We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parame...We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.展开更多
Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achievin...Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.展开更多
This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the pred...This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.展开更多
Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions tha...To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,whic...Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,which is characterized by less adverse reaction and convenient operation.It has been widely used in the treatment of various diseases.This review introduces six major clinical applications of TEAS,named analgesia,regulation of gastrointestinal function,improvement of reproductive function,enhancement of cognitive function,promotion of limb function recovery and relief of fatigue.Besides,TEAS has been ap-plied to the treatment of other chronic diseases such as hypertension and diabetes,achieving satisfactory clinical effects.However,two crucial challenges are encountered in the development of TEAS.One is the lack of standardization in the selection of parameters such as waveform,frequency,intensity and stimula-tion duration.The other is the limitation on the flexibility in the acupoint selection.This review analyzes key issues that need to be addressed in the current clinical application of TEAS,such as the selection of parameters and acupoints,and this review provides a certain reference value for optimizing regimens of TEAS and promoting its development and application.展开更多
This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parame...This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parameters.These pathogens included Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,Pseudomonas aeruginosa,and Staphylococcus aureus.A total of 1951 men of infertile couples were recruited between 23 March 2023,and 17 May 2023,at the Department of Reproductive Medicine of The First People’s Hospital of Yunnan Province(Kunming,China).Multiplex polymerase chain reaction and capillary electrophoresis were used for HPV genotyping.Polymerase chain reaction and electrophoresis were also used to detect the presence of other STIs.The overall prevalence of HPV infection was 12.4%.The top five prevalent HPV subtypes were types 56,52,43,16,and 53 among those tested positive for HPV.Other common infections with high prevalence rates were Ureaplasma urealyticum(28.3%),Ureaplasma parvum(20.4%),and Enterococcus faecalis(9.5%).The prevalence rates of HPV coinfection with Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,and Staphylococcus aureus were 24.8%,25.4%,10.6%,6.4%,2.4%,7.9%,5.9%,0.9%,and 1.3%,respectively.The semen volume and total sperm count were greatly decreased by HPV infection alone.Coinfection with HPV and Ureaplasma urealyticum significantly reduced sperm motility and viability.Our study shows that coinfection with STIs is highly prevalent in the semen of infertile men and that coinfection with pathogens can seriously affect semen parameters,emphasizing the necessity of semen screening for STIs.展开更多
The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied...The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied angle shear tests(VASTs)were designed using acoustic emission(AE)detection and digital image correlation technologies to evaluate the mechanical behaviors of typical red sandstone.AE signal parameters revealed differences in the number and intensity of microcracks within the sandstone,with a test angle(α)of 50°identified as a significant turning point for its failure properties.Whenα³50°,microcrack activity intensified,and the proportion of tensile cracks increased.Asαincreased,the number of fragments generated after failure decreased,fragment sizes became smaller,and the crack network simplified.Cracks extended from the two cut slits at the ends of the rock,gradually penetrating along the centerline towards the central location,as observed from the evolution of the strain concentration field.Both cohesion(c)and internal friction angle(ϕ)measured in VAST were lower than those measured under conventional triaxial compression.展开更多
The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fi...The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fitting methods often overestimate the charge transfer overpotential,leading to substantial errors in reaction rate constant measurements.These inaccuracies hinder the accurate prediction of voltage profiles and overall cell performance.In this study,we propose the characteristic time-decomposed overpotential(CTDO)method,which employs a single-layer particle electrode(SLPE)structure to eliminate interference overpotentials.By leveraging the distribution of relaxation times(DRT),our method effectively isolates the characteristic time of the charge transfer process,enabling a more precise determination of the reaction rate constant.Simulation results indicate that our approach reduces measurement errors to below 2%,closely aligning with theoretical values.Furthermore,experimental validation demonstrates an 80% reduction in error compared to the conventional galvanostatic intermittent titration technique(GITT)method.Overall,this study provides a novel voltage-based approach for determining the reaction rate constant,enhancing the applicability of theoretical analysis in electrode structural design and facilitating rapid battery optimization.展开更多
Austenitic stainless steel(ASS)is a common material used in high-pressure hydrogen systems.Prolonged exposure to high-pressure hydrogen can cause hydrogen embrittlement(HE),raising significant safety concerns.Selectiv...Austenitic stainless steel(ASS)is a common material used in high-pressure hydrogen systems.Prolonged exposure to high-pressure hydrogen can cause hydrogen embrittlement(HE),raising significant safety concerns.Selective Laser Melting(SLM),known for its high precision,is a promising additive manufacturing technology that has been widely adopted across various industries.Studies have reported that under certain SLM manufacturing conditions and process parameters,the HE resistance of SLM ASS is significantly better than that of conventionally manufactured(CM)ASS,showing great potential for application in high-pressure hydrogen systems.Thus,studying the HE of SLM ASS is crucial for further improving the safety of high-pressure hydrogen systems.This paper provides an overview of the SLM process,reviews the mechanisms of HE and their synergistic effects,and analyzes the HE characteristics of SLM ASS.Additionally,it examines the influence of unique microstructures and SLM process variables on HE of SLM ASS and offers recommendations for future research to enhance the safety of high-pressure hydrogen systems.展开更多
The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.Howev...The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.展开更多
A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that th...A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.展开更多
Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in c...Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.展开更多
The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods fo...The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods for centroid measurement often necessitate custom equipment and specialized positioning devices,leading to high costs and limited accuracy.Here,we present a centroid measurement method that integrates 3D scanning technology,enabling accurate measurement of centroid across various types of objects without the need for specialized positioning fixtures.A theoretical framework for centroid measurement was established,which combined the principle of the multi-point weighing method with 3D scanning technology.The measurement accuracy was evaluated using a designed standard component.Experimental results demonstrate that the discrepancies between the theoretical and the measured centroid of a standard component with various materials and complex shapes in the X,Y,and Z directions are 0.003 mm,0.009 mm,and 0.105 mm,respectively,yielding a spatial deviation of 0.106 mm.Qualitative verification was conducted through experimental validation of three distinct types.They confirmed the reliability of the proposed method,which allowed for accurate centroid measurements of various products without requiring positioning fixtures.This advancement significantly broadened the applicability and scope of centroid measurement devices,offering new theoretical insights and methodologies for the measurement of complex parts and systems.展开更多
Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in speci...Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.展开更多
The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Ma...The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Many scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated ambiguity.The goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site selection.The characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these uncertainties.To tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s method.Following this,an intelligent algorithm is suggested.The steps of the suggested algorithm are explained with an example that explains itself.The compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their viability.This study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,respectively.It uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior literature.It is simpler for the decisionmakers to look at each option separately because the decision is uncertain.Comparing the computed results,it is discovered that they are consistent and dependable because of their preferred properties.展开更多
To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and l...To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and low energy consumption was proposed.But the unreasonable process parameters may lead to the inferior consistency of the forming quality of single-channel multilayer in Joule heat additive manufacturing process,and it is difficult to reach the condition for forming thinwalled parts.Orthogonal experiments were designed to fabricate single-channel multilayer samples with varying numbers of layers,and their forming quality was evaluated.The influence of printing current,forming speed,and contact pressure on the forming quality of the single-channel multilayer was analyzed.The optimal process parameters were obtained and the quality characterization of the experiment results was conducted.Results show that the printing current has the most significant influence on the forming quality of the single-channel multilayer.Under the optimal process parameters,the forming section is well fused and the surface is continuously smooth.The surface roughness of a single-channel 3-layer sample is 0.16μm,and the average Vickers hardness of cross section fusion zone is 317 HV,which lays a foundation for the subsequent use of Joule heat additive manufacturing technique to form thinwall parts.展开更多
In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the...In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the identification of these weak influence parameters in the complex systems and propose a identification model based on the parameter recursion.As an application,three parameters of the steam generator are identified,that is,the valve opening,the valve CV value,and the reference water level,in which the valve opening and the reference water level are weak influence parameters under most operating conditions.Numerical simulation results show that,in comparison with the multi-layer perceptron(MLP),the identification error rate is decreased.Actually,the average identification error rate for the valve opening decreases by 0.96%,for the valve CV decreases by 0.002%,and for the reference water level decreases by 12%after one recursion.After two recursions,the average identification error rate for the valve opening decreases by 11.07%,for the valve CV decreases by 2.601%,and for the reference water level decreases by 95.79%.This method can help to improve the control of the steam generator.展开更多
Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parame...Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.展开更多
基金supported in part by the National Key Research and Development Program of China (Grant No.2020YFC2201504)the National Natural Science Foundation of China (Grant Nos.12588101 and 12535002)。
文摘We investigate the null tests of cosmic accelerated expansion by using the baryon acoustic oscillation(BAO)data measured by the dark energy spectroscopic instrument(DESI)and reconstruct the dimensionless Hubble parameter E(z)from the DESI BAO Alcock-Paczynski(AP)data using Gaussian process to perform the null test.We find strong evidence of accelerated expansion from the DESI BAO AP data.By reconstructing the deceleration parameter q(z) from the DESI BAO AP data,we find that accelerated expansion persisted until z■0.7 with a 99.7%confidence level.Additionally,to provide insights into the Hubble tension problem,we propose combining the reconstructed E(z) with D_(H)/r_(d) data to derive a model-independent result r_(d)h=99.8±3.1 Mpc.This result is consistent with measurements from cosmic microwave background(CMB)anisotropies using the ΛCDM model.We also propose a model-independent method for reconstructing the comoving angular diameter distance D_(M)(z) from the distance modulus μ,using SNe Ia data and combining this result with DESI BAO data of D_(M)/r_(d) to constrain the value of r_(d).We find that the value of r_(d),derived from this model-independent method,is smaller than that obtained from CMB measurements,with a significant discrepancy of at least 4.17σ.All the conclusions drawn in this paper are independent of cosmological models and gravitational theories.
基金supported by Key Program of National Natural Science Foundation of China(U2368215)the Science and Technology Research and Development Program Project of China Railway Group Co.,Ltd.(N2023J056).
文摘Accurately predicting fatigue life under multiaxial fatigue damage conditions is essential for ensuring the safety of critical components in service.However,due to the complexity of fatigue failure mechanisms,achieving accurate multiaxial fatigue life predictions remains challenging.Traditional multiaxial fatigue prediction models are often limited by specific material properties and loading conditions,making it difficult to maintain reliable life prediction results beyond these constraints.This paper presents a study on the impact of seven key feature quantities on multiaxial fatigue life,using Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),and Fully Connected Neural Networks(FCNN)within a deep learning framework.Fatigue test results from eight metal specimens were analyzed to identify these feature quantities,which were then extracted as critical time-series features.Using a CNN-LSTM network,these features were combined to form a feature matrix,which was subsequently input into an FCNN to predict metal fatigue life.A comparison of the fatigue life prediction results from the STFAN model with those from traditional prediction models—namely,the equivalent strain method,the maximum shear strain method,and the critical plane method—shows that the majority of predictions for the five metal materials and various loading conditions based on the STFAN model fall within an error band of 1.5 times.Additionally,all data points are within an error band of 2 times.These findings indicate that the STFAN model provides superior prediction accuracy compared to the traditional models,highlighting its broad applicability and high precision.
基金supported by the National Key R&D Program of China[grant number 2023YFC3008004]。
文摘This study introduces a new ocean surface friction velocity scheme and a modified Thompson cloud microphysics parameterization scheme into the CMA-TYM model.The impact of these two parameterization schemes on the prediction of the movement track and intensity of Typhoon Kompasu in 2021 is examined.Additionally,the possible reasons for their effects on tropical cyclone(TC)intensity prediction are analyzed.Statistical results show that both parameterization schemes improve the predictions of Typhoon Kompasu’s track and intensity.The influence on track prediction becomes evident after 60 h of model integration,while the significant positive impact on intensity prediction is observed after 66 h.Further analysis reveals that these two schemes affect the timing and magnitude of extreme TC intensity values by influencing the evolution of the TC’s warm-core structure.
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金supported by the Beijing Natural Science Foundation(No.L212029)the National Natural Science Foundation of China(No.62271043).
文摘To meet the requirements of electromagnetic(EM)theory and applied physics,this study presents an overview of the state-of-the-art research on obtaining the EM properties of media and points out potential solutions that can break through the bottlenecks of current methods.Firstly,based on the survey of three mainstream approaches for acquiring EM properties of media,we identify the difficulties when implementing them in realistic environments.With a focus on addressing these problems and challenges,we propose a novel paradigm for obtaining the EM properties of multi-type media in realistic environments.Particularly,within this paradigm,we describe the implementation approach of the key technology,namely“multipath extraction using heterogeneous wave propagation data in multi-spectrum cases”.Finally,the latest measurement and simulation results show that the EM properties of multi-type media in realistic environments can be precisely and efficiently acquired by the methodology proposed in this study.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
基金Supported by Shanghai 2020“Science and Technology Innovation Action Plan”Medical Innovation Research Special Program:20Y21902800Shanghai Municipal Health Commission Shanghai Three-Year Action Plan to Further Accelerate the Development of Traditional Chinese Medicine Inheritance and Innovation:ZY(2021-2023)−0302)+1 种基金Shanghai Key Specialty(Acupuncture)Construction Project:shslczdzk04701Shanghai 2024"Science and Technology Innovation Action Plan"star cultivation(Sail special):24YF2740600.
文摘Transcutaneous electrical acupoint stimulation(TEAS)is a kind of physical therapy that use electric cur-rent through the electrodes placed on the surface of acupoints to produce clinical effects in the human body,which is characterized by less adverse reaction and convenient operation.It has been widely used in the treatment of various diseases.This review introduces six major clinical applications of TEAS,named analgesia,regulation of gastrointestinal function,improvement of reproductive function,enhancement of cognitive function,promotion of limb function recovery and relief of fatigue.Besides,TEAS has been ap-plied to the treatment of other chronic diseases such as hypertension and diabetes,achieving satisfactory clinical effects.However,two crucial challenges are encountered in the development of TEAS.One is the lack of standardization in the selection of parameters such as waveform,frequency,intensity and stimula-tion duration.The other is the limitation on the flexibility in the acupoint selection.This review analyzes key issues that need to be addressed in the current clinical application of TEAS,such as the selection of parameters and acupoints,and this review provides a certain reference value for optimizing regimens of TEAS and promoting its development and application.
基金supported by the Yunnan Provincial Key Laboratory of Clinical Virology(No.202002AG070062)the Key Projects of Yunnan Province Science and Technology Department(No.202302AA310044)the National Natural Science Foundation of China(No.82060664).
文摘This study primarily aimed to investigate the prevalence of human papillomavirus(HPV)and other common pathogens of sexually transmitted infections(STIs)in spermatozoa of infertile men and their effects on semen parameters.These pathogens included Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,Pseudomonas aeruginosa,and Staphylococcus aureus.A total of 1951 men of infertile couples were recruited between 23 March 2023,and 17 May 2023,at the Department of Reproductive Medicine of The First People’s Hospital of Yunnan Province(Kunming,China).Multiplex polymerase chain reaction and capillary electrophoresis were used for HPV genotyping.Polymerase chain reaction and electrophoresis were also used to detect the presence of other STIs.The overall prevalence of HPV infection was 12.4%.The top five prevalent HPV subtypes were types 56,52,43,16,and 53 among those tested positive for HPV.Other common infections with high prevalence rates were Ureaplasma urealyticum(28.3%),Ureaplasma parvum(20.4%),and Enterococcus faecalis(9.5%).The prevalence rates of HPV coinfection with Ureaplasma urealyticum,Ureaplasma parvum,Chlamydia trachomatis,Mycoplasma genitalium,herpes simplex virus 2,Neisseria gonorrhoeae,Enterococcus faecalis,Streptococcus agalactiae,and Staphylococcus aureus were 24.8%,25.4%,10.6%,6.4%,2.4%,7.9%,5.9%,0.9%,and 1.3%,respectively.The semen volume and total sperm count were greatly decreased by HPV infection alone.Coinfection with HPV and Ureaplasma urealyticum significantly reduced sperm motility and viability.Our study shows that coinfection with STIs is highly prevalent in the semen of infertile men and that coinfection with pathogens can seriously affect semen parameters,emphasizing the necessity of semen screening for STIs.
基金Project(52374150)supported by the National Natural Science Foundation of ChinaProject(2021RC3007)supported by the Science and Technology Innovation Program of Hunan Province,China。
文摘The mechanical parameters and failure characteristics of sandstone under compressive-shear stress states provide crucial theoretical references for underground engineering construction.In this study,a series of varied angle shear tests(VASTs)were designed using acoustic emission(AE)detection and digital image correlation technologies to evaluate the mechanical behaviors of typical red sandstone.AE signal parameters revealed differences in the number and intensity of microcracks within the sandstone,with a test angle(α)of 50°identified as a significant turning point for its failure properties.Whenα³50°,microcrack activity intensified,and the proportion of tensile cracks increased.Asαincreased,the number of fragments generated after failure decreased,fragment sizes became smaller,and the crack network simplified.Cracks extended from the two cut slits at the ends of the rock,gradually penetrating along the centerline towards the central location,as observed from the evolution of the strain concentration field.Both cohesion(c)and internal friction angle(ϕ)measured in VAST were lower than those measured under conventional triaxial compression.
基金supported by the National Key R&D Program of China 2022YFB2404300the National Natural Science Foundation of China U22B2069the China Postdoctoral Science Foundation 2024M761006。
文摘The reaction rate constant is a crucial kinetic parameter that governs the charge and discharge performance of batteries,particularly in high-rate and thick-electrode applications.However,conventional estimation or fitting methods often overestimate the charge transfer overpotential,leading to substantial errors in reaction rate constant measurements.These inaccuracies hinder the accurate prediction of voltage profiles and overall cell performance.In this study,we propose the characteristic time-decomposed overpotential(CTDO)method,which employs a single-layer particle electrode(SLPE)structure to eliminate interference overpotentials.By leveraging the distribution of relaxation times(DRT),our method effectively isolates the characteristic time of the charge transfer process,enabling a more precise determination of the reaction rate constant.Simulation results indicate that our approach reduces measurement errors to below 2%,closely aligning with theoretical values.Furthermore,experimental validation demonstrates an 80% reduction in error compared to the conventional galvanostatic intermittent titration technique(GITT)method.Overall,this study provides a novel voltage-based approach for determining the reaction rate constant,enhancing the applicability of theoretical analysis in electrode structural design and facilitating rapid battery optimization.
基金finncially supported by the National Natural Science Foundation of China(No.52075183)the Guangdong Basic and Applied Research Fundamental(No.2023A1515010692)the Key-Area Research and Development Program of Guangdong Province(Nos.2024B1111080002 and 2020B0404020004).
文摘Austenitic stainless steel(ASS)is a common material used in high-pressure hydrogen systems.Prolonged exposure to high-pressure hydrogen can cause hydrogen embrittlement(HE),raising significant safety concerns.Selective Laser Melting(SLM),known for its high precision,is a promising additive manufacturing technology that has been widely adopted across various industries.Studies have reported that under certain SLM manufacturing conditions and process parameters,the HE resistance of SLM ASS is significantly better than that of conventionally manufactured(CM)ASS,showing great potential for application in high-pressure hydrogen systems.Thus,studying the HE of SLM ASS is crucial for further improving the safety of high-pressure hydrogen systems.This paper provides an overview of the SLM process,reviews the mechanisms of HE and their synergistic effects,and analyzes the HE characteristics of SLM ASS.Additionally,it examines the influence of unique microstructures and SLM process variables on HE of SLM ASS and offers recommendations for future research to enhance the safety of high-pressure hydrogen systems.
基金financially supported by the National Natural Science Foundation of China(No.52174297).
文摘The endpoint carbon content in the converter is critical for the quality of steel products,and accurately predicting this parameter is an effective way to reduce alloy consumption and improve smelting efficiency.However,most scholars currently focus on modifying methods to enhance model accuracy,while overlooking the extent to which input parameters influence accuracy.To address this issue,in this study,a prediction model for the endpoint carbon content in the converter was developed using factor analysis(FA)and support vector machine(SVM)optimized by improved particle swarm optimization(IPSO).Analysis of the factors influencing the endpoint carbon content during the converter smelting process led to the identification of 21 input parameters.Subsequently,FA was used to reduce the dimensionality of the data and applied to the prediction model.The results demonstrate that the performance of the FA-IPSO-SVM model surpasses several existing methods,such as twin support vector regression and support vector machine.The model achieves hit rates of 89.59%,96.21%,and 98.74%within error ranges of±0.01%,±0.015%,and±0.02%,respectively.Finally,based on the prediction results obtained by sequentially removing input parameters,the parameters were classified into high influence(5%-7%),medium influence(2%-5%),and low influence(0-2%)categories according to their varying degrees of impact on prediction accuracy.This classi-fication provides a reference for selecting input parameters in future prediction models for endpoint carbon content.
基金financially supported by the National Natural Science Foundation of China(Grant No.42172292)Taishan Scholars Project Special Funding,and Shandong Energy Group(Grant No.SNKJ 2022A01-R26).
文摘A conceptual model of intermittent joints is introduced to the cyclic shear test in the laboratory to explore the effects of loading parameters on its shear behavior under cyclic shear loading.The results show that the loading parameters(initial normal stress,normal stiffness,and shear velocity)determine propagation paths of the wing and secondary cracks in rock bridges during the initial shear cycle,creating different morphologies of macroscopic step-path rupture surfaces and asperities on them.The differences in stress state and rupture surface induce different cyclic shear responses.It shows that high initial normal stress accelerates asperity degradation,raises shear resistance,and promotes compression of intermittent joints.In addition,high normal stiffness provides higher normal stress and shear resistance during the initial cycles and inhibits the dilation and compression of intermittent joints.High shear velocity results in a higher shear resistance,greater dilation,and greater compression.Finally,shear strength is most sensitive to initial normal stress,followed by shear velocity and normal stiffness.Moreover,average dilation angle is most sensitive to initial normal stress,followed by normal stiffness and shear velocity.During the shear cycles,frictional coefficient is affected by asperity degradation,backfilling of rock debris,and frictional area,exhibiting a non-monotonic behavior.
基金supported by the National Key R&D Program of China [grant number 2023YFF0805202]the National Natural Science Foun-dation of China [grant number 42175045]the Strategic Priority Research Program of the Chinese Academy of Sciences [grant number XDB42000000]。
文摘Atlantic Meridional Overturning Circulation(AMOC)plays a central role in long-term climate variations through its heat and freshwater transports,which can collapse under a rapid increase of greenhouse gas forcing in climate models.Previous studies have suggested that the deviation of model parameters is one of the major factors in inducing inaccurate AMOC simulations.In this work,with a low-resolution earth system model,the authors try to explore whether a reasonable adjustment of the key model parameter can help to re-establish the AMOC after its collapse.Through a new optimization strategy,the extra freshwater flux(FWF)parameter is determined to be the dominant one affecting the AMOC’s variability.The traditional ensemble optimal interpolation(EnOI)data assimilation and new machine learning methods are adopted to optimize the FWF parameter in an abrupt 4×CO_(2) forcing experiment to improve the adaptability of model parameters and accelerate the recovery of AMOC.The results show that,under an abrupt 4×CO_(2) forcing in millennial simulations,the AMOC will first collapse and then re-establish by the default FWF parameter slowly.However,during the parameter adjustment process,the saltier and colder sea water over the North Atlantic region are the dominant factors in usefully improving the adaptability of the FWF parameter and accelerating the recovery of AMOC,according to their physical relationship with FWF on the interdecadal timescale.
基金supported by National Natural Science Foundation of China(No.52176122).
文摘The centroid coordinate serves as a critical control parameter in motion systems,including aircraft,missiles,rockets,and drones,directly influencing their motion dynamics and control performance.Traditional methods for centroid measurement often necessitate custom equipment and specialized positioning devices,leading to high costs and limited accuracy.Here,we present a centroid measurement method that integrates 3D scanning technology,enabling accurate measurement of centroid across various types of objects without the need for specialized positioning fixtures.A theoretical framework for centroid measurement was established,which combined the principle of the multi-point weighing method with 3D scanning technology.The measurement accuracy was evaluated using a designed standard component.Experimental results demonstrate that the discrepancies between the theoretical and the measured centroid of a standard component with various materials and complex shapes in the X,Y,and Z directions are 0.003 mm,0.009 mm,and 0.105 mm,respectively,yielding a spatial deviation of 0.106 mm.Qualitative verification was conducted through experimental validation of three distinct types.They confirmed the reliability of the proposed method,which allowed for accurate centroid measurements of various products without requiring positioning fixtures.This advancement significantly broadened the applicability and scope of centroid measurement devices,offering new theoretical insights and methodologies for the measurement of complex parts and systems.
基金supported by the National Key R&D Program of China(No.2021YFB0301200)National Natural Science Foundation of China(No.62025208).
文摘Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning paradigm.While this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader deployment.Parameter-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational efficiency.Among these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA variants.Despite these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains underexplored.This study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM architectures.Our investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do so.These insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized LLMs.Moreover,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller one.This study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM fine-tuning in resource-constrained environments.
文摘The dramatic rise in the number of people living in cities has made many environmental and social problems worse.The search for a productive method for disposing of solid waste is the most notable of these problems.Many scholars have referred to it as a fuzzy multi-attribute or multi-criteria decision-making problem using various fuzzy set-like approaches because of the inclusion of criteria and anticipated ambiguity.The goal of the current study is to use an innovative methodology to address the expected uncertainties in the problem of solid waste site selection.The characteristics(or sub-attributes)that decision-makers select and the degree of approximation they accept for various options can both be indicators of these uncertainties.To tackle these problems,a novel mathematical structure known as the fuzzy parameterized possibility single valued neutrosophic hypersoft expert set(ρˆ-set),which is initially described,is integrated with a modified version of Sanchez’s method.Following this,an intelligent algorithm is suggested.The steps of the suggested algorithm are explained with an example that explains itself.The compatibility of solid waste management sites and systems is discussed,and rankings are established along with detailed justifications for their viability.This study’s strengths lie in its application of fuzzy parameterization and possibility grading to effectively handle the uncertainties embodied in the parameters’nature and alternative approximations,respectively.It uses specific mathematical formulations to compute the fuzzy parameterized degrees and possibility grades that are missing from the prior literature.It is simpler for the decisionmakers to look at each option separately because the decision is uncertain.Comparing the computed results,it is discovered that they are consistent and dependable because of their preferred properties.
基金Shaanxi Province Qin Chuangyuan“Scientist+Engineer”Team Construction Project(2022KXJ-071)2022 Qin Chuangyuan Achievement Transformation Incubation Capacity Improvement Project(2022JH-ZHFHTS-0012)+1 种基金Shaanxi Province Key Research and Development Plan-“Two Chains”Integration Key Project-Qin Chuangyuan General Window Industrial Cluster Project(2023QCY-LL-02)Xixian New Area Science and Technology Plan(2022-YXYJ-003,2022-XXCY-010)。
文摘To overcome the shortage of complex equipment,large volume,and high energy consumption in space capsule manufacturing,a novel sliding pressure Joule heat fuse additive manufacturing technique with reduced volume and low energy consumption was proposed.But the unreasonable process parameters may lead to the inferior consistency of the forming quality of single-channel multilayer in Joule heat additive manufacturing process,and it is difficult to reach the condition for forming thinwalled parts.Orthogonal experiments were designed to fabricate single-channel multilayer samples with varying numbers of layers,and their forming quality was evaluated.The influence of printing current,forming speed,and contact pressure on the forming quality of the single-channel multilayer was analyzed.The optimal process parameters were obtained and the quality characterization of the experiment results was conducted.Results show that the printing current has the most significant influence on the forming quality of the single-channel multilayer.Under the optimal process parameters,the forming section is well fused and the surface is continuously smooth.The surface roughness of a single-channel 3-layer sample is 0.16μm,and the average Vickers hardness of cross section fusion zone is 317 HV,which lays a foundation for the subsequent use of Joule heat additive manufacturing technique to form thinwall parts.
文摘In complex systems,there is a kind of parameters having only a minor impact on the outputs in most cases,but their accurate values are still critical for the operation of systems.In this paper,the authors focus on the identification of these weak influence parameters in the complex systems and propose a identification model based on the parameter recursion.As an application,three parameters of the steam generator are identified,that is,the valve opening,the valve CV value,and the reference water level,in which the valve opening and the reference water level are weak influence parameters under most operating conditions.Numerical simulation results show that,in comparison with the multi-layer perceptron(MLP),the identification error rate is decreased.Actually,the average identification error rate for the valve opening decreases by 0.96%,for the valve CV decreases by 0.002%,and for the reference water level decreases by 12%after one recursion.After two recursions,the average identification error rate for the valve opening decreases by 11.07%,for the valve CV decreases by 2.601%,and for the reference water level decreases by 95.79%.This method can help to improve the control of the steam generator.
文摘Accurately predicting environmental parameters in solar greenhouses is crucial for achieving precise environmental control.In solar greenhouses,temperature,humidity,and light intensity are crucial environmental parameters.The monitoring platform collected data on the internal environment of the solar greenhouse for one year,including temperature,humidity,and light intensity.Additionally,meteorological data,comprising outdoor temperature,outdoor humidity,and outdoor light intensity,was gathered during the same time frame.The characteristics and interrelationships among these parameters were investigated by a thorough analysis.The analysis revealed that environmental parameters in solar greenhouses displayed characteristics such as temporal variability,non-linearity,and periodicity.These parameters exhibited complex coupling relationships.Notably,these characteristics and coupling relationships exhibited pronounced seasonal variations.The multi-parameter multi-step prediction model for solar greenhouse(MPMS-SGH)was introduced,aiming to accurately predict three key greenhouse environmental parameters,and the model had certain seasonal adaptability.MPMS-SGH was structured with multiple layers,including an input layer,a preprocessing layer,a feature extraction layer,and a prediction layer.The input layer was used to generate the original sequence matrix,which included indoor temperature,indoor humidity,indoor light intensity,as well as outdoor temperature and outdoor light intensity.Then the preprocessing layer normalized,decomposed,and positionally encoded the original sequence matrix.In the feature extraction layer,the time attention mechanism and frequency attention mechanism were used to extract features from the trend component and the seasonal component,respectively.Finally,the prediction layer used a multi-layer perceptron to perform multi-step prediction of indoor environmental parameters(i.e.temperature,humidity,and light intensity).The parameter selection experiment evaluated the predictive performance of MPMS-SGH on input and output sequences of different lengths.The results indicated that with a constant output sequence length,the prediction accuracy of MPMS-SGH was firstly increased and then decreased with the increase of input sequence length.Specifically,when the input sequence length was 100,MPMS-SGH had the highest prediction accuracy,with RMSE of 0.22℃,0.28%,and 250lx for temperature,humidity,and light intensity,respectively.When the length of the input sequence remained constant,as the length of the output sequence increased,the accuracy of the model in predicting the three environmental parameters was continuously decreased.When the length of the output sequence exceeded 45,the prediction accuracy of MPMS-SGH was significantly decreased.In order to achieve the best balance between model size and performance,the input sequence length of MPMS-SGH was set to be 100,while the output sequence length was set to be 35.To assess MPMS-SGH’s performance,comparative experiments with four prediction models were conducted:SVR,STL-SVR,LSTM,and STL-LSTM.The results demonstrated that MPMS-SGH surpassed all other models,achieving RMSE of 0.15℃for temperature,0.38%for humidity,and 260lx for light intensity.Additionally,sequence decomposition can contribute to enhancing MPMS-SGH’s prediction performance.To further evaluate MPMS-SGH’s capabilities,its prediction accuracy was tested across different seasons for greenhouse environmental parameters.MPMS-SGH had the highest accuracy in predicting indoor temperature and the lowest accuracy in predicting humidity.And the accuracy of MPMS-SGH in predicting environmental parameters of the solar greenhouse fluctuated with seasons.MPMS-SGH had the highest accuracy in predicting the temperature inside the greenhouse on sunny days in spring(R^(2)=0.91),the highest accuracy in predicting the humidity inside the greenhouse on sunny days in winter(R^(2)=0.83),and the highest accuracy in predicting the light intensity inside the greenhouse on cloudy days in autumm(R^(2)=0.89).MPMS-SGH had the lowest accuracy in predicting three environmental parameters in a sunny summer greenhouse.
