Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography(CGH).Current data-driven deep learning algorithms face the challenge that the labeled training dataset...Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography(CGH).Current data-driven deep learning algorithms face the challenge that the labeled training datasets limit the training performance and generalization.The model-driven deep learning introduces the diffraction model into the neural network.It eliminates the need for the labeled training dataset and has been extensively applied to hologram generation.However,the existing model-driven deep learning algorithms face the problem of insufficient constraints.In this study,we propose a model-driven neural network capable of high-fidelity 4K computer-generated hologram generation,called 4K Diffraction Model-driven Network(4K-DMDNet).The constraint of the reconstructed images in the frequency domain is strengthened.And a network structure that combines the residual method and sub-pixel convolution method is built,which effectively enhances the fitting ability of the network for inverse problems.The generalization of the 4K-DMDNet is demonstrated with binary,grayscale and 3D images.High-quality full-color optical reconstructions of the 4K holograms have been achieved at the wavelengths of 450 nm,520 nm,and 638 nm.展开更多
Model-Driven Engineering (MDE) by reframing software development as the transformation of high-level models, promises lots of gains to Software Engineering in terms of productivity, quality and reusability. Although a...Model-Driven Engineering (MDE) by reframing software development as the transformation of high-level models, promises lots of gains to Software Engineering in terms of productivity, quality and reusability. Although a number of empirical studies have established the reality of these gains, there are still lots of reluctances toward the adoption of MDE in practice. This resistance can be explained by several technological and social factors among which a natural scepticism toward novel approaches. In this paper we attempt to provide arguments to help alleviate this scepticism by conducting an assessment of a MDE approach. Our goal is to show that although this MDE is novel, it retains similarities with the conventional Software Engineering approach while automating aspects of it.展开更多
Although the Model-Driven paradigm is being accepted in the research environment as a very useful and powerful option for effective software development, its real application in the enterprise context is still a chall...Although the Model-Driven paradigm is being accepted in the research environment as a very useful and powerful option for effective software development, its real application in the enterprise context is still a challenge for software engineering. Several causes can be stacked out, but one of them can be the lack of tool support for the efficient application of this paradigm. This paper presents a set of tools, grouped in a suite named NDT-Suite, which under the Model-Driven paradigm offer a suitable solution for software development. These tools explore different options that this paradigm can improve such as, development, quality assurance or requirement treatment. Besides, this paper analyses how they are being successfully applied in the industry.展开更多
The IEC 61131-3 standard defines a model and a set of programming languages for the development of industrial automation software. It is widely accepted by industry and most of the commercial tool vendors advertise co...The IEC 61131-3 standard defines a model and a set of programming languages for the development of industrial automation software. It is widely accepted by industry and most of the commercial tool vendors advertise compliance with it. On the other side, Model Driven Development (MDD) has been proved as a quite successful paradigm in general-purpose computing. This was the motivation for exploiting the benefits of MDD in the industrial automation domain. With the emerging IEC 61131 specification that defines an object-oriented (OO) extension to the function block model, there will be a push to the industry to better exploit the benefits of MDD in automation systems development. This work discusses possible alternatives to integrate the current but also the emerging specification of IEC 61131 in the model driven development process of automation systems. IEC 61499, UML and SysML are considered as possible alternatives to allow the developer to work in higher layers of abstraction than the one supported by IEC 61131 and to more effectively move from requirement specifications into the implementation model of the system.展开更多
Web Service Composition provides an opportunity for enterprises to increase the ability to adapt themselves to frequent changes in users' requirements by integrating existing services. Our research has focused on ...Web Service Composition provides an opportunity for enterprises to increase the ability to adapt themselves to frequent changes in users' requirements by integrating existing services. Our research has focused on proposing a framework to support dynamic composition and to use both SOAP-based and RESTful Web services simultaneously in composite services. In this paper a framework called "Model-driven Dynamic Composition of Heterogeneous Service" (MDCHeS) is introduced. It is elaborated in three different ways;each represents a particular view of the framework: data view, which consists of a Meta model and composition elements as well their relationships;process view, which introduces composition phases and used models in each phase;and component view, which shows an abstract view of the components and their interactions. In order to increase the dynamicity of MDCHeS framework, Model Driven Architecture and proxy based ideas are used.展开更多
Reasonable prediction of concrete creep is the basis of studying long-term deflection of concrete structures.In this paper,a hybrid model-driven and data-driven(HMD)method for predicting concrete creep is proposed by ...Reasonable prediction of concrete creep is the basis of studying long-term deflection of concrete structures.In this paper,a hybrid model-driven and data-driven(HMD)method for predicting concrete creep is proposed by using the sequence integration strategy.Then,a novel uncertainty prediction model(UPM)is developed considering uncertainty quantification.Finally,the effectiveness of the proposed method is validated by using the North-western University(NU)database of creep,and the effect of uncertainty on prediction results are also discussed.The analysis results show that the proposed HMD method outperforms the model-driven and three data-driven methods,including the genetic algorithm-back propagation neural network(GA-BPNN),particle swarm optimization-support vector regression(PSO-SVR)and convolutional neural network only method,in accuracy and time efficiency.The proposed UPM of concrete creep not only ensures relatively good prediction accuracy,but also quantifies the model and measurement uncertainties during the prediction process.Additionally,although incorporating measurement uncertainty into concrete creep prediction can improve the prediction performance of UPM,the prediction interval of the creep compliance is more sensitive to model uncertainty than to measurement uncertainty,and the mean contribution of variance attributed to the model uncertainty to the total variance is about 90%.展开更多
The automatic cutting of intersecting pipes is a challenging task in manufacturing.For improved automation and accuracy,this paper proposes a model-driven path planning approach for the robotic plasma cutting of a bra...The automatic cutting of intersecting pipes is a challenging task in manufacturing.For improved automation and accuracy,this paper proposes a model-driven path planning approach for the robotic plasma cutting of a branch pipe with a single Y-groove.Firstly,it summarizes the intersection forms and introduces a dual-pipe intersection model.Based on this model,the moving three-plane structure(a description unit of the geometric characteristics of the intersecting curve)is constructed,and a geometric model of the branch pipe with a single Y-groove is defined.Secondly,a novel mathematical model for plasma radius and taper compensation is established.Then,the compensation model and groove model are integrated by establishing movable frames.Thirdly,to prevent collisions between the plasma torch and workpiece,the torch height is planned and a branch pipe-rotating scheme is proposed.Through the established models and moving frames,the planned path description of cutting robot is provided in this novel scheme.The accuracy of the proposed method is verified by simulations and robotic cutting experiments.展开更多
The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinea...The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinear in nature,pose challenges for accurate description through physical models.While field data provides insights into real-world effects,its limited volume and quality restrict its utility.Complementing this,numerical simulation models offer effective support.To harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination model.Leveraging fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs.Through the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well conditions.The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets.展开更多
基金We are grateful for financial supports from National Natural Science Foundation of China(62035003,61775117)China Postdoctoral Science Foundation(BX2021140)Tsinghua University Initiative Scientific Research Program(20193080075).
文摘Deep learning offers a novel opportunity to achieve both high-quality and high-speed computer-generated holography(CGH).Current data-driven deep learning algorithms face the challenge that the labeled training datasets limit the training performance and generalization.The model-driven deep learning introduces the diffraction model into the neural network.It eliminates the need for the labeled training dataset and has been extensively applied to hologram generation.However,the existing model-driven deep learning algorithms face the problem of insufficient constraints.In this study,we propose a model-driven neural network capable of high-fidelity 4K computer-generated hologram generation,called 4K Diffraction Model-driven Network(4K-DMDNet).The constraint of the reconstructed images in the frequency domain is strengthened.And a network structure that combines the residual method and sub-pixel convolution method is built,which effectively enhances the fitting ability of the network for inverse problems.The generalization of the 4K-DMDNet is demonstrated with binary,grayscale and 3D images.High-quality full-color optical reconstructions of the 4K holograms have been achieved at the wavelengths of 450 nm,520 nm,and 638 nm.
文摘Model-Driven Engineering (MDE) by reframing software development as the transformation of high-level models, promises lots of gains to Software Engineering in terms of productivity, quality and reusability. Although a number of empirical studies have established the reality of these gains, there are still lots of reluctances toward the adoption of MDE in practice. This resistance can be explained by several technological and social factors among which a natural scepticism toward novel approaches. In this paper we attempt to provide arguments to help alleviate this scepticism by conducting an assessment of a MDE approach. Our goal is to show that although this MDE is novel, it retains similarities with the conventional Software Engineering approach while automating aspects of it.
文摘Although the Model-Driven paradigm is being accepted in the research environment as a very useful and powerful option for effective software development, its real application in the enterprise context is still a challenge for software engineering. Several causes can be stacked out, but one of them can be the lack of tool support for the efficient application of this paradigm. This paper presents a set of tools, grouped in a suite named NDT-Suite, which under the Model-Driven paradigm offer a suitable solution for software development. These tools explore different options that this paradigm can improve such as, development, quality assurance or requirement treatment. Besides, this paper analyses how they are being successfully applied in the industry.
文摘The IEC 61131-3 standard defines a model and a set of programming languages for the development of industrial automation software. It is widely accepted by industry and most of the commercial tool vendors advertise compliance with it. On the other side, Model Driven Development (MDD) has been proved as a quite successful paradigm in general-purpose computing. This was the motivation for exploiting the benefits of MDD in the industrial automation domain. With the emerging IEC 61131 specification that defines an object-oriented (OO) extension to the function block model, there will be a push to the industry to better exploit the benefits of MDD in automation systems development. This work discusses possible alternatives to integrate the current but also the emerging specification of IEC 61131 in the model driven development process of automation systems. IEC 61499, UML and SysML are considered as possible alternatives to allow the developer to work in higher layers of abstraction than the one supported by IEC 61131 and to more effectively move from requirement specifications into the implementation model of the system.
文摘Web Service Composition provides an opportunity for enterprises to increase the ability to adapt themselves to frequent changes in users' requirements by integrating existing services. Our research has focused on proposing a framework to support dynamic composition and to use both SOAP-based and RESTful Web services simultaneously in composite services. In this paper a framework called "Model-driven Dynamic Composition of Heterogeneous Service" (MDCHeS) is introduced. It is elaborated in three different ways;each represents a particular view of the framework: data view, which consists of a Meta model and composition elements as well their relationships;process view, which introduces composition phases and used models in each phase;and component view, which shows an abstract view of the components and their interactions. In order to increase the dynamicity of MDCHeS framework, Model Driven Architecture and proxy based ideas are used.
基金supported by the National Natural Science Foundation of China(Grant Nos.52208166 and 52108135)the National Key Research and Development Program of China(No.2021YFB2600900)+1 种基金the Science and Technology Innovation Program of Hunan Province(No.2022RC1186)the Aid program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan Province.
文摘Reasonable prediction of concrete creep is the basis of studying long-term deflection of concrete structures.In this paper,a hybrid model-driven and data-driven(HMD)method for predicting concrete creep is proposed by using the sequence integration strategy.Then,a novel uncertainty prediction model(UPM)is developed considering uncertainty quantification.Finally,the effectiveness of the proposed method is validated by using the North-western University(NU)database of creep,and the effect of uncertainty on prediction results are also discussed.The analysis results show that the proposed HMD method outperforms the model-driven and three data-driven methods,including the genetic algorithm-back propagation neural network(GA-BPNN),particle swarm optimization-support vector regression(PSO-SVR)and convolutional neural network only method,in accuracy and time efficiency.The proposed UPM of concrete creep not only ensures relatively good prediction accuracy,but also quantifies the model and measurement uncertainties during the prediction process.Additionally,although incorporating measurement uncertainty into concrete creep prediction can improve the prediction performance of UPM,the prediction interval of the creep compliance is more sensitive to model uncertainty than to measurement uncertainty,and the mean contribution of variance attributed to the model uncertainty to the total variance is about 90%.
基金the National Natural Science Foundation of China(Grant No.62103234)the Shandong Provincial Natural Science Foundation(Grant Nos.ZR2021QF027,ZR2022QF031).
文摘The automatic cutting of intersecting pipes is a challenging task in manufacturing.For improved automation and accuracy,this paper proposes a model-driven path planning approach for the robotic plasma cutting of a branch pipe with a single Y-groove.Firstly,it summarizes the intersection forms and introduces a dual-pipe intersection model.Based on this model,the moving three-plane structure(a description unit of the geometric characteristics of the intersecting curve)is constructed,and a geometric model of the branch pipe with a single Y-groove is defined.Secondly,a novel mathematical model for plasma radius and taper compensation is established.Then,the compensation model and groove model are integrated by establishing movable frames.Thirdly,to prevent collisions between the plasma torch and workpiece,the torch height is planned and a branch pipe-rotating scheme is proposed.Through the established models and moving frames,the planned path description of cutting robot is provided in this novel scheme.The accuracy of the proposed method is verified by simulations and robotic cutting experiments.
基金supported by the China Postdoctoral Science Foundation(2021M702304)Natural Science Foundation of Shandong Province(ZR20210E260).
文摘The production capacity of shale oil reservoirs after hydraulic fracturing is influenced by a complex interplay involving geological characteristics,engineering quality,and well conditions.These relationships,nonlinear in nature,pose challenges for accurate description through physical models.While field data provides insights into real-world effects,its limited volume and quality restrict its utility.Complementing this,numerical simulation models offer effective support.To harness the strengths of both data-driven and model-driven approaches,this study established a shale oil production capacity prediction model based on a machine learning combination model.Leveraging fracturing development data from 236 wells in the field,a data-driven method employing the random forest algorithm is implemented to identify the main controlling factors for different types of shale oil reservoirs.Through the combination model integrating support vector machine(SVM)algorithm and back propagation neural network(BPNN),a model-driven shale oil production capacity prediction model is developed,capable of swiftly responding to shale oil development performance under varying geological,fluid,and well conditions.The results of numerical experiments show that the proposed method demonstrates a notable enhancement in R2 by 22.5%and 5.8%compared to singular machine learning models like SVM and BPNN,showcasing its superior precision in predicting shale oil production capacity across diverse datasets.
