Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation to...Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools.In this paper,we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model.The proposed ensemble model is composed of two levels of regression models.The first level consists of three strong models namely,random forest,support vector regression,and light gradient boosting machine.Whereas the second level is based on the ElasticNet regression model,which receives the prediction results from the models in the first level for refinement and producing the final optimal result.To achieve the best performance of these regression models,the advanced squirrel search optimization algorithm(ASSOA)is utilized to search for the optimal set of hyper-parameters of each model.Experimental results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark dataset.The findings indicate that the proposed approach results in root mean square error(RMSE)of(0.013),mean absolute error(MAE)of(0.004),and mean bias error(MBE)of(0.0017).These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.展开更多
Varicocele has a prevalence of 15%in the population and represents a primary cause of infertility in 40%of cases and a secondary cause in approximately 80%of cases.It is considered the major correctable cause of male ...Varicocele has a prevalence of 15%in the population and represents a primary cause of infertility in 40%of cases and a secondary cause in approximately 80%of cases.It is considered the major correctable cause of male infertility.Despite its high prevalence in the infertile population,a large number of patients with varicocele do not experience reproductive difficulties.For this reason,it is still highly debated which parameters could be used to predict which patients with varicocele will be most likely to benefit from its repair.The main international and European guidelines state that treatment should only be considered in infertile patients with abnormal sperm quality.However,these guidelines do not help physicians to identify which of these patients may benefit from the treatment.Therefore,this narrative review collects the evidence in the literature on the usefulness of some factors as predictors of improvement,highlighting how some of them may be effective in an initial selection of patients to be treated,while others are promising but further studies are needed.Finally,a brief consideration on the possible role of artificial intelligence is proposed.展开更多
Astronomical spectroscopy is crucial for exploring the physical properties,chemical composition,and kinematic behavior of celestial objects.With continuous advancements in observational technology,astronomical spectro...Astronomical spectroscopy is crucial for exploring the physical properties,chemical composition,and kinematic behavior of celestial objects.With continuous advancements in observational technology,astronomical spectroscopy faces the dual challenges of rapidly expanding data volumes and relatively lagging data processing capabilities.In this context,the rise of artificial intelligence technologies offers an innovative solution to address these challenges.This paper analyzes the latest developments in the application of machine learning for astronomical spectral data mining and discusses future research directions in AI-based spectral studies.However,the application of machine learning technologies presents several challenges.The high complexity of models often comes with insufficient interpretability,complicating scientific understanding.Moreover,the large-scale computational demands place higher requirements on hardware resources,leading to a significant increase in computational costs.AI-based astronomical spectroscopy research should advance in the following key directions.First,develop efficient data augmentation techniques to enhance model generalization capabilities.Second,explore more interpretable model designs to ensure the reliability and transparency of scientific conclusions.Third,optimize computational efficiency and reduce the threshold for deep-learning applications through collaborative innovations in algorithms and hardware.Furthermore,promoting the integration of cross-band data processing is essential to achieve seamless integration and comprehensive analysis of multi-source data,providing richer,multidimensional information to uncover the mysteries of the universe.展开更多
This research introduces a spectrum-based physics-informed neural network(SP-PINN)model to significantly improve the accuracy of calculation of two-phase flow parameters,surpassing existing methods that have limitatio...This research introduces a spectrum-based physics-informed neural network(SP-PINN)model to significantly improve the accuracy of calculation of two-phase flow parameters,surpassing existing methods that have limitations in global and continuous data sampling.SP-PINNs address the challenges of traditional methods in terms of continuous sampling by integrating the spectral analysis and pressure correction into the Navier-Stokes(N-S)equations,enhancing the predictive accuracy especially in critical regions like gas-phase boundaries and velocity peaks.The novel introduction of a pressure-correction module within SP-PINNs mitigates prediction errors,achieving a substantial reduction to 1‰compared with the conventional physics-informed neural network(PINN)approaches.Experimental applications validate the model’s ability to accurately and rapidly predict flow parameters with different sampling time intervals,with the computation time of predicting unsampled data less than 0.01 s.Such advancements signify a 100-fold improvement over traditional DNS calculations,underscoring the model’s potential in the real-time calculation and analysis of multiphase flow dynamics.展开更多
In this paper,a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed.Firstly,the invariable parameters in the forming process including shot size,mass flo...In this paper,a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed.Firstly,the invariable parameters in the forming process including shot size,mass flow,peening distance and peening angle are determined according to the empirical and machine type.Then,the optimal value of air pressure for the whole shot peening is selected by the experimental data.Finally,the feeding speed for every shot peening path is predicted by regression equation.The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object,and four experiments are conducted.In order to design specimens for acquiring the forming data,one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens,which were peened along the middle of the two stringers.The most striking finding of this experiment is that the outer shape error range is below 3.9%,so the plate specimens can be used in predicting feeding speed of the integral panel.The second experiment is performed and results show that when the coverage reaches the limit of 80%,the minimum feeding speed is 50 mm/s.By this feeding speed,the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object,and the optimal air pressure is 0.15 MPa.Then,the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment,and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method.The algorithm is validated by forming a test part and the average deviation is 0.496 mm.It is shown that the approach can realize the forming of the integral panel precisely.展开更多
Based on experimental data of line heating, the methods of vector mapping, plane projection, and coordinate converting are presented to establish the spectra for line heating distortion discipline which shows the rela...Based on experimental data of line heating, the methods of vector mapping, plane projection, and coordinate converting are presented to establish the spectra for line heating distortion discipline which shows the relationship between process parameters and distortion parameters of line heating. Back-propagation network （BP-net） is used to modify tile spectra. Mathematical models for optimizing line heating techniques parameters, which include two-objective functions, are constructed. To convert the multi-objective optimization into a single-objective one, the method of changifig weight coefficient is used, and then the individual fitness function is built up, Taking the number of heating lines, distance between the heating lines＇ border （line space）, and shrink quantity of lines as three restrictive conditions, a hierarchy genetic algorithm （HGA） code is established by making use of information provided by the spectra, in which inner coding and outer coding adopt different heredity arithmetic operators in inherent operating, The numerical example shows that the spectra for line heating distortion discipline presented here can provide accurate information required by techniques parameter prediction of line heating process and the technique parameter optimization method based on HGA provided here can obtain good results for hull plate.展开更多
A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variable...A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.展开更多
In view of aircraft engine health condition parameters prediction,an ensemble ELM based prediction approach is proposed in this paper. In the approach,the AdaBoost. RT algorithm is improved to adjust its threshold ada...In view of aircraft engine health condition parameters prediction,an ensemble ELM based prediction approach is proposed in this paper. In the approach,the AdaBoost. RT algorithm is improved to adjust its threshold adaptively,and is utilized as the basic framework to establish the ensemble learning model using ELM as weak learners. The proposed approach is evaluated through the prediction of the actual engine fuel flow deviation time series,and the results demonstrate that this approach is feasible for the prediction of aircraft engine health condition parameters. The performance of the proposed approach is compared with single ELM, single process neural network ( PNN) ,and a similar ensemble ELM based approach using AdaBoost. RT as basic framework. The results show that,the proposed approach is more accurate than single ELM and single PNN,and no worse than the ensemble prediction approach for contrast,furthermore,the given approach is more convenient for practical application. Therefore,the proposed approach is better suited to the prediction of aircraft engine health parameters.展开更多
To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year...To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.展开更多
To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal test...To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.展开更多
Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distribu...Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.展开更多
This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weat...This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.展开更多
Diameter distribution models play an important role in forest inventories,growth prediction,and management.The Weibull probability density function is widely used in forestry.Although a number of methods have been pro...Diameter distribution models play an important role in forest inventories,growth prediction,and management.The Weibull probability density function is widely used in forestry.Although a number of methods have been proposed to predict or recover the Weibull distribution,their applicability and predictive performance for the major tree species of China remain to be determined.Trees in sample plots of three even-aged coniferous species(Larix olgensis,Pinus sylvestris and Pinus koraiensis)were measured both in un-thinned and thinned stands to develop parameter prediction models for the Weibull probability density function.Ordinary least squares(OLS)and maximum likelihood regression(MLER),as well as cumulative distribution function regression(CDFR)were used,and their performance compared.The results show that MLER and CDFR were better than OLS in predicting diameter distributions of tree plantations.CDFR produced the best results in terms of fitting statistics.Based on the error statistics calculated for different age groups,CDFR was considered the most suitable method for developing prediction models for Weibull parameters in coniferous plantations.展开更多
AIM： To determine survival parameters as well as char-acteristics of patients with this syndrome. METHODS： The investigation was conducted over a period of eight years, as a prospective, non-random-ized, clinical st...AIM： To determine survival parameters as well as char-acteristics of patients with this syndrome. METHODS： The investigation was conducted over a period of eight years, as a prospective, non-random-ized, clinical study which included 204 patients, treated by chronic hemodialysis. Most patients received hemo-dialysis 12 h per week. As vascular access for hemodi-alysis all subjects had an arteriovenous fstulae. Based on surveys the respondents were divided into groups of patients with and without digital hypoperfusion isch-emic syndrome. Gender, demographic and anthropo-metric characteristics, together with comorbidity and certain habits, were recorded. During this period 34.8% patients died.RESULTS： Patients with digital hypoperfusion ischemic syndrome were older than those without ischemia （P = 0.01）. Hemodialysis treatment lasted signifcantly lon-ger in the patients with digital hypoperfusion ischemic syndrome （P = 0.02）. The incidence of cardiovascular disease （P 〈 0.001） and diabetes mellitus （P = 0.01）, as well as blood fow through the arteriovenous fstula （ P = 0.036）, were higher in patients with digital hypoper-fusion ischemic syndrome. Statistically significant dif-ferences also existed in relation to oxygen saturation （P = 0.04）. Predictive parameters of survival for patients with digital hypoperfusion ischemic syndrome were： adequacy of hemodialysis （B = -3.604, P 〈 0.001）, hypertension （B = -0.920, P = 0.018）, smoking （B = -0.901, P = 0.049）, diabetes mellitus （B = 1.227, P = 0.005）, erythropoietin therapy （B = 1.274, P = 0.002） and hemodiafltration （B = -1.242, P = 0.033）. Kaplan-Meier survival analysis indicated that subjects with and without digital hypoperfusion ischemic syndrome dif-fered regarding the length of survival （P 〈 0.001）, i.e. , patients with confrmed digital hypoperfusion ischemic syndrome died earlier.CONCLUSION： Survival was signifcantly longer in the patients without digital hypoperfusion ischemic syn-drome.展开更多
A novel grooving method for eliminating the bending-induced collapse of hexagonal honeycombs has been proposed,which lies in determining the appropriate grooving parameters,including the grooving spacing,angle,and dep...A novel grooving method for eliminating the bending-induced collapse of hexagonal honeycombs has been proposed,which lies in determining the appropriate grooving parameters,including the grooving spacing,angle,and depth.To this end,a framework built upon the experiment-based,machine learning approach for grooving parameters prediction was presented.The continuously grooved honeycomb bending experiments with various radii,honeycomb types,and thicknesses were carried out,and then the deformation level of honeycombs at different grooving spacing was quantitatively evaluated.A criterion for determining the grooving spacing was proposed by setting an appropriate tolerance for the out-of-plane compression strength.It was found that as the curvature increases,the grooving spacing increases due to the deformation level of honeycombs being more severe at a smaller bending radius.Besides,the grooving spacing drops as the honeycomb thickness increases,and the cell size has a positive effect on the grooving spacing,while the relative density has a negative effect on the grooving spacing.Furthermore,the data-driven Gaussian Process(GP)was trained from the collected data to predict the grooving spacing efficiently.The grooving angle and depth were calculated using the geometrical relationship of honeycombs before and after bending.Finally,the grooving parameters design and verification of a honeycomb sandwich fairing part were conducted based on the proposed grooving method.展开更多
Rock physics inversion is to use seismic elastic properties of underground strata for predicting reservoir petrophysical parameters.The Markov chain Monte Carlo(MCMC)algorithm is commonly used to solve rock physics in...Rock physics inversion is to use seismic elastic properties of underground strata for predicting reservoir petrophysical parameters.The Markov chain Monte Carlo(MCMC)algorithm is commonly used to solve rock physics inverse problems.However,all the parameters to be inverted are iterated simultaneously in the conventional MCMC algorithm.What is obtained is an optimal solution of combining the petrophysical parameters with being inverted.This study introduces the alternating direction(AD)method into the MCMC algorithm(i.e.the optimized MCMC algorithm)to ensure that each petrophysical parameter can get the optimal solution and improve the convergence of the inversion.Firstly,the Gassmann equations and Xu-White model are used to model shaly sandstone,and the theoretical relationship between seismic elastic properties and reservoir petrophysical parameters is established.Then,in the framework of Bayesian theory,the optimized MCMC algorithm is used to generate a Markov chain to obtain the optimal solution of each physical parameter to be inverted and obtain the maximum posterior density of the physical parameter.The proposed method is applied to actual logging and seismic data and the results show that the method can obtain more accurate porosity,saturation,and clay volume.展开更多
Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time ...Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine （ELM）, to improve the efficiency of LOD predictions. Earth orientation parameters （EOP） C04 time-series provides daily values from International Earth Rotation and Reference Systems Service （IERS）, which serves as our database. First, the known predictable effects that can be described by functional models-such as the effects of solid earth, ocean tides, or seasonal atmospheric variations--are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data （i.e., the irregular and quasi-periodic variations） are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks （GRNN）, and adaptive network-based fuzzy inference systems （ANFIS）. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction tech- niques, the mean-absolute-error （MAE） from the proposed method is compared with that from the EOP prediction comparison campaign （EOP PCC）. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.展开更多
Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electro...Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electrochemical energy storage system. A novel failure prediction modeling method of lithium ion battery based on distributed parameter estimation and single particle model is proposed in this work. Lithium ion concentration in the anode of lithium ion battery is an unmeasurable distributed variable. Failure prediction system can estimate lithium ion concentration online, track the failure residual which is the difference between the estimated value and the ideal value. The precaution signal will be triggered when the failure residual is beyond the predefined failure precaution threshold, and the failure countdown prediction module will be activated. The remaining time of the severe failure threshold can be estimated by the failure countdown prediction module according to the changing rate of the failure residual. A simulation example verifies that lithium ion concentration in the anode of lithium ion battery can be estimated exactly and effectively by the failure prediction model. The precaution signal can be triggered reliably, and the remaining time of the severe failure can be forecasted accurately by the failure countdown prediction module.展开更多
Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machin...Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.展开更多
Prosodic control is an important part of speech synthesis system. Prosodic parameters choice right or wrong influences the quality of synthetic speech directly. At present, text to speech system has less effective des...Prosodic control is an important part of speech synthesis system. Prosodic parameters choice right or wrong influences the quality of synthetic speech directly. At present, text to speech system has less effective describe to reflect data relationships in the corpus. A new research approach - data mining technology to discover those relationships by association rules modeling is presented. And a new algorithm for generating association rules of prosodic parameters including pitch parameters and duration parameters from corpus is developed. The output rules improve the correctness of syllable choice in text to speech system.展开更多
基金The authors received funding for this study from the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number(IFP2021-033).
文摘Employing machine learning techniques in predicting the parameters of metamaterial antennas has a significant impact on the reduction of the time needed to design an antenna with optimal parameters using simulation tools.In this paper,we propose a new approach for predicting the bandwidth of metamaterial antenna using a novel ensemble model.The proposed ensemble model is composed of two levels of regression models.The first level consists of three strong models namely,random forest,support vector regression,and light gradient boosting machine.Whereas the second level is based on the ElasticNet regression model,which receives the prediction results from the models in the first level for refinement and producing the final optimal result.To achieve the best performance of these regression models,the advanced squirrel search optimization algorithm(ASSOA)is utilized to search for the optimal set of hyper-parameters of each model.Experimental results show that the proposed two-level ensemble model could achieve a robust prediction of the bandwidth of metamaterial antenna when compared with the recently published ensemble models based on the same publicly available benchmark dataset.The findings indicate that the proposed approach results in root mean square error(RMSE)of(0.013),mean absolute error(MAE)of(0.004),and mean bias error(MBE)of(0.0017).These results are superior to the other competing ensemble models and can predict the antenna bandwidth more accurately.
文摘Varicocele has a prevalence of 15%in the population and represents a primary cause of infertility in 40%of cases and a secondary cause in approximately 80%of cases.It is considered the major correctable cause of male infertility.Despite its high prevalence in the infertile population,a large number of patients with varicocele do not experience reproductive difficulties.For this reason,it is still highly debated which parameters could be used to predict which patients with varicocele will be most likely to benefit from its repair.The main international and European guidelines state that treatment should only be considered in infertile patients with abnormal sperm quality.However,these guidelines do not help physicians to identify which of these patients may benefit from the treatment.Therefore,this narrative review collects the evidence in the literature on the usefulness of some factors as predictors of improvement,highlighting how some of them may be effective in an initial selection of patients to be treated,while others are promising but further studies are needed.Finally,a brief consideration on the possible role of artificial intelligence is proposed.
基金supported by the National Key R&D Program of China(2021YFC2203502 and 2022YFF0711502)the National Natural Science Foundation of China(NSFC)(12173077)+4 种基金the Tianshan Talent Project of Xinjiang Uygur Autonomous Region(2022TSYCCX0095 and 2023TSYCCX0112)the Scientific Instrument Developing Project of the Chinese Academy of Sciences(PTYQ2022YZZD01)China National Astronomical Data Center(NADC)the Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of Finance of China(MOF)and administrated by the Chinese Academy of SciencesNatural Science Foundation of Xinjiang Uygur Autonomous Region(2022D01A360).
文摘Astronomical spectroscopy is crucial for exploring the physical properties,chemical composition,and kinematic behavior of celestial objects.With continuous advancements in observational technology,astronomical spectroscopy faces the dual challenges of rapidly expanding data volumes and relatively lagging data processing capabilities.In this context,the rise of artificial intelligence technologies offers an innovative solution to address these challenges.This paper analyzes the latest developments in the application of machine learning for astronomical spectral data mining and discusses future research directions in AI-based spectral studies.However,the application of machine learning technologies presents several challenges.The high complexity of models often comes with insufficient interpretability,complicating scientific understanding.Moreover,the large-scale computational demands place higher requirements on hardware resources,leading to a significant increase in computational costs.AI-based astronomical spectroscopy research should advance in the following key directions.First,develop efficient data augmentation techniques to enhance model generalization capabilities.Second,explore more interpretable model designs to ensure the reliability and transparency of scientific conclusions.Third,optimize computational efficiency and reduce the threshold for deep-learning applications through collaborative innovations in algorithms and hardware.Furthermore,promoting the integration of cross-band data processing is essential to achieve seamless integration and comprehensive analysis of multi-source data,providing richer,multidimensional information to uncover the mysteries of the universe.
基金Supported by the National Natural Science Foundation of China(No.62304022)。
文摘This research introduces a spectrum-based physics-informed neural network(SP-PINN)model to significantly improve the accuracy of calculation of two-phase flow parameters,surpassing existing methods that have limitations in global and continuous data sampling.SP-PINNs address the challenges of traditional methods in terms of continuous sampling by integrating the spectral analysis and pressure correction into the Navier-Stokes(N-S)equations,enhancing the predictive accuracy especially in critical regions like gas-phase boundaries and velocity peaks.The novel introduction of a pressure-correction module within SP-PINNs mitigates prediction errors,achieving a substantial reduction to 1‰compared with the conventional physics-informed neural network(PINN)approaches.Experimental applications validate the model’s ability to accurately and rapidly predict flow parameters with different sampling time intervals,with the computation time of predicting unsampled data less than 0.01 s.Such advancements signify a 100-fold improvement over traditional DNS calculations,underscoring the model’s potential in the real-time calculation and analysis of multiphase flow dynamics.
基金supported by the National Level Project of China。
文摘In this paper,a progressive approach to predict the multiple shot peening process parameters for complex integral panel is proposed.Firstly,the invariable parameters in the forming process including shot size,mass flow,peening distance and peening angle are determined according to the empirical and machine type.Then,the optimal value of air pressure for the whole shot peening is selected by the experimental data.Finally,the feeding speed for every shot peening path is predicted by regression equation.The integral panel part with thickness from 2 mm to 5 mm and curvature radius from 3200 mm to 16000 mm is taken as a research object,and four experiments are conducted.In order to design specimens for acquiring the forming data,one experiment is conducted to compare the curvature radius of the plate and stringer-structural specimens,which were peened along the middle of the two stringers.The most striking finding of this experiment is that the outer shape error range is below 3.9%,so the plate specimens can be used in predicting feeding speed of the integral panel.The second experiment is performed and results show that when the coverage reaches the limit of 80%,the minimum feeding speed is 50 mm/s.By this feeding speed,the forming curvature radius of the specimens with different thickness from the third experiment is measured and compared with the research object,and the optimal air pressure is 0.15 MPa.Then,the plate specimens with thickness from 2 mm to 5 mm are peened in the fourth experiment,and the measured curvature radius data are used to calculate the feeding speed of different shot peening path by regressive analysis method.The algorithm is validated by forming a test part and the average deviation is 0.496 mm.It is shown that the approach can realize the forming of the integral panel precisely.
文摘Based on experimental data of line heating, the methods of vector mapping, plane projection, and coordinate converting are presented to establish the spectra for line heating distortion discipline which shows the relationship between process parameters and distortion parameters of line heating. Back-propagation network （BP-net） is used to modify tile spectra. Mathematical models for optimizing line heating techniques parameters, which include two-objective functions, are constructed. To convert the multi-objective optimization into a single-objective one, the method of changifig weight coefficient is used, and then the individual fitness function is built up, Taking the number of heating lines, distance between the heating lines＇ border （line space）, and shrink quantity of lines as three restrictive conditions, a hierarchy genetic algorithm （HGA） code is established by making use of information provided by the spectra, in which inner coding and outer coding adopt different heredity arithmetic operators in inherent operating, The numerical example shows that the spectra for line heating distortion discipline presented here can provide accurate information required by techniques parameter prediction of line heating process and the technique parameter optimization method based on HGA provided here can obtain good results for hull plate.
基金supported by the National Special Fund for Major Research Instrument Development(2011YQ140145)111 Project (B07009)+1 种基金the National Natural Science Foundation of China(11002013)Defense Industrial Technology Development Program(A2120110001 and B2120110011)
文摘A new numerical technique named interval finite difference method is proposed for the steady-state temperature field prediction with uncertainties in both physical parameters and boundary conditions. Interval variables are used to quantitatively describe the uncertain parameters with limited information. Based on different Taylor and Neumann series, two kinds of parameter perturbation methods are presented to approximately yield the ranges of the uncertain temperature field. By comparing the results with traditional Monte Carlo simulation, a numerical example is given to demonstrate the feasibility and effectiveness of the proposed method for solving steady-state heat conduction problem with uncertain-but-bounded parameters.
基金Sponsored by the National High-tech Research and Development Program of China (Grant No. 2012AA040911-1)the National Natural Science Foundation of China (Grant No. 60939003)
文摘In view of aircraft engine health condition parameters prediction,an ensemble ELM based prediction approach is proposed in this paper. In the approach,the AdaBoost. RT algorithm is improved to adjust its threshold adaptively,and is utilized as the basic framework to establish the ensemble learning model using ELM as weak learners. The proposed approach is evaluated through the prediction of the actual engine fuel flow deviation time series,and the results demonstrate that this approach is feasible for the prediction of aircraft engine health condition parameters. The performance of the proposed approach is compared with single ELM, single process neural network ( PNN) ,and a similar ensemble ELM based approach using AdaBoost. RT as basic framework. The results show that,the proposed approach is more accurate than single ELM and single PNN,and no worse than the ensemble prediction approach for contrast,furthermore,the given approach is more convenient for practical application. Therefore,the proposed approach is better suited to the prediction of aircraft engine health parameters.
基金Research special fund of the Ministry of Health public service sectors funded projects(201202010)The 12th Five-year Key Project of Beijing Education Sciences Research Institute(AAA12011)
文摘To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.
基金the National Natural Science Foundation of China (Nos. 50674083 and 51074162) for its financial support
文摘To solve the problems of blindness and inefficiency existing in the determination of meso-level mechanical parameters of particle flow code (PFC) models, we firstly designed and numerically carried out orthogonal tests on rock samples to investigate the correlations between macro-and meso-level mechanical parameters of rock-like bonded granular materials. Then based on the artificial intelligent technology, the intelligent prediction systems for nine meso-level mechanical parameters of PFC models were obtained by creating, training and testing the prediction models with the set of data got from the orthogonal tests. Lastly the prediction systems were used to predict the meso-level mechanical parameters of one kind of sandy mudstone, and according to the predicted results the macroscopic properties of the rock were obtained by numerical tests. The maximum relative error between the numerical test results and real rock properties is 3.28% which satisfies the precision requirement in engineering. It shows that this paper provides a fast and accurate method for the determination of meso-level mechanical parameters of PFC models.
文摘Superconductive properties for oxides were predicted by artificial neural network (ANN) method with structural and chemical parameters as inputs. The predicted properties include superconductivity for oxides, distributed ranges of the superconductive transition temperature (Tc) for complex oxides, and Tc values for cuprate superconductors. The calculated results indicated that the adjusted ANN can be used to predict superconductive properties for unknown oxides.
文摘This paper presents a novel artificial intelligence (AI) based approach to predict crucial meteorological parameters such as temperature,pressure,and wind speed,typically calculated from computationally intensive weather research and forecasting (WRF) model.Accurate meteorological data is indispensable for simulating the release of radioactive effluents,especially in dispersion modeling for nuclear emergency decision support systems.Simulation of meteorological conditions during nuclear emergencies using the conventional WRF model is very complex and time-consuming.Therefore,a new artificial neural network (ANN) based technique was proposed as a viable alternative for meteorological prediction.A multi-input multi-output neural network was trained using historical site-specific meteorological data to forecast the meteorological parameters.Comprehensive evaluation of this technique was conducted to test its performance in forecasting various parameters including atmospheric pressure,temperature,and wind speed components in both East-West and North-South directions.The performance of developed network was evaluated on an unknown dataset,and acquired results are within the acceptable range for all meteorological parameters.Results show that ANNs possess the capability to forecast meteorological parameters,such as temperature and pressure,at multiple spatial locations within a grid with high accuracy,utilizing input data from a single station.However,accuracy is slightly compromised when predicting wind speed components.Root mean square error (RMSE) was utilized to report the accuracy of predicted results,with values of 1.453℃for temperature,77 Pa for predicted pressure,1.058 m/s for the wind speed of U-component and 0.959 m/s for the wind speed of V-component.In conclusion,this approach offers a precise,efficient,and wellinformed method for administrative decision-making during nuclear emergencies.
基金supported by the Natural Science Foundation of China(32071758 and U21A20244)the Fundamental Research Funds for the Central Universities of China(No.2572020BA01)。
文摘Diameter distribution models play an important role in forest inventories,growth prediction,and management.The Weibull probability density function is widely used in forestry.Although a number of methods have been proposed to predict or recover the Weibull distribution,their applicability and predictive performance for the major tree species of China remain to be determined.Trees in sample plots of three even-aged coniferous species(Larix olgensis,Pinus sylvestris and Pinus koraiensis)were measured both in un-thinned and thinned stands to develop parameter prediction models for the Weibull probability density function.Ordinary least squares(OLS)and maximum likelihood regression(MLER),as well as cumulative distribution function regression(CDFR)were used,and their performance compared.The results show that MLER and CDFR were better than OLS in predicting diameter distributions of tree plantations.CDFR produced the best results in terms of fitting statistics.Based on the error statistics calculated for different age groups,CDFR was considered the most suitable method for developing prediction models for Weibull parameters in coniferous plantations.
基金In Part by the Ministry of Education and Science of Serbia,Grant Ⅲ41010by the Pristina/K Mitrovica Medical Faculty,Serbia,Junior Project Number 07/09
文摘AIM： To determine survival parameters as well as char-acteristics of patients with this syndrome. METHODS： The investigation was conducted over a period of eight years, as a prospective, non-random-ized, clinical study which included 204 patients, treated by chronic hemodialysis. Most patients received hemo-dialysis 12 h per week. As vascular access for hemodi-alysis all subjects had an arteriovenous fstulae. Based on surveys the respondents were divided into groups of patients with and without digital hypoperfusion isch-emic syndrome. Gender, demographic and anthropo-metric characteristics, together with comorbidity and certain habits, were recorded. During this period 34.8% patients died.RESULTS： Patients with digital hypoperfusion ischemic syndrome were older than those without ischemia （P = 0.01）. Hemodialysis treatment lasted signifcantly lon-ger in the patients with digital hypoperfusion ischemic syndrome （P = 0.02）. The incidence of cardiovascular disease （P 〈 0.001） and diabetes mellitus （P = 0.01）, as well as blood fow through the arteriovenous fstula （ P = 0.036）, were higher in patients with digital hypoper-fusion ischemic syndrome. Statistically significant dif-ferences also existed in relation to oxygen saturation （P = 0.04）. Predictive parameters of survival for patients with digital hypoperfusion ischemic syndrome were： adequacy of hemodialysis （B = -3.604, P 〈 0.001）, hypertension （B = -0.920, P = 0.018）, smoking （B = -0.901, P = 0.049）, diabetes mellitus （B = 1.227, P = 0.005）, erythropoietin therapy （B = 1.274, P = 0.002） and hemodiafltration （B = -1.242, P = 0.033）. Kaplan-Meier survival analysis indicated that subjects with and without digital hypoperfusion ischemic syndrome dif-fered regarding the length of survival （P 〈 0.001）, i.e. , patients with confrmed digital hypoperfusion ischemic syndrome died earlier.CONCLUSION： Survival was signifcantly longer in the patients without digital hypoperfusion ischemic syn-drome.
基金the National Natural Science Foundation of China(No.11902256)the Natural Science Basic Research Program of Shaanxi,China(No.2019JQ-479).
文摘A novel grooving method for eliminating the bending-induced collapse of hexagonal honeycombs has been proposed,which lies in determining the appropriate grooving parameters,including the grooving spacing,angle,and depth.To this end,a framework built upon the experiment-based,machine learning approach for grooving parameters prediction was presented.The continuously grooved honeycomb bending experiments with various radii,honeycomb types,and thicknesses were carried out,and then the deformation level of honeycombs at different grooving spacing was quantitatively evaluated.A criterion for determining the grooving spacing was proposed by setting an appropriate tolerance for the out-of-plane compression strength.It was found that as the curvature increases,the grooving spacing increases due to the deformation level of honeycombs being more severe at a smaller bending radius.Besides,the grooving spacing drops as the honeycomb thickness increases,and the cell size has a positive effect on the grooving spacing,while the relative density has a negative effect on the grooving spacing.Furthermore,the data-driven Gaussian Process(GP)was trained from the collected data to predict the grooving spacing efficiently.The grooving angle and depth were calculated using the geometrical relationship of honeycombs before and after bending.Finally,the grooving parameters design and verification of a honeycomb sandwich fairing part were conducted based on the proposed grooving method.
基金supported by the National Natural Science Foundation of China(No.42174146)CNPC major forwardlooking basic science and technology projects(No.2021DJ0204).
文摘Rock physics inversion is to use seismic elastic properties of underground strata for predicting reservoir petrophysical parameters.The Markov chain Monte Carlo(MCMC)algorithm is commonly used to solve rock physics inverse problems.However,all the parameters to be inverted are iterated simultaneously in the conventional MCMC algorithm.What is obtained is an optimal solution of combining the petrophysical parameters with being inverted.This study introduces the alternating direction(AD)method into the MCMC algorithm(i.e.the optimized MCMC algorithm)to ensure that each petrophysical parameter can get the optimal solution and improve the convergence of the inversion.Firstly,the Gassmann equations and Xu-White model are used to model shaly sandstone,and the theoretical relationship between seismic elastic properties and reservoir petrophysical parameters is established.Then,in the framework of Bayesian theory,the optimized MCMC algorithm is used to generate a Markov chain to obtain the optimal solution of each physical parameter to be inverted and obtain the maximum posterior density of the physical parameter.The proposed method is applied to actual logging and seismic data and the results show that the method can obtain more accurate porosity,saturation,and clay volume.
基金supported by the West Light Foundation of the Chinese Academy of Sciences
文摘Traditional artificial neural networks （ANN） such as back-propagation neural networks （BPNN） provide good predictions of length-of-day （LOD）. However, the determination of network topology is difficult and time consuming. Therefore, we propose a new type of neural network, extreme learning machine （ELM）, to improve the efficiency of LOD predictions. Earth orientation parameters （EOP） C04 time-series provides daily values from International Earth Rotation and Reference Systems Service （IERS）, which serves as our database. First, the known predictable effects that can be described by functional models-such as the effects of solid earth, ocean tides, or seasonal atmospheric variations--are removed a priori from the C04 time-series. Only the residuals after the subtraction of a priori model from the observed LOD data （i.e., the irregular and quasi-periodic variations） are employed for training and predictions. The predicted LOD is the sum of a prior extrapolation model and the ELM predictions of the residuals. Different input patterns are discussed and compared to optimize the network solution. The prediction results are analyzed and compared with those obtained by other machine learning-based prediction methods, including BPNN, generalization regression neural networks （GRNN）, and adaptive network-based fuzzy inference systems （ANFIS）. It is shown that while achieving similar prediction accuracy, the developed method uses much less training time than other methods. Furthermore, to conduct a direct comparison with the existing prediction tech- niques, the mean-absolute-error （MAE） from the proposed method is compared with that from the EOP prediction comparison campaign （EOP PCC）. The results indicate that the accuracy of the proposed method is comparable with that of the former techniques. The implementation of the proposed method is simple.
基金This work was supported by the Fundamental Research Funds for the Central Universities （No.2017JBM003）, the National Natural Science Foundation of China （No.61575053, No.61504008）, and the Specialized Research Fund for the Doctoral Program of Higher Education of China （No.20130009120042）.
文摘Lithium ion battery has typical character of distributed parameter system, and can be described precisely by partial differential equations and multi-physics theory because lithium ion battery is a complicated electrochemical energy storage system. A novel failure prediction modeling method of lithium ion battery based on distributed parameter estimation and single particle model is proposed in this work. Lithium ion concentration in the anode of lithium ion battery is an unmeasurable distributed variable. Failure prediction system can estimate lithium ion concentration online, track the failure residual which is the difference between the estimated value and the ideal value. The precaution signal will be triggered when the failure residual is beyond the predefined failure precaution threshold, and the failure countdown prediction module will be activated. The remaining time of the severe failure threshold can be estimated by the failure countdown prediction module according to the changing rate of the failure residual. A simulation example verifies that lithium ion concentration in the anode of lithium ion battery can be estimated exactly and effectively by the failure prediction model. The precaution signal can be triggered reliably, and the remaining time of the severe failure can be forecasted accurately by the failure countdown prediction module.
文摘Performance parameter prediction technology is the core research content of aeroengine health management,and more and more machine learning algorithms have been applied in the field.Regularized extreme learning machine(RELM)is one of them.However,the regularization parameter determination of RELM consumes computational resources,which makes it unsuitable in the field of aeroengine performance parameter prediction with a large amount of data.This paper uses the forward and backward segmentation(FBS)algorithms to improve the RELM performance,and introduces an adaptive step size determination method and an improved solution mechanism to obtain a new machine learning algorithm.While maintaining good generalization,the new algorithm is not sensitive to regularization parameters,which greatly saves computing resources.The experimental results on the public data sets prove the above conclusions.Finally,the new algorithm is applied to the prediction of aero-engine performance parameters,and the excellent prediction performance is achieved.
基金This work was supported by the 863 National High Technology Project and the National Natural Science Foundation of China (No. 60275014).
文摘Prosodic control is an important part of speech synthesis system. Prosodic parameters choice right or wrong influences the quality of synthetic speech directly. At present, text to speech system has less effective describe to reflect data relationships in the corpus. A new research approach - data mining technology to discover those relationships by association rules modeling is presented. And a new algorithm for generating association rules of prosodic parameters including pitch parameters and duration parameters from corpus is developed. The output rules improve the correctness of syllable choice in text to speech system.
