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
The cubic stiffness force model(CSFM)and Bouc-Wen model(BWM)are introduced and compared innovatively.The unknown coefficients of the nonlinear models are identified by the genetic algorithm combined with experiments.B...The cubic stiffness force model(CSFM)and Bouc-Wen model(BWM)are introduced and compared innovatively.The unknown coefficients of the nonlinear models are identified by the genetic algorithm combined with experiments.By fitting the identified nonlinear coefficients under different excitation amplitudes,the nonlinear vibration responses of the system are predicted.The results show that the accuracy of the BWM is higher than that of the CSFM,especially in the non-resonant region.However,the optimization time of the BWM is longer than that of the CSFM.展开更多
Dielectric elastomers(DEs)require balanced electric actuation performance and mechanical integrity under applied voltages.Incorporating high dielectric particles as fillers provides extensive design space to optimize ...Dielectric elastomers(DEs)require balanced electric actuation performance and mechanical integrity under applied voltages.Incorporating high dielectric particles as fillers provides extensive design space to optimize concentration,morphology,and distribution for improved actuation performance and material modulus.This study presents an integrated framework combining finite element modeling(FEM)and deep learning to optimize the microstructure of DE composites.FEM first calculates actuation performance and the effective modulus across varied filler combinations,with these data used to train a convolutional neural network(CNN).Integrating the CNN into a multi-objective genetic algorithm generates designs with enhanced actuation performance and material modulus compared to the conventional optimization approach based on FEM approach within the same time.This framework harnesses artificial intelligence to navigate vast design possibilities,enabling optimized microstructures for high-performance DE composites.展开更多
Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale pr...Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites.An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture.The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions.Three RVE models of cuboid,hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale.Besides,the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models.The prediction results at the mesoscale correspond closely to the experimental values.The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration.The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.展开更多
For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model f...For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.展开更多
In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluste...In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.展开更多
In order to formally reason and verify web services composition described by web services choreography specification WS-CDL,a typed formal model named typed Abstract WS-CDL(web services choreography description langu...In order to formally reason and verify web services composition described by web services choreography specification WS-CDL,a typed formal model named typed Abstract WS-CDL(web services choreography description language)for WS-CDL specifications is proposed.In typed Abstract WS-CDL,the syntax of type and session,typing rules and operational semantics are formalized;the collaborations of web services are formally described by sessions;the operational semantics of a session can help to formally reason the execution of the choreography;the typing rules can help to formally check the data type consistency of exchanged information between web services and capture run-time errors due to type mismatches.Particularly,the concepts of type assumption set extension and type assumption set compatibility are proposed,and the merging algorithm of type assumption sets is defined so as to eliminate type assumption conflict.Based on the formal model,typed mapping rules for mapping web services choreography to orchestration is also defined.With the typed mapping rules,orchestration stubs and their type assumption sets can be generated from a given choreography; thus, web services composition can be verified at choreography and orchestration levels,respectively.The model is proved to have properties of type safety,and how the model can help to reason and verify web services composition is illustrated through a case study.展开更多
Building a model for a object through model composition is a interesting topics, this paper research the interface composit ion of models in the Zeigler’s modeling methodology DEVS.
Safety evaluation of toppling rock slopes developing in reservoir areas is crucial. To reduce the uncertainty of safety evaluation, this study developed a composite cloud model, which improved the combination weights ...Safety evaluation of toppling rock slopes developing in reservoir areas is crucial. To reduce the uncertainty of safety evaluation, this study developed a composite cloud model, which improved the combination weights of the decision-making trial and evaluation laboratory (DEMATEL) and criteria importance through intercriteria correlation (CRITIC) methods. A safety evaluation system was developed according to in situ monitoring data. The backward cloud generator was used to calculate the numerical characteristics of a cloud model of quantitative indices, and different virtual clouds were used to synthesize some clouds into a generalized one. The synthesized numerical characteristics were calculated to comprehensively evaluate the safety of toppling rock slopes. A case study of a toppling rock slope near the Huangdeng Hydropower Station in China was conducted using monitoring data collected since operation of the hydropower project began. The results indicated that the toppling rock slope was moderately safe with a low safety margin. The composite cloud model considers the fuzziness and randomness of safety evaluation and enables interchange between qualitative and quantitative knowledge. This study provides a new theoretical method for evaluating the safety of toppling rock slopes. It can aid in the predication, control, and even prevention of disasters.展开更多
Ongoing climate changes have a direct impact on forest growth;they also affect natural fire regimes,with further implications for forest composition.Understanding of how these will affect forests on decadal-to-centenn...Ongoing climate changes have a direct impact on forest growth;they also affect natural fire regimes,with further implications for forest composition.Understanding of how these will affect forests on decadal-to-centennial timescales is limited.Here we use reconstructions of past vegetation,fire regimes and climate during the Holocene to examine the relative importance of changes in climate and fire regimes for the abundance of key tree species in northeastern China.We reconstructed vegetation changes and fire regimes based on pollen and charcoal records from Gushantun peatland.We then used generalized linear modelling to investigate the impact of reconstructed changes in summer temperature,annual precipitation,background levels of fire,fire frequency and fire magnitude to identify the drivers of decadal-to-centennial changes in forest openness and composition.Changes in climate and fire regimes have independent impacts on the abundance of the key tree taxa.Climate variables are generally more important than fire variables in determining the abundance of individual taxa.Precipitation is the only determinant of forest openness,but summer temperature is more important than precipitation for individual tree taxa with warmer summers causing a decrease in cold-tolerant conifers and an increase in warmth-demanding broadleaved trees.Both background level and fire frequency have negative relationships with the abundance of most tree taxa;only Pinus increases as fire frequency increases.The magnitude of individual fires does not have a significant impact on species abundance on this timescale.Both climate and fire regime characteristics must be considered to understand changes in forest composition on the decadal-to-centennial timescale.There are differences,both in sign and magnitude,in the response of individual tree species to individual drivers.展开更多
A global two-dimensional zonally averaged chemistry model is developed to study the chemi-cal composition of atmosphere. The region of the model is from 90°S to 90°N and from the ground to the altitude of 20...A global two-dimensional zonally averaged chemistry model is developed to study the chemi-cal composition of atmosphere. The region of the model is from 90°S to 90°N and from the ground to the altitude of 20 km with a resolution of 5° x 1 km. The wind field is residual circulation calcu-lated from diabatic rate. 34 species and 104 chemical and photochemical reactions are considered in the model. The sources of CH4, CO and NOx, which are divided into seasonal sources and non-seasonal sources, are parameterized as a function of latitude and time. The chemical composi-tion of atmosphere was simulated with emission level of CH4, CO and NOx in 1990. The results are compared with observations and other model results, showing that the model is successful to simu-late the atmospheric chemical composition and distribution of CH4. Key words Global two-dimensional chemistry model - Atmospheric composition - Emission This work was supported by the State Key Program for basic research “ Climate Dynamics and Cli-mate Prediction Theory” (Pandeng-yu-21).The authors would like to express their thanks to the National Oceanic and Atmospheric Administration (NOAA), Climate Monitoring and Diagnostics Laboratory (CMDL), Carbon Cycle Group for providing the observational data of CO and CH4.展开更多
In this paper, a model based colored Petri net (CPN) to provide semantic support for web service composition is proposed, and the reliability and maintainability of composite services are improved. The composite con...In this paper, a model based colored Petri net (CPN) to provide semantic support for web service composition is proposed, and the reliability and maintainability of composite services are improved. The composite constructs in the model are sequence, concurrent, choice, loop and replace. The web service is formally defined by a CPN. A closed composing algebra is defined to obtain a framework which enables declarative composition of web services. Availability, confidentiality, and integrity of composite service are analyzed within the framework of the model based CPN.展开更多
In order to provide more insights into the damage propagation composite wind turbine blades(blade)under cyclic fatigue loading,a stiffness degradation model for blade is proposed based on the full-scale fatigue testin...In order to provide more insights into the damage propagation composite wind turbine blades(blade)under cyclic fatigue loading,a stiffness degradation model for blade is proposed based on the full-scale fatigue testing of a blade.A novel non-linear fatigue damage accumulation model is proposed using the damage assessment theories of composite laminates for the first time.Then,a stiffness degradation model is established based on the correlation of fatigue damage and residual stiffness of the composite laminates.Finally,a stiffness degradation model for the blade is presented based on the full-scale fatigue testing.The scientific rationale of the proposed stiffness model of blade is verified by using full-scale fatigue test data of blade with a total length of 52.5 m.The results indicate that the proposed stiffness degradation model of the blade agrees well with the fatigue testing results of this blade.This work provides a basis for evaluating the fatigue damage and lifetime of blade under cyclic fatigue loading.展开更多
Diesel molecular compositional model has important application for diesel quality prediction,blending,and molecular-level process model development.In this paper,different types of diesel molecular compositional and b...Diesel molecular compositional model has important application for diesel quality prediction,blending,and molecular-level process model development.In this paper,different types of diesel molecular compositional and blending models were constructed based on the SU-BEM framework.More than 1500 representative molecules were selected to form the molecular structure library.The probability density functions(PDFs)combination was determined by experimental data and experience.A quadratic optimization strategy combining genetic algorithm with local optimization algorithm was adopted to improve the accuracy of the compositional model.The model results show good agreement with the experimental data.The diesel blending model was constructed at the molecular-level based on the above diesel compositional models.The properties of the blending model accord with the experimental regulations.It is proved that the compositional models and blending model constructed have high accuracy and strong prediction capability,and are applicable to the industrial process.展开更多
The dividing wall column (DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control (MPC) is an advanced control strategy that has a...The dividing wall column (DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control (MPC) is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol, n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and tem- perature inferent/al control are considered. The multiobjective genetic algor/thm function "gamult/obj" in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and tempera- ture inferential control, resulting in a more stable and superior performance with lower values of integral of squared error (ISE).展开更多
To improve the naphtha composition prediction model based on molecular type homologous series matrix (MTHS), this paper puts forward a novel molecular matrix to characterize the naphtha composition and the norreal d...To improve the naphtha composition prediction model based on molecular type homologous series matrix (MTHS), this paper puts forward a novel molecular matrix to characterize the naphtha composition and the norreal distribution hypothesis to better describe the molecular composition distribution within each homologous series of the molecular matrix. Through prediction calculation of eight groups of naphtha samples and eight groups of gasoline samples, it is verified that the normal distribution hypothesis is more applicable than gamma distribution hypothesis for the prediction model. According to the prediction results of the samples, the restrain range of normal distribution parameters during model computing process is summarized. With the bulk properties of naphtha samples and the value range of distribution parameters as input conditions, this study utilizes the improved novel molecular matrix to predict the composition of naphtha samples. As the results show, the novel molecular matrix can predict more detailed composition information of naphtha and improve prediction accuracy with less unknown parameters.展开更多
A simple hydration model is used here by taking the composition of the cement and the initial water: cementratio (w/c) into account explicitly. Its conceptual basis is a combination of the Avrami equation and Bentz’s...A simple hydration model is used here by taking the composition of the cement and the initial water: cementratio (w/c) into account explicitly. Its conceptual basis is a combination of the Avrami equation and Bentz’s modelbased on simple spatial considerations. In this model, the Avrami equation determines the initial reaction, andBentz’s model describes the following hydration stage. The model favors engineers for it relies on one experimentalparameter and has a reliable approximation in the practice.展开更多
Ancient glass relics are easily weathered by the influence of buried environment, and the internal elements exchange with the environmental elements in large quantities, resulting in changes in their composition ratio...Ancient glass relics are easily weathered by the influence of buried environment, and the internal elements exchange with the environmental elements in large quantities, resulting in changes in their composition ratio. Archaeological research can often detect the component content of glass relics after weathering, but it is difficult to obtain the corresponding component content before weathering. It is necessary to predict the chemical composition of glass relics before weathering in order to accurately identify the type of glass relics and repair them. To solve this problem, this paper proposes a distributed matching strategy, and studies the influence of weathering on the composition content of glass through compositional correlation analysis and linear regression statistical methods, so as to build a prediction model of the composition content of glass relics before weathering. The results show that the composition prediction model of glass cultural relics constructed by the distribution matching strategy has a good prediction ability, which is consistent with the change trend of the composition ratio of linear regression analysis. Moreover, the model is simple and easy to operate, which is convenient for popularization and application, and provides theoretical basis and reference value for further research on the composition and accurate classification of glass cultural relics.展开更多
This study investigates the ground and structural response of adjacent raft foundations induced by largescale surcharge by ore in soft soil areas through a 130g centrifuge modeling test with an innovative layered load...This study investigates the ground and structural response of adjacent raft foundations induced by largescale surcharge by ore in soft soil areas through a 130g centrifuge modeling test with an innovative layered loading device.The prototype of the test is a coastal iron ore yard with a natural foundation of deep soft soil.Therefore,it is necessary to adopt some measures to reduce the influence of the large-scale surcharge on the adjacent raft foundation,such as installing stone columns for foundation treatment.Under an acceleration of 130 g,the model conducts similar simulations of iron ore,stone columns,and raft foundation structures.The tested soil mass has dimensions of 900 mm×700 mm×300 mm(lengthwidthdepth),which is remodeled from the soil extracted from the drilling holes.The test conditions are consistent with the actual engineering conditions and the effects of four-level loading conditions on the composite foundation of stone columns,unreinforced zone,and raft foundations are studied.An automatic layer-by-layer loading device was innovatively developed to simulate the loading process of actual engineering more realistically.The composite foundation of stone columns had a large settlement after the loading,forming an obvious settlement trough and causing the surface of the unreinforced zone to rise.The 12 m surcharge loading causes a horizontal displacement of 13.19 cm and a vertical settlement of 1.37 m in the raft foundation.The stone columns located on both sides of the unreinforced zone suffered significant shear damage at the sand-mud interface.Due to the reinforcement effect of stone columns,the sand layer below the top of the stone columns moves less.Meanwhile,the horizontal earth pressure in the raft foundation zone increases slowly.The stone columns will form new drainage channels and accelerate the dissipation of excess pore pressure.展开更多
The shear behavior of backfill-rock composites is crucial for mine safety and the management of surface subsidence.For exposing the shear failure mechanism of backfill-rock composites,we conducted shear tests on backf...The shear behavior of backfill-rock composites is crucial for mine safety and the management of surface subsidence.For exposing the shear failure mechanism of backfill-rock composites,we conducted shear tests on backfill-rock composites under three constant normal loads,compared with the unfilled rock.To investigate the macro-and meso-failure characteristics of the samples in the shear tests,the cracking behavior of samples was recorded by a high-speed camera and acoustic emission monitoring.In parallel with the experimental test,the numerical models of backfill-rock composites and unfilled rock were established using the discrete element method to analyze the continuous-discontinuous shearing process.Based on the damage mechanics and statistics,a novel shear constitutive model was proposed to describe mechanical behavior.The results show that backfill-rock composites had a special bimodal phenomenon of shearing load-deformation curve,i.e.the first shearing peak corresponded to rock break and the second shearing peak induced by the broken of aeolian sand-cement/fly ash paste backfill.Moreover,the shearing characteristic curves of the backfill-rock composites could be roughly divided into four stages,i.e.the shear failure of the specimens experienced:stage I:stress concentration;stage II:crack propagation;stage III:crack coalescence;stage IV:shearing friction.The numerical simulation shows that the existence of aeolian sand-cement/fly ash paste backfill inevitably altered the coalescence type and failure mode of the specimens and had a strengthening effect on the shear strength of backfillrock composites.Based on damage mechanics and statistics,a shear constitutive model was proposed to describe the shear fracture characteristics of specimens,especially the bimodal phenomenon.Finally,the micro-and meso-mechanisms of shear failure were discussed by combining the micro-test and numerical results.The research can advance the better understanding of the shear behavior of backfill-rock composites and contribute to the safety of mining engineering.展开更多
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.
文摘The cubic stiffness force model(CSFM)and Bouc-Wen model(BWM)are introduced and compared innovatively.The unknown coefficients of the nonlinear models are identified by the genetic algorithm combined with experiments.By fitting the identified nonlinear coefficients under different excitation amplitudes,the nonlinear vibration responses of the system are predicted.The results show that the accuracy of the BWM is higher than that of the CSFM,especially in the non-resonant region.However,the optimization time of the BWM is longer than that of the CSFM.
基金supported by the National Key Research and Development Program of China(Grant No.2022YFB3707803)the National Natural Science Foundation of China(Grant Nos.12072179 and 11672168)+1 种基金the Key Research Project of Zhejiang Lab(Grant No.2021PE0AC02)Shanghai Engineering Research Center for Inte-grated Circuits and Advanced Display Materials.
文摘Dielectric elastomers(DEs)require balanced electric actuation performance and mechanical integrity under applied voltages.Incorporating high dielectric particles as fillers provides extensive design space to optimize concentration,morphology,and distribution for improved actuation performance and material modulus.This study presents an integrated framework combining finite element modeling(FEM)and deep learning to optimize the microstructure of DE composites.FEM first calculates actuation performance and the effective modulus across varied filler combinations,with these data used to train a convolutional neural network(CNN).Integrating the CNN into a multi-objective genetic algorithm generates designs with enhanced actuation performance and material modulus compared to the conventional optimization approach based on FEM approach within the same time.This framework harnesses artificial intelligence to navigate vast design possibilities,enabling optimized microstructures for high-performance DE composites.
基金Supported by Science Center for Gas Turbine Project of China (Grant No.P2022-B-IV-014-001)Frontier Leading Technology Basic Research Special Project of Jiangsu Province of China (Grant No.BK20212007)the BIT Research and Innovation Promoting Project of China (Grant No.2022YCXZ019)。
文摘Thermal conductivity is one of the most significant criterion of three-dimensional carbon fiber-reinforced SiC matrix composites(3D C/SiC).Represent volume element(RVE)models of microscale,void/matrix and mesoscale proposed in this work are used to simulate the thermal conductivity behaviors of the 3D C/SiC composites.An entirely new process is introduced to weave the preform with three-dimensional orthogonal architecture.The 3D steady-state analysis step is created for assessing the thermal conductivity behaviors of the composites by applying periodic temperature boundary conditions.Three RVE models of cuboid,hexagonal and fiber random distribution are respectively developed to comparatively study the influence of fiber package pattern on the thermal conductivities at the microscale.Besides,the effect of void morphology on the thermal conductivity of the matrix is analyzed by the void/matrix models.The prediction results at the mesoscale correspond closely to the experimental values.The effect of the porosities and fiber volume fractions on the thermal conductivities is also taken into consideration.The multi-scale models mentioned in this paper can be used to predict the thermal conductivity behaviors of other composites with complex structures.
文摘For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.
文摘In view of the composition analysis and identification of ancient glass products, L1 regularization, K-Means cluster analysis, elbow rule and other methods were comprehensively used to build logical regression, cluster analysis, hyper-parameter test and other models, and SPSS, Python and other tools were used to obtain the classification rules of glass products under different fluxes, sub classification under different chemical compositions, hyper-parameter K value test and rationality analysis. Research can provide theoretical support for the protection and restoration of ancient glass relics.
基金The National Natural Science Foundation of China(No.60403027,60773191,70771043)the National High Technology Research and Development Program of China(863 Program)(No.2007AA01Z403)
文摘In order to formally reason and verify web services composition described by web services choreography specification WS-CDL,a typed formal model named typed Abstract WS-CDL(web services choreography description language)for WS-CDL specifications is proposed.In typed Abstract WS-CDL,the syntax of type and session,typing rules and operational semantics are formalized;the collaborations of web services are formally described by sessions;the operational semantics of a session can help to formally reason the execution of the choreography;the typing rules can help to formally check the data type consistency of exchanged information between web services and capture run-time errors due to type mismatches.Particularly,the concepts of type assumption set extension and type assumption set compatibility are proposed,and the merging algorithm of type assumption sets is defined so as to eliminate type assumption conflict.Based on the formal model,typed mapping rules for mapping web services choreography to orchestration is also defined.With the typed mapping rules,orchestration stubs and their type assumption sets can be generated from a given choreography; thus, web services composition can be verified at choreography and orchestration levels,respectively.The model is proved to have properties of type safety,and how the model can help to reason and verify web services composition is illustrated through a case study.
文摘Building a model for a object through model composition is a interesting topics, this paper research the interface composit ion of models in the Zeigler’s modeling methodology DEVS.
基金supported by the Natural Science Foundation of China(Grant No.51939004)the Fundamental Research Funds for the Central Universities(Grant No.B210204009)the China Huaneng Group Science and Technology Project(Grant No.HNKJ18-H24).
文摘Safety evaluation of toppling rock slopes developing in reservoir areas is crucial. To reduce the uncertainty of safety evaluation, this study developed a composite cloud model, which improved the combination weights of the decision-making trial and evaluation laboratory (DEMATEL) and criteria importance through intercriteria correlation (CRITIC) methods. A safety evaluation system was developed according to in situ monitoring data. The backward cloud generator was used to calculate the numerical characteristics of a cloud model of quantitative indices, and different virtual clouds were used to synthesize some clouds into a generalized one. The synthesized numerical characteristics were calculated to comprehensively evaluate the safety of toppling rock slopes. A case study of a toppling rock slope near the Huangdeng Hydropower Station in China was conducted using monitoring data collected since operation of the hydropower project began. The results indicated that the toppling rock slope was moderately safe with a low safety margin. The composite cloud model considers the fuzziness and randomness of safety evaluation and enables interchange between qualitative and quantitative knowledge. This study provides a new theoretical method for evaluating the safety of toppling rock slopes. It can aid in the predication, control, and even prevention of disasters.
基金This work was supported by the National Nature Science Foundation of China(awards 42,271,162,41,971,100)the Natural Science Foundation of Jilin Province(award 20220101149JC)the Scholarship Fund from China Scholarship Council(award 202,206,620,038).
文摘Ongoing climate changes have a direct impact on forest growth;they also affect natural fire regimes,with further implications for forest composition.Understanding of how these will affect forests on decadal-to-centennial timescales is limited.Here we use reconstructions of past vegetation,fire regimes and climate during the Holocene to examine the relative importance of changes in climate and fire regimes for the abundance of key tree species in northeastern China.We reconstructed vegetation changes and fire regimes based on pollen and charcoal records from Gushantun peatland.We then used generalized linear modelling to investigate the impact of reconstructed changes in summer temperature,annual precipitation,background levels of fire,fire frequency and fire magnitude to identify the drivers of decadal-to-centennial changes in forest openness and composition.Changes in climate and fire regimes have independent impacts on the abundance of the key tree taxa.Climate variables are generally more important than fire variables in determining the abundance of individual taxa.Precipitation is the only determinant of forest openness,but summer temperature is more important than precipitation for individual tree taxa with warmer summers causing a decrease in cold-tolerant conifers and an increase in warmth-demanding broadleaved trees.Both background level and fire frequency have negative relationships with the abundance of most tree taxa;only Pinus increases as fire frequency increases.The magnitude of individual fires does not have a significant impact on species abundance on this timescale.Both climate and fire regime characteristics must be considered to understand changes in forest composition on the decadal-to-centennial timescale.There are differences,both in sign and magnitude,in the response of individual tree species to individual drivers.
文摘A global two-dimensional zonally averaged chemistry model is developed to study the chemi-cal composition of atmosphere. The region of the model is from 90°S to 90°N and from the ground to the altitude of 20 km with a resolution of 5° x 1 km. The wind field is residual circulation calcu-lated from diabatic rate. 34 species and 104 chemical and photochemical reactions are considered in the model. The sources of CH4, CO and NOx, which are divided into seasonal sources and non-seasonal sources, are parameterized as a function of latitude and time. The chemical composi-tion of atmosphere was simulated with emission level of CH4, CO and NOx in 1990. The results are compared with observations and other model results, showing that the model is successful to simu-late the atmospheric chemical composition and distribution of CH4. Key words Global two-dimensional chemistry model - Atmospheric composition - Emission This work was supported by the State Key Program for basic research “ Climate Dynamics and Cli-mate Prediction Theory” (Pandeng-yu-21).The authors would like to express their thanks to the National Oceanic and Atmospheric Administration (NOAA), Climate Monitoring and Diagnostics Laboratory (CMDL), Carbon Cycle Group for providing the observational data of CO and CH4.
基金Project supported by the National Natural Science Foundation of China (Grant No.60403027)
文摘In this paper, a model based colored Petri net (CPN) to provide semantic support for web service composition is proposed, and the reliability and maintainability of composite services are improved. The composite constructs in the model are sequence, concurrent, choice, loop and replace. The web service is formally defined by a CPN. A closed composing algebra is defined to obtain a framework which enables declarative composition of web services. Availability, confidentiality, and integrity of composite service are analyzed within the framework of the model based CPN.
基金supported by the Science and Technology Programs of Gansu Province,China(Nos.21JR1RA248,20JR10RA264)the Young Scholars Science Foundation of Lanzhou Jiaotong University,China(Nos.2020039,2020017)the Special Funds for Guiding Local Scientific and Technological Development by the Central Government,China(No.22ZY1QA005)。
文摘In order to provide more insights into the damage propagation composite wind turbine blades(blade)under cyclic fatigue loading,a stiffness degradation model for blade is proposed based on the full-scale fatigue testing of a blade.A novel non-linear fatigue damage accumulation model is proposed using the damage assessment theories of composite laminates for the first time.Then,a stiffness degradation model is established based on the correlation of fatigue damage and residual stiffness of the composite laminates.Finally,a stiffness degradation model for the blade is presented based on the full-scale fatigue testing.The scientific rationale of the proposed stiffness model of blade is verified by using full-scale fatigue test data of blade with a total length of 52.5 m.The results indicate that the proposed stiffness degradation model of the blade agrees well with the fatigue testing results of this blade.This work provides a basis for evaluating the fatigue damage and lifetime of blade under cyclic fatigue loading.
基金supported by the SINOPEC R&D Program(grant number 119014-1)
文摘Diesel molecular compositional model has important application for diesel quality prediction,blending,and molecular-level process model development.In this paper,different types of diesel molecular compositional and blending models were constructed based on the SU-BEM framework.More than 1500 representative molecules were selected to form the molecular structure library.The probability density functions(PDFs)combination was determined by experimental data and experience.A quadratic optimization strategy combining genetic algorithm with local optimization algorithm was adopted to improve the accuracy of the compositional model.The model results show good agreement with the experimental data.The diesel blending model was constructed at the molecular-level based on the above diesel compositional models.The properties of the blending model accord with the experimental regulations.It is proved that the compositional models and blending model constructed have high accuracy and strong prediction capability,and are applicable to the industrial process.
基金Supported by the National Natural Science Foundation of China(21676299,21476261and 21606255)
文摘The dividing wall column (DWC) is considered as a major breakthrough in distillation technology and has good prospect of industrialization. Model predictive control (MPC) is an advanced control strategy that has acquired extensive applications in various industries. In this study, MPC is applied to the process for separating ethanol, n-propanol, and n-butanol ternary mixture in a fully thermally coupled DWC. Both composition control and tem- perature inferent/al control are considered. The multiobjective genetic algor/thm function "gamult/obj" in Matlab is used for the weight tuning of MPC. Comparisons are made between the control performances of MPC and PI strategies. Simulation results show that although both MPC and PI schemes can stabilize the DWC in case of feed disturbances, MPC generally behaves better than the PI strategy for both composition control and tempera- ture inferential control, resulting in a more stable and superior performance with lower values of integral of squared error (ISE).
基金Supported by the National Natural Science Foundation of China(U1462206)
文摘To improve the naphtha composition prediction model based on molecular type homologous series matrix (MTHS), this paper puts forward a novel molecular matrix to characterize the naphtha composition and the norreal distribution hypothesis to better describe the molecular composition distribution within each homologous series of the molecular matrix. Through prediction calculation of eight groups of naphtha samples and eight groups of gasoline samples, it is verified that the normal distribution hypothesis is more applicable than gamma distribution hypothesis for the prediction model. According to the prediction results of the samples, the restrain range of normal distribution parameters during model computing process is summarized. With the bulk properties of naphtha samples and the value range of distribution parameters as input conditions, this study utilizes the improved novel molecular matrix to predict the composition of naphtha samples. As the results show, the novel molecular matrix can predict more detailed composition information of naphtha and improve prediction accuracy with less unknown parameters.
基金The work was supported by Yunnan Local Colleges Applied Basic Research Projects(No.2018FH001-119)Science Research Foundation of Yunnan Education Department of China(Nos.2019J0734,2019J0733,2017ZZX177 and 2018JS422)+2 种基金the Candidate Talents Training Fund of Yunnan Province(Project No.2015HB064)National Natural Science Foundation of China(No.11802265)The authors(MBY and QLH)gratefully acknowledge the financial support from the Hundred Talents Program of Yuxi(Grant 2019).
文摘A simple hydration model is used here by taking the composition of the cement and the initial water: cementratio (w/c) into account explicitly. Its conceptual basis is a combination of the Avrami equation and Bentz’s modelbased on simple spatial considerations. In this model, the Avrami equation determines the initial reaction, andBentz’s model describes the following hydration stage. The model favors engineers for it relies on one experimentalparameter and has a reliable approximation in the practice.
文摘Ancient glass relics are easily weathered by the influence of buried environment, and the internal elements exchange with the environmental elements in large quantities, resulting in changes in their composition ratio. Archaeological research can often detect the component content of glass relics after weathering, but it is difficult to obtain the corresponding component content before weathering. It is necessary to predict the chemical composition of glass relics before weathering in order to accurately identify the type of glass relics and repair them. To solve this problem, this paper proposes a distributed matching strategy, and studies the influence of weathering on the composition content of glass through compositional correlation analysis and linear regression statistical methods, so as to build a prediction model of the composition content of glass relics before weathering. The results show that the composition prediction model of glass cultural relics constructed by the distribution matching strategy has a good prediction ability, which is consistent with the change trend of the composition ratio of linear regression analysis. Moreover, the model is simple and easy to operate, which is convenient for popularization and application, and provides theoretical basis and reference value for further research on the composition and accurate classification of glass cultural relics.
基金funding support from National Key Research and Development Program of China(Grant No.2021YFF0502200)National Natural Science Foundation of China(Grant Nos.52022070 and 51978516).
文摘This study investigates the ground and structural response of adjacent raft foundations induced by largescale surcharge by ore in soft soil areas through a 130g centrifuge modeling test with an innovative layered loading device.The prototype of the test is a coastal iron ore yard with a natural foundation of deep soft soil.Therefore,it is necessary to adopt some measures to reduce the influence of the large-scale surcharge on the adjacent raft foundation,such as installing stone columns for foundation treatment.Under an acceleration of 130 g,the model conducts similar simulations of iron ore,stone columns,and raft foundation structures.The tested soil mass has dimensions of 900 mm×700 mm×300 mm(lengthwidthdepth),which is remodeled from the soil extracted from the drilling holes.The test conditions are consistent with the actual engineering conditions and the effects of four-level loading conditions on the composite foundation of stone columns,unreinforced zone,and raft foundations are studied.An automatic layer-by-layer loading device was innovatively developed to simulate the loading process of actual engineering more realistically.The composite foundation of stone columns had a large settlement after the loading,forming an obvious settlement trough and causing the surface of the unreinforced zone to rise.The 12 m surcharge loading causes a horizontal displacement of 13.19 cm and a vertical settlement of 1.37 m in the raft foundation.The stone columns located on both sides of the unreinforced zone suffered significant shear damage at the sand-mud interface.Due to the reinforcement effect of stone columns,the sand layer below the top of the stone columns moves less.Meanwhile,the horizontal earth pressure in the raft foundation zone increases slowly.The stone columns will form new drainage channels and accelerate the dissipation of excess pore pressure.
文摘The shear behavior of backfill-rock composites is crucial for mine safety and the management of surface subsidence.For exposing the shear failure mechanism of backfill-rock composites,we conducted shear tests on backfill-rock composites under three constant normal loads,compared with the unfilled rock.To investigate the macro-and meso-failure characteristics of the samples in the shear tests,the cracking behavior of samples was recorded by a high-speed camera and acoustic emission monitoring.In parallel with the experimental test,the numerical models of backfill-rock composites and unfilled rock were established using the discrete element method to analyze the continuous-discontinuous shearing process.Based on the damage mechanics and statistics,a novel shear constitutive model was proposed to describe mechanical behavior.The results show that backfill-rock composites had a special bimodal phenomenon of shearing load-deformation curve,i.e.the first shearing peak corresponded to rock break and the second shearing peak induced by the broken of aeolian sand-cement/fly ash paste backfill.Moreover,the shearing characteristic curves of the backfill-rock composites could be roughly divided into four stages,i.e.the shear failure of the specimens experienced:stage I:stress concentration;stage II:crack propagation;stage III:crack coalescence;stage IV:shearing friction.The numerical simulation shows that the existence of aeolian sand-cement/fly ash paste backfill inevitably altered the coalescence type and failure mode of the specimens and had a strengthening effect on the shear strength of backfillrock composites.Based on damage mechanics and statistics,a shear constitutive model was proposed to describe the shear fracture characteristics of specimens,especially the bimodal phenomenon.Finally,the micro-and meso-mechanisms of shear failure were discussed by combining the micro-test and numerical results.The research can advance the better understanding of the shear behavior of backfill-rock composites and contribute to the safety of mining engineering.