The IES dynamic time-scale scheduling strategy based on multiple load forecasting errors

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.

Multiple Time-scale Characteristics of Runoff Variations in Middle Reaches of Huolin River and Their Effects

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.

Assessment of the methods for determining net radiation at different time-scales of meteorological variables

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.

A structure-preserving algorithm for time-scale non-shiftedHamiltonian systems

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.

AN IMPROVED ALGORITHM OF GMM VOICE CONVERSION SYSTEM BASED ON CHANGING THE TIME-SCALE

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.

MEASUREMENT-BASED ADMISSION CONTROL USING MULTIPLE TIME-SCALES TEST

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.

基于Intrinsic Time-scale Decomposition算法的扰动信号特征量提取问题研究

由于电能质量扰动信号具有非线性的特点,当下算法无法有效提取电能质量扰动信号,采取Intrinsic Timescale Decomposition算法,可以有效提取电能质量扰动信号的频率、幅值、相位等特征量。利用Intrinsic Time-scale Decomposition算法提取电能质量扰动信号的Proper Rotation Component分量,后对Proper Rotation Component分量进行Hilbert变换以求得电能质量扰动信号的相位和瞬时频率,再根据电能质量扰动信号的高频突变点得到电能质量扰动信号的起止时间;根据envelope函数求得衰减因子。利用软件开发程序MATHEMATICA对电能质量扰动信号进行分析,结果表明,Intrinsic Time-scale Decomposition算法可以很好地提取电能质量扰动信号特征量,并且具有极佳的抗噪性能。

EEG Feature Learning Model Based on Intrinsic Time-Scale Decomposition and Adaptive Huber Loss

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.

Non-saturation Throughput of S-ALOHA Using the Time-Scale Decomposition Technique

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.

An Effective Numerical Calculation Method for Multi-Time-Scale Mathematical Models in Systems Biology

The improvements of high-throughput experimental devices such as microarray and mass spectrometry have allowed an effective acquisition of biological comprehensive data which include genome, transcriptome, proteome, and metabolome (multi-layered omics data). In Systems Biology, we try to elucidate various dynamical characteristics of biological functions with applying the omics data to detailed mathematical model based on the central dogma. However, such mathematical models possess multi-time-scale properties which are often accompanied by time-scale differences seen among biological layers. The differences cause time stiff problem, and have a grave influence on numerical calculation stability. In the present conventional method, the time stiff problem remained because the calculation of all layers was implemented by adaptive time step sizes of the smallest time-scale layer to ensure stability and maintain calculation accuracy. In this paper, we designed and developed an effective numerical calculation method to improve the time stiff problem. This method consisted of ahead, backward, and cumulative algorithms. Both ahead and cumulative algorithms enhanced calculation efficiency of numerical calculations via adjustments of step sizes of each layer, and reduced the number of numerical calculations required for multi-time-scale models with the time stiff problem. Backward algorithm ensured calculation accuracy in the multi-time-scale models. In case studies which were focused on three layers system with 60 times difference in time-scale order in between layers, a proposed method had almost the same calculation accuracy compared with the conventional method in spite of a reduction of the total amount of the number of numerical calculations. Accordingly, the proposed method is useful in a numerical analysis of multi-time-scale models with time stiff problem.

Modeling of Fluid Turbulence Modification Using Two-time-scale Dissipation Models and Accounting for the Particle Wake Effect

Presently developed two-phase turbulence models under-predict the gas turbulent fluctuation, because their turbulence modification models cannot fully reflect the effect of particles. In this paper, a two-time-scale dis- sipation model of turbulence modification, developed for the two-phase velocity correlation and for the dissipation rate of gas turbulent kinetic energy, is proposed and used to simulate sudden-expansion and swirling gas-particle flows. The proposed two-time scale model gives better results than the single-time scale model. Besides, a gas tur- bulence augmentation model accounting for the finite-size particle wake effect in the gas Reynolds stress equation is proposed. The proposed turbulence modification models are used to simulate two-phase pipe flows. It can prop- erly predict both turbulence reduction and turbulence enhancement for a certain size of particles observed in ex- periments.

Real-time Subsynchronous Control Interaction Monitoring Using Improved Intrinsic Time-scale Decomposition

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.

A Hybrid State Estimation Approach for Integrated Heat and Electricity Networks Considering Time-scale Characteristics

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.

Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines

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.

ε-Dependent Controllability for Two Time-Scale Systems

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.

SIMULATION OF THE INTERFACED STRUCTURAL COMPONENT BASED ON A MULTIPLE-TIME-SCALE ALGORITHM

A multiple-time-scale algorithm is developed to numerically simulate certain structural components in civil structures where local defects inevitably exist. Spatially, the size of local defects is relatively small compared to the structural scale. Different length scales should be adopted considering the efficiency and computational cost. In the principle of physics, different length scales are stipulated to correspond to different time scales. This concept lays the foundation of the framework for this multiple-time-scale algorithm. A multiple-time-scale algorithm, which involves different time steps for different regions, while enforcing the compatibility of displacement, force and stress fields across the interface, is proposed. Furthermore, a defected beam component is studied as a numerical sample. The structural component is divided into two regions： a coarse one and a fine one; a micro-defect exists in the fine region and the finite element sizes of the two regions are diametrically different. Correspondingly, two different time steps are adopted. With dynamic load applied to the beam, stress and displacement distribution of the defected beam is investigated from the global and local perspectives. The numerical sample reflects that the proposed algorithm is physically rational and computationally efficient in the potential damage simulation of civil structures.

Application of matched filters in time-scale domain for elastography

Elasticity imaging offers the possibility of detecting changes in elastic properties and assesses the biomechanical properties of soft tissue with increased sensitivity and spatial resolution compared with conventional palpation. However, the range of applied strains is limited by the concomitant increase of echo signal decorrelation, The decorrelation is mainly introduced by diffuse scattering, while the regular scattering is highly correlated. Because the regular scattering and diffuse scattering localize with different patterns in different ranges of time-scale plane, a new method is put forward to detect the regular scattering with matched filters based on wavelet transform using Generalized Likelihood Aatio Test (GLRT). The simulation results illustrate that the change in estimated mean interscatterer spacing introduced by a SNR of -10 dB is 1.1±2.8%. Thus, by tracking the highly correlated regular scattering, the internal strain can be estimated based on the change in interscatterer spacing under the condition of large surface deformation. The experiment studies show that the internal strain can be estimated up to 10% applied deformation in phantom and 5% strain in porcine liver.

A NUMERICAL STUDY ON THE INTERANNUAL TIME-SCALE LOW-FREQUENCY OSCILLATION

Low-frequency oscillation (LFO) of a large-scale flow pattern is an important observational characteristic feature. In this paper, under the forcing of annual periodic variation a two-layer quasi-geostrophic low- spectrum model is used for carrying out a prolonged numerical integration of more than 30 model years. In the model atmosphere, the interannual time-scale LFO is implicitly reproduced. The result is quite agreeable with the observational evidence.

Adaptive Fault Tolerant Control of Multi-time-scale Singularly Perturbed Systems

This paper studies the fault tolerant control, adaptive approach, for linear time-invariant two-time-scale and three-time-scale singularly perturbed systems in presence of actuator faults and external disturbances. First, the full order system will be controlled using v-dependent control law. The corresponding Lyapunov equation is ill-conditioned due to the presence of slow and fast phenomena. Secondly, a time-scale decomposition of the Lyapunov equation is carried out using singular perturbation method to avoid the numerical stiffness. A composite control law based on local controllers of the slow and fast subsystems is also used to make the control law ε-independent. The designed fault tolerant control guarantees the robust stability of the global closed-loop singularly perturbed system despite loss of effectiveness of actuators. The stability is proved based on the Lyapunov stability theory in the case where the singular perturbation parameter is sufficiently small. A numerical example is provided to illustrate the proposed method.

Asymptotic Expansions of Backward Equations for Two-time-scale Markov Chains in Continuous Time

This work develops asymptotic expansions for solutions of systems of backward equations of time- inhomogeneous Maxkov chains in continuous time. Owing to the rapid progress in technology and the increasing complexity in modeling, the underlying Maxkov chains often have large state spaces, which make the computa- tional tasks ihfeasible. To reduce the complexity, two-time-scale formulations are used. By introducing a small parameter ε〉 0 and using suitable decomposition and aggregation procedures, it is formulated as a singular perturbation problem. Both Markov chains having recurrent states only and Maxkov chains including also tran- sient states are treated. Under certain weak irreducibility and smoothness conditions of the generators, the desired asymptotic expansions axe constructed. Then error bounds are obtained.