The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters...The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.展开更多
Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete par...Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.展开更多
A head-related transfer function （HRTF） model for fast and real-time synthesizing multiple virtual sound sources is proposed. A head-related impulse response （HRIR, time- domain version of HRTF） is first decompose...A head-related transfer function （HRTF） model for fast and real-time synthesizing multiple virtual sound sources is proposed. A head-related impulse response （HRIR, time- domain version of HRTF） is first decomposed by a two-level wavelet packet and then represented by a model composed of subband filters and reconstruction filters. The coefficients of the subband filters are the zero interpolation of the wavelet coefficients of the HRIR. The coefficients of the reconstruction filters can be calculated from the wavelet function. The model is simplified by applying a threshold method to reduce the wavelet coefficients. The calculated results indicate that for a model with 30 wavelet coefficients, the error of reconstructed HRIR is about 1%. And the result of a psychoacoustic test shows that a model with 35 wavelet coefficients is perceptually indistinguishable from the original HRIR. When multiple virtual sound sources are synthesized simultaneously, the computational cost of the proposed model is much less than the traditional HRTF filters.展开更多
Based on the fact that the real inductor and the real capacitor are fractional order in nature and the fractional calculus,the transfer function modeling and analysis of the open-loop Buck converter in a continuous co...Based on the fact that the real inductor and the real capacitor are fractional order in nature and the fractional calculus,the transfer function modeling and analysis of the open-loop Buck converter in a continuous conduction mode（CCM） operation are carried out in this paper.The fractional order small signal model and the corresponding equivalent circuit of the open-loop Buck converter in a CCM operation are presented.The transfer functions from the input voltage to the output voltage,from the input voltage to the inductor current,from the duty cycle to the output voltage,from the duty cycle to the inductor current,and the output impedance of the open-loop Buck converter in CCM operation are derived,and their bode diagrams and step responses are calculated,respectively.It is found that all the derived fractional order transfer functions of the system are influenced by the fractional orders of the inductor and the capacitor.Finally,the realization of the fractional order inductor and the fractional order capacitor is designed,and the corresponding PSIM circuit simulation results of the open-loop Buck converter in CCM operation are given to confirm the correctness of the derivations and the theoretical analysis.展开更多
Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the...Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.展开更多
The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these...The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these structures.Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications.展开更多
Applying 3-dimension finite difference method, the distribution characteristics of horizontal field transfer functions for rectangular conductor have been computed, and the law of distribution for Re-part and Im-part ...Applying 3-dimension finite difference method, the distribution characteristics of horizontal field transfer functions for rectangular conductor have been computed, and the law of distribution for Re-part and Im-part has been given. The influences of source field period, the conductivity, the buried depth and the length of the conductor on the transfer functions were studied. The extrema of transfer functions appear at the center, the four corners and around the edges of conductor, and move with the edges. This feature demonstrates that around the edges are best places for transfer functions' observation.展开更多
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti...Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.展开更多
Based on the 500-hPa geopotential height field series of T106 numerical forecast products, by empirical orthogonal function （EOF） time-space separation, and on the hypotheses of EOF space-models being stable, the EO...Based on the 500-hPa geopotential height field series of T106 numerical forecast products, by empirical orthogonal function （EOF） time-space separation, and on the hypotheses of EOF space-models being stable, the EOF time coefficient series were taken as dynamical statistic model variables. The dynamic system reconstruction idea and genetic algorithm were introduced to make the dynamical model parameters optimized, and a nonlinear dynamic statistic model of EOF separating time coefficient series was established. By the model time integral and EOF time-space reconstruction, a medium/long-range forecast of subtropical high was carried out. The results show that the dynamical model forecast and T106 numerical forecast were approximately similar in the short-range forecast （≤5 days）, but in the medium/long-range forecast （≥5 days）, the forecast results of dynamical model was superior to that of T106 numerical products. A new method and idea were presented for diagnosing and forecasting complicated weathers such as subtropical high, and showed a better application outlook.展开更多
In this paper,the forecasting equations of a 2nd-order space-time differential remainder are deduced from the Navier-Stokes primitive equations and Eulerian operator by Taylor-series expansion.Here we introduce a cubi...In this paper,the forecasting equations of a 2nd-order space-time differential remainder are deduced from the Navier-Stokes primitive equations and Eulerian operator by Taylor-series expansion.Here we introduce a cubic spline numerical model(Spline Model for short),which is with a quasi-Lagrangian time-split integration scheme of fitting cubic spline/bicubic surface to all physical variable fields in the atmospheric equations on spherical discrete latitude-longitude mesh.A new algorithm of"fitting cubic spline—time step integration—fitting cubic spline—……"is developed to determine their first-and2nd-order derivatives and their upstream points for time discrete integral to the governing equations in Spline Model.And the cubic spline function and its mathematical polarities are also discussed to understand the Spline Model’s mathematical foundation of numerical analysis.It is pointed out that the Spline Model has mathematical laws of"convergence"of the cubic spline functions contracting to the original functions as well as its 1st-order and 2nd-order derivatives.The"optimality"of the 2nd-order derivative of the cubic spline functions is optimal approximation to that of the original functions.In addition,a Hermite bicubic patch is equivalent to operate on a grid for a 2nd-order derivative variable field.Besides,the slopes and curvatures of a central difference are identified respectively,with a smoothing coefficient of 1/3,three-point smoothing of that of a cubic spline.Then the slopes and curvatures of a central difference are calculated from the smoothing coefficient 1/3 and three-point smoothing of that of a cubic spline,respectively.Furthermore,a global simulation case of adiabatic,non-frictional and"incompressible"model atmosphere is shown with the quasi-Lagrangian time integration by using a global Spline Model,whose initial condition comes from the NCEP reanalysis data,along with quasi-uniform latitude-longitude grids and the so-called"shallow atmosphere"Navier-Stokes primitive equations in the spherical coordinates.The Spline Model,which adopted the Navier-Stokes primitive equations and quasi-Lagrangian time-split integration scheme,provides an initial ideal case of global atmospheric circulation.In addition,considering the essentially non-linear atmospheric motions,the Spline Model could judge reasonably well simple points of any smoothed variable field according to its fitting spline curvatures that must conform to its physical interpretation.展开更多
This paper studies the renewable power forecasting task with a more advanced formulation,the probabilistic forecasts of day-ahead power generation sequences of multiple renewable power plants without breaching the pri...This paper studies the renewable power forecasting task with a more advanced formulation,the probabilistic forecasts of day-ahead power generation sequences of multiple renewable power plants without breaching the privacy of data in each plant.To realize such a task,an advanced domain-invariant feature learning embedded federated learning(DIFL)framework is proposed to coordinate the development of a system of deep networkbased models serving as multiple clients and one server.In DIFL,each client,which serves each local renew-able power plant,maps its raw data input into latent features via a local feature extractor and generates power output sequence probabilistic forecasts via a locally hosted forecasting model.The cloud-hosted server first aggregates the knowledge from models of clients and next dispatches the aggregated model back to each client for facilitating each local feature extractor to identify domain-invariant features via interacting with a server-side discriminator.Therefore,only desensitized data,such as parameters of the models,are allowed to be transmitted among end users for preserving local data privacy of power plants.To verify the advantages of the DIFL,a preliminary exploration of its theoretical property is first conducted.Next,computational studies are performed to benchmark the DIFL against famous baselines based on datasets collected from commercial renewable power plants.Results further confirm that,in terms of the averaged performance,the DIFL consistently realizes im-provements against all benchmarks based on both real wind farm and solar power plant datasets.展开更多
Biological inspirations are good design mimicry resources. This paper proposes a function based approach for modeling and transformation of bio-inspiration design knowledge. A general functional modeling method for bi...Biological inspirations are good design mimicry resources. This paper proposes a function based approach for modeling and transformation of bio-inspiration design knowledge. A general functional modeling method for biological domain and engineering domain design knowledge is introduced. Functional similarity based bio-inspiration transformation between biological domain and engineering domain is proposed. The biological function topology transfer and analog solution recomposition are also discussed in this paper.展开更多
Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most o...Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most of which are based on the global rock bolt response evaluated in pull-out tests.This paper presents a laboratory experimental setup aiming to capture the rock formation effect,while using distributed fiber optic sensing to quantify the effect of the confinement and the reinforcement pull-out behavior on a more local level.It is shown that the behavior along the sample itself varies,with certain points exhibiting stress drops with crack formation.Some edge effects related to the kinematic freedom of the grout to dilate are also observed.Regardless,it was found that the mid-level response is quite similar to the average response along the sample.The ability to characterize the variation of the response along the sample is one of the many advantages high-resolution fiber optic sensing allows in such investigations.The paper also offers a plasticity-based hardening load transfer function,representing a"slice"of the anchor.The paper describes in detail the development of the model and the calibration/determination of its parameters.The suggested model captures well the coupled behavior in which the pull-out process leads to an increase in the confining stress due to dilative behavior.展开更多
The key difficulty of restoring a fuzzy image is to estimate its point spread function( PSF). In the paper,PSF is modelled based on modulation transfer function( MTF). The first step is calculating the image MTF. In t...The key difficulty of restoring a fuzzy image is to estimate its point spread function( PSF). In the paper,PSF is modelled based on modulation transfer function( MTF). The first step is calculating the image MTF. In the traditional slanted-edge method,a sub-block is always manually extracted from original image and its MTF will be viewed as the result of the whole image. However,handcraft extraction is inefficient and will lead to inaccurate results. Given this,an automatic MTF computation algorithm is proposed,which extracts and screens out all the effective sub-blocks and calculates their average MTF as the final result. Then,a two-dimensional MTF restoration model is constructed by multiplying the horizontal and vertical MTF,and it is combined with conventional image restoration methods to restore fuzzy image. Experimental results indicate the proposed method implementes a fast and accurate MTF computation and the MTF model improves the performance of conventional restoration methods significantly.展开更多
3D chip stacking is considered known to overcome conventional 2D-IC issues, using through silicon vias to ensure vertical signal transmission. From any point source, embedded or not, we calculate the impedance spread ...3D chip stacking is considered known to overcome conventional 2D-IC issues, using through silicon vias to ensure vertical signal transmission. From any point source, embedded or not, we calculate the impedance spread out;our ultimate goal will to study substrate noise via impedance field method. For this, our approach is twofold: a compact Green function or a Transmission Line Model over a multi-layered substrate is derived by solving Poisson’s equation analytically. The Discrete Cosine Transform (DCT) and its variations are used for rapid evaluation. Using this technique, the substrate coupling and loss in IC’s can be analyzed. We implement our algorithm in MATLAB;it permits to extract impedances between any pair of embedded contacts. Comparisons are performed using finite element methods.展开更多
Plant capacity for water storage leads to time lags between basal stem sap flow and transpiration in various woody plants. Internal water storage depends on the sizes of woody plants. However, the changes and its infl...Plant capacity for water storage leads to time lags between basal stem sap flow and transpiration in various woody plants. Internal water storage depends on the sizes of woody plants. However, the changes and its influencing factors in time lags of basal stem flow during the development of herbaceous plants including crops remain unclear. A field experiment was conducted in an arid region of Northwest China to examine the time lag characteristics of sap flow in seed-maize and to calibrate the transpiration modeling. Cross-correlation analysis was used to estimate the time lags between stem sap flow and meteorological driving factors including solar radiation（R_s） and vapor pressure deficit of the air（VPD_（air））. Results indicate that the changes in seed-maize stem sap flow consistently lagged behind the changes in R_s and preceded the changes in VPD_（air） both on hourly and daily scales, suggesting that light-mediated stomatal closures drove sap flow responses. The time lag in the maize＇s sap flow differed significantly during different growth stages and the difference was potentially due to developmental changes in capacitance tissue and/or xylem during ontogenesis. The time lags between stem sap flow and R_s in both female plants and male plants corresponded to plant use of stored water and were independent of total plant water use. Time lags of sap flow were always longer in male plants than in female plants. Theoretically, dry soil may decrease the speed by which sap flow adjusts ahead of shifts in VPD_（air） in comparison with wet soil and also increase the speed by which sap flow adjusts to R_s. However, sap flow lags that were associated with R_s before irrigation and after irrigation in female plants did not shift. Time series analysis method provided better results for simulating seed-maize sap flow with advantages of allowing for fewer variables to be included. This approach would be helpful in improving the accuracy of estimation for canopy transpiration and conductance using meteorological measurements.展开更多
The co-adsorption behaviors of SO2 and H2 O on face-centered cubic Cu（100） ideal surface were studied using the GGA-r PBE method of density functional theory（DFT） with slab models. The optimized structures of sing...The co-adsorption behaviors of SO2 and H2 O on face-centered cubic Cu（100） ideal surface were studied using the GGA-r PBE method of density functional theory（DFT） with slab models. The optimized structures of single H2 O and SO2 on Cu（100） surface were calculated at the coverage of 0.25 ML（molecular layer） and 0.5 ML. The results show that there was no obvious chemical adsorption of them on Cu（100） surface. The adsorbed structures, adsorption energy and electronic properties including difference charge density, valence charge density, Bader charge analysis and partial density of states（PDOS） of co-adsorbed structures of H2 O and SO2 were investigated to illustrate the interaction between adsorbates and surface. H2 O and SO2 can adsorb on surface of Cu atoms chemically via molecule form at the coverage of 0.25 ML, while H2 O dissociated into OH adsorbed on surface and H bonded with SO2 which keeps away from surface at the coverage of 0.5 ML.展开更多
Two mono iron complexes Fe（CO）2PR3（NN） （R = Cy （3）, Ph （4）, NN = o-phenylenediamine dianion ligand, N2H2Ph2-） derived from the ligand substitution of Fe（CO）3hPR3 by the NN ligand were isolated and structur...Two mono iron complexes Fe（CO）2PR3（NN） （R = Cy （3）, Ph （4）, NN = o-phenylenediamine dianion ligand, N2H2Ph2-） derived from the ligand substitution of Fe（CO）3hPR3 by the NN ligand were isolated and structurally characterized by single crystal X-ray diffraction. They have a similar first coordination sphere and oxidation state of the iron center as the [Fe]-hydrogenase active site, and can be a model of it IR demonstrated that the effect of the NN ligand on the coordinated CO stretch- ing frequencies was due to its excellent electron donating ability. The reversible protonation/deprotonation of the NN ligand was identified by infrared spectroscopy and density functional theory computation. The NN ligand is an effective proton acceptor as the internal base of the cysteine thiolate ligand in [Fe]-hydrogenase. The electrochemical properties of complexes 3, 4 were investigated by cyclic voltammograms. Complex 3 catalyzed the transfer hydrogenation of benzoquinone to hydroquinone effectively under mild conditions.展开更多
基金sponsored by the National Natural Science Foundation of China(61333002)Open Research Foundation of the State Key Laboratory of Geodesy and Earth’s Dynamics(SKLGED2018-5-4-E)+5 种基金Foundation of the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems(ACIA2017002)111 projects under Grant(B17040)Open Research Project of the Hubei Key Laboratory of Intelligent Geo-Information Processing(KLIGIP-2017A02)supported by the Three Gorges Research Center for geo-hazardMinistry of Education cooperation agreements of Krasnoyarsk Science Center and Technology BureauRussian Academy of Sciences。
文摘The Earth’s natural pulse electromagnetic field data consists typically of an underlying variation tendency of intensity and irregularities.The change tendency may be related to the occurrence of earthquake disasters.Forecasting of the underlying intensity trend plays an important role in the analysis of data and disaster monitoring.Combining chaos theory and the radial basis function neural network,this paper proposes a forecasting model of the chaotic radial basis function neural network to conduct underlying intensity trend forecasting by the Earth’s natural pulse electromagnetic field signal.The main strategy of this forecasting model is to obtain parameters as the basis for optimizing the radial basis function neural network and to forecast the reconstructed Earth’s natural pulse electromagnetic field data.In verification experiments,we employ the 3 and 6 days’data of two channels as training samples to forecast the 14 and 21-day Earth’s natural pulse electromagnetic field data respectively.According to the forecasting results and absolute error results,the chaotic radial basis function forecasting model can fit the fluctuation trend of the actual signal strength,effectively reduce the forecasting error compared with the traditional radial basis function model.Hence,this network may be useful for studying the characteristics of the Earth’s natural pulse electromagnetic field signal before a strong earthquake and we hope it can contribute to the electromagnetic anomaly monitoring before the earthquake.
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
基金partly supported by the Natural Science Foundation of China under Grant Nos.71101100 and 70731160635New Teachers’Fund for Doctor Stations,Ministry of Education under Grant No.20110181120047+5 种基金Excellent Youth Fund of Sichuan University under Grant No.2013SCU04A08China Postdoctoral Science Foundation under Grant Nos.2011M500418,2012T50148 and 2013M530753Frontier and Cross-innovation Foundation of Sichuan University under Grant No.skqy201352Soft Science Foundation of Sichuan Province under Grant No.2013ZR0016Humanities and Social Sciences Youth Foundation of the Ministry of Education of China under Grant No.11YJC870028Selfdetermined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE under Grant No.CCNU13F030
文摘Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.
基金supported by the National Nature Science Fund of China(50938003,10774049)State Key Lab of Subtropical Building Science,South China University of Technology
文摘A head-related transfer function （HRTF） model for fast and real-time synthesizing multiple virtual sound sources is proposed. A head-related impulse response （HRIR, time- domain version of HRTF） is first decomposed by a two-level wavelet packet and then represented by a model composed of subband filters and reconstruction filters. The coefficients of the subband filters are the zero interpolation of the wavelet coefficients of the HRIR. The coefficients of the reconstruction filters can be calculated from the wavelet function. The model is simplified by applying a threshold method to reduce the wavelet coefficients. The calculated results indicate that for a model with 30 wavelet coefficients, the error of reconstructed HRIR is about 1%. And the result of a psychoacoustic test shows that a model with 35 wavelet coefficients is perceptually indistinguishable from the original HRIR. When multiple virtual sound sources are synthesized simultaneously, the computational cost of the proposed model is much less than the traditional HRTF filters.
基金Project supported by the National Natural Science Foundation of China (Grant No. 51007068)the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20100201120028)+2 种基金the Natural Science Basic Research Plan in Shaanxi Province of China (Grant No. 2012JQ7026)the Fundamental Research Funds for the Central Universities of China (Grant No. 2012jdgz09)the State Key Laboratory of Electrical Insulation and Power Equipment of China (Grant No. EIPE12303)
文摘Based on the fact that the real inductor and the real capacitor are fractional order in nature and the fractional calculus,the transfer function modeling and analysis of the open-loop Buck converter in a continuous conduction mode（CCM） operation are carried out in this paper.The fractional order small signal model and the corresponding equivalent circuit of the open-loop Buck converter in a CCM operation are presented.The transfer functions from the input voltage to the output voltage,from the input voltage to the inductor current,from the duty cycle to the output voltage,from the duty cycle to the inductor current,and the output impedance of the open-loop Buck converter in CCM operation are derived,and their bode diagrams and step responses are calculated,respectively.It is found that all the derived fractional order transfer functions of the system are influenced by the fractional orders of the inductor and the capacitor.Finally,the realization of the fractional order inductor and the fractional order capacitor is designed,and the corresponding PSIM circuit simulation results of the open-loop Buck converter in CCM operation are given to confirm the correctness of the derivations and the theoretical analysis.
文摘Trend forecasting is an important aspect in fault diagnosis and work state supervision. The principle, where Grey theory is applied in fault forecasting, is that the forecast system is considered as a Grey system; the existing known information is used to infer the unknown information's character, state and development trend in a fault pattern, and to make possible forecasting and decisions for future development. It involves the whitenization of a Grey process. But the traditional equal time interval Grey GM (1,1) model requires equal interval data and needs to bring about accumulating addition generation and reversion calculations. Its calculation is very complex. However, the non equal interval Grey GM (1,1) model decreases the condition of the primitive data when establishing a model, but its requirement is still higher and the data were pre processed. The abrasion primitive data of plant could not always satisfy these modeling requirements. Therefore, it establishes a division method suited for general data modeling and estimating parameters of GM (1,1), the standard error coefficient that was applied to judge accuracy height of the model was put forward; further, the function transform to forecast plant abrasion trend and assess GM (1,1) parameter was established. These two models need not pre process the primitive data. It is not only suited for equal interval data modeling, but also for non equal interval data modeling. Its calculation is simple and convenient to use. The oil spectrum analysis acted as an example. The two GM (1,1) models put forward in this paper and the new information model and its comprehensive usage were investigated. The example shows that the two models are simple and practical, and worth expanding and applying in plant fault diagnosis.
基金supported by the Fundamental Research Funds for the Central Universities(No.226-2024-00038),China.
文摘The internal structures of cells as the basic units of life are a major wonder of the microscopic world.Cellular images provide an intriguing window to help explore and understand the composition and function of these structures.Scientific imagery combined with artistic expression can further expand the potential of imaging in educational dissemination and interdisciplinary applications.
文摘Applying 3-dimension finite difference method, the distribution characteristics of horizontal field transfer functions for rectangular conductor have been computed, and the law of distribution for Re-part and Im-part has been given. The influences of source field period, the conductivity, the buried depth and the length of the conductor on the transfer functions were studied. The extrema of transfer functions appear at the center, the four corners and around the edges of conductor, and move with the edges. This feature demonstrates that around the edges are best places for transfer functions' observation.
文摘Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.
基金the National Natural Science Foundation of China(40375019)the Tropical Marine and Meteorological Science Foundation(200609).
文摘Based on the 500-hPa geopotential height field series of T106 numerical forecast products, by empirical orthogonal function （EOF） time-space separation, and on the hypotheses of EOF space-models being stable, the EOF time coefficient series were taken as dynamical statistic model variables. The dynamic system reconstruction idea and genetic algorithm were introduced to make the dynamical model parameters optimized, and a nonlinear dynamic statistic model of EOF separating time coefficient series was established. By the model time integral and EOF time-space reconstruction, a medium/long-range forecast of subtropical high was carried out. The results show that the dynamical model forecast and T106 numerical forecast were approximately similar in the short-range forecast （≤5 days）, but in the medium/long-range forecast （≥5 days）, the forecast results of dynamical model was superior to that of T106 numerical products. A new method and idea were presented for diagnosing and forecasting complicated weathers such as subtropical high, and showed a better application outlook.
文摘In this paper,the forecasting equations of a 2nd-order space-time differential remainder are deduced from the Navier-Stokes primitive equations and Eulerian operator by Taylor-series expansion.Here we introduce a cubic spline numerical model(Spline Model for short),which is with a quasi-Lagrangian time-split integration scheme of fitting cubic spline/bicubic surface to all physical variable fields in the atmospheric equations on spherical discrete latitude-longitude mesh.A new algorithm of"fitting cubic spline—time step integration—fitting cubic spline—……"is developed to determine their first-and2nd-order derivatives and their upstream points for time discrete integral to the governing equations in Spline Model.And the cubic spline function and its mathematical polarities are also discussed to understand the Spline Model’s mathematical foundation of numerical analysis.It is pointed out that the Spline Model has mathematical laws of"convergence"of the cubic spline functions contracting to the original functions as well as its 1st-order and 2nd-order derivatives.The"optimality"of the 2nd-order derivative of the cubic spline functions is optimal approximation to that of the original functions.In addition,a Hermite bicubic patch is equivalent to operate on a grid for a 2nd-order derivative variable field.Besides,the slopes and curvatures of a central difference are identified respectively,with a smoothing coefficient of 1/3,three-point smoothing of that of a cubic spline.Then the slopes and curvatures of a central difference are calculated from the smoothing coefficient 1/3 and three-point smoothing of that of a cubic spline,respectively.Furthermore,a global simulation case of adiabatic,non-frictional and"incompressible"model atmosphere is shown with the quasi-Lagrangian time integration by using a global Spline Model,whose initial condition comes from the NCEP reanalysis data,along with quasi-uniform latitude-longitude grids and the so-called"shallow atmosphere"Navier-Stokes primitive equations in the spherical coordinates.The Spline Model,which adopted the Navier-Stokes primitive equations and quasi-Lagrangian time-split integration scheme,provides an initial ideal case of global atmospheric circulation.In addition,considering the essentially non-linear atmospheric motions,the Spline Model could judge reasonably well simple points of any smoothed variable field according to its fitting spline curvatures that must conform to its physical interpretation.
基金supported in part by the Hong Kong RGC General Research Fund Project with No.11213124in part by Hong Kong ITC Innovation and Technology Fund Project with No.ITS/034/22MS+2 种基金in part by in part by Guangdong Provincial Basic and Applied Basic Research-Offshore Wind Power Joint Fund Project under Grant 2022A1515240066in part by Guangdong Province Technological Project with No.2023A0505030014in part by the Shenzhen-Hong Kong-Macao Science&Technology Category C Project with No.SGDX20220530111205037.
文摘This paper studies the renewable power forecasting task with a more advanced formulation,the probabilistic forecasts of day-ahead power generation sequences of multiple renewable power plants without breaching the privacy of data in each plant.To realize such a task,an advanced domain-invariant feature learning embedded federated learning(DIFL)framework is proposed to coordinate the development of a system of deep networkbased models serving as multiple clients and one server.In DIFL,each client,which serves each local renew-able power plant,maps its raw data input into latent features via a local feature extractor and generates power output sequence probabilistic forecasts via a locally hosted forecasting model.The cloud-hosted server first aggregates the knowledge from models of clients and next dispatches the aggregated model back to each client for facilitating each local feature extractor to identify domain-invariant features via interacting with a server-side discriminator.Therefore,only desensitized data,such as parameters of the models,are allowed to be transmitted among end users for preserving local data privacy of power plants.To verify the advantages of the DIFL,a preliminary exploration of its theoretical property is first conducted.Next,computational studies are performed to benchmark the DIFL against famous baselines based on datasets collected from commercial renewable power plants.Results further confirm that,in terms of the averaged performance,the DIFL consistently realizes im-provements against all benchmarks based on both real wind farm and solar power plant datasets.
基金the National Basic Research Program(973)of China(Nos.2011CB707503 and2011CB013305)the National Natural Science Foundation of China(Nos.51075262,51305260,51275293,51121063,50575142 and 51005148)+4 种基金the"ShuGuang"Project of Shanghai Municipal Education Commissionand Shanghai Education Development Foundation(No.12SG14)the Project of Shanghai Committee of Science and Technology(Nos.11JC1406100,13111102800 and 11BA1405300)the National KeyScientific Instruments and Equipment Development Program of China(Nos.2013YQ03065105 and2011YQ030114)the Program for New Century Excellent Talents in University(No.NCET-08-0361)the National High Technology Research and DevelopmentProgram(863)of China(No.2008AA04Z113)
文摘Biological inspirations are good design mimicry resources. This paper proposes a function based approach for modeling and transformation of bio-inspiration design knowledge. A general functional modeling method for biological domain and engineering domain design knowledge is introduced. Functional similarity based bio-inspiration transformation between biological domain and engineering domain is proposed. The biological function topology transfer and analog solution recomposition are also discussed in this paper.
基金funding support from the Israeli Ministry of Housing and Construction(Grant No.2028286).
文摘Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most of which are based on the global rock bolt response evaluated in pull-out tests.This paper presents a laboratory experimental setup aiming to capture the rock formation effect,while using distributed fiber optic sensing to quantify the effect of the confinement and the reinforcement pull-out behavior on a more local level.It is shown that the behavior along the sample itself varies,with certain points exhibiting stress drops with crack formation.Some edge effects related to the kinematic freedom of the grout to dilate are also observed.Regardless,it was found that the mid-level response is quite similar to the average response along the sample.The ability to characterize the variation of the response along the sample is one of the many advantages high-resolution fiber optic sensing allows in such investigations.The paper also offers a plasticity-based hardening load transfer function,representing a"slice"of the anchor.The paper describes in detail the development of the model and the calibration/determination of its parameters.The suggested model captures well the coupled behavior in which the pull-out process leads to an increase in the confining stress due to dilative behavior.
基金Supported by the National High Technology Research and Development Programme of China(No.2012AA12A305)the National Key Technology R&D Program of the Ministry of Science and Technology(No.2013BAH03B01)+1 种基金Fundamental Research Funds for the Central Universities of China(No.2042015kf0059)China Postdoctoral Science Foundation(No.2015M582277)
文摘The key difficulty of restoring a fuzzy image is to estimate its point spread function( PSF). In the paper,PSF is modelled based on modulation transfer function( MTF). The first step is calculating the image MTF. In the traditional slanted-edge method,a sub-block is always manually extracted from original image and its MTF will be viewed as the result of the whole image. However,handcraft extraction is inefficient and will lead to inaccurate results. Given this,an automatic MTF computation algorithm is proposed,which extracts and screens out all the effective sub-blocks and calculates their average MTF as the final result. Then,a two-dimensional MTF restoration model is constructed by multiplying the horizontal and vertical MTF,and it is combined with conventional image restoration methods to restore fuzzy image. Experimental results indicate the proposed method implementes a fast and accurate MTF computation and the MTF model improves the performance of conventional restoration methods significantly.
文摘3D chip stacking is considered known to overcome conventional 2D-IC issues, using through silicon vias to ensure vertical signal transmission. From any point source, embedded or not, we calculate the impedance spread out;our ultimate goal will to study substrate noise via impedance field method. For this, our approach is twofold: a compact Green function or a Transmission Line Model over a multi-layered substrate is derived by solving Poisson’s equation analytically. The Discrete Cosine Transform (DCT) and its variations are used for rapid evaluation. Using this technique, the substrate coupling and loss in IC’s can be analyzed. We implement our algorithm in MATLAB;it permits to extract impedances between any pair of embedded contacts. Comparisons are performed using finite element methods.
基金support from the National Key Basic Research Program of China (2016YFC0400207)the National Natural Science Foundation of China (51439006, 91425302)the 111 Program of Introducing Talents of Discipline to Universities (B14002)
文摘Plant capacity for water storage leads to time lags between basal stem sap flow and transpiration in various woody plants. Internal water storage depends on the sizes of woody plants. However, the changes and its influencing factors in time lags of basal stem flow during the development of herbaceous plants including crops remain unclear. A field experiment was conducted in an arid region of Northwest China to examine the time lag characteristics of sap flow in seed-maize and to calibrate the transpiration modeling. Cross-correlation analysis was used to estimate the time lags between stem sap flow and meteorological driving factors including solar radiation（R_s） and vapor pressure deficit of the air（VPD_（air））. Results indicate that the changes in seed-maize stem sap flow consistently lagged behind the changes in R_s and preceded the changes in VPD_（air） both on hourly and daily scales, suggesting that light-mediated stomatal closures drove sap flow responses. The time lag in the maize＇s sap flow differed significantly during different growth stages and the difference was potentially due to developmental changes in capacitance tissue and/or xylem during ontogenesis. The time lags between stem sap flow and R_s in both female plants and male plants corresponded to plant use of stored water and were independent of total plant water use. Time lags of sap flow were always longer in male plants than in female plants. Theoretically, dry soil may decrease the speed by which sap flow adjusts ahead of shifts in VPD_（air） in comparison with wet soil and also increase the speed by which sap flow adjusts to R_s. However, sap flow lags that were associated with R_s before irrigation and after irrigation in female plants did not shift. Time series analysis method provided better results for simulating seed-maize sap flow with advantages of allowing for fewer variables to be included. This approach would be helpful in improving the accuracy of estimation for canopy transpiration and conductance using meteorological measurements.
基金Project(51222106)supported by the National Natural Science Foundation of ChinaProject(230201306500002)supported by the Fundamental Research Funds for the Central Universities+1 种基金ChinaProject(2014CB643300)supported by National Basic Research Program of China
文摘The co-adsorption behaviors of SO2 and H2 O on face-centered cubic Cu（100） ideal surface were studied using the GGA-r PBE method of density functional theory（DFT） with slab models. The optimized structures of single H2 O and SO2 on Cu（100） surface were calculated at the coverage of 0.25 ML（molecular layer） and 0.5 ML. The results show that there was no obvious chemical adsorption of them on Cu（100） surface. The adsorbed structures, adsorption energy and electronic properties including difference charge density, valence charge density, Bader charge analysis and partial density of states（PDOS） of co-adsorbed structures of H2 O and SO2 were investigated to illustrate the interaction between adsorbates and surface. H2 O and SO2 can adsorb on surface of Cu atoms chemically via molecule form at the coverage of 0.25 ML, while H2 O dissociated into OH adsorbed on surface and H bonded with SO2 which keeps away from surface at the coverage of 0.5 ML.
基金supported by the National Natural Science Foundation of China(21103121,21276187)Tianjin Municipal Natural Science Foundation(13JCQNJC05800)the Specialized Research Fund for the Doctoral Program of Higher Education(20121317110009)~~
文摘Two mono iron complexes Fe（CO）2PR3（NN） （R = Cy （3）, Ph （4）, NN = o-phenylenediamine dianion ligand, N2H2Ph2-） derived from the ligand substitution of Fe（CO）3hPR3 by the NN ligand were isolated and structurally characterized by single crystal X-ray diffraction. They have a similar first coordination sphere and oxidation state of the iron center as the [Fe]-hydrogenase active site, and can be a model of it IR demonstrated that the effect of the NN ligand on the coordinated CO stretch- ing frequencies was due to its excellent electron donating ability. The reversible protonation/deprotonation of the NN ligand was identified by infrared spectroscopy and density functional theory computation. The NN ligand is an effective proton acceptor as the internal base of the cysteine thiolate ligand in [Fe]-hydrogenase. The electrochemical properties of complexes 3, 4 were investigated by cyclic voltammograms. Complex 3 catalyzed the transfer hydrogenation of benzoquinone to hydroquinone effectively under mild conditions.
