal time-scales can transport these warmer waters to the surface contributing to sea ice melting. Additionally, high resolution sea ice fraction and ocean currents obtained from satellite and in situ data are used to investigate in detail mechanisms associated with persistent extreme sea ice expansion and contraction on intraseasonal time-scales. These case studies reveal that atmospheric circumpolar waves on intraseasonal time-scales can induce contrasting anomalies of about ±20% in sea ice concentration at the Weddell and western Antarctica Peninsula margins within less than 30 d. This study shows that extreme anomalies in sea ice may lag between 5–25 d(1–5 pentads) the ocean-atmospheric forcing on intraseasonal time-scales.
基金supported by the National Key Research and Development Project(Grant No.2018YFC1900800-5)the National Natural Science Foundation of China(Grant Nos.61890930-5,61622301,61903010,62021003,62103012)Beijing Nova Program(Grant No.20240484694)。
文摘The biodegradability evaluation of petrochemical wastewater is vital for regulating the petrochemical wastewater treatment process.Nevertheless,the essential datasets derived by instruments with different sampling scales are characterized by multiple time scales,making it challenging for the existing data-driven biodegradability evaluation methods to achieve feasible results.In this paper,an intelligent evaluation method is proposed based on multiple time-scale analyses to ensure realtime and accurate biodegradability evaluation of the petrochemical wastewater treatment process.Firstly,a multiple time-scale reconfiguration method is introduced to regularize the datasets consistently by regulating the time-series characteristics of the collected variables.Moreover,missing data for large time-scale variables are supplemented by linear interpolation.Secondly,a multi-scale feature extraction algorithm based on partial least squares is designed to obtain biodegradability feature variables and remove noise and redundant information.Thirdly,an intelligent evaluation model based on a dynamic fuzzy min-max neural network is established to realize the classification of biodegradability.Finally,the proposed evaluation method is applied to the practical petrochemical wastewater treatment process.The experimental results demonstrate that the proposed method can provide real-time and accurate evaluation of the petrochemical wastewater biodegradability.
基金supported by Joint Fund of the Ministry of Education f or Equipment Pre-research (6141A20223)。
文摘Since the dynamical system and control system of the missile are typically nonlinear, an effective acceleration tracking autopilot is designed using the dynamic surface control(DSC)technique in order to make the missile control system more robust despite the uncertainty of the dynamical parameters and the presence of disturbances. Firstly, the nonlinear mathematical model of the tail-controlled missile is decomposed into slow acceleration dynamics and fast pitch rate dynamics based on the naturally existing time scale separation. Secondly, the controller based on DSC is designed after obtaining the linear dynamics characteristics of the slow and fast subsystems. An extended state observer is used to detect the uncertainty of the system state variables and aerodynamic parameters to achieve the compensation of the control law. The closed-loop stability of the controller is derived and rigorously analyzed. Finally, the effectiveness and robustness of the design is verified by Monte Carlo simulation considering different initial conditions and parameter uptake. Simulation results illustrate that the missile autopilot based DSC controller achieves better performance and robustness than the other two well-known autopilots.The method proposed in this paper is applied to the design of a missile autopilot, and the results show that the acceleration tracking autopilot based on the DSC controller can ensure accurate tracking of the required commands and has better performance.
基金supported by the CAS Project for Young Scientists in Basic Research(YSBR-096)the National Major Scientific Instruments and Equipment Development Project of China(No.11627901)+1 种基金the National Key Research and Development Program of China(Nos.2021YFF0701202,2021YFA1600703)the National Natural Science Foundation of China(Nos.U1932205,12275343).
文摘The fast X-ray imaging beamline(BL16U2)at Shanghai Synchrotron Radiation Facility(SSRF)is a new beamline that provides X-ray micro-imaging capabilities across a wide range of time scales,spanning from 100 ps toμs and ms.This beamline has been specifically designed to facilitate the investigation of a wide range of rapid phenomena,such as the deformation and failure of materials subjected to intense dynamic loads.In addition,it enables the study of high-pressure and high-speed fuel spray processes in automotive engines.The light source of this beamline is a cryogenic permanent magnet undulator(CPMU)that is cooled by liquid nitrogen.This CPMU can generate X-ray photons within an energy range of 8.7-30 keV.The beamline offers two modes of operation:monochromatic beam mode with a liquid nitrogen-cooled double-crystal monochromator(DCM)and pink beam mode with the first crystal of the DCM out of the beam path.Four X-ray imaging methods were implemented in BL16U2:single-pulse ultrafast X-ray imaging,microsecond-resolved X-ray dynamic imaging,millisecond-resolved X-ray dynamic micro-CT,and high-resolution quantitative micro-CT.Furthermore,BL16U2 is equipped with various in situ impact loading systems,such as a split Hopkinson bar system,light gas gun,and fuel spray chamber.Following the completion of the final commissioning in 2021 and subsequent trial operations in 2022,the beamline has been officially available to users from 2023.
文摘In the process of oil recovery,experiments are usually carried out on core samples to evaluate the recovery of oil,so the numerical data are fitted into a non-dimensional equation called scaling-law.This will be essential for determining the behavior of actual reservoirs.The global non-dimensional time-scale is a parameter for predicting a realistic behavior in the oil field from laboratory data.This non-dimensional universal time parameter depends on a set of primary parameters that inherit the properties of the reservoir fluids and rocks and the injection velocity,which dynamics of the process.One of the practical machine learning(ML)techniques for regression/classification problems is gradient boosting(GB)regression.The GB produces a prediction model as an ensemble of weak prediction models that can be done at each iteration by matching a least-squares base-learner with the current pseudoresiduals.Using a randomization process increases the execution speed and accuracy of GB.Hence in this study,we developed a stochastic regression model of gradient boosting(SGB)to forecast oil recovery.Different nondimensional time-scales have been used to generate data to be used with machine learning techniques.The SGB method has been found to be the best machine learning technique for predicting the non-dimensional time-scale,which depends on oil/rock properties.
文摘Most of the classical self-similar traffic models are asymptotic in nature. Therefore, it is crucial for an appropriate buffer design of a switch and queuing based performance evaluation. In this paper, we investigate delay and loss behavior of the switch under self-similar fixed length packet traffic by modeling it as CMMPP/D/1 and CMMPP/D/1/K, respectively, where Circulant Markov Modulated Poisson Process (CMMPP) is fitted by equating the variance of CMMPP and that of self-similar traffic. CMMPP model is already the validated one to emulate the self-similar characteristics. We compare the analytical results with the simulation ones.
基金supported by the National Natural Science Foundation of China(NSFC)(No.51537006)the China Postdoctoral Science Foundation(No.2019M650675)
文摘State estimation(SE)usually serves as the basic function of the energy management system(EMS).In this paper,the time-scale characteristics of the integrated heat and electricity networks are studied and an SE model is established.Then,a two-stage iterative algorithm is proposed to estimate the time delay of heat power transportation in the pipeline.Meanwhile,to accommodate the measuring resolutions of the integrated network,a hybrid SE approach is developed based on the two-stage iterative algorithm.Results show that,in both steady and dynamic processes,the two-stage estimator has good accuracy and convergence.The hybrid estimator has good performance on tracking the variation of the states in the heating network,even when the available measurements are limited.
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supported in part by the National Natural Science Foundation of China(No.51907133)in part by the Fundamental Research Funds for the Central Universities(No.YJ201911).
文摘In recent years,subsynchronous control interaction(SSCI)has frequently taken place in renewable-connected power systems.To counter this issue,utilities have been seeking tools for fast and accurate identification of SSCI events.The main challenges of SSCI monitoring are the time-varying nature and uncertain modes of SSCI events.Accordingly,this paper presents a simple but effective method that takes advantage of intrinsic time-scale decomposition(ITD).The main purpose is to improve the accuracy and robustness of ITD by incorporating the least-squares method.Results show that the proposed method strikes a good balance between dynamic performance and estimation accuracy.More importantly,the method does not require any prior information,and its performance is therefore not affected by the frequency constitution of the SSCI.Comprehensive comparative studies are conducted to demonstrate the usefulness of the method through synthetic signals,electromagnetic temporary program(EMTP)simulations,and field-recorded SSCI data.Finally,real-time simulation tests are conducted to show the feasibility of the method for real-time monitoring.
基金Supported by the National Science Fund for Distinguished Young Scholars(No.60625304)the National Natural Science Foundation of China(No.90716021)the Specialized Research Fund for the Doctoral Program of Higher Education of MOE,China(No.20050003049)
文摘This paper investigates the controllability of two time-scale systems using both the time-scale separation model and the slow-fast order reduction model. This work considers the effect of a singular perturbation parameter on the model transformations to improve the criterion precision. The Maclaurin expansion method and homotopy arithmetic are introduced to obtain t-dependent controllability criteria. Examples indicate that the s-dependent controllability criteria are more accurate and that the controllability of two time-scale systems does not change during model transformations with these more accurate forms.
