The Nei's improved genetic distance(DA)and gene flow(Nm)were measured using sixteen microsatellite markers.Dendograms based on DA genetic distance using the neighbor-joining(NJ)method and STRUCTURE program were co...The Nei's improved genetic distance(DA)and gene flow(Nm)were measured using sixteen microsatellite markers.Dendograms based on DA genetic distance using the neighbor-joining(NJ)method and STRUCTURE program were constructed to analyze the genetic structure and relationship among 10 Chinese indigenous chicken breeds.The results showed that dendograms of DA genetic distance using the NJ method divided the 10 chicken breeds into two main clusters;one consisted of breeds of low weight body(CHA,TTB,XIA,GUS and BAI),the other contained heavier breeds(LAN,DAG,YOU,XIS and LUY).In the lighter breeds,TIB and CHA clustered together,as did XIA and GUS.In the heavier breeds,XIS and LUY was clustered together in one branch,but LAN,DAG and YOU clustered in independent branches.The results were consistent with Nm estimates among the 10 indigenous chicken breeds.The STRUCTURE program properly inferred the presence of genetic structure despite not pre-defining the origin of individuals.The genetic cluster inferred by STRUCTURE was basically the same as that from the DA distance clustering method.An advantage of the STRUCTURE program was its ability to identify the migrants and admixed individuals in the 10 chicken populations;this could not be achieved by use of the DA distance clustering method.展开更多
This paper proposes a model-based prognostics method that couples the Extended Kalman Filter(EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack ...This paper proposes a model-based prognostics method that couples the Extended Kalman Filter(EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled EKF-linearization method provides satisfactory results: the EKF algorithm well identifies the model parameters, and the linearization method gives comparable prediction results to Monte Carlo(MC) method while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful.展开更多
In re-entry, the drilling riser hanging to the holding vessel takes on a free hanging state, waiting to be moved from the initial random position to the wellhead. For the re-entry, dynamics calculation is often done t...In re-entry, the drilling riser hanging to the holding vessel takes on a free hanging state, waiting to be moved from the initial random position to the wellhead. For the re-entry, dynamics calculation is often done to predict the riser motion or evaluate the structural safety. A dynamics calculation method based on Flexible Segment Model （FSM） is proposed for free hanging marine risers. In FSM, a riser is discretized into a series of flexible segments. For each flexible segment, its deflection feature and external forces are analyzed independently. For the whole riser, the nonlinear governing equations are listed according to the moment equilibrium at nodes. For the solution of the nonlinear equations, a linearization iteration scheme is provided in the paper. Owing to its flexibility, each segment can match a long part of the riser body, which enables that good results can be obtained even with a small number of segments. Moreover, the linearization iteration scheme can avoid widely used Newton-Rapson iteration scheme in which the calculation stability is influenced by the initial points. The FSM-based dynamics calculation is timesaving and stable, so suitable for the shape prediction or real-time control of free hanging marine risers.展开更多
This work was focused on the model-based design method of two-axis four-actuator(TAFA) fast steering mirror system(FSM), in order to improve the design efficiency. The structure and operation principle commonality of ...This work was focused on the model-based design method of two-axis four-actuator(TAFA) fast steering mirror system(FSM), in order to improve the design efficiency. The structure and operation principle commonality of normal TAFA FSM were investigated. Based on the structure and the commonality, the conditions of single-axis idea, high-frequency resonance and coupling were modeled gradually. Combining these models, a holonomic system model was established to reflect and predict the performance of TAFA FSM. A model-based design method was proposed based on the holonomic system model. The design flow and design concept of the method were described. In accordance with the method, a TAFA FSM was designed. Simulations and experiments of the FSM were done, and the results of them were compared. The compared results indicate that the holonomic system model can well reflect and predict the performance of TAFA FSM. The bandwidth of TAFA FSM is more than 250 Hz; adjust time is less than 15 ms;overshoot is less than 8%; position accuracy is better than 10 μrad; the FSM prototype can satisfy the requirements.展开更多
In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionall...In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.展开更多
Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study comp...Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study compares the performance of practical iterative reallocation algorithms with model-based clustering algorithms.The data is from forest vegetation in Virginia(United States),the Hyrcanian Forest(Asia),and European beech forests.Practical iterative reallocation algorithms were applied as non-hierarchical methods and Finite Gaussian mixture modeling was used as a model-based clustering method.Due to limitations on dimensionality in model-based clustering,principal coordinates analysis was employed to reduce the dataset’s dimensions.A log transformation was applied to achieve a normal distribution for the pseudo-species data before calculating the Bray-Curtis dissimilarity.The findings indicate that the reallocation of misclassified objects based on silhouette width(OPTSIL)with Flexible-β(-0.25)had the highest mean among the tested clustering algorithms with Silhouette width 1(REMOS1)with Flexible-β(-0.25)second.However,model-based clustering performed poorly.Based on these results,it is recommended using OPTSIL with Flexible-β(-0.25)and REMOS1 with Flexible-β(-0.25)for forest vegetation classification instead of model-based clustering particularly for heterogeneous datasets common in forest vegetation community data.展开更多
Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while...Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.展开更多
This paper studies the problem of designing a modelbased decentralized dynamic periodic event-triggering mechanism(DDPETM)for networked control systems(NCSs)subject to packet losses and external disturbances.Firstly,t...This paper studies the problem of designing a modelbased decentralized dynamic periodic event-triggering mechanism(DDPETM)for networked control systems(NCSs)subject to packet losses and external disturbances.Firstly,the entire NCSs,comprising the triggering mechanism,packet losses and output-based controller,are unified into a hybrid dynamical framework.Secondly,by introducing dynamic triggering variables,the DDPETM is designed to conserve network resources while guaranteeing desired performance properties and tolerating the maximum allowable number of successive packet losses.Thirdly,some stability conditions are derived using the Lyapunov approach.Differing from the zero-order-hold(ZOH)case,the model-based control sufficiently exploits the model information at the controller side.Between two updates,the controller predicts the plant state based on the models and received feedback information.With the model-based control,less transmission may be expected than with ZOH.Finally,numerical examples and comparative experiments demonstrate the effectiveness of the proposed method.展开更多
In order to solve the problem of difficult modeling and identification caused by time-variable parameters,multiple inputs and outputs and unstable open loop,a subsystem model-based close-loop grey-box identification m...In order to solve the problem of difficult modeling and identification caused by time-variable parameters,multiple inputs and outputs and unstable open loop,a subsystem model-based close-loop grey-box identification method was put forward when consider the main coupling effects of hydraulic Stewart platform.Firstly,the whole system is divided into three TITO(Two Input Two Output) subsystems according to the characteristics of the pseudo-mass matrix,hence transfer function matrix model of the subsystem can also be found.Secondly,since the Stewart platform is unstable,the close-loop transfer model of the subsystem is derived under the proportional controllers.The inverse M serial is adopted as the identification signal to get the experimental data.All parameters of the subsystem are determined in close-loop indirect identification by PEM(Prediction Error Method).Finally,a case study validates the correctness and effectiveness of the subsystem model-based close-loop grey-box identification method for hydraulic Stewart platform.展开更多
In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to ...In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.展开更多
In the study of spiral galaxy morphology,spiral arm structures are valuable for intuitively reflecting active physical and chemical processes within galaxies.However,long-term scarcity of high-quality one-,three-,and ...In the study of spiral galaxy morphology,spiral arm structures are valuable for intuitively reflecting active physical and chemical processes within galaxies.However,long-term scarcity of high-quality one-,three-,and four-armed galaxy samples has limited deep learning model performance.To address this,this study developed a spiral galaxy data simulation program with a three-stage workflow:first,screening highly reliable training samples;second,selecting the best-performing Imagen architecture as the generative model after comparing nine mainstream ones;finally,training Imagen to generate an open data set of 9402 one-/three-armed galaxies,expanding the original sample size by 6 times.Multi-dimensional evaluations verified reliability and usability:Fréchet Inception Distance scores for N=1 and N=3 tasks were 6.05 and 9.13;the t-distributed Stochastic Neighbor Embedding showed generated data covered and expanded real data distribution;the Structural Similarity Index Measure confirmed no sample duplication.In downstream validation,data augmentation improved seven classification models'average accuracy by 8.7%(DenseNet peaked at 97%),and SHapley Additive exPlanations analysis showed model decisions focused on spiral arm topology.In conclusion,the program and data set support spiral galaxy morphology deep learning research and are publicly available at https://github.com/TuAstroAILab/AstroGS.展开更多
The spread of fire and smoke during a fire incident plays a crucial role in rescuing people from the burning building. So it is important for the decision makers (the head of rescue staff) to get a prediction about th...The spread of fire and smoke during a fire incident plays a crucial role in rescuing people from the burning building. So it is important for the decision makers (the head of rescue staff) to get a prediction about the spread of fire inside the building through computational techniques like numerical fire simulations. But these techniques require advanced mathematical knowledge and are very time consuming. This paper presents a new method which employs a set of pre-simulated and model-based scenarios to find the closest one to the real fire and present its results to the decision makers. For this purpose, we shift the performance consuming numerical fire simulations into a former phase by integration of these simulations into the planning process of the building. This is realized by enhancing the methods of Building Information Modeling (BIM). To provide the fire simulation results during a real case, our new concept includes a scenario database where all simulated fire scenarios will be collected. In a real case, a special search algorithm will go through this database to find the closest pre-simulated fire scenario to the real fire on the basis of reported information from the burning building.展开更多
Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode...Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.展开更多
In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical m...In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical model,and its validity was verified using a simple impact test.A crushable discrete element method(DEM)framework is built based on the previously established theoretical model.The tensile strength,which considers the fractal theory,size effect,and Weibull variation,was assigned to each generated particle.The assigned strength is then used for crush detection by comparing it with its maximum tensile stress.Mass conservation is ensured by inserting a series of sub-particles whose total mass was equal to the quality loss.Based on the crushable DEM framework,a numerical simulation of the crushing behavior of a pebble bed with hollow cylindrical geometry under a uniaxial compression test was performed.The results of this investigation showed that the particle withstands the external load by contact and sliding at the beginning of the compression process,and the results confirmed that crushing can be considered an important method of resisting the increasing external load.A relatively regular particle arrangement aids in resisting the load and reduces the occurrence of particle crushing.However,a limit exists to the promotion of resistance.When the strain increases beyond this limit,the distribution of the crushing position tends to be isotropic over the entire pebble bed.The theoretical model and crushable DEM framework provide a new method for exploring the pebble bed in a fusion reactor,considering particle crushing.展开更多
Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision...Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.展开更多
The application of nitrogen fertilizers in agricultural fields can lead to the release of nitrogen-containing gases(NCGs),such as NO_(x),NH_(3) and N_(2)O,which can significantly impact regional atmospheric environmen...The application of nitrogen fertilizers in agricultural fields can lead to the release of nitrogen-containing gases(NCGs),such as NO_(x),NH_(3) and N_(2)O,which can significantly impact regional atmospheric environment and con-tribute to global climate change.However,there remain considerable research gaps in the accurate measurement of NCGs emissions from agricultural fields,hindering the development of effective emission reduction strategies.We improved an open-top dynamic chambers(OTDCs)system and evaluated the performance by comparing the measured and given fluxes of the NCGs.The results showed that the measured fluxes of NO,N_(2)O and NH_(3)were 1%,2%and 7%lower than the given fluxes,respectively.For the determination of NH_(3) concentration,we employed a stripping coil-ion chromatograph(SC-IC)analytical technique,which demonstrated an absorption efficiency for atmospheric NH_(3) exceeding 96.1%across sampling durations of 6 to 60 min.In the summer maize season,we utilized the OTDCs system to measure the exchange fluxes of NO,NH_(3),and N_(2)O from the soil in the North China Plain.Substantial emissions of NO,NH_(3) and N_(2)O were recorded following fertilization,with peaks of 107,309,1239 ng N/(m^(2)·s),respectively.Notably,significant NCGs emissions were observed following sus-tained heavy rainfall one month after fertilization,particularly with NH_(3) peak being 4.5 times higher than that observed immediately after fertilization.Our results demonstrate that the OTDCs system accurately reflects the emission characteristics of soil NCGs and meets the requirements for long-term and continuous flux observation.展开更多
Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The t...Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.展开更多
At present,there is currently a lack of unified standard methods for the determination of antimony content in groundwater in China.The precision and trueness of related detection technologies have not yet been systema...At present,there is currently a lack of unified standard methods for the determination of antimony content in groundwater in China.The precision and trueness of related detection technologies have not yet been systematically and quantitatively evaluated,which limits the effective implementation of environmental monitoring.In response to this key technical gap,this study aimed to establish a standardized method for determining antimony in groundwater using Hydride Generation–Atomic Fluorescence Spectrometry(HG-AFS).Ten laboratories participated in inter-laboratory collaborative tests,and the statistical analysis of the test data was carried out in strict accordance with the technical specifications of GB/T 6379.2—2004 and GB/T 6379.4—2006.The consistency and outliers of the data were tested by Mandel's h and k statistics,the Grubbs test and the Cochran test,and the outliers were removed to optimize the data,thereby significantly improving the reliability and accuracy.Based on the optimized data,parameters such as the repeatability limit(r),reproducibility limit(R),and method bias value(δ)were determined,and the trueness of the method was statistically evaluated.At the same time,precision-function relationships were established,and all results met the requirements.The results show that the lower the antimony content,the lower the repeatability limit(r)and reproducibility limit(R),indicating that the measurement error mainly originates from the detection limit of the method and instrument sensitivity.Therefore,improving the instrument sensitivity and reducing the detection limit are the keys to controlling the analytical error and improving precision.This study provides reliable data support and a solid technical foundation for the establishment and evaluation of standardized methods for the determination of antimony content in groundwater.展开更多
A tablet consisting of direct-acting antiviral agents,ledipasvir（a NS5 A protein inhibitor） and sofosbuvir（a NS5 B polymerase inhibitor）,is the first fixed-dose preparation used in the antiviral therapy of hepatit...A tablet consisting of direct-acting antiviral agents,ledipasvir（a NS5 A protein inhibitor） and sofosbuvir（a NS5 B polymerase inhibitor）,is the first fixed-dose preparation used in the antiviral therapy of hepatitis C.A model-based meta-analysis of ledipasvir and GS331007,the primary metabolite of sofosbuvir,enabled the integration of pharmacokinetic（PK） information from separate clinical trials and the quantitative characterization of the population pharmacokinetics of these two drugs.A systematic publication search was conducted for the clinical studies of ledipasvir and sofosbuvir.A total of 401 arm-level aggregate concentrations of GS331007 and 188 concentrations of ledipasvir were used for PK modeling.A two-compartment disposition model was used for both ledipasvir and GS331007.Zero-order absorption was applied for ledipasvir PK modeling,and a combined zero- and first-order absorption was used for the modeling of GS331007.Absorption lag was observed in concentration-time profiles of both ledipasvir and GS331007.To aid the development of direct-acting antiviral drugs,our established PK models provided a basis for the further PK-viral kinetic studies of ledipasvir and sofosbuvir.展开更多
As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and s...As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.展开更多
基金supported by the Program of National Technological Basis from Ministry of Science and Technology of China(No.2005DKA21101)the National Natural Science Foundation of China(No.30700572)
文摘The Nei's improved genetic distance(DA)and gene flow(Nm)were measured using sixteen microsatellite markers.Dendograms based on DA genetic distance using the neighbor-joining(NJ)method and STRUCTURE program were constructed to analyze the genetic structure and relationship among 10 Chinese indigenous chicken breeds.The results showed that dendograms of DA genetic distance using the NJ method divided the 10 chicken breeds into two main clusters;one consisted of breeds of low weight body(CHA,TTB,XIA,GUS and BAI),the other contained heavier breeds(LAN,DAG,YOU,XIS and LUY).In the lighter breeds,TIB and CHA clustered together,as did XIA and GUS.In the heavier breeds,XIS and LUY was clustered together in one branch,but LAN,DAG and YOU clustered in independent branches.The results were consistent with Nm estimates among the 10 indigenous chicken breeds.The STRUCTURE program properly inferred the presence of genetic structure despite not pre-defining the origin of individuals.The genetic cluster inferred by STRUCTURE was basically the same as that from the DA distance clustering method.An advantage of the STRUCTURE program was its ability to identify the migrants and admixed individuals in the 10 chicken populations;this could not be achieved by use of the DA distance clustering method.
基金partially funded by the National Natural Science Foundation of China (No.51805262)
文摘This paper proposes a model-based prognostics method that couples the Extended Kalman Filter(EKF) and a new developed linearization method. The proposed prognostics method is developed in the context of fatigue crack propagation in fuselage panels where the model parameters are unknown and the crack propagation is affected by different types of uncertainties. The coupled method is composed of two steps. The first step employs EKF to estimate the unknown model parameters and the current damage state. In the second step, the proposed efficient linearization method is applied to compute analytically the statistical distribution of the damage evolution path in some future time. A numerical case study is implemented to evaluate the performance of the proposed method. The results show that the coupled EKF-linearization method provides satisfactory results: the EKF algorithm well identifies the model parameters, and the linearization method gives comparable prediction results to Monte Carlo(MC) method while leading to very significant computational cost saving. The proposed prognostics method for fatigue crack growth can be used for developing predictive maintenance strategy for an aircraft fleet, in which case, the computational cost saving is significantly meaningful.
基金supported by the National Natural Science Foundation of China (Grant No. 51009092)the Doctoral Foundation of Education Ministry of China (Grant No. 20090073120013)the Scientific Research Foundation of State Education Ministry for the Returned Overseas Chinese Scholars
文摘In re-entry, the drilling riser hanging to the holding vessel takes on a free hanging state, waiting to be moved from the initial random position to the wellhead. For the re-entry, dynamics calculation is often done to predict the riser motion or evaluate the structural safety. A dynamics calculation method based on Flexible Segment Model （FSM） is proposed for free hanging marine risers. In FSM, a riser is discretized into a series of flexible segments. For each flexible segment, its deflection feature and external forces are analyzed independently. For the whole riser, the nonlinear governing equations are listed according to the moment equilibrium at nodes. For the solution of the nonlinear equations, a linearization iteration scheme is provided in the paper. Owing to its flexibility, each segment can match a long part of the riser body, which enables that good results can be obtained even with a small number of segments. Moreover, the linearization iteration scheme can avoid widely used Newton-Rapson iteration scheme in which the calculation stability is influenced by the initial points. The FSM-based dynamics calculation is timesaving and stable, so suitable for the shape prediction or real-time control of free hanging marine risers.
基金Projects(51135009)supported by the National Natural Science Foundation of China
文摘This work was focused on the model-based design method of two-axis four-actuator(TAFA) fast steering mirror system(FSM), in order to improve the design efficiency. The structure and operation principle commonality of normal TAFA FSM were investigated. Based on the structure and the commonality, the conditions of single-axis idea, high-frequency resonance and coupling were modeled gradually. Combining these models, a holonomic system model was established to reflect and predict the performance of TAFA FSM. A model-based design method was proposed based on the holonomic system model. The design flow and design concept of the method were described. In accordance with the method, a TAFA FSM was designed. Simulations and experiments of the FSM were done, and the results of them were compared. The compared results indicate that the holonomic system model can well reflect and predict the performance of TAFA FSM. The bandwidth of TAFA FSM is more than 250 Hz; adjust time is less than 15 ms;overshoot is less than 8%; position accuracy is better than 10 μrad; the FSM prototype can satisfy the requirements.
基金the support of EPIC - Energy Production Innovation Center, hosted by the University of Campinas (UNICAMP) and sponsored by Equinor Brazil and FAPESP - Sao Paulo Research Foundation (2021/04878- 7 and 2017/15736-3)financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nível Superior Brasil (CAPES) - Financing Code 001
文摘In the area of reservoir engineering,the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system's infrastructure.Traditionally,this optimization process was centered on a single objective,such as net present value,return on investment,cumulative oil production,or cumulative water production.However,the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach.Mul-tiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making.In response to this challenge,researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria,employing the formidable tools of multi-objective optimization algorithms.These algorithms delve into the intricate terrain of production strategy design,seeking to strike a delicate balance between the often-contrasting objectives.Over the years,a plethora of these algorithms have emerged,ranging from a priori methods to a posteriori approach,each offering unique insights and capabilities.This survey endeavors to encapsulate,catego-rize,and scrutinize these invaluable contributions to field development optimization,which grapple with the complexities of multiple conflicting objective functions.Beyond the overview of existing methodologies,we delve into the persisting challenges faced by researchers and practitioners alike.Notably,the application of multi-objective optimization techniques to production optimization is hin-dered by the resource-intensive nature of reservoir simulation,especially when confronted with inherent uncertainties.As a result of this survey,emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future.As intelligent and more efficient algo-rithms continue to evolve,the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable.This discussion on future prospects aims to inspire critical research,guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.
基金financially supported by the vice chancellor for research and technology of Urmia University
文摘Numerous clustering algorithms are valuable in pattern recognition in forest vegetation,with new ones continually being proposed.While some are well-known,others are underutilized in vegetation science.This study compares the performance of practical iterative reallocation algorithms with model-based clustering algorithms.The data is from forest vegetation in Virginia(United States),the Hyrcanian Forest(Asia),and European beech forests.Practical iterative reallocation algorithms were applied as non-hierarchical methods and Finite Gaussian mixture modeling was used as a model-based clustering method.Due to limitations on dimensionality in model-based clustering,principal coordinates analysis was employed to reduce the dataset’s dimensions.A log transformation was applied to achieve a normal distribution for the pseudo-species data before calculating the Bray-Curtis dissimilarity.The findings indicate that the reallocation of misclassified objects based on silhouette width(OPTSIL)with Flexible-β(-0.25)had the highest mean among the tested clustering algorithms with Silhouette width 1(REMOS1)with Flexible-β(-0.25)second.However,model-based clustering performed poorly.Based on these results,it is recommended using OPTSIL with Flexible-β(-0.25)and REMOS1 with Flexible-β(-0.25)for forest vegetation classification instead of model-based clustering particularly for heterogeneous datasets common in forest vegetation community data.
基金supported by the Postdoctoral Fellowship Program of CPSF(Grant No.GZC20242194)the National Natural Science Foundation of China(Grant Nos.52175251 and 52205268)+1 种基金the Industry Key Technology Research Fund Project of Northwestern Polytechnical University(Grant No.HYGJXM202318)the National Basic Scientific Research Program(Grant No.JCKY2021206B005).
文摘Unlike traditional propeller-driven underwater vehicles,blended-wing-body underwater gliders(BWBUGs)achieve zigzag gliding through periodic adjustments of their net buoyancy,enhancing their cruising capabilities while mini-mizing energy consumption.However,enhancing gliding performance is challenging due to the complex system design and limited design experience.To address this challenge,this paper introduces a model-based,multidisciplinary system design optimization method for BWBUGs at the conceptual design stage.First,a model-based,multidisciplinary co-simulation design framework is established to evaluate both system-level and disciplinary indices of BWBUG performance.A data-driven,many-objective multidisciplinary optimization is subsequently employed to explore the design space,yielding 32 Pareto optimal solutions.Finally,a model-based physical system simulation,which represents the design with the largest hyper-volume contribution among the 32 final designs,is established.Its gliding perfor-mance,validated by component behavior,lays the groundwork for constructing the entire system’s digital prototype.In conclusion,this model-based,multidisciplinary design optimization method effectively generates design schemes for innovative underwater vehicles,facilitating the development of digital prototypes.
基金supported by the National Natural Science Foundation of China(U21A20477,61722302,61573069,61903290)the Fundamental Research Funds for the Central Universities(DUT19ZD218).
文摘This paper studies the problem of designing a modelbased decentralized dynamic periodic event-triggering mechanism(DDPETM)for networked control systems(NCSs)subject to packet losses and external disturbances.Firstly,the entire NCSs,comprising the triggering mechanism,packet losses and output-based controller,are unified into a hybrid dynamical framework.Secondly,by introducing dynamic triggering variables,the DDPETM is designed to conserve network resources while guaranteeing desired performance properties and tolerating the maximum allowable number of successive packet losses.Thirdly,some stability conditions are derived using the Lyapunov approach.Differing from the zero-order-hold(ZOH)case,the model-based control sufficiently exploits the model information at the controller side.Between two updates,the controller predicts the plant state based on the models and received feedback information.With the model-based control,less transmission may be expected than with ZOH.Finally,numerical examples and comparative experiments demonstrate the effectiveness of the proposed method.
文摘In order to solve the problem of difficult modeling and identification caused by time-variable parameters,multiple inputs and outputs and unstable open loop,a subsystem model-based close-loop grey-box identification method was put forward when consider the main coupling effects of hydraulic Stewart platform.Firstly,the whole system is divided into three TITO(Two Input Two Output) subsystems according to the characteristics of the pseudo-mass matrix,hence transfer function matrix model of the subsystem can also be found.Secondly,since the Stewart platform is unstable,the close-loop transfer model of the subsystem is derived under the proportional controllers.The inverse M serial is adopted as the identification signal to get the experimental data.All parameters of the subsystem are determined in close-loop indirect identification by PEM(Prediction Error Method).Finally,a case study validates the correctness and effectiveness of the subsystem model-based close-loop grey-box identification method for hydraulic Stewart platform.
基金Supported in part by Natural Science Foundation of Guangxi(2023GXNSFAA026246)in part by the Central Government's Guide to Local Science and Technology Development Fund(GuikeZY23055044)in part by the National Natural Science Foundation of China(62363003)。
文摘In this paper,we consider the maximal positive definite solution of the nonlinear matrix equation.By using the idea of Algorithm 2.1 in ZHANG(2013),a new inversion-free method with a stepsize parameter is proposed to obtain the maximal positive definite solution of nonlinear matrix equation X+A^(*)X|^(-α)A=Q with the case 0<α≤1.Based on this method,a new iterative algorithm is developed,and its convergence proof is given.Finally,two numerical examples are provided to show the effectiveness of the proposed method.
基金supported by the National Natural Science Foundation of China(NSFC,grant No.U1731128)the support of the Association for Astronomy X A.I.(A3),funded by the Science and Education Integration Funding of the University of Chinese Academy of Sciences。
文摘In the study of spiral galaxy morphology,spiral arm structures are valuable for intuitively reflecting active physical and chemical processes within galaxies.However,long-term scarcity of high-quality one-,three-,and four-armed galaxy samples has limited deep learning model performance.To address this,this study developed a spiral galaxy data simulation program with a three-stage workflow:first,screening highly reliable training samples;second,selecting the best-performing Imagen architecture as the generative model after comparing nine mainstream ones;finally,training Imagen to generate an open data set of 9402 one-/three-armed galaxies,expanding the original sample size by 6 times.Multi-dimensional evaluations verified reliability and usability:Fréchet Inception Distance scores for N=1 and N=3 tasks were 6.05 and 9.13;the t-distributed Stochastic Neighbor Embedding showed generated data covered and expanded real data distribution;the Structural Similarity Index Measure confirmed no sample duplication.In downstream validation,data augmentation improved seven classification models'average accuracy by 8.7%(DenseNet peaked at 97%),and SHapley Additive exPlanations analysis showed model decisions focused on spiral arm topology.In conclusion,the program and data set support spiral galaxy morphology deep learning research and are publicly available at https://github.com/TuAstroAILab/AstroGS.
文摘The spread of fire and smoke during a fire incident plays a crucial role in rescuing people from the burning building. So it is important for the decision makers (the head of rescue staff) to get a prediction about the spread of fire inside the building through computational techniques like numerical fire simulations. But these techniques require advanced mathematical knowledge and are very time consuming. This paper presents a new method which employs a set of pre-simulated and model-based scenarios to find the closest one to the real fire and present its results to the decision makers. For this purpose, we shift the performance consuming numerical fire simulations into a former phase by integration of these simulations into the planning process of the building. This is realized by enhancing the methods of Building Information Modeling (BIM). To provide the fire simulation results during a real case, our new concept includes a scenario database where all simulated fire scenarios will be collected. In a real case, a special search algorithm will go through this database to find the closest pre-simulated fire scenario to the real fire on the basis of reported information from the burning building.
文摘Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.
基金supported by Anhui Provincial Natural Science Foundation(2408085QA030)Natural Science Research Project of Anhui Educational Committee,China(2022AH050825)+3 种基金Medical Special Cultivation Project of Anhui University of Science and Technology(YZ2023H2C008)the Excellent Research and Innovation Team of Anhui Province,China(2022AH010052)the Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology,China(2021yjrc51)Collaborative Innovation Program of Hefei Science Center,CAS,China(2019HSC-CIP006).
文摘In this paper,a novel method for investigating the particle-crushing behavior of breeding particles in a fusion blanket is proposed.The fractal theory and Weibull distribution are combined to establish a theoretical model,and its validity was verified using a simple impact test.A crushable discrete element method(DEM)framework is built based on the previously established theoretical model.The tensile strength,which considers the fractal theory,size effect,and Weibull variation,was assigned to each generated particle.The assigned strength is then used for crush detection by comparing it with its maximum tensile stress.Mass conservation is ensured by inserting a series of sub-particles whose total mass was equal to the quality loss.Based on the crushable DEM framework,a numerical simulation of the crushing behavior of a pebble bed with hollow cylindrical geometry under a uniaxial compression test was performed.The results of this investigation showed that the particle withstands the external load by contact and sliding at the beginning of the compression process,and the results confirmed that crushing can be considered an important method of resisting the increasing external load.A relatively regular particle arrangement aids in resisting the load and reduces the occurrence of particle crushing.However,a limit exists to the promotion of resistance.When the strain increases beyond this limit,the distribution of the crushing position tends to be isotropic over the entire pebble bed.The theoretical model and crushable DEM framework provide a new method for exploring the pebble bed in a fusion reactor,considering particle crushing.
基金funded by the Beijing Engineering Research Center of Electric Rail Transportation.
文摘Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.
基金supported by the National Key Research and Develop-ment Program(No.2022YFC3701103)the National Natural Science Foundation of China(Nos.42130714 and 41931287).
文摘The application of nitrogen fertilizers in agricultural fields can lead to the release of nitrogen-containing gases(NCGs),such as NO_(x),NH_(3) and N_(2)O,which can significantly impact regional atmospheric environment and con-tribute to global climate change.However,there remain considerable research gaps in the accurate measurement of NCGs emissions from agricultural fields,hindering the development of effective emission reduction strategies.We improved an open-top dynamic chambers(OTDCs)system and evaluated the performance by comparing the measured and given fluxes of the NCGs.The results showed that the measured fluxes of NO,N_(2)O and NH_(3)were 1%,2%and 7%lower than the given fluxes,respectively.For the determination of NH_(3) concentration,we employed a stripping coil-ion chromatograph(SC-IC)analytical technique,which demonstrated an absorption efficiency for atmospheric NH_(3) exceeding 96.1%across sampling durations of 6 to 60 min.In the summer maize season,we utilized the OTDCs system to measure the exchange fluxes of NO,NH_(3),and N_(2)O from the soil in the North China Plain.Substantial emissions of NO,NH_(3) and N_(2)O were recorded following fertilization,with peaks of 107,309,1239 ng N/(m^(2)·s),respectively.Notably,significant NCGs emissions were observed following sus-tained heavy rainfall one month after fertilization,particularly with NH_(3) peak being 4.5 times higher than that observed immediately after fertilization.Our results demonstrate that the OTDCs system accurately reflects the emission characteristics of soil NCGs and meets the requirements for long-term and continuous flux observation.
基金Supported by the National Natural Science Foundation of China under Grant No.51975138the High-Tech Ship Scientific Research Project from the Ministry of Industry and Information Technology under Grant No.CJ05N20the National Defense Basic Research Project under Grant No.JCKY2023604C006.
文摘Marine thin plates are susceptible to welding deformation owing to their low structural stiffness.Therefore,the efficient and accurate prediction of welding deformation is essential for improving welding quality.The traditional thermal elastic-plastic finite element method(TEP-FEM)can accurately predict welding deformation.However,its efficiency is low because of the complex nonlinear transient computation,making it difficult to meet the needs of rapid engineering evaluation.To address this challenge,this study proposes an efficient prediction method for welding deformation in marine thin plate butt welds.This method is based on the coupled temperature gradient-thermal strain method(TG-TSM)that integrates inherent strain theory with a shell element finite element model.The proposed method first extracts the distribution pattern and characteristic value of welding-induced inherent strain through TEP-FEM analysis.This strain is then converted into the equivalent thermal load applied to the shell element model for rapid computation.The proposed method-particularly,the gradual temperature gradient-thermal strain method(GTG-TSM)-achieved improved computational efficiency and consistent precision.Furthermore,the proposed method required much less computation time than the traditional TEP-FEM.Thus,this study lays the foundation for future prediction of welding deformation in more complex marine thin plates.
基金supported by the National Natural Science Foundation of China(Project No.42307555).
文摘At present,there is currently a lack of unified standard methods for the determination of antimony content in groundwater in China.The precision and trueness of related detection technologies have not yet been systematically and quantitatively evaluated,which limits the effective implementation of environmental monitoring.In response to this key technical gap,this study aimed to establish a standardized method for determining antimony in groundwater using Hydride Generation–Atomic Fluorescence Spectrometry(HG-AFS).Ten laboratories participated in inter-laboratory collaborative tests,and the statistical analysis of the test data was carried out in strict accordance with the technical specifications of GB/T 6379.2—2004 and GB/T 6379.4—2006.The consistency and outliers of the data were tested by Mandel's h and k statistics,the Grubbs test and the Cochran test,and the outliers were removed to optimize the data,thereby significantly improving the reliability and accuracy.Based on the optimized data,parameters such as the repeatability limit(r),reproducibility limit(R),and method bias value(δ)were determined,and the trueness of the method was statistically evaluated.At the same time,precision-function relationships were established,and all results met the requirements.The results show that the lower the antimony content,the lower the repeatability limit(r)and reproducibility limit(R),indicating that the measurement error mainly originates from the detection limit of the method and instrument sensitivity.Therefore,improving the instrument sensitivity and reducing the detection limit are the keys to controlling the analytical error and improving precision.This study provides reliable data support and a solid technical foundation for the establishment and evaluation of standardized methods for the determination of antimony content in groundwater.
基金Janssen Research & DevelopmentChina，Pfizer Scholarship for Pharmacometrics during this project
文摘A tablet consisting of direct-acting antiviral agents,ledipasvir（a NS5 A protein inhibitor） and sofosbuvir（a NS5 B polymerase inhibitor）,is the first fixed-dose preparation used in the antiviral therapy of hepatitis C.A model-based meta-analysis of ledipasvir and GS331007,the primary metabolite of sofosbuvir,enabled the integration of pharmacokinetic（PK） information from separate clinical trials and the quantitative characterization of the population pharmacokinetics of these two drugs.A systematic publication search was conducted for the clinical studies of ledipasvir and sofosbuvir.A total of 401 arm-level aggregate concentrations of GS331007 and 188 concentrations of ledipasvir were used for PK modeling.A two-compartment disposition model was used for both ledipasvir and GS331007.Zero-order absorption was applied for ledipasvir PK modeling,and a combined zero- and first-order absorption was used for the modeling of GS331007.Absorption lag was observed in concentration-time profiles of both ledipasvir and GS331007.To aid the development of direct-acting antiviral drugs,our established PK models provided a basis for the further PK-viral kinetic studies of ledipasvir and sofosbuvir.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants(52275471 and 52120105008)the Beijing Outstanding Young Scientist Program,and the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘As pivotal supporting technologies for smart manufacturing and digital engineering,model-based and data-driven methods have been widely applied in many industrial fields,such as product design,process monitoring,and smart maintenance.While promising,both methods have issues that need to be addressed.For example,model-based methods are limited by low computational accuracy and a high computational burden,and data-driven methods always suffer from poor interpretability and redundant features.To address these issues,the concept of data-model fusion(DMF)emerges as a promising solution.DMF involves integrating model-based methods with data-driven methods by incorporating big data into model-based methods or embedding relevant domain knowledge into data-driven methods.Despite growing efforts in the field of DMF,a unanimous definition of DMF remains elusive,and a general framework of DMF has been rarely discussed.This paper aims to address this gap by providing a thorough overview and categorization of both data-driven methods and model-based methods.Subsequently,this paper also presents the definition and categorization of DMF and discusses the general framework of DMF.Moreover,the primary seven applications of DMF are reviewed within the context of smart manufacturing and digital engineering.Finally,this paper directs the future directions of DMF.
