The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communica...The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communications using a finite number of pilots.On the other hand,Deep Learning(DL)approaches have been very successful in wireless OFDM communications.However,whether they will work underwater is still a mystery.For the first time,this paper compares two categories of DL-based UWA OFDM receivers:the DataDriven(DD)method,which performs as an end-to-end black box,and the Model-Driven(MD)method,also known as the model-based data-driven method,which combines DL and expert OFDM receiver knowledge.The encoder-decoder framework and Convolutional Neural Network(CNN)structure are employed to establish the DD receiver.On the other hand,an unfolding-based Minimum Mean Square Error(MMSE)structure is adopted for the MD receiver.We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios.Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers.It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.展开更多
In a context where urban satellite image processing technologies are undergoing rapid evolution,this article presents an innovative and rigorous approach to satellite image classification applied to urban planning.Thi...In a context where urban satellite image processing technologies are undergoing rapid evolution,this article presents an innovative and rigorous approach to satellite image classification applied to urban planning.This research proposes an integrated methodological framework,based on the principles of model-driven engineering(MDE),to transform a generic meta-model into a meta-model specifically dedicated to urban satellite image classification.We implemented this transformation using the Atlas Transformation Language(ATL),guaranteeing a smooth and consistent transition from platform-independent model(PIM)to platform-specific model(PSM),according to the principles of model-driven architecture(MDA).The application of this IDM methodology enables advanced structuring of satellite data for targeted urban planning analyses,making it possible to classify various urban zones such as built-up,cultivated,arid and water areas.The novelty of this approach lies in the automation and standardization of the classification process,which significantly reduces the need for manual intervention,and thus improves the reliability,reproducibility and efficiency of urban data analysis.By adopting this method,decision-makers and urban planners are provided with a powerful tool for systematically and consistently analyzing and interpreting satellite images,facilitating decision-making in critical areas such as urban space management,infrastructure planning and environmental preservation.展开更多
In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning ...In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning and scheduling plays a pivotal role in realizing these objectives such as improving production efficiency,saving energy,reducing carbon emissions,and enhancing quality.However,current practices in steel enterprises are largely dependent on experience-driven manual decision approaches supported by information systems,which are inadequate to meet the complex requirements of the industry.This study explores the current situation in production planning and scheduling,analyzes the characteristics and limitations of existing methods,and emphasizes the necessity and trends of intelligent systems.It surveys the current literature on production planning and scheduling in steel enterprises and analyzes the theoretical advancements and practical challenges associated with combinatorial and sequential optimization in this field.A key focus is on the limitations of current models and algorithms in effectively addressing the multi-objective and multiconstraint characteristics of steel produc-tion.To overcome these challenges,a novel framework for intelligent production planning and scheduling is proposed.This framework leverages data-and knowledge-driven decision-making and scenario adaptability,enabling the system to respond dynamically to real-time production conditions and market fluctuations.By integrating artificial intelligence and advanced optimization methodologies,the proposed framework improves the efficiency,cost-effectiveness,and environmental sustainability of steel manufacturing.展开更多
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 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
To reduce complexity, the combat effectiveness simulation system(CESS) is often decomposed into static structure,physical behavior, and cognitive behavior, and model abstraction is layered onto domain invariant knowle...To reduce complexity, the combat effectiveness simulation system(CESS) is often decomposed into static structure,physical behavior, and cognitive behavior, and model abstraction is layered onto domain invariant knowledge(DIK) and application variant knowledge(AVK) levels. This study concentrates on the specification of CESS’s physical behaviors at the DIK level of abstraction, and proposes a model driven framework for efficiently developing simulation models within model-driven engineering(MDE). Technically, this framework integrates the four-layer metamodeling architecture and a set of model transformation techniques with the objective of reducing model heterogeneity and enhancing model continuity. As a proof of concept, a torpedo example is illustrated to explain how physical models are developed following the proposed framework. Finally, a combat scenario is constructed to demonstrate the availability, and a further verification is shown by a reasonable agreement between simulation results and field observations.展开更多
Software testing has been attracting a lot of attention for effective software development.In model driven approach,Unified Modelling Language(UML)is a conceptual modelling approach for obligations and other features ...Software testing has been attracting a lot of attention for effective software development.In model driven approach,Unified Modelling Language(UML)is a conceptual modelling approach for obligations and other features of the system in a model-driven methodology.Specialized tools interpret these models into other software artifacts such as code,test data and documentation.The generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the requirements.This paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm(BGA).For generating the test cases,both diagrams were converted into their corresponding intermediate graphical forms namely,Activity Diagram Graph(ADG)and State Chart Diagram Graph(SCDG).Then both graphs will be combined to form a single graph called,Activity State Chart Diagram Graph(ASCDG).Both graphs were then joined to create a single graph known as the Activity State Chart Diagram Graph(ASCDG).Next,the ASCDG will be optimized using BGA to generate the test data.A case study involving a withdrawal from the automated teller machine(ATM)of a bank was employed to demonstrate the approach.The approach successfully identified defects in various ATM functions such as messaging and operation.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
Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are g...Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.展开更多
Testing in Software Engineering is one of the most important phases although, unfortunately, it cannot be always successfully fulfilled due to time constraints. In most cases, the development phase takes more time tha...Testing in Software Engineering is one of the most important phases although, unfortunately, it cannot be always successfully fulfilled due to time constraints. In most cases, the development phase takes more time than it was estimated, entailing negative effects on the testing phase. The delay increases even more in Research and Development (R + D) projects, where the real time to execute tasks is more difficult to control. Model Driven Engineering (MDE) offers a solution to avoid testing costs without affecting the execution quality of the applied test. This paper presents a practical overview of a Model Driven Testing (MDT)-based methodology and its impact on CALIPSOneo project, which was carried out in liaison with Airbus Defense and Space and, particularly, with the Product Lifecycle Management (PLM) department.展开更多
This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awirele...This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design.A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design.A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries,expressed using Neo4j Cypher,to provide insights from the stored data for decision support.As proof of concept,a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture.Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected.The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.展开更多
The emerging Model-Driven Engineering (MDE) paradigm advocates the use of models as first-class citizens in the software development process, while artifacts such as documentation and source-code can be quickly produc...The emerging Model-Driven Engineering (MDE) paradigm advocates the use of models as first-class citizens in the software development process, while artifacts such as documentation and source-code can be quickly produced from those models by using automated transformations. Even though many MDE-oriented approaches, languages and tools have been developed in the recent past, there is no standard that concretely defines a specific sequence of steps to obtain a functional software system from a model. Thus, the existing approaches present numerous differences among themselves, because each one handles the problems inherent to software development in its own way. This paper presents and discusses a reference model for the comparative study of current MDE approaches in the scope of web-application development. This reference model focuses on relevant aspects such as modeling language scope (domain, business-logic, user-interface), usage of patterns, separation of concerns, model transformations, tool support, and deployment details like web-platform independence and traditional programming required. The ultimate goal of this paper is to determine the aspects that will be of greater importance in future web-oriented MDE languages.展开更多
Recent estimates indicate that more than half the software market belongs to enterprise applications. One of the greatest challenges in these is in conducting the complex process of adaptation of pre-packaged applicat...Recent estimates indicate that more than half the software market belongs to enterprise applications. One of the greatest challenges in these is in conducting the complex process of adaptation of pre-packaged applications, such as Oracle or SAP, to the organization needs. Although very detailed, structured and well documented methods govern this process, the consulting team implementing the method must spend much manual effort in making sure that the guidelines of the method are followed as intended by the method author. The problem is exacerbated by the diversity of skills and roles of team members, and the many sorts of communications of collaboration that methods prescribe. By enhancing the metamodel in which the methods are defined, we automatically produce a CASE tool (so to speak) for the applications of these methods. Our results are successfully employed in a number of large, ongoing projects with demonstrable, non-meager saving.展开更多
CPS （cyber-physics system） engineering brings new revolutionary opportunities for multi-disciplinary and complex processes, like oil extraction from oil sands. Based on an established unified feature modeling scheme...CPS （cyber-physics system） engineering brings new revolutionary opportunities for multi-disciplinary and complex processes, like oil extraction from oil sands. Based on an established unified feature modeling scheme, a software modeling framework to simulate the process of SAGD （steam-assisted gravity drainage） is proposed. The main purpose of this work was to apply CPS in the complex production engineering informatics modeling as it applied to SAGD. Existing physics models and simulation algorithms for main SAGD phenomena were reviewed, and an integrated ontology model via a feature-based approach has been developed. Conservation laws were used as the governing principles, while the transport phenomena were modelled via the primary phenomenon features. The representation of typical data flows targeting to the functional simulation scenarios by applying the concept of phenomenon features was also done. The definition of this feature type represents a new expansion of the emerging unified feature scheme for engineering software modeling. Slotted liners were taken as the well-completion option and their design and specifications were included in the case study model. The unique representation of the planned software design is developed and expressed with graphical diagrams of the UML （unified modelling language） convention.展开更多
Efficiency of supply chains management mostly depends on the process coordination and information integration between the supply chain companies.The well-known integrated circuit design houses,the wafer fabrication in...Efficiency of supply chains management mostly depends on the process coordination and information integration between the supply chain companies.The well-known integrated circuit design houses,the wafer fabrication industries, and the integrated circuit packaging/testing business has together formed a contiguous supply chain from materials to system in Taiwan during the past decades.Logistic management of the wafer hence becomes the key linkage in the semiconductor foundry supply chain.The objective of this paper is to develop the wafer warehouse management system for global wafer logistics.Current operations for wafer logistics management are firstly reviewed. The system requirements are analyzed by the model-driven business transformation approach.The business operation model and the platform-independent solution architecture for the wafer logistics management are constructed.A prototype information system is also developed for validation.Results of this research can improve the effectiveness and efficiency in wafer logistics management for the semiconductor foundry supply chain.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
This paper presents model-based approach to process-control software development. The presented approach enables modelling of control software in a straightforward manner and, at the same time, on a high level of abst...This paper presents model-based approach to process-control software development. The presented approach enables modelling of control software in a straightforward manner and, at the same time, on a high level of abstraction. The essence of the presented approach is a high-level, domain-specific modelling language ProcGraph, which is based on three types of diagrams that describe the modelled system using a domain-oriented hierarchical structure of interdependent procedural control entities and state-transition diagrams describing the behaviour of the procedural control entities. The presented concept is demonstrated by means of higher-level model segments of a real process-control application that deals with the micronisation process in the production of titanium dioxide. The presented industrial case shows that the application of ProcGraph provides adequate expressive power for an elegant preparation of graphic specifications in a transparent and easy way.展开更多
基金funded in part by the National Natural Science Foundation of China under Grant 62401167 and 62192712in part by the Key Laboratory of Marine Environmental Survey Technology and Application,Ministry of Natural Resources,P.R.China under Grant MESTA-2023-B001in part by the Stable Supporting Fund of National Key Laboratory of Underwater Acoustic Technology under Grant JCKYS2022604SSJS007.
文摘The Underwater Acoustic(UWA)channel is bandwidth-constrained and experiences doubly selective fading.It is challenging to acquire perfect channel knowledge for Orthogonal Frequency Division Multiplexing(OFDM)communications using a finite number of pilots.On the other hand,Deep Learning(DL)approaches have been very successful in wireless OFDM communications.However,whether they will work underwater is still a mystery.For the first time,this paper compares two categories of DL-based UWA OFDM receivers:the DataDriven(DD)method,which performs as an end-to-end black box,and the Model-Driven(MD)method,also known as the model-based data-driven method,which combines DL and expert OFDM receiver knowledge.The encoder-decoder framework and Convolutional Neural Network(CNN)structure are employed to establish the DD receiver.On the other hand,an unfolding-based Minimum Mean Square Error(MMSE)structure is adopted for the MD receiver.We analyze the characteristics of different receivers by Monte Carlo simulations under diverse communications conditions and propose a strategy for selecting a proper receiver under different communication scenarios.Field trials in the pool and sea are also conducted to verify the feasibility and advantages of the DL receivers.It is observed that DL receivers perform better than conventional receivers in terms of bit error rate.
文摘In a context where urban satellite image processing technologies are undergoing rapid evolution,this article presents an innovative and rigorous approach to satellite image classification applied to urban planning.This research proposes an integrated methodological framework,based on the principles of model-driven engineering(MDE),to transform a generic meta-model into a meta-model specifically dedicated to urban satellite image classification.We implemented this transformation using the Atlas Transformation Language(ATL),guaranteeing a smooth and consistent transition from platform-independent model(PIM)to platform-specific model(PSM),according to the principles of model-driven architecture(MDA).The application of this IDM methodology enables advanced structuring of satellite data for targeted urban planning analyses,making it possible to classify various urban zones such as built-up,cultivated,arid and water areas.The novelty of this approach lies in the automation and standardization of the classification process,which significantly reduces the need for manual intervention,and thus improves the reliability,reproducibility and efficiency of urban data analysis.By adopting this method,decision-makers and urban planners are provided with a powerful tool for systematically and consistently analyzing and interpreting satellite images,facilitating decision-making in critical areas such as urban space management,infrastructure planning and environmental preservation.
基金supported by the Key Program of the National Natural Science Foundation of China(Nos.52334008 and 51734004).
文摘In the context of reducing its carbon emissions,the Chinese steel industry is currently undergoing an intelligent transformation to enhance its profitability and sustainability.The optimization of production planning and scheduling plays a pivotal role in realizing these objectives such as improving production efficiency,saving energy,reducing carbon emissions,and enhancing quality.However,current practices in steel enterprises are largely dependent on experience-driven manual decision approaches supported by information systems,which are inadequate to meet the complex requirements of the industry.This study explores the current situation in production planning and scheduling,analyzes the characteristics and limitations of existing methods,and emphasizes the necessity and trends of intelligent systems.It surveys the current literature on production planning and scheduling in steel enterprises and analyzes the theoretical advancements and practical challenges associated with combinatorial and sequential optimization in this field.A key focus is on the limitations of current models and algorithms in effectively addressing the multi-objective and multiconstraint characteristics of steel produc-tion.To overcome these challenges,a novel framework for intelligent production planning and scheduling is proposed.This framework leverages data-and knowledge-driven decision-making and scenario adaptability,enabling the system to respond dynamically to real-time production conditions and market fluctuations.By integrating artificial intelligence and advanced optimization methodologies,the proposed framework improves the efficiency,cost-effectiveness,and environmental sustainability of steel manufacturing.
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.
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.
基金supported by the National Natural Science Foundation of China(61273198)
文摘To reduce complexity, the combat effectiveness simulation system(CESS) is often decomposed into static structure,physical behavior, and cognitive behavior, and model abstraction is layered onto domain invariant knowledge(DIK) and application variant knowledge(AVK) levels. This study concentrates on the specification of CESS’s physical behaviors at the DIK level of abstraction, and proposes a model driven framework for efficiently developing simulation models within model-driven engineering(MDE). Technically, this framework integrates the four-layer metamodeling architecture and a set of model transformation techniques with the objective of reducing model heterogeneity and enhancing model continuity. As a proof of concept, a torpedo example is illustrated to explain how physical models are developed following the proposed framework. Finally, a combat scenario is constructed to demonstrate the availability, and a further verification is shown by a reasonable agreement between simulation results and field observations.
基金support from the Deanship of Scientific Research,University of Hail,Saudi Arabia through the project Ref.(RG-191315).
文摘Software testing has been attracting a lot of attention for effective software development.In model driven approach,Unified Modelling Language(UML)is a conceptual modelling approach for obligations and other features of the system in a model-driven methodology.Specialized tools interpret these models into other software artifacts such as code,test data and documentation.The generation of test cases permits the appropriate test data to be determined that have the aptitude to ascertain the requirements.This paper focuses on optimizing the test data obtained from UML activity and state chart diagrams by using Basic Genetic Algorithm(BGA).For generating the test cases,both diagrams were converted into their corresponding intermediate graphical forms namely,Activity Diagram Graph(ADG)and State Chart Diagram Graph(SCDG).Then both graphs will be combined to form a single graph called,Activity State Chart Diagram Graph(ASCDG).Both graphs were then joined to create a single graph known as the Activity State Chart Diagram Graph(ASCDG).Next,the ASCDG will be optimized using BGA to generate the test data.A case study involving a withdrawal from the automated teller machine(ATM)of a bank was employed to demonstrate the approach.The approach successfully identified defects in various ATM functions such as messaging and operation.
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.
基金supported by the National Natural Science Foundation of China(61273198)
文摘Combat system effectiveness simulation (CSES) is a special type of complex system simulation. Three non-functional requirements (NFRs), i.e. model composability, domain specific modeling, and model evolvability, are gaining higher priority from CSES users when evaluating different modeling methodologies for CSES. Traditional CSES modeling methodologies are either domain-neutral (lack of domain characteristics consideration and limited support for model composability) or domain-oriented (lack of openness and evolvability) and fall short of the three NFRs. Inspired by the concept of architecture in systems engineering and software engineering fields, we extend it into a concept of model architecture for complex simulation systems, and propose a model architecture-oriented modeling methodology in which the model architecture plays a central role in achieving the three NFRs. Various model-driven engineering (MDE) approaches and technologies, including simulation modeling platform (SMP), unified modeling language (UML), domain specific modeling (DSM), eclipse modeling framework (EMF), graphical modeling framework (GMF), and so forth, are applied where possible in representing the CSES model architecture and its components' behaviors from physical and cognitive domain aspects. A prototype CSES system, called weapon effectiveness simulation system (WESS), and a non-trivial air-combat simulation example are presented to demonstrate the methodology.
文摘Testing in Software Engineering is one of the most important phases although, unfortunately, it cannot be always successfully fulfilled due to time constraints. In most cases, the development phase takes more time than it was estimated, entailing negative effects on the testing phase. The delay increases even more in Research and Development (R + D) projects, where the real time to execute tasks is more difficult to control. Model Driven Engineering (MDE) offers a solution to avoid testing costs without affecting the execution quality of the applied test. This paper presents a practical overview of a Model Driven Testing (MDT)-based methodology and its impact on CALIPSOneo project, which was carried out in liaison with Airbus Defense and Space and, particularly, with the Product Lifecycle Management (PLM) department.
文摘This work presents the design of an Internet of Things(IoT)edge-based system based on model transformation and complete weighted graph to detect violations of social distancing measures in indoor public places.Awireless sensor network based on Bluetooth Low Energy is introduced as the infrastructure of the proposed design.A hybrid model transformation strategy for generating a graph database to represent groups of people is presented as a core middleware layer of the detecting system’s proposed architectural design.A Neo4j graph database is used as a target implementation generated from the proposed transformational system to store all captured real-time IoT data about the distances between individuals in an indoor area and answer user predefined queries,expressed using Neo4j Cypher,to provide insights from the stored data for decision support.As proof of concept,a discrete-time simulation model was adopted for the design of a COVID-19 physical distancing measures case study to evaluate the introduced system architecture.Twenty-one weighted graphs were generated randomly and the degrees of violation of distancing measures were inspected.The experimental results demonstrate the capability of the proposed system design to detect violations of COVID-19 physical distancing measures within an enclosed area.
文摘The emerging Model-Driven Engineering (MDE) paradigm advocates the use of models as first-class citizens in the software development process, while artifacts such as documentation and source-code can be quickly produced from those models by using automated transformations. Even though many MDE-oriented approaches, languages and tools have been developed in the recent past, there is no standard that concretely defines a specific sequence of steps to obtain a functional software system from a model. Thus, the existing approaches present numerous differences among themselves, because each one handles the problems inherent to software development in its own way. This paper presents and discusses a reference model for the comparative study of current MDE approaches in the scope of web-application development. This reference model focuses on relevant aspects such as modeling language scope (domain, business-logic, user-interface), usage of patterns, separation of concerns, model transformations, tool support, and deployment details like web-platform independence and traditional programming required. The ultimate goal of this paper is to determine the aspects that will be of greater importance in future web-oriented MDE languages.
文摘Recent estimates indicate that more than half the software market belongs to enterprise applications. One of the greatest challenges in these is in conducting the complex process of adaptation of pre-packaged applications, such as Oracle or SAP, to the organization needs. Although very detailed, structured and well documented methods govern this process, the consulting team implementing the method must spend much manual effort in making sure that the guidelines of the method are followed as intended by the method author. The problem is exacerbated by the diversity of skills and roles of team members, and the many sorts of communications of collaboration that methods prescribe. By enhancing the metamodel in which the methods are defined, we automatically produce a CASE tool (so to speak) for the applications of these methods. Our results are successfully employed in a number of large, ongoing projects with demonstrable, non-meager saving.
文摘CPS （cyber-physics system） engineering brings new revolutionary opportunities for multi-disciplinary and complex processes, like oil extraction from oil sands. Based on an established unified feature modeling scheme, a software modeling framework to simulate the process of SAGD （steam-assisted gravity drainage） is proposed. The main purpose of this work was to apply CPS in the complex production engineering informatics modeling as it applied to SAGD. Existing physics models and simulation algorithms for main SAGD phenomena were reviewed, and an integrated ontology model via a feature-based approach has been developed. Conservation laws were used as the governing principles, while the transport phenomena were modelled via the primary phenomenon features. The representation of typical data flows targeting to the functional simulation scenarios by applying the concept of phenomenon features was also done. The definition of this feature type represents a new expansion of the emerging unified feature scheme for engineering software modeling. Slotted liners were taken as the well-completion option and their design and specifications were included in the case study model. The unique representation of the planned software design is developed and expressed with graphical diagrams of the UML （unified modelling language） convention.
文摘Efficiency of supply chains management mostly depends on the process coordination and information integration between the supply chain companies.The well-known integrated circuit design houses,the wafer fabrication industries, and the integrated circuit packaging/testing business has together formed a contiguous supply chain from materials to system in Taiwan during the past decades.Logistic management of the wafer hence becomes the key linkage in the semiconductor foundry supply chain.The objective of this paper is to develop the wafer warehouse management system for global wafer logistics.Current operations for wafer logistics management are firstly reviewed. The system requirements are analyzed by the model-driven business transformation approach.The business operation model and the platform-independent solution architecture for the wafer logistics management are constructed.A prototype information system is also developed for validation.Results of this research can improve the effectiveness and efficiency in wafer logistics management for the semiconductor foundry supply chain.
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%.
文摘This paper presents model-based approach to process-control software development. The presented approach enables modelling of control software in a straightforward manner and, at the same time, on a high level of abstraction. The essence of the presented approach is a high-level, domain-specific modelling language ProcGraph, which is based on three types of diagrams that describe the modelled system using a domain-oriented hierarchical structure of interdependent procedural control entities and state-transition diagrams describing the behaviour of the procedural control entities. The presented concept is demonstrated by means of higher-level model segments of a real process-control application that deals with the micronisation process in the production of titanium dioxide. The presented industrial case shows that the application of ProcGraph provides adequate expressive power for an elegant preparation of graphic specifications in a transparent and easy way.
