Phase reconstruction plays a pivotal role in biology, medical imaging, and wavefront sensing. However, multiple measurements and adjustments are usually required for conventional schemes, which inevitably reduces the ...Phase reconstruction plays a pivotal role in biology, medical imaging, and wavefront sensing. However, multiple measurements and adjustments are usually required for conventional schemes, which inevitably reduces the quality of phase imaging. Here, based on multi-channel metasurface and quantum entanglement source, a simple and integrated quantum analog operation system is proposed to realize quantitative phase reconstruction with a high signal-to-noise ratio (SNR) under a low signal photon level. Without additional measurements and adjustments, four differential images necessary for the phase reconstruction are captured simultaneously. The non-local correlation of entangled photon pairs enables to remotely manipulate working modes of the system. Besides, the consistency of entangled photon pairs in time domain makes it possible to achieve a high SNR imaging by trigger detection. The results may potentially empower the application of metasurfaces in optical chip, wave function reconstruction, and label-free biology imaging.展开更多
Rare earth molten salt electrolytic slag(RMES)has emerged as a promising secondary resource for rare earth elements(REEs).This study introduces an innovative leaching technique for extracting REEs from RMES under atmo...Rare earth molten salt electrolytic slag(RMES)has emerged as a promising secondary resource for rare earth elements(REEs).This study introduces an innovative leaching technique for extracting REEs from RMES under atmospheric conditions,employing an alkali phase reconstruction method followed by an acid leaching process.Additionally,the external electric field was employed to enhance the reaction.Under the optimal reaction conditions:NaOH initial concentration of 70 wt%,NaOH-slag mass ratio of 4:1,temperature of 160℃,current density of 1000 A/m^(2),reaction time of 90 min,stirring speed of 300 r/min,HCl concentration of 4 mol/L,liquid-solid ratio of 15:1,and leaching time of 20 min,the leaching efficiencies of Nd and Pr reach up to 99.21%and 99.14%,respectively.Phase analysis indicates that the rare earth fluorides transform into rare earth hydroxides,significantly enhancing their solubility in acid solution.The imposition of an external electric field leads to pronounced disruption of the RMES surface,thereby promoting the formation of stable reactive oxygen species in the alkaline medium.This facilitates the decomposition of fluorinated rare earths and hastens the phase reconstruction,resulting in an enhanced leaching process.The achieved leaching efficiency with an external electric field is 37%higher than that without an electric field.展开更多
Magnesia-carbon refractories were prepared using fused magnesia,flake graphite and metal aluminum powder as starting materials,phenolic resin as the binder,heat-treating at 1300-1600℃in nitrogen atmosphere.The phase ...Magnesia-carbon refractories were prepared using fused magnesia,flake graphite and metal aluminum powder as starting materials,phenolic resin as the binder,heat-treating at 1300-1600℃in nitrogen atmosphere.The phase reconstruction and the microstructure evolution of the obtained magnesia-carbon refractories were analyzed.The formation mechanisms of magnesia crystals with different morphologies by chemical vapor deposition were revealed.The results show that at 1300-1500℃,the non-oxides within the specimens are aluminum carbide(Al_(4)C_(3)),aluminum nitride(AlN)and magnesium aluminum nitride(Mg_(3)Al_(n)N_(n+2),n=2 or 3);at 1600℃,the diffraction characteristic peak intensity of Al_(4)C_(3) and AlN decreases sharply,and sharp diffraction characteristic peaks of nitrogen aluminum carbide(Al_(7)C_(3)N_(3))appear.Mg(g)is produced by the aluminothermic reduction and carbothermal reduction of magnesia.On the surface of the specimens,Mg(g)reacts with oxygen to form MgO whiskers.Inside the specimens,Mg(g)and O_(2)(g)undergo a CVD chemical deposition reaction to form cubic MgO crystals.There is a phase relationship between flake AlN and flake Mg3AlnNn+2,and they are so associated with each other that the morphology is difficult to distinguish.展开更多
In optical three-dimensional shape measurement, a method of improving the measurement precision for phase reconstruction without phase unwrapping is analyzed in detail. Intensities of any five consecutive pixels that ...In optical three-dimensional shape measurement, a method of improving the measurement precision for phase reconstruction without phase unwrapping is analyzed in detail. Intensities of any five consecutive pixels that lie in the x-axis direction of the phase domain are given. Partial derivatives of the phase function in the x- and y-axis directions are obtained with a phase-shifting mechanism, the origin of which is analysed. Furthermore, to avoid phase unwrapping in the phase reconstruction, we derive the gradient of the phase function and perform a two-dimensional integral along the x- and y-axis directions. The reconstructed phase can be obtained directly by performing numerical integration, and thus it is of great convenience for phase reconstruction. Finally, the results of numerical simulations and practical experiments verify the correctness of the proposed method.展开更多
Adaptive optics（AO） systems are widespread and considered as an essential part of any large aperture telescope for obtaining a high resolution imaging at present.To enlarge the imaging field of view（FOV）,multi-las...Adaptive optics（AO） systems are widespread and considered as an essential part of any large aperture telescope for obtaining a high resolution imaging at present.To enlarge the imaging field of view（FOV）,multi-laser guide stars（LGSs） are currently being investigated and used for the large aperture optical telescopes.LGS measurement is necessary and pivotal to obtain the cumulative phase distortion along a target in the multi-LGSs AO system.We propose a high precision phase reconstruction algorithm to estimate the phase for a target with an uncertain turbulence profile based on the interpolation.By comparing with the conventional average method,the proposed method reduces the root mean square（RMS） error from 130 nm to 85 nm with a 30% reduction for narrow FOV.We confirm that such phase reconstruction algorithm is validated for both narrow field AO and wide field AO.展开更多
Deep-blue perovskite light-emitting diodes(PeLEDs)based on reduced-dimensional perovskites(RDPs)still face a few challenges including severe trap-assisted nonradiative recombination,sluggish exciton transfer,and undes...Deep-blue perovskite light-emitting diodes(PeLEDs)based on reduced-dimensional perovskites(RDPs)still face a few challenges including severe trap-assisted nonradiative recombination,sluggish exciton transfer,and undesirable bathochromic shift of the electroluminescence spectra,impeding the realization of high-performance PeLEDs.Herein,an in situ chlorination(isCl)post-treatment strategy was employed to regulate phase reconstruction and renovate multiple defects of RDPs,leading to superior carrier cooling of 0.88 ps,extraordinary exciton binding energy of 122.53 meV,and higher photoluminescence quantum yield of 60.9%for RDP films with deep-blue emission at 450 nm.The phase regulation is accomplished via fluorine-derived hydrogen bonds that suppress the formation of small-n phases.Multiple defects,including halide vacancies(shallow-state defects)and lead-chloride antisite defects(deepstate defects),are renovated via C=O coordination and hydroxy-group-derived hydrogen bonds.Consequently,deepblue PeLEDs with a record maximum external quantum efficiency of 6.17%and stable electroluminescence at 454 nm were demonstrated,representing the best-performing deep-blue PeLEDs.展开更多
All-inorganic CsPbBr3 perovskite polycrystalline films,renowned for their remarkable optoelectronic properties,solution processability,and enhanced stability over organic–inorganic counterparts,are emerging as next-g...All-inorganic CsPbBr3 perovskite polycrystalline films,renowned for their remarkable optoelectronic properties,solution processability,and enhanced stability over organic–inorganic counterparts,are emerging as next-generation gain media for high-performance lasers.However,due to the limited understanding of how to realize population inversion under slow carrier injection,and a lack of convenient strategies to suppress Auger recombination while retaining low optical loss,achieving high-performance quasi-continuous-wave(quasi-CW)or CW lasing based on CsPbBr_(3)films at ambient temperature is still challenging.We devised a phase reconstruction strategy employing volatile ammonium,which achieves substantial suppression of Auger recombination through elimination of low-dimensional phase impurities and remains low optical loss via precisely controlled film crystallization dynamics.Importantly,this strategy emphasizes the critical role of Auger recombination suppression for high-performance lasing under slower carrier injection.Ultimately,an ultralow amplified spontaneous emission threshold of 9.6μJ cm^(-2)was achieved under quasi-continuous nanosecond-pulsed excitation,which facilitated the realization of a single-mode vertical-cavity surface-emitting laser with a threshold of 17.3μJ cm^(-2)and a quality factor of 3850 under quasi-CW pumping.We represent the exceptional performance of quasi-CW lasing to date,offering valuable insights for future advancements in high-performance CW lasing and even electrically driven lasers.展开更多
Silicon(Si)has been proven to significantly enhance CO_(2)reduction efficiency of numerous catalysts.However,the lack of research on Si's effect on manganese ferrite catalytic performance presents a challenge in u...Silicon(Si)has been proven to significantly enhance CO_(2)reduction efficiency of numerous catalysts.However,the lack of research on Si's effect on manganese ferrite catalytic performance presents a challenge in using high-silica manganese ore to prepare cost-effective,efficient manganese-based ferrite decarbonization catalysts.This work prepared Nano-MnFe_(2)O_(4) materials containing 0.0%-9.3%Si(2.3%increments,mass fraction)by solid-phase synthesis(1300℃,air)and nano-grinding.The effect,mechanism,and enhancement strategy of Si on the low-temperature catalytic reduction of CO_(2)to carbon(C)performance of Nano-MnFe_(2)O_(4) were systematically investigated.Results showed that introducing Si transformed Nano-MnFe_(2)O_(4) into Nano-Mn_(X)Fe_(3-X)O_(4)(0.75<X<1.00)with degraded catalysis and Nano-FeYMnZSiO_(4) with almost no activity,lowering CO_(2)reduction to C from 5.55 mmol/g to 2.81 mmol/g.Increasing Si concentration simultaneously decreased the content and X-value of Nano-Mn_(X)Fe_(3-X)O_(4),causing fewer oxygen vacancies and declined CO_(2)reduction.A phase reconstruction strategy to eliminate Si impact and enhance catalysis was proposed,i.e.,adding MgO,increasing Mn/Fe ratio,and nano-grinding followed by magnetic separation.Nano-Mg_(0.19)Mn_(1.70)Fe_(1.11)O_(4) reconstructed from Nano-MnFe_(2)O_(4) with 2.3%Si exhibited 8.82 mmol/g CO_(2)reduction and 100%carbon conversion at 350℃.Mg^(2+)doping strengthened the conversion of Mn^(2+)to highly catalytically active Mn^(3+)and Mn^(4+)ions,increasing oxygen vacancies and electron transport for C=O rupture.展开更多
The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstructio...The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstruction theory.With the correlation dimensions obtained from the phase space reconstruction,the chaotic behavior of EN was quantitatively evaluated.The results show that both electrochemical potential noise (EPN) and electrochemical current noise (ECN) have chaotic properties.The correlation dimensions of EPN increase with corrosion extent,while those of ECN seem nearly unchanged.The increased correlation dimensions of EPN during the degradation process are associated with the increased susceptibility to local corrosion.展开更多
Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The cl...Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.展开更多
Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and art...Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and artificial neural networks. Training data construction and networks structure were determined by the phase space reconstruction, and establishing nonlinear relationship of phase points with neural networks, the forecasting model of the resource quantity of the surface water was brought forward. The keystone of the way and the detailed arithmetic of the network training were given. The example shows that the model has highly forecasting precision.展开更多
A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler...A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.展开更多
To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase spa...To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.展开更多
In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decompo...In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decomposition of chaotic dynamical system is essentially a projection of chaotic attractor on the axes of space opened by the wavelet filter vectors, which corresponds to the time-delayed embedding method of phase space reconstruction proposed by Packard and Takens. The experimental results show that, the structure of dynamical trajectory of chaotic system on the wavelet space is much similar to the original system, and the nonlinear invariants such as correlation dimension, Lyapunov exponent and Kolmogorov entropy are still reserved. It demonstrates that wavelet decomposition is effective for characterizing chaotic dynamical system.展开更多
Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body...Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.展开更多
In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an ar...In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.展开更多
Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environme...Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.展开更多
A new method for predicting the trend of displacement evolution of surroundingrock was presented in this paper.According to the nonlinear characteristics of displace-ment time series of underground engineering surroun...A new method for predicting the trend of displacement evolution of surroundingrock was presented in this paper.According to the nonlinear characteristics of displace-ment time series of underground engineering surrounding rock,based on phase spacereconstruction theory and the powerful nonlinear mapping ability of support vector ma-chines,the information offered by the time series datum sets was fully exploited and thenon-linearity of the displacement evolution system of surrounding rock was well described.The example suggests that the methods based on phase space reconstruction and modi-fied v-SVR algorithm are very accurate,and the study can help to build the displacementforecast system to analyze the stability of underground engineering surrounding rock.展开更多
Epilepsy is a chronic neurological disorder that affects the function of the brain in people of all ages.It manifests in the electroencephalogram(EEG) signal which records the electrical activity of the brain.Various ...Epilepsy is a chronic neurological disorder that affects the function of the brain in people of all ages.It manifests in the electroencephalogram(EEG) signal which records the electrical activity of the brain.Various image processing,signal processing,and machine-learning based techniques are employed to analyze epilepsy,using spatial and temporal features.The nervous system that generates the EEG signal is considered nonlinear and the EEG signals exhibit chaotic behavior.In order to capture these nonlinear dynamics,we use reconstructed phase space(RPS) representation of the signal.Earlier studies have primarily addressed seizure detection as a binary classification(normal vs.ictal) problem and rarely as a ternary class(normal vs.interictal vs.ictal)problem.We employ transfer learning on a pre-trained deep neural network model and retrain it using RPS images of the EEG signal.The classification accuracy of the model for the binary classes is(98.5±1.5)% and(95±2)% for the ternary classes.The performance of the convolution neural network(CNN) model is better than the other existing statistical approach for all performance indicators such as accuracy,sensitivity,and specificity.The result of the proposed approach shows the prospect of employing RPS images with CNN for predicting epileptic seizures.展开更多
The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi-...The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.展开更多
基金supported by the National Natural Science Foundation of China(Grants No.62221002,Grants No.12174097).
文摘Phase reconstruction plays a pivotal role in biology, medical imaging, and wavefront sensing. However, multiple measurements and adjustments are usually required for conventional schemes, which inevitably reduces the quality of phase imaging. Here, based on multi-channel metasurface and quantum entanglement source, a simple and integrated quantum analog operation system is proposed to realize quantitative phase reconstruction with a high signal-to-noise ratio (SNR) under a low signal photon level. Without additional measurements and adjustments, four differential images necessary for the phase reconstruction are captured simultaneously. The non-local correlation of entangled photon pairs enables to remotely manipulate working modes of the system. Besides, the consistency of entangled photon pairs in time domain makes it possible to achieve a high SNR imaging by trigger detection. The results may potentially empower the application of metasurfaces in optical chip, wave function reconstruction, and label-free biology imaging.
基金supported by the Natural Science Foundation of the Jiangxi Province(20224BAB204038)the Young Elite Scientists Sponsorship Program by CAST(2022QNRC001)+2 种基金the Ganzhou City Science and Technology Innovation Talent Project(2023CYZ26999)Jiangxi Provincial Key Laboratory of LowCarbon Processing and Utilization of Strategic Metal Mineral Resources(2023SSY01041)the Jiangxi Province Graduate Student Innovation Special Fund Project(YC2023-S642)。
文摘Rare earth molten salt electrolytic slag(RMES)has emerged as a promising secondary resource for rare earth elements(REEs).This study introduces an innovative leaching technique for extracting REEs from RMES under atmospheric conditions,employing an alkali phase reconstruction method followed by an acid leaching process.Additionally,the external electric field was employed to enhance the reaction.Under the optimal reaction conditions:NaOH initial concentration of 70 wt%,NaOH-slag mass ratio of 4:1,temperature of 160℃,current density of 1000 A/m^(2),reaction time of 90 min,stirring speed of 300 r/min,HCl concentration of 4 mol/L,liquid-solid ratio of 15:1,and leaching time of 20 min,the leaching efficiencies of Nd and Pr reach up to 99.21%and 99.14%,respectively.Phase analysis indicates that the rare earth fluorides transform into rare earth hydroxides,significantly enhancing their solubility in acid solution.The imposition of an external electric field leads to pronounced disruption of the RMES surface,thereby promoting the formation of stable reactive oxygen species in the alkaline medium.This facilitates the decomposition of fluorinated rare earths and hastens the phase reconstruction,resulting in an enhanced leaching process.The achieved leaching efficiency with an external electric field is 37%higher than that without an electric field.
文摘Magnesia-carbon refractories were prepared using fused magnesia,flake graphite and metal aluminum powder as starting materials,phenolic resin as the binder,heat-treating at 1300-1600℃in nitrogen atmosphere.The phase reconstruction and the microstructure evolution of the obtained magnesia-carbon refractories were analyzed.The formation mechanisms of magnesia crystals with different morphologies by chemical vapor deposition were revealed.The results show that at 1300-1500℃,the non-oxides within the specimens are aluminum carbide(Al_(4)C_(3)),aluminum nitride(AlN)and magnesium aluminum nitride(Mg_(3)Al_(n)N_(n+2),n=2 or 3);at 1600℃,the diffraction characteristic peak intensity of Al_(4)C_(3) and AlN decreases sharply,and sharp diffraction characteristic peaks of nitrogen aluminum carbide(Al_(7)C_(3)N_(3))appear.Mg(g)is produced by the aluminothermic reduction and carbothermal reduction of magnesia.On the surface of the specimens,Mg(g)reacts with oxygen to form MgO whiskers.Inside the specimens,Mg(g)and O_(2)(g)undergo a CVD chemical deposition reaction to form cubic MgO crystals.There is a phase relationship between flake AlN and flake Mg3AlnNn+2,and they are so associated with each other that the morphology is difficult to distinguish.
基金Project supported by the National Natural Science Foundation of China (Grant No. 61144006)
文摘In optical three-dimensional shape measurement, a method of improving the measurement precision for phase reconstruction without phase unwrapping is analyzed in detail. Intensities of any five consecutive pixels that lie in the x-axis direction of the phase domain are given. Partial derivatives of the phase function in the x- and y-axis directions are obtained with a phase-shifting mechanism, the origin of which is analysed. Furthermore, to avoid phase unwrapping in the phase reconstruction, we derive the gradient of the phase function and perform a two-dimensional integral along the x- and y-axis directions. The reconstructed phase can be obtained directly by performing numerical integration, and thus it is of great convenience for phase reconstruction. Finally, the results of numerical simulations and practical experiments verify the correctness of the proposed method.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11174274,11174279,61205021,11204299,61475152,and 61405194)State Key Laboratory of Applied Optics,Changchun Institute of Optics,Fine Mechanics and Physics,Chinese Academy of Sciences
文摘Adaptive optics（AO） systems are widespread and considered as an essential part of any large aperture telescope for obtaining a high resolution imaging at present.To enlarge the imaging field of view（FOV）,multi-laser guide stars（LGSs） are currently being investigated and used for the large aperture optical telescopes.LGS measurement is necessary and pivotal to obtain the cumulative phase distortion along a target in the multi-LGSs AO system.We propose a high precision phase reconstruction algorithm to estimate the phase for a target with an uncertain turbulence profile based on the interpolation.By comparing with the conventional average method,the proposed method reduces the root mean square（RMS） error from 130 nm to 85 nm with a 30% reduction for narrow FOV.We confirm that such phase reconstruction algorithm is validated for both narrow field AO and wide field AO.
基金supported by the following grants:the National Youth Science Funds of China(Grant No.52302172)the Key Program of the National Natural Science Foundation of China(Grant No.52032004)+2 种基金the National Natural Science Foundation of China(Grant No.21902135,92056204)Y.B.Zhao would like to acknowledge the support of the National Natural Science Foundation of China(Grant No.61905206,12364054,and 11804294)Besides,we extend gratitude to the Theoretical and Computational Chemistry Team from Shiyanjia Lab for providing invaluable assistance(www.shiyanjia.com).
文摘Deep-blue perovskite light-emitting diodes(PeLEDs)based on reduced-dimensional perovskites(RDPs)still face a few challenges including severe trap-assisted nonradiative recombination,sluggish exciton transfer,and undesirable bathochromic shift of the electroluminescence spectra,impeding the realization of high-performance PeLEDs.Herein,an in situ chlorination(isCl)post-treatment strategy was employed to regulate phase reconstruction and renovate multiple defects of RDPs,leading to superior carrier cooling of 0.88 ps,extraordinary exciton binding energy of 122.53 meV,and higher photoluminescence quantum yield of 60.9%for RDP films with deep-blue emission at 450 nm.The phase regulation is accomplished via fluorine-derived hydrogen bonds that suppress the formation of small-n phases.Multiple defects,including halide vacancies(shallow-state defects)and lead-chloride antisite defects(deepstate defects),are renovated via C=O coordination and hydroxy-group-derived hydrogen bonds.Consequently,deepblue PeLEDs with a record maximum external quantum efficiency of 6.17%and stable electroluminescence at 454 nm were demonstrated,representing the best-performing deep-blue PeLEDs.
基金financially supported by the“Pioneer”and“Leading Goose”R&D Program of Zhejiang(Grant No.2024C01191)the National Natural Science Foundation of China(Grant Nos.U22A20133 and 52072337)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.2024QZJH10,X.D.)the Shanxi-Zheda Institute of Advanced Materials and Chemical Engineering(Grant No.2022SZ-TD004,H.H.).
文摘All-inorganic CsPbBr3 perovskite polycrystalline films,renowned for their remarkable optoelectronic properties,solution processability,and enhanced stability over organic–inorganic counterparts,are emerging as next-generation gain media for high-performance lasers.However,due to the limited understanding of how to realize population inversion under slow carrier injection,and a lack of convenient strategies to suppress Auger recombination while retaining low optical loss,achieving high-performance quasi-continuous-wave(quasi-CW)or CW lasing based on CsPbBr_(3)films at ambient temperature is still challenging.We devised a phase reconstruction strategy employing volatile ammonium,which achieves substantial suppression of Auger recombination through elimination of low-dimensional phase impurities and remains low optical loss via precisely controlled film crystallization dynamics.Importantly,this strategy emphasizes the critical role of Auger recombination suppression for high-performance lasing under slower carrier injection.Ultimately,an ultralow amplified spontaneous emission threshold of 9.6μJ cm^(-2)was achieved under quasi-continuous nanosecond-pulsed excitation,which facilitated the realization of a single-mode vertical-cavity surface-emitting laser with a threshold of 17.3μJ cm^(-2)and a quality factor of 3850 under quasi-CW pumping.We represent the exceptional performance of quasi-CW lasing to date,offering valuable insights for future advancements in high-performance CW lasing and even electrically driven lasers.
基金supported by National Natural Science Foundations of China[No.52074361]and[No.72088101]the Science and Technology Innovation Program of Hunan Province[No.2023RC1025]and[2024RC3022].
文摘Silicon(Si)has been proven to significantly enhance CO_(2)reduction efficiency of numerous catalysts.However,the lack of research on Si's effect on manganese ferrite catalytic performance presents a challenge in using high-silica manganese ore to prepare cost-effective,efficient manganese-based ferrite decarbonization catalysts.This work prepared Nano-MnFe_(2)O_(4) materials containing 0.0%-9.3%Si(2.3%increments,mass fraction)by solid-phase synthesis(1300℃,air)and nano-grinding.The effect,mechanism,and enhancement strategy of Si on the low-temperature catalytic reduction of CO_(2)to carbon(C)performance of Nano-MnFe_(2)O_(4) were systematically investigated.Results showed that introducing Si transformed Nano-MnFe_(2)O_(4) into Nano-Mn_(X)Fe_(3-X)O_(4)(0.75<X<1.00)with degraded catalysis and Nano-FeYMnZSiO_(4) with almost no activity,lowering CO_(2)reduction to C from 5.55 mmol/g to 2.81 mmol/g.Increasing Si concentration simultaneously decreased the content and X-value of Nano-Mn_(X)Fe_(3-X)O_(4),causing fewer oxygen vacancies and declined CO_(2)reduction.A phase reconstruction strategy to eliminate Si impact and enhance catalysis was proposed,i.e.,adding MgO,increasing Mn/Fe ratio,and nano-grinding followed by magnetic separation.Nano-Mg_(0.19)Mn_(1.70)Fe_(1.11)O_(4) reconstructed from Nano-MnFe_(2)O_(4) with 2.3%Si exhibited 8.82 mmol/g CO_(2)reduction and 100%carbon conversion at 350℃.Mg^(2+)doping strengthened the conversion of Mn^(2+)to highly catalytically active Mn^(3+)and Mn^(4+)ions,increasing oxygen vacancies and electron transport for C=O rupture.
基金Supported by Major State Basic Research Program of China ("973" Program,No. 2011CB610505)Specialized Research Fund for the Doctoral Program of Higher Education (No. 20120032110029)
文摘The degradation process of organosol coated tinplate in beverage was investigated by electrochemical noise (EN) technique combined with morphology characterization.EN data were analyzed using phase space reconstruction theory.With the correlation dimensions obtained from the phase space reconstruction,the chaotic behavior of EN was quantitatively evaluated.The results show that both electrochemical potential noise (EPN) and electrochemical current noise (ECN) have chaotic properties.The correlation dimensions of EPN increase with corrosion extent,while those of ECN seem nearly unchanged.The increased correlation dimensions of EPN during the degradation process are associated with the increased susceptibility to local corrosion.
基金Project (No. 50437010) supported by the Key Program of the Na-tional Natural Science Foundation of China
文摘Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.
基金Supported by 863 Program of China(2002AA2Z4291) Henan Innovation Project for University Prominent Research Talents(2005KYCX015)Henan Project for University Prominent Talents
文摘Proposed a new method to disclose the complicated non-linearity structure of the water-resource system, introducing chaos theory into the hydrology and water resources field, and combined with the chaos theory and artificial neural networks. Training data construction and networks structure were determined by the phase space reconstruction, and establishing nonlinear relationship of phase points with neural networks, the forecasting model of the resource quantity of the surface water was brought forward. The keystone of the way and the detailed arithmetic of the network training were given. The example shows that the model has highly forecasting precision.
基金Supported by the National Natural Science Foundation of China(51406031)
文摘A new method of nonlinear analysis is established by combining phase space reconstruction and data reduction sub-frequency band wavelet. This method is applied to two types of chaotic dynamic systems(Lorenz and Rssler) to examine the anti-noise ability for complex systems. Results show that the nonlinear dynamic system analysis method resists noise and reveals the internal dynamics of a weak signal from noise pollution. On this basis, the vertical upward gas–liquid two-phase flow in a 2 mm × 0.81 mm small rectangular channel is investigated. The frequency and energy distributions of the main oscillation mode are revealed by analyzing the time–frequency spectra of the pressure signals of different flow patterns. The positive power spectral density of singular-value frequency entropy and the damping ratio are extracted to characterize the evolution of flow patterns and achieve accurate recognition of different vertical upward gas–liquid flow patterns(bubbly flow:100%, slug flow: 92%, churn flow: 96%, annular flow: 100%). The proposed analysis method will enrich the dynamics theory of multi-phase flow in small channel.
基金Sponsored by the National Eleventh Five year Plan Key Project of Ministry of Science and Technology of China (Grant No. 2006BAJ03A05-05)
文摘To make elevator group control system better follow the change of elevator traffic flow (ETF) in order to adjust the control strategy,the prediction method of support vector machine (SVM) in combination with phase space reconstruction has been proposed for ETF.Firstly,the phase space reconstruction for elevator traffic flow time series (ETFTS) is processed.Secondly,the small data set method is applied to calculate the largest Lyapunov exponent to judge the chaotic property of ETF.Then prediction model of ETFTS based on SVM is founded.Finally,the method is applied to predict the time series for the incoming and outgoing passenger flow respectively using ETF data collected in some building.Meanwhile,it is compared with RBF neural network model.Simulation results show that the trend of factual traffic flow is better followed by predictive traffic flow.SVM algorithm has much better prediction performance.The fitting and prediction of ETF with better effect are realized.
基金supported by the Natural Science Foundation of Fujian Province of China (Grant Nos. 2010J01210 and T0750008)
文摘In view of the disadvantages of the traditional phase space reconstruction method, this paper presents the method of phase space reconstruction based on the wavelet decomposition and indicates that the wavelet decomposition of chaotic dynamical system is essentially a projection of chaotic attractor on the axes of space opened by the wavelet filter vectors, which corresponds to the time-delayed embedding method of phase space reconstruction proposed by Packard and Takens. The experimental results show that, the structure of dynamical trajectory of chaotic system on the wavelet space is much similar to the original system, and the nonlinear invariants such as correlation dimension, Lyapunov exponent and Kolmogorov entropy are still reserved. It demonstrates that wavelet decomposition is effective for characterizing chaotic dynamical system.
基金the National Key R&D Program of China(No.2022YFC2904103)the Key Program of the National Natural Science Foundation of China(No.52034001)+1 种基金the 111 Project(No.B20041)the China National Postdoctoral Program for Innovative Talents(No.BX20230041)。
文摘Traditional research believes that the filling body can effectively control stress concentration while ignoring the problems of unknown stability and the complex and changeable stress distribution of the filling body–surrounding rock combination under high-stress conditions.Current monitoring data processing methods cannot fully consider the complexity of monitoring objects,the diversity of monitoring methods,and the dynamics of monitoring data.To solve this problem,this paper proposes a phase space reconstruction and stability prediction method to process heterogeneous information of backfill–surrounding rock combinations.The three-dimensional monitoring system of a large-area filling body–surrounding rock combination in Longshou Mine was constructed by using drilling stress,multipoint displacement meter,and inclinometer.Varied information,such as the stress and displacement of the filling body–surrounding rock combination,was continuously obtained.Combined with the average mutual information method and the false nearest neighbor point method,the phase space of the heterogeneous information of the filling body–surrounding rock combination was then constructed.In this paper,the distance between the phase point and its nearest point was used as the index evaluation distance to evaluate the stability of the filling body–surrounding rock combination.The evaluated distances(ED)revealed a high sensitivity to the stability of the filling body–surrounding rock combination.The new method was then applied to calculate the time series of historically ED for 12 measuring points located at Longshou Mine.The moments of mutation in these time series were at least 3 months ahead of the roadway return dates.In the ED prediction experiments,the autoregressive integrated moving average model showed a higher prediction accuracy than the deep learning models(long short-term memory and Transformer).Furthermore,the root-mean-square error distribution of the prediction results peaked at 0.26,thus outperforming the no-prediction method in 70%of the cases.
基金National High Technology Research and Development Program of China(No.2007AA04Z109)Open Research Fund of State Key Laboratory of Digital Manufacturing Equipment and Technology at Huazhong University of Science and Technology,China(No.DMETKF2009006)
文摘In order to manage and control semiconductor wafer fabrication system (SWFS) more effectively,the daily throughput prediction data of wafer fab are often used in the planning and scheduling of SWFS.In this paper,an artificial neural network (ANN) prediction method based on phase space reconstruction (PSR) and ant colony optimization (ACO) is presented,in which the phase space reconstruction theory is used to reconstruct the daily throughput time series,the ANN is used to construct the daily throughput prediction model,and the ACO is used to train the connection weight and bias values of the neural network prediction model.Testing with factory operation data and comparing with the traditional method show that the proposed methodology is effective.
基金The National Natural Science Foundation of China under contract No.62275228the S&T Program of Hebei under contract Nos 19273901D and 20373301Dthe Hebei Natural Science Foundation under contract No.F2020203066.
文摘Marine life is very sensitive to changes in pH.Even slight changes can cause ecosystems to collapse.Therefore,understanding the future pH of seawater is of great significance for the protection of the marine environment.At present,the monitoring method of seawater pH has been matured.However,how to accurately predict future changes has been lacking effective solutions.Based on this,the model of bidirectional gated recurrent neural network with multi-headed self-attention based on improved complete ensemble empirical mode decomposition with adaptive noise combined with phase space reconstruction(ICPBGA)is proposed to achieve seawater pH prediction.To verify the validity of this model,pH data of two monitoring sites in the coastal sea area of Beihai,China are selected to verify the effect.At the same time,the ICPBGA model is compared with other excellent models for predicting chaotic time series,and root mean square error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and coefficient of determination(R2)are used as performance evaluation indicators.The R2 of the ICPBGA model at Sites 1 and 2 are above 0.9,and the prediction errors are also the smallest.The results show that the ICPBGA model has a wide range of applicability and the most satisfactory prediction effect.The prediction method in this paper can be further expanded and used to predict other marine environmental indicators.
文摘A new method for predicting the trend of displacement evolution of surroundingrock was presented in this paper.According to the nonlinear characteristics of displace-ment time series of underground engineering surrounding rock,based on phase spacereconstruction theory and the powerful nonlinear mapping ability of support vector ma-chines,the information offered by the time series datum sets was fully exploited and thenon-linearity of the displacement evolution system of surrounding rock was well described.The example suggests that the methods based on phase space reconstruction and modi-fied v-SVR algorithm are very accurate,and the study can help to build the displacementforecast system to analyze the stability of underground engineering surrounding rock.
文摘Epilepsy is a chronic neurological disorder that affects the function of the brain in people of all ages.It manifests in the electroencephalogram(EEG) signal which records the electrical activity of the brain.Various image processing,signal processing,and machine-learning based techniques are employed to analyze epilepsy,using spatial and temporal features.The nervous system that generates the EEG signal is considered nonlinear and the EEG signals exhibit chaotic behavior.In order to capture these nonlinear dynamics,we use reconstructed phase space(RPS) representation of the signal.Earlier studies have primarily addressed seizure detection as a binary classification(normal vs.ictal) problem and rarely as a ternary class(normal vs.interictal vs.ictal)problem.We employ transfer learning on a pre-trained deep neural network model and retrain it using RPS images of the EEG signal.The classification accuracy of the model for the binary classes is(98.5±1.5)% and(95±2)% for the ternary classes.The performance of the convolution neural network(CNN) model is better than the other existing statistical approach for all performance indicators such as accuracy,sensitivity,and specificity.The result of the proposed approach shows the prospect of employing RPS images with CNN for predicting epileptic seizures.
文摘The neutral network forecasting model based on the phase space reconstruction was proposed. First, through reconstructing the phase space, the time series of single variable was done excursion and expanded into multi- dimension series which included the ergodic information and more rich information could be excavated. Then, on the basis of the embedding dimension of the time series, the structure form of neutral network was constructed, of which the node number in input layer was the embedding dimension of the time series minus 1, and the node number in output layers was 1. Finally, as an example, the model was applied for water yield of mine forecasting. The result shows that the model has good fitting accuracy and forecasting precision.
