Investigation of Tools, Techniques and Languages for Model Driven Software Product Lines (SPL): A Systematic Literature Review

Software development is a complex and difficult task that requires the investment of sufficient resources and carries major risk of failure. Model Driven Engineering (MDE) focuses on creating software models and automating code generation from the models. Model Driven Software Development (MDSD) offers significantly more effective approaches. These approaches improve the way of building software. Model driven approaches partially increase developer productivity, decrease the cost of software construction, improve software reusability, and make software more maintainable. This paper investigates the methods where Model Driven Software Development is integrated with Software Product Line (SPL). This SLR has been conducted to identify 71 research works published since 2014. We have collected 18 tools, 14 techniques and 17 languages used for MDSD for SPL. We analyze which technique is suitable for SPL. We compare the techniques on the basis of features provided by these tools to understand the better-quality results.

Fault Diagnosis Technology Based on Model Driven

Fault diagnosis is an important application of the power grids monitoring system. Under the situation of continuous development of smart grid, it brings new challenges to the fault diagnosis technology. A fault diagnosis expert system based on model driven approach is proposed in this paper. And the corresponding fault modeling technology based on Fault Logic Description Language (FLDL) is described step by step. Practices show that this system could meet the requirements of processing fault alarm information rapidly and reliably by operator.

Incorporating AOSD to Enhance Model Driven Architecture

Various software development approaches in the present scenario are best suited for a specific application. The software development strategies include both merits and demerits when talked in terms of generalization. The Model Driven Architecture (MDA) describes software development based on models on various levels of abstraction. The model driven software development process consists of sequence of model transformations between various models. This paper presents incorporation of the merits of Aspect Oriented Software Development (AOSD) like modularization, reusability and reduced complexity into Model Driven Architecture (MDA) software development strategy. The proposed Model Driven Software Development approach which combines the merits of MDA and AOSD meets the ever changing and challenging demand of the enterprise software development.

Data and Model Driven Task Offloading Strategy in the Dynamic Mobile Edge Computing System

Mobile Edge Computing(MEC)provides communication and computational capabilities for the industrial Internet,meeting the demands of latency-sensitive tasks.Nevertheless,traditional model-driven task offloading strategies face challenges in adapting to situations with unknown network communication status and computational capabilities.This limitation becomes notably significant in complex industrial networks of high-speed railway.Motivated by these considerations,a data and model-driven task offloading problem is proposed in this paper.A redundant communication network is designed to adapt to anomalous channel states when tasks are offloaded to edge servers.The link switching mechanism is executed by the train according to the attributes of the completed task.The task offloading optimization problem is formulated by introducing data-driven prediction of communication states into the traditional model.Furthermore,the optimal strategy is achieved by employing the informer-based prediction algorithm and the quantum particle swarm optimization method,which effectively tackle real-time optimization problems due to their low time complexity.The simulations illustrate that the data and model-driven task offloading strategy can predict the communication state in advance,thus reducing the cost of the system and improving its robustness.

Data-Driven Model Identification and Control of the Inertial Systems

In the synthesis of the control algorithm for complex systems, we are often faced with imprecise or unknown mathematical models of the dynamical systems, or even with problems in finding a mathematical model of the system in the open loop. To tackle these difficulties, an approach of data-driven model identification and control algorithm design based on the maximum stability degree criterion is proposed in this paper. The data-driven model identification procedure supposes the finding of the mathematical model of the system based on the undamped transient response of the closed-loop system. The system is approximated with the inertial model, where the coefficients are calculated based on the values of the critical transfer coefficient, oscillation amplitude and period of the underdamped response of the closed-loop system. The data driven control design supposes that the tuning parameters of the controller are calculated based on the parameters obtained from the previous step of system identification and there are presented the expressions for the calculation of the tuning parameters. The obtained results of data-driven model identification and algorithm for synthesis the controller were verified by computer simulation.

Full field reservoir modeling of shale assets using advanced data-driven analytics

Hydrocarbon production from shale has attracted much attention in the recent years. When applied to this prolific and hydrocarbon rich resource plays, our understanding of the complexities of the flow mechanism（sorption process and flow behavior in complex fracture systems- induced or natural） leaves much to be desired. In this paper, we present and discuss a novel approach to modeling, history matching of hydrocarbon production from a Marcellus shale asset in southwestern Pennsylvania using advanced data mining, pattern recognition and machine learning technologies. In this new approach instead of imposing our understanding of the flow mechanism, the impact of multi-stage hydraulic fractures, and the production process on the reservoir model, we allow the production history, well log, completion and hydraulic fracturing data to guide our model and determine its behavior. The uniqueness of this technology is that it incorporates the so-called ＂hard data＂ directly into the reservoir model, so that the model can be used to optimize the hydraulic fracture process. The ＂hard data＂ refers to field measurements during the hydraulic fracturing process such as fluid and proppant type and amount, injection pressure and rate as well as proppant concentration. This novel approach contrasts with the current industry focus on the use of ＂soft data＂（non-measured, interpretive data such as frac length, width,height and conductivity） in the reservoir models. The study focuses on a Marcellus shale asset that includes 135 wells with multiple pads, different landing targets, well length and reservoir properties. The full field history matching process was successfully completed using this data driven approach thus capturing the production behavior with acceptable accuracy for individual wells and for the entire asset.

Assessing a Model-Driven Web-Application Engineering Approach

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.

Event-driven process execution model for process virtual machine

Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.

Nonadiabatic Population Transfer in a Tangent-Pulse Driven Quantum Model

Fine control of the dynamics of a quantum system is the key element to perform quantum information processing and coherent manipulations for atomic and molecular systems. We propose a control protocol using a tangentpulse driven model and demonstrate that it indicates a desirable design, i.e., of being both fast and accurate for population transfer. As opposed to other existing strategies, a remarkable character of the present scheme is that high velocity of the nonadiabatic evolution itself not only will not lead to unwanted transitions but also can suppress the error caused by the truncation of the driving pulse.

NUMERICAL MODELLING OF THREE-DIMENSION CHARACTERISTICS OF WIND-DRIVEN CURRENT IN THE BOHAI SEA

Three- dimension (3-D) wind-driven currents in the Bohai Sea in both winter and summer are calculated by using a 3- D barotropic steady model, and the results are consistent with observed flow char -acteristics. Based on the results, 3- D characteristics of flow, currents at different depths, compensated flow in the lower layer , long and narrow alongshore current, the area of upwelling and downwelling, main circulation in vertical profile, and the current in Bohai Strait are discussed.

Towards a Model-Driven IEC 61131-Based Development Process in Industrial Automation

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.

Modeling and Assessment of Buoyancy-Driven Stratified Airflow in High-Space Industrial Hall

In industrial environment,heat sources often are contaminant sources and health threatening contaminants are mainly passive,so a detailed understanding of airflow mode can assist in work environment hygiene measurement and prevention.This paper presented a numerical investigation of stratified airflow scenario in a high-space industrial hall with validated commercial code and experimentally acquired boundary conditions.Based upon an actually undergoing engineering project,this study investigated the performance of the buoyancy-driven displacement ventilation in a large welding hall with big components manufactured.The results have demonstrated that stratified airflow sustained by thermal buoyancy provides zoning effect in terms of clean and polluted regions except minor stagnant eddy areas.The competition between negative buoyant jets from displacement radial diffusers and positive buoyant plume from bulk object constitutes the complex transport characteristics under and above stratification interface.Entrainment,downdraft and turbulent eddy motion complicate the upper mixing zone,but the exhaust outlet plays a less important role in the whole field flow.And the corresponding suggestions concerning computational stability and convergence,further improvements in modelling and measurements were given.

MDCHeS: Model-Driven Dynamic Composition of Heterogeneous Service

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.

NDT-Suite: A Methodological Tool Solution in the Model-Driven Engineering Paradigm

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.

Floquet Bound States in a Driven Two-Particle Bose-Hubbard Model with an Impurity

We investigate how the driving field affects the bound states in the one-dimensional two-particle Bose-Hubbard model with an impurity. In the high-frequency regime, compared with the static lattice [Phys. Rev. Lett. 109 （2012） 116405], a new type of Floquet bound state can be obtained even for a weak particle-particle interaction by tuning the driving amplitude. Moreover, the localization degree of the F1oquet bound molecular state can be adjusted by tuning the driving frequency, and even the Floquet bound molecular state can be changed into the Floquet extended state when the driving frequency is below a critical value. Our results provide an efficient way to manipulate bound states in the many-body systems.

Fast parallel algorithm for three-dimensional distance-driven model in iterative computed tomography reconstruction

The projection matrix model is used to describe the physical relationship between reconstructed object and projection.Such a model has a strong influence on projection and backprojection,two vital operations in iterative computed tomographic reconstruction.The distance-driven model（DDM） is a state-of-the-art technology that simulates forward and back projections.This model has a low computational complexity and a relatively high spatial resolution;however,it includes only a few methods in a parallel operation with a matched model scheme.This study introduces a fast and parallelizable algorithm to improve the traditional DDM for computing the parallel projection and backprojection operations.Our proposed model has been implemented on a GPU（graphic processing unit） platform and has achieved satisfactory computational efficiency with no approximation.The runtime for the projection and backprojection operations with our model is approximately 4.5 s and 10.5 s per loop,respectively,with an image size of 256×256×256 and 360 projections with a size of 512×512.We compare several general algorithms that have been proposed for maximizing GPU efficiency by using the unmatched projection/backprojection models in a parallel computation.The imaging resolution is not sacrificed and remains accurate during computed tomographic reconstruction.

Wind-Driven,Double-Gyre,Ocean Circulation in a Reduced-Gravity,2.5-Layer,Lattice Boltzmann Model

A coupled lattice Boltzmann （LB） model with second-order accuracy is applied to the reduced-gravity, shallow water, 2.5-layer model for wind-driven double-gyre ocean circulation. By introducing the secondorder integral approximation for the collision operator, the model becomes fully explicit. The Coriolis force and other external forces are included in the model with second-order accuracy, which is consistent with the discretization accuracy of the LB equation. The feature of the multiple equilibria solutions is found in the numerical experiments under different Reynolds numbers based on this LB scheme. With the Reynolds number increasing from 3000 to 4000, the solution of this model is destabilized from the anti-symmetric double-gyre solution to the subtropic gyre solution and then to the subpolar gyre solution. The transitions between these equilibria states are also found in some parameter ranges. The time-dependent variability of the circulation based on this LB simulation is also discussed for varying viscosity regimes. The flow of this model exhibits oscillations with different timescales varying from subannual to interannual. The corresponding statistical oscillation modes are obtained by spectral analysis. By analyzing the spatiotemporal structures of these modes, it is found that the subannual oscillation with a 9-month period originates from the barotropic Rossby basin mode, and the interarmual oscillations with periods ranging from 1.5 years to 4.6 years originate from the recirculation gyre modes, which include the barotropic and the baroclinic recirculation gyre modes.

Dynamic Modeling and Motion Simulation for A Winged Hybrid-Driven Underwater Glider

PETREL, a winged hybrid-driven underwater glider is a novel and practical marine survey platform which combines the features of legacy underwater glider and conventional AUV （autonomous underwater vehicle）. It can be treated as a multi-rigid-body system with a floating base and a particular hydrodynamic profile. In this paper, theorems on linear and angular momentum are used to establish the dynamic equations of motion of each rigid body and the effect of translational and rotational motion of internal masses on the attitude control are taken into consideration. In addition, due to the unique external shape with fixed wings and deflectable rudders and the dual-drive operation in thrust and glide modes, the approaches of building dynamic model of conventional AUV and hydrodynamic model of submarine are introduced, and the tailored dynamic equations of the hybrid glider are formulated. Moreover, the behaviors of motion in glide and thrust operation are analyzed based on the simulation and the feasibility of the dynamic model is validated by data from lake field trials.

A Data-Driven Car-Following Model Based on the Random Forest

The car-following models are the research basis of traffic flow theory and microscopic traffic simulation. Among the previous work, the theory-driven models are dominant, while the data-driven ones are relatively rare. In recent years, the related technologies of Intelligent Transportation System (ITS) re- presented by the Vehicles to Everything (V2X) technology have been developing rapidly. Utilizing the related technologies of ITS, the large-scale vehicle microscopic trajectory data with high quality can be acquired, which provides the research foundation for modeling the car-following behavior based on the data-driven methods. According to this point, a data-driven car-following model based on the Random Forest (RF) method was constructed in this work, and the Next Generation Simulation (NGSIM) dataset was used to calibrate and train the constructed model. The Artificial Neural Network (ANN) model, GM model, and Full Velocity Difference (FVD) model are em- ployed to comparatively verify the proposed model. The research results suggest that the model proposed in this work can accurately describe the car- following behavior with better performance under multiple performance indicators.