Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints...Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints,notably the no-cloning theorem,which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography,secure communication,and error correction.While existing quantum circuit representations implicitly honor such constraints,they lack formal mechanisms for early-stage verification in software design.Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software.This paper presents a formal metamodeling framework using UML-style notation and and Object Constraint Language(OCL)to systematically capture and enforce the no-cloning theorem within quantum software models.The proposed metamodel formalizes key quantum concepts—such as entanglement and teleportation—and encodes enforceable invariants that reflect core quantum mechanical laws.The framework’s effectiveness is validated by analyzing two critical edge cases—conditional copying with CNOT gates and quantum teleportation—through instance model evaluations.These cases demonstrate that the metamodel can capture nuanced scenarios that are often mistaken as violations of the no-cloning theorem but are proven compliant under formal analysis.Thus,these serve as constructive validations that demonstrate the metamodel’s expressiveness and correctness in representing operations that may appear to challenge the no-cloning theorem but,upon rigorous analysis,are shown to comply with it.The approach supports early detection of conceptual design errors,promoting correctness prior to implementation.The framework’s extensibility is also demonstrated by modeling projective measurement,further reinforcing its applicability to broader quantum software engineering tasks.By integrating the rigor of metamodeling with fundamental quantum mechanical principles,this work provides a structured,model-driven approach that enables traditional software engineers to address quantum computing challenges.It offers practical insights into embedding quantum correctness at the modeling level and advances the development of reliable,error-resilient quantum software systems.展开更多
The Electro-Hydrostatic Actuator(EHA)is a typical hydro-mechatronic control system.Due to the limited accuracy of measurement,inadequate knowledge,and vague judgments,hybrid uncertainties,including aleatory and episte...The Electro-Hydrostatic Actuator(EHA)is a typical hydro-mechatronic control system.Due to the limited accuracy of measurement,inadequate knowledge,and vague judgments,hybrid uncertainties,including aleatory and epistemic uncertainties,inevitably exist in the performance assessment of EHA systems.Existing methods ignored the hybrid uncertainties which can hardly obtain a satisfactory result while wasting a lot of time on the experimental design.To overcome this drawback,a metamodeling method for hybrid uncertainty propagation of EHA systems is developed via an active learning Gaussian Process(GP)model.The proposed method is bifurcated into three pillars:(A)Initializing the GP model and generating the optimum candidate sampling set by an Optimized Max-Minimize Distance(OMMD)algorithm,which aims to maximize the minimum distance between the added samples and original samples,(B)maximizing the learning function and generating new samples by a developed farthest or nearest judgment strategy,while updating the original GP model,and(C)judging the convergence by three uncertainty metrics,i.e.,the area metric,maximum variance metric,and the mean value metric.A numerical example is exemplified to evaluate the effectiveness and efficiency of the proposed method.Meanwhile,the EHA system of aircrafts is examined to show the application of the proposed method for high-dimensional problems.The effects of the uncertainties in the Proportional-Integral-Differential(PID)of the EHA system are also examined.展开更多
Domain-specific metamodeling language(DSMML) defined by informal method cannot strictly represent its structural semantics,so its properties such as consistency cannot be holistically and systematically verified.In re...Domain-specific metamodeling language(DSMML) defined by informal method cannot strictly represent its structural semantics,so its properties such as consistency cannot be holistically and systematically verified.In response,the paper proposes a formal representation of the structural semantics of DSMML named extensible markup language(XML) based metamodeling language(XMML) and its metamodels consistency verification method.Firstly,we describe our approach of formalization,based on this,the method of consistency verification of XMML and its metamodels based on first-order logical inference is presented;then,the formalization automatic mapping engine for metamodels is designed to show the feasibility of our formal method.展开更多
Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensiv...Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.展开更多
A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximatio...A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.展开更多
Recently,the ontological metamodel plays an increasingly important role to specify systems in two forms:ontology and metamodel.Ontology is a descriptive model representing reality by a set of concepts,their interrelat...Recently,the ontological metamodel plays an increasingly important role to specify systems in two forms:ontology and metamodel.Ontology is a descriptive model representing reality by a set of concepts,their interrelations,and constraints.On the other hand,metamodel is a more classical,but more powerful model in which concepts and relationships are represented in a prescriptive way.This study firstly clarifies the difference between the two approaches,then explains their advantages and limitations,and attempts to explore a general ontological metamodeling framework by integrating each characteristic,in order to implement semantic simulation model engineering.As a proof of concept,this paper takes the combat effectiveness simulation systems as a motivating case,uses the proposed framework to define a set of ontological composable modeling frameworks,and presents an underwater targets search scenario for running simulations and analyzing results.Finally,this paper expects that this framework will be generally used in other fields.展开更多
The electric power transfer capability on the Manitoba-Ontario interconnection depends on various system operating conditions such as area generation patterns and ambient temperatures. This work models the power netwo...The electric power transfer capability on the Manitoba-Ontario interconnection depends on various system operating conditions such as area generation patterns and ambient temperatures. This work models the power network as a black-box function, which is evaluated with the system reliability analysis techniques to determine the maximum transfer capability under a given operating condition. A metamodel or an approximation model of the maximized power transfer capability is built based on the sampled system responses and optimized with respect to the corresponding operating conditions. An optimal metamodel is implemented as a prototype software tool, PTCanalyzer, and applied to Manitoba-Ontario interconnection power transfer calculations. This optimized metamodel technique provides an in-depth understanding of the dependency of the power transfer capability on system operating conditions and proves to be an effective tool in optimizing the operation planning of the interconnection for a given power system configuration. The PTCanalyzer has the potential to be used for optimization of other power network interconnections.展开更多
Global urbanization causes more environmental stresses in cities and energy efficiency is one of major concerns for urban sustainability.The variable importance techniques have been widely used in building energy anal...Global urbanization causes more environmental stresses in cities and energy efficiency is one of major concerns for urban sustainability.The variable importance techniques have been widely used in building energy analysis to determine key factors influencing building energy use.Most of these applications,however,use only one type of variable importance approaches.Therefore,this paper proposes a procedure of conducting two types of variable importance analysis(predictive and variance-based)to determine robust and effective energy saving measures in urban buildings.These two variable importance methods belong to metamodeling techniques,which can significantly reduce computational cost of building energy simulation models for urban buildings.The predictive importance analysis is based on the prediction errors of metamodels to obtain importance rankings of inputs,while the variance-based variable importance can explore non-linear effects and interactions among input variables based on variance decomposition.The campus buildings are used to demonstrate the application of the method proposed to explore characteristic of heating energy,cooling energy,electricity,and carbon emissions of buildings.The results indicate that the combination of two types of metamodeling variable importance analysis can provide fast and robust analysis to improve energy efficiency of urban buildings.The carbon emissions can be reduced approximately 30%after using a few of effective energy efficiency measures and more aggressive measures can lead to the 60%of reduction of carbon emissions.Moreover,this research demonstrates the application of parallel computing to expedite building energy analysis in urban environment since more multi-core computers become increasingly available.展开更多
For the energy-related issues that the world is facing nowadays,the renovation of the building stock is one of the major challenges.The objective of this study is to present an approach that helps to develop a multi-c...For the energy-related issues that the world is facing nowadays,the renovation of the building stock is one of the major challenges.The objective of this study is to present an approach that helps to develop a multi-criteria decision support tool dedicated to the rehabilitation of sustainable and passive energy housing by integrating heating energy needs,economic,social and environmental criteria throughout the life cycle stages of the building.The methodology consists of developing metamodels to predict heating energy needs from polynomial regression,design of experiments method and thermo-aeraulic simulations of building behavior.This metamodel is used to carry out a combinatorial study of real technical solutions.The methodology was applied to a real-life existing building located in La Rochelle city(France)based on an in-situ energy diagnosis.Three multicriteria analysis methods were studied and compared:weighted sum,Min-Max and Pareto concept.Furthermore,technical constraints as well as owner preferences and performance constraints have been studied.Optimal technical solutions have been obtained in order to meet the various criteria studied.In addition,window shading and natural ventilation have been proposed to reduce the thermal discomfort rate in summer.This study was extended to all French regions.Finally,this method can be transformed into a decision support tool which will be useful for architects,engineers and stockholders.展开更多
The formal specification of design patterns is central to pattern research and is the foundation of solving various pattern-related problems.In this paper,we propose a metamodeling approach for pattern specification,i...The formal specification of design patterns is central to pattern research and is the foundation of solving various pattern-related problems.In this paper,we propose a metamodeling approach for pattern specification,in which a pattern is modeled as a meta-level class and its participants are meta-level references.Instead of defining a new metamodel,we reuse the Unified Modeling Language(UML)metamodel and incorporate the concepts of Variable and Set into our approach,which are unavailable in the UML but essential for pattern specification.Our approach provides straightforward solutions for pattern-related problems,such as pattern instantiation,evolution,and implementation.By integrating the solutions into a single framework,we can construct a pattern management system,in which patterns can be instantiated,evolved,and implemented in a correct and manageable way.展开更多
There is a growing need for accurate and interpretable machine learning models of thermal comfort in buildings.Physics-informed machine learning could address this need by adding physical consistency to such models.Th...There is a growing need for accurate and interpretable machine learning models of thermal comfort in buildings.Physics-informed machine learning could address this need by adding physical consistency to such models.This paper presents metamodeling of thermal comfort in non-air-conditioned buildings using physics-informed machine learning.The studied metamodel incorporated knowledge of both quasi-steady-state heat transfer and dynamic simulation results.Adaptive thermal comfort in an office located in cold and hot European climates was studied with the number of overheating hours as index.A one-at-a-time method was used to gain knowledge from dynamic simulation with TRNSYS software.This knowledge was used to filter the training data and to choose probability distributions for metamodel forms alternative to polynomial.The response of the dynamic model was positively skewed;and thus,the symmetric logistic and hyperbolic secant distributions were inappropriate and outperformed by positively skewed distributions.Incorporating physical knowledge into the metamodel was much more effective than doubling the size of the training sample.The highly flexible Kumaraswamy distribution provided the best performance with R2 equal to 0.9994 for the cold climate and 0.9975 for the hot climate.Physics-informed machine learning could combine the strength of both physics and machine learning models,and could therefore support building design with flexible,accurate and interpretable metamodels.展开更多
Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace(referred to as CATT gear),a mathematical model for determinin...Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace(referred to as CATT gear),a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis.In this study,a mathematical relationship between design parameters and contact stress is formulated using the KrigingMetamodel.To enhance the model’s accuracy,we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle Swarm optimization algorithm,leveraging the strengths of each.Additionally,the“parental inheritance+self-learning”optimization model is used to fine-tune the KrigingMetamodel’s parameters.Following this,amathematicalmodel for calculating the contact stress of Variable Hyperbolic Circular-Arc-Tooth-Trace(VH-CATT)gears using the optimized Kriging model was developed.We then examined how different gear parameters affect the VH-CATT gears’contact stress.Our simulation results show:(1)Improvements in R2,RMSE,and RMAE.R2 rose from0.9852 to 0.9974(a 1.22%increase),nearing 1,suggesting the optimized Kriging Metamodel’s global error is minimized.Meanwhile,RMSE dropped from3.9210 to 1.6492,a decline of 57.94%.The global error of the GA-IQPSO-Kriging algorithm was also reduced,with RMAE decreasing by 58.69%from 0.1823 to 0.0753,showing the algorithm’s enhanced precision.In a comparison of ten experimental groups selected randomly,the GA-IQPSO-Kriging and FEM-based contact analysis methods were used to measure contact stress.Results revealed a maximum error of 12.11667 MPA,which represents 2.85%of the real value.(2)Several factors,including the pressure angle,tooth width,modulus,and tooth line radius,are inversely related to contact stress.The descending order of their impact on the contact stress is:tooth line radius>modulus>pressure angle>tooth width.(3)Complex interactions are noted among various parameters.Specifically,when the tooth line radius interacts with parameters such as pressure angle,tooth width,and modulus,the resulting stress contour is nonlinear,showcasing amultifaceted contour plane.However,when tooth width,modulus,and pressure angle interact,the stress contour is nearly linear,and the contour plane is simpler,indicating a weaker coupling among these factors.展开更多
Estimating the failure probability of highly reliable structures in practice engineering,such as aeronautical components,is challenging because of the strong-coupling and the small failure probability traits.In this p...Estimating the failure probability of highly reliable structures in practice engineering,such as aeronautical components,is challenging because of the strong-coupling and the small failure probability traits.In this paper,an Expanded Learning Intelligent Back Propagation(EL-IBP)neural network approach is developed:firstly,to accurately characterize the engineering response coupling relationships,a high-fidelity Intelligent-optimized Back Propagation(IBP)neural network metamodel is developed;furthermore,to elevate the analysis efficacy for small failure assessment,a novel expanded learning strategy for adaptive IBP metamodeling is proposed.Three numerical examples and one typical practice engineering case are analyzed,to validate the effectiveness and engineering application value of the proposed method.Methods comparison shows that the ELIBP method holds significant efficiency and accuracy superiorities in engineering issues.The current study may shed a light on pushing the adaptive metamodeling technique deeply toward complex engineering reliability analysis.展开更多
SPEM(software process engineering metamodel)是国际标准化组织制定的标准元模型,正日益成为软件过程建模领域的行业标准,但在过程执行方面,SPEM还存在不足.将软件过程看作是一种特殊的工作流,提出了一种应用工作流运行机制支持软件...SPEM(software process engineering metamodel)是国际标准化组织制定的标准元模型,正日益成为软件过程建模领域的行业标准,但在过程执行方面,SPEM还存在不足.将软件过程看作是一种特殊的工作流,提出了一种应用工作流运行机制支持软件过程执行的方法.通过将SPEM模型转换为XPDL(XML process definition language)模型,利用XPDL引擎支持SPEM模型的执行.制定了SPEM和XPDL之间的映射规则,设计了转换算法并开发了转换引擎.该方法被应用在SoftPM项目中,成功地基于XPDL引擎Shark实现了对软件过程模型的执行支持.展开更多
Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increase...Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.展开更多
Callovo-Oxfordian(COx)claystone has been considered as a potential host rock for geological radioactive waste disposal in France(Cigéo project).During the exploitation phase(100 years),the stability of drifts(e.g...Callovo-Oxfordian(COx)claystone has been considered as a potential host rock for geological radioactive waste disposal in France(Cigéo project).During the exploitation phase(100 years),the stability of drifts(e.g.galleries/alveoli)within the disposal is assured by the liner,which includes two layers:concrete arch segment and compressible material.The latter exhibits a significant deformation capacity(about 50%)under low stress(<3 MPa).Although the response of these underground structures can be governed by complex thermo-hydro-mechanical coupling,the creep behavior of COx claystone has been considered as the main factor controlling the increase of stress state in the concrete liner and hence the long-term stability of drifts.Therefore,by focusing only on the purely mechanical behavior,this study aims at investigating the uncertainty effect of the COx claystone time-dependent properties on the stability of an alveolus of Cigéo during the exploitation period.To describe the creep behavior of COx claystone,we use Lemaitre’s viscoplastic model with three parameters whose uncertainties are identified from laboratory creep tests.For the reliability analysis,an extension of a well-known Kriging metamodeling technique is proposed to assess the exceedance probability of acceptable stress in the concrete liner of the alveolus.The open-source code Code_Aster is chosen for the direct numerical evaluations of the performance function.The Kriging-based reliability analysis elucidates the effect of the uncertainty of COx claystone on the long-term stability of the concrete liner.Moreover,the role of the compressible material layer between the concrete liner and the host rock is also highlighted.展开更多
Raising software abstraction and re-use levels are key success factors for producing quality software products. Model-driven architecture (MDA) is an OMG initiative following this trend by mapping a conceptual model o...Raising software abstraction and re-use levels are key success factors for producing quality software products. Model-driven architecture (MDA) is an OMG initiative following this trend by mapping a conceptual model of application specified in platform independent model (PIM), to one or more platform specific models (PSM) automatically. Because there is little previous work tackling the development problem from specification through to implementation, this paper proposes End to End Development engineering (E2EDE) method using MDA methodology. E2EDE is intended to fill the mapping gap between PIM and PSM in MDA. The notion of variability is utilized from software product line and used to model design decisions in PSM. PIM is equipped with Nonfunctional requirements which borrowed from Design pattern to inform design decisions;thereby guiding the mapping process. In addition we have developed a strategic PSM for messaging systems can be configured to produce different applications such as the helpdesk system which is used as a case study.展开更多
Many advanced mathematical models of biochemical, biophysical and other processes in systems biology can be described by parametrized systems of nonlinear differential equations. Due to complexity of the models, a pro...Many advanced mathematical models of biochemical, biophysical and other processes in systems biology can be described by parametrized systems of nonlinear differential equations. Due to complexity of the models, a problem of their simplification has become of great importance. In particular, rather challengeable methods of estimation of parameters in these models may require such simplifications. The paper offers a practical way of constructing approximations of nonlinearly parametrized functions by linearly parametrized ones. As the idea of such approximations goes back to Principal Component Analysis, we call the corresponding transformation Principal Component Transform. We show that this transform possesses the best individual fit property, in the sense that the corresponding approximations preserve most information (in some sense) about the original function. It is also demonstrated how one can estimate the error between the given function and its approximations. In addition, we apply the theory of tensor products of compact operators in Hilbert spaces to justify our method for the case of the products of parametrized functions. Finally, we provide several examples, which are of relevance for systems biology.展开更多
The success of system modernization depends on the existence of technical frameworks for information integration and tool interoperation like the Model Driven Architecture （MDA）. Reverse engineering techniques play ...The success of system modernization depends on the existence of technical frameworks for information integration and tool interoperation like the Model Driven Architecture （MDA）. Reverse engineering techniques play a crucial role in system modernization. This paper describes how to reverse engineering activity diagrams from object oriented code in the MDA context focusing on transformations at model and metamodel levels. A framework to reverse engineering MDA models from object oriented code that distinguishes three different abstraction levels linked to models, metamodels and formal specifications, is described. At model level, transformations are based on static and dynamic analysis. At metamodel level, transformations are specified as 0CL （Object Constraint Language） contracts between M0F （Meta Object Facility） metamodels which control the consistency of these transformations. The level of formal specification includes algebraic specifications of MOF metamodels and metamodel-based transformations. This paper analyzes a recovery process of activity diagrams from Java code by applying static and dynamic analysis and shows a formalization of this process in terms of MOF metamodels. The authors validate their approach by using Eclipse Modeling Framework, Ecore metamodels and ATL （Atlas Transformation Language）.展开更多
文摘Quantum software development utilizes quantum phenomena such as superposition and entanglement to address problems that are challenging for classical systems.However,it must also adhere to critical quantum constraints,notably the no-cloning theorem,which prohibits the exact duplication of unknown quantum states and has profound implications for cryptography,secure communication,and error correction.While existing quantum circuit representations implicitly honor such constraints,they lack formal mechanisms for early-stage verification in software design.Addressing this constraint at the design phase is essential to ensure the correctness and reliability of quantum software.This paper presents a formal metamodeling framework using UML-style notation and and Object Constraint Language(OCL)to systematically capture and enforce the no-cloning theorem within quantum software models.The proposed metamodel formalizes key quantum concepts—such as entanglement and teleportation—and encodes enforceable invariants that reflect core quantum mechanical laws.The framework’s effectiveness is validated by analyzing two critical edge cases—conditional copying with CNOT gates and quantum teleportation—through instance model evaluations.These cases demonstrate that the metamodel can capture nuanced scenarios that are often mistaken as violations of the no-cloning theorem but are proven compliant under formal analysis.Thus,these serve as constructive validations that demonstrate the metamodel’s expressiveness and correctness in representing operations that may appear to challenge the no-cloning theorem but,upon rigorous analysis,are shown to comply with it.The approach supports early detection of conceptual design errors,promoting correctness prior to implementation.The framework’s extensibility is also demonstrated by modeling projective measurement,further reinforcing its applicability to broader quantum software engineering tasks.By integrating the rigor of metamodeling with fundamental quantum mechanical principles,this work provides a structured,model-driven approach that enables traditional software engineers to address quantum computing challenges.It offers practical insights into embedding quantum correctness at the modeling level and advances the development of reliable,error-resilient quantum software systems.
基金the National Natural Science Foundation of China(Nos.72301057,72271044,72331002,and 52305010)the Sichuan Science and Technology Program,China(No.2023YFG0157).
文摘The Electro-Hydrostatic Actuator(EHA)is a typical hydro-mechatronic control system.Due to the limited accuracy of measurement,inadequate knowledge,and vague judgments,hybrid uncertainties,including aleatory and epistemic uncertainties,inevitably exist in the performance assessment of EHA systems.Existing methods ignored the hybrid uncertainties which can hardly obtain a satisfactory result while wasting a lot of time on the experimental design.To overcome this drawback,a metamodeling method for hybrid uncertainty propagation of EHA systems is developed via an active learning Gaussian Process(GP)model.The proposed method is bifurcated into three pillars:(A)Initializing the GP model and generating the optimum candidate sampling set by an Optimized Max-Minimize Distance(OMMD)algorithm,which aims to maximize the minimum distance between the added samples and original samples,(B)maximizing the learning function and generating new samples by a developed farthest or nearest judgment strategy,while updating the original GP model,and(C)judging the convergence by three uncertainty metrics,i.e.,the area metric,maximum variance metric,and the mean value metric.A numerical example is exemplified to evaluate the effectiveness and efficiency of the proposed method.Meanwhile,the EHA system of aircrafts is examined to show the application of the proposed method for high-dimensional problems.The effects of the uncertainties in the Proportional-Integral-Differential(PID)of the EHA system are also examined.
基金the Yunnan Provincial Department of Education Research Fund Key Project(No.2011z025)General Project(No.2011y214)
文摘Domain-specific metamodeling language(DSMML) defined by informal method cannot strictly represent its structural semantics,so its properties such as consistency cannot be holistically and systematically verified.In response,the paper proposes a formal representation of the structural semantics of DSMML named extensible markup language(XML) based metamodeling language(XMML) and its metamodels consistency verification method.Firstly,we describe our approach of formalization,based on this,the method of consistency verification of XMML and its metamodels based on first-order logical inference is presented;then,the formalization automatic mapping engine for metamodels is designed to show the feasibility of our formal method.
基金supported by National Natural Science Foundation of China (Grant No.60572007)National Basic Research Program of China(973 Program,Grant No.613580202)
文摘Metamodeling techniques have been used in robust optimization to reduce the high computational cost of the uncertainty analysis and improve the performance of robust optimization problems with computationally expensive simulation models. Existing metamodels main focus on polynomial regression（PR）, neural networks（NN） and Kriging models, these metamodels are not well suited for large-scale robust optimization problems with small size training sets and high nonlinearity. To address the problem, a reduced approximation model technique based on support vector regression（SVR） is introduced in order to improve the accuracy of metamodels. A robust optimization method based on SVR is presented for problems that involve high dimension and nonlinear. First appropriate design parameter samples are selected by experimental design theories, then the response samples are obtained from the simulations such as finite element analysis, the SVR metamodel is constructed and treated as the mean and the variance of the objective performance functions. Combining other constraints, the robust optimization model is formed which can be solved by genetic algorithm （GA）. The applicability of the method developed is demonstrated using a case of two-bar structure system study. The performances of SVR were compared with those of PR, Kriging and back-propagation neural networks（BPNN）, the comparison results show that the prediction accuracy of the SVR metamodel was higher than those of other metamodels under uncertainty. The robust optimization solutions are near to the real result, and the proposed method is found to be accurate and efficient for robust optimization. This reaserch provides an efficient method for robust optimization problems with complex structure.
基金Supported by National"863"Program of China (No.2006AA04Z127) .
文摘A method for optimizing automotive doors under multiple criteria involving the side impact, stiffness, natural frequency, and structure weight is presented. Metamodeling technique is employed to construct approximations to replace the high computational simulation models. The approximating functions for stiffness and natural frequency are constructed using Taylor series approximation. Three popular approximation techniques,i.e.polynomial response surface (PRS), stepwise regression (SR), and Kriging are studied on their accuracy in the construction of side impact functions. Uniform design is employed to sample the design space of the door impact analysis. The optimization problem is solved by a multi-objective genetic algorithm. It is found that SR technique is superior to PRS and Kriging techniques in terms of accuracy in this study. The numerical results demonstrate that the method successfully generates a well-spread Pareto optimal set. From this Pareto optimal set, decision makers can select the most suitable design according to the vehicle program and its application.
基金the National Natural Science Foundation of China(61273198).
文摘Recently,the ontological metamodel plays an increasingly important role to specify systems in two forms:ontology and metamodel.Ontology is a descriptive model representing reality by a set of concepts,their interrelations,and constraints.On the other hand,metamodel is a more classical,but more powerful model in which concepts and relationships are represented in a prescriptive way.This study firstly clarifies the difference between the two approaches,then explains their advantages and limitations,and attempts to explore a general ontological metamodeling framework by integrating each characteristic,in order to implement semantic simulation model engineering.As a proof of concept,this paper takes the combat effectiveness simulation systems as a motivating case,uses the proposed framework to define a set of ontological composable modeling frameworks,and presents an underwater targets search scenario for running simulations and analyzing results.Finally,this paper expects that this framework will be generally used in other fields.
文摘The electric power transfer capability on the Manitoba-Ontario interconnection depends on various system operating conditions such as area generation patterns and ambient temperatures. This work models the power network as a black-box function, which is evaluated with the system reliability analysis techniques to determine the maximum transfer capability under a given operating condition. A metamodel or an approximation model of the maximized power transfer capability is built based on the sampled system responses and optimized with respect to the corresponding operating conditions. An optimal metamodel is implemented as a prototype software tool, PTCanalyzer, and applied to Manitoba-Ontario interconnection power transfer calculations. This optimized metamodel technique provides an in-depth understanding of the dependency of the power transfer capability on system operating conditions and proves to be an effective tool in optimizing the operation planning of the interconnection for a given power system configuration. The PTCanalyzer has the potential to be used for optimization of other power network interconnections.
基金supported by the National Natural Science Foundation of China(No.51778416)the Key Projects of Philosophy and Social Sciences Research,Ministry of Education of China“Research on Green Design in Sustainable Development”(contract No.16JZDH014,approval No.16JZD014).
文摘Global urbanization causes more environmental stresses in cities and energy efficiency is one of major concerns for urban sustainability.The variable importance techniques have been widely used in building energy analysis to determine key factors influencing building energy use.Most of these applications,however,use only one type of variable importance approaches.Therefore,this paper proposes a procedure of conducting two types of variable importance analysis(predictive and variance-based)to determine robust and effective energy saving measures in urban buildings.These two variable importance methods belong to metamodeling techniques,which can significantly reduce computational cost of building energy simulation models for urban buildings.The predictive importance analysis is based on the prediction errors of metamodels to obtain importance rankings of inputs,while the variance-based variable importance can explore non-linear effects and interactions among input variables based on variance decomposition.The campus buildings are used to demonstrate the application of the method proposed to explore characteristic of heating energy,cooling energy,electricity,and carbon emissions of buildings.The results indicate that the combination of two types of metamodeling variable importance analysis can provide fast and robust analysis to improve energy efficiency of urban buildings.The carbon emissions can be reduced approximately 30%after using a few of effective energy efficiency measures and more aggressive measures can lead to the 60%of reduction of carbon emissions.Moreover,this research demonstrates the application of parallel computing to expedite building energy analysis in urban environment since more multi-core computers become increasingly available.
文摘For the energy-related issues that the world is facing nowadays,the renovation of the building stock is one of the major challenges.The objective of this study is to present an approach that helps to develop a multi-criteria decision support tool dedicated to the rehabilitation of sustainable and passive energy housing by integrating heating energy needs,economic,social and environmental criteria throughout the life cycle stages of the building.The methodology consists of developing metamodels to predict heating energy needs from polynomial regression,design of experiments method and thermo-aeraulic simulations of building behavior.This metamodel is used to carry out a combinatorial study of real technical solutions.The methodology was applied to a real-life existing building located in La Rochelle city(France)based on an in-situ energy diagnosis.Three multicriteria analysis methods were studied and compared:weighted sum,Min-Max and Pareto concept.Furthermore,technical constraints as well as owner preferences and performance constraints have been studied.Optimal technical solutions have been obtained in order to meet the various criteria studied.In addition,window shading and natural ventilation have been proposed to reduce the thermal discomfort rate in summer.This study was extended to all French regions.Finally,this method can be transformed into a decision support tool which will be useful for architects,engineers and stockholders.
基金Project(Nos.61070226 and 61003181)supported by the NationalNatural Science Foundation of China
文摘The formal specification of design patterns is central to pattern research and is the foundation of solving various pattern-related problems.In this paper,we propose a metamodeling approach for pattern specification,in which a pattern is modeled as a meta-level class and its participants are meta-level references.Instead of defining a new metamodel,we reuse the Unified Modeling Language(UML)metamodel and incorporate the concepts of Variable and Set into our approach,which are unavailable in the UML but essential for pattern specification.Our approach provides straightforward solutions for pattern-related problems,such as pattern instantiation,evolution,and implementation.By integrating the solutions into a single framework,we can construct a pattern management system,in which patterns can be instantiated,evolved,and implemented in a correct and manageable way.
文摘There is a growing need for accurate and interpretable machine learning models of thermal comfort in buildings.Physics-informed machine learning could address this need by adding physical consistency to such models.This paper presents metamodeling of thermal comfort in non-air-conditioned buildings using physics-informed machine learning.The studied metamodel incorporated knowledge of both quasi-steady-state heat transfer and dynamic simulation results.Adaptive thermal comfort in an office located in cold and hot European climates was studied with the number of overheating hours as index.A one-at-a-time method was used to gain knowledge from dynamic simulation with TRNSYS software.This knowledge was used to filter the training data and to choose probability distributions for metamodel forms alternative to polynomial.The response of the dynamic model was positively skewed;and thus,the symmetric logistic and hyperbolic secant distributions were inappropriate and outperformed by positively skewed distributions.Incorporating physical knowledge into the metamodel was much more effective than doubling the size of the training sample.The highly flexible Kumaraswamy distribution provided the best performance with R2 equal to 0.9994 for the cold climate and 0.9975 for the hot climate.Physics-informed machine learning could combine the strength of both physics and machine learning models,and could therefore support building design with flexible,accurate and interpretable metamodels.
基金supported by the National Natural Science Foundation of China(Project No.51875370)the Natural Science Foundation of Sichuan Province(Project Nos.2022NSFSC0454,2022NSFSC1975)+2 种基金Sichuan Science and Technology Program(Project No.2023ZYD0139)the University Key Laboratory of Sichuan in Process Equipment and Control Engineering(No.GK201905)Key Laboratory of Fluid and Power Machinery,Ministry of Education(No.LTDL2020-006).
文摘Although there is currently no unified standard theoretical formula for calculating the contact stress of cylindrical gears with a circular arc tooth trace(referred to as CATT gear),a mathematical model for determining the contact stress of CATT gear is essential for studying how parameters affect its contact stress and building the contact stress limit state equation for contact stress reliability analysis.In this study,a mathematical relationship between design parameters and contact stress is formulated using the KrigingMetamodel.To enhance the model’s accuracy,we propose a new hybrid algorithm that merges the genetic algorithm with the Quantum Particle Swarm optimization algorithm,leveraging the strengths of each.Additionally,the“parental inheritance+self-learning”optimization model is used to fine-tune the KrigingMetamodel’s parameters.Following this,amathematicalmodel for calculating the contact stress of Variable Hyperbolic Circular-Arc-Tooth-Trace(VH-CATT)gears using the optimized Kriging model was developed.We then examined how different gear parameters affect the VH-CATT gears’contact stress.Our simulation results show:(1)Improvements in R2,RMSE,and RMAE.R2 rose from0.9852 to 0.9974(a 1.22%increase),nearing 1,suggesting the optimized Kriging Metamodel’s global error is minimized.Meanwhile,RMSE dropped from3.9210 to 1.6492,a decline of 57.94%.The global error of the GA-IQPSO-Kriging algorithm was also reduced,with RMAE decreasing by 58.69%from 0.1823 to 0.0753,showing the algorithm’s enhanced precision.In a comparison of ten experimental groups selected randomly,the GA-IQPSO-Kriging and FEM-based contact analysis methods were used to measure contact stress.Results revealed a maximum error of 12.11667 MPA,which represents 2.85%of the real value.(2)Several factors,including the pressure angle,tooth width,modulus,and tooth line radius,are inversely related to contact stress.The descending order of their impact on the contact stress is:tooth line radius>modulus>pressure angle>tooth width.(3)Complex interactions are noted among various parameters.Specifically,when the tooth line radius interacts with parameters such as pressure angle,tooth width,and modulus,the resulting stress contour is nonlinear,showcasing amultifaceted contour plane.However,when tooth width,modulus,and pressure angle interact,the stress contour is nearly linear,and the contour plane is simpler,indicating a weaker coupling among these factors.
基金co-supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52105136)the Hong Kong Scholars Program,China(No.XJ2022013).
文摘Estimating the failure probability of highly reliable structures in practice engineering,such as aeronautical components,is challenging because of the strong-coupling and the small failure probability traits.In this paper,an Expanded Learning Intelligent Back Propagation(EL-IBP)neural network approach is developed:firstly,to accurately characterize the engineering response coupling relationships,a high-fidelity Intelligent-optimized Back Propagation(IBP)neural network metamodel is developed;furthermore,to elevate the analysis efficacy for small failure assessment,a novel expanded learning strategy for adaptive IBP metamodeling is proposed.Three numerical examples and one typical practice engineering case are analyzed,to validate the effectiveness and engineering application value of the proposed method.Methods comparison shows that the ELIBP method holds significant efficiency and accuracy superiorities in engineering issues.The current study may shed a light on pushing the adaptive metamodeling technique deeply toward complex engineering reliability analysis.
基金Supported by the National Natural Science Foundation of China under Grant No.60273026(国家自然科学基金)the National High-Tech Research and Development Plan of China under Grant No.2002AA116060(国家高技术研究发展计划(863))
文摘SPEM(software process engineering metamodel)是国际标准化组织制定的标准元模型,正日益成为软件过程建模领域的行业标准,但在过程执行方面,SPEM还存在不足.将软件过程看作是一种特殊的工作流,提出了一种应用工作流运行机制支持软件过程执行的方法.通过将SPEM模型转换为XPDL(XML process definition language)模型,利用XPDL引擎支持SPEM模型的执行.制定了SPEM和XPDL之间的映射规则,设计了转换算法并开发了转换引擎.该方法被应用在SoftPM项目中,成功地基于XPDL引擎Shark实现了对软件过程模型的执行支持.
基金supported by the National Natural Science Foundation of China(No.61627810)。
文摘Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space.Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model.In this paper,an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed.The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points.To avoid the clustering of samples in the local region,an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation.Besides,the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design.Finally,six benchmark problems and an engineering case are applied to examine the performance of the proposed method.The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.
文摘Callovo-Oxfordian(COx)claystone has been considered as a potential host rock for geological radioactive waste disposal in France(Cigéo project).During the exploitation phase(100 years),the stability of drifts(e.g.galleries/alveoli)within the disposal is assured by the liner,which includes two layers:concrete arch segment and compressible material.The latter exhibits a significant deformation capacity(about 50%)under low stress(<3 MPa).Although the response of these underground structures can be governed by complex thermo-hydro-mechanical coupling,the creep behavior of COx claystone has been considered as the main factor controlling the increase of stress state in the concrete liner and hence the long-term stability of drifts.Therefore,by focusing only on the purely mechanical behavior,this study aims at investigating the uncertainty effect of the COx claystone time-dependent properties on the stability of an alveolus of Cigéo during the exploitation period.To describe the creep behavior of COx claystone,we use Lemaitre’s viscoplastic model with three parameters whose uncertainties are identified from laboratory creep tests.For the reliability analysis,an extension of a well-known Kriging metamodeling technique is proposed to assess the exceedance probability of acceptable stress in the concrete liner of the alveolus.The open-source code Code_Aster is chosen for the direct numerical evaluations of the performance function.The Kriging-based reliability analysis elucidates the effect of the uncertainty of COx claystone on the long-term stability of the concrete liner.Moreover,the role of the compressible material layer between the concrete liner and the host rock is also highlighted.
文摘Raising software abstraction and re-use levels are key success factors for producing quality software products. Model-driven architecture (MDA) is an OMG initiative following this trend by mapping a conceptual model of application specified in platform independent model (PIM), to one or more platform specific models (PSM) automatically. Because there is little previous work tackling the development problem from specification through to implementation, this paper proposes End to End Development engineering (E2EDE) method using MDA methodology. E2EDE is intended to fill the mapping gap between PIM and PSM in MDA. The notion of variability is utilized from software product line and used to model design decisions in PSM. PIM is equipped with Nonfunctional requirements which borrowed from Design pattern to inform design decisions;thereby guiding the mapping process. In addition we have developed a strategic PSM for messaging systems can be configured to produce different applications such as the helpdesk system which is used as a case study.
文摘Many advanced mathematical models of biochemical, biophysical and other processes in systems biology can be described by parametrized systems of nonlinear differential equations. Due to complexity of the models, a problem of their simplification has become of great importance. In particular, rather challengeable methods of estimation of parameters in these models may require such simplifications. The paper offers a practical way of constructing approximations of nonlinearly parametrized functions by linearly parametrized ones. As the idea of such approximations goes back to Principal Component Analysis, we call the corresponding transformation Principal Component Transform. We show that this transform possesses the best individual fit property, in the sense that the corresponding approximations preserve most information (in some sense) about the original function. It is also demonstrated how one can estimate the error between the given function and its approximations. In addition, we apply the theory of tensor products of compact operators in Hilbert spaces to justify our method for the case of the products of parametrized functions. Finally, we provide several examples, which are of relevance for systems biology.
文摘The success of system modernization depends on the existence of technical frameworks for information integration and tool interoperation like the Model Driven Architecture （MDA）. Reverse engineering techniques play a crucial role in system modernization. This paper describes how to reverse engineering activity diagrams from object oriented code in the MDA context focusing on transformations at model and metamodel levels. A framework to reverse engineering MDA models from object oriented code that distinguishes three different abstraction levels linked to models, metamodels and formal specifications, is described. At model level, transformations are based on static and dynamic analysis. At metamodel level, transformations are specified as 0CL （Object Constraint Language） contracts between M0F （Meta Object Facility） metamodels which control the consistency of these transformations. The level of formal specification includes algebraic specifications of MOF metamodels and metamodel-based transformations. This paper analyzes a recovery process of activity diagrams from Java code by applying static and dynamic analysis and shows a formalization of this process in terms of MOF metamodels. The authors validate their approach by using Eclipse Modeling Framework, Ecore metamodels and ATL （Atlas Transformation Language）.
