Model Transformation Approach Research Articles

Assistance in Model Driven Development: Toward an Automated Transformation Design Process

Model driven engineering aims to shorten the development cycle by focusing on abstractions and partially automating code generation. We long lived in the myth of automatic Model Driven Development (MDD) with promising approaches, techniques, and tools. Describing models should be a main concern in software development as well as model verification and model transformation to get running applications from high level models. We revisit the subject of MDD through the prism of experimentation and open mindness. In this article, we explore assistance for the stepwise transition from the model to the code to reduce the time between the analysis model and implementation. The current state of practice requires methods and tools. We provide a general process and detailed transformation specifications where reverse-engineering may play its part. We advocate a model transformation approach in which transformations remain simple, the complexity lies in the process of transformation that is adaptable and configurable. We demonstrate the usefulness, and scalability of our proposed MDD process by conducting experiments. We conduct experiments within a simple case study in software automation systems. It is both representative and scalable. The models are written in UML; the transformations are implemented mainly using ATL, and the programs are deployed on Android and Lego EV3. Last we report the lessons learned from experimentation for future community work.

Inter-terminal transportation for an offshore port integrating an inland container depot

Offshore ports, which are located on islands or away from the hinterland, are usually connected with the hinterland via bridges for truck transportation. With the rapid growth of port throughput, the bridges are likely to become a bottleneck restricting the development of port business. Inland container depots (ICD) have been introduced as satellite terminals to address congestion issues on bridges by allowing a proportion of drayage trucks to pick up and drop off containers at the ICD instead of going to the container terminals of the offshore ports. Containers are transported between the ICD and container terminals by inter-terminal transportation (ITT) trucks owned by port authorities. The ICD is expected to mitigate the bridge congestion by transferring the travel demands of drayage trucks in peak hours to the travel demands of ITT trucks in off-peak hours. We propose a methodology for analyzing the influence of ICDs on port efficiency. An integer linear programming model is developed to optimize the movements of containers, drayage trucks and ITT trucks in an offshore port area with the objective of minimizing the costs of drayage trucks and ITT trucks and the penalty for late delivery of containers. Two model transformation approaches are proposed to reduce the solution space of the model for improving the computational efficiency of the algorithm. The influence of ICDs on port efficiency is evaluated through a real-world case study using the proposed methodology. The results provide insights for port authorities to make strategic decisions.

Traceable Multi-view Model Integration: A Transformation Pipeline for Agile Production Systems Engineering

Purpose.Agile Production Systems Engineering (PSE) is characterised by parallel and iterative engineering of several disciplines. This multi-view engineering requires capabilities for tracing changes to support configuration management of PSE assets. Yet, traditional model transformation approaches in PSE do not preserve local views and hierarchies on concepts of PSE assets, such as plans and configurations. Thus, tracing multi-view changes to PSE assets is challenging.Method.Following the Design Science approach, we (i) elicit requirements for tracing multi-view changes to PSE assets from a domain analysis in automotive manufacturing; (ii) introduce and evaluate the Traceable Multi-view Model Transformation (TMvMT) process; and (iii) propose the TMvMT pipeline architecture to provide traceable model integration capabilities for agile PSE.Results.In a feasibility study on robot cell models, we evaluate the TMvMT process and architecture regarding the requirements for traceability compared to traditional approaches.Conclusion.The proposed TMvMT approach provides traceability of changes in multi-view modelling as a basis through the separation of modelling transformation steps and provision of clear input and output artefacts to achieve traceable configuration management and validation of system designs for production system assets in agile PSE.

Event-triggered consensus control for singular multi-agent systems based on observers

This paper is devoted to the problem of event-triggered consensus for a class of general linear singular multi-agent systems with undirected topology. Considering that the system states are not measurable, state observers are designed first, and then a new observer-based event-triggered sampling mechanism is proposed, which naturally avoids Zeno behavior. Furthermore, a distributed event-triggered consensus protocol is designed. By employing the Lyapunov-Krasovskii functional method and model transformation approach, sufficient conditions are obtained such that the consensus of the singular multi-agent systems can be achieved, while the explicit dynamic expression of the consensus function is also given. Finally, a numerical example is included to illustrate the effectiveness of the proposed results.

Fusion steady‐state robust filtering for uncertain multisensor networked systems with application to autoregressive moving average signal estimates

AbstractThis article is concerned with the centralized fusion (CF) robust steady‐state Kalman filtering problem for a class of multisensor networked systems with mixed uncertainties including state‐dependent and noise‐dependent multiplicative noises, missing measurements, packet dropouts, and uncertain noise variances. By using a model transformation approach, the initial multisensor system under study is transformed into a multi‐model multisensor system only with uncertain noise variances. By introducing an augmented state vector, the CF system is obtained. In the light of the minimax robust estimation method, and based on the worst‐case fusion system with conservative upper bounds of uncertain noise variances, the CF robust steady‐state Kalman estimators (predictor, filter, and smoother) are proposed in a unified form. By means of the permutation matrices and Lyapunov equation method, the robustness of CF estimators is proved. The accuracy relations among the robust local and fusion steady‐state estimators are proved. An example applied to autoregressive moving average signal estimation is put forward, and the robust CF steady‐state signal estimators are proposed. Simulation experiment shows the correctness of the proposed approach.

Simulating hybrid SysML models: a model transformation approach under the DEVS framework

Simulating hybrid SysML models: a model transformation approach under the DEVS framework

Optimal Placement of PMU for Fast Robust Power System Dynamic State Estimation Using UKF–GBDT Technique

This paper proposes a fast and robust dynamic state estimation technique based on model transformation method using the proposed hybrid technique. The proposed hybrid method is the combination of Unscented Kalman Filter (UKF) and Gradient Boosting Decision Tree (GBDT), hence commonly referred to as the UKF–GBDT technique. The proposed model transformation approach is accomplished by taking the active power generator measured as input variable and derived frequency as rate of change of frequency measurements of phasor measurement units (PMU) as dynamic generator output variable model. The proposed hybrid technique is also formulated to deal with data quality issues, and the rate of change of frequency and frequency measurements may be skewed in the presence of rigorous disruption or communication problems. This permits to obtain discrete-time linear dynamic equations in state space based on the linear Kalman filter (LKF). With this proper control, this model alleviates filter divergence problems, which can be a severe issue if the nonlinear model is utilized in greatly strained operating system conditions, and gives quick estimate of rotor speeds together with angles through transient modes if only the transient stability with control is concerned. In the case of long-term dynamics, the outcome of governor’s response in long-term system dynamics is offset together with mechanical power at rotor speed and the state vector angles for joint evaluation. At last, the performance of the proposed method is simulated in MATLAB/Simulink and the performance is compared to the existing methods like UKF, GBDT and ANN. The proposed technique is simulated under three case studies like IEEE 14-, 30- and 118-bus systems.

A model transformation approach for multiscale modeling of software architectures applied to smart cities

SummaryModeling and specifying correct software systems is a challenging task that can be supported by providing appropriate modeling abstractions. This article proposes an approach for graphical multiscale modeling of such systems using model transformation techniques. The approach is founded on a guided rule‐based iterative modeling process ensuring controlled transition from a coarse‐grained description to a fine‐grained description. It provides also user‐friendly graphical descriptions by extension of UML notations, hence preserving the common practices from software architectures design. The iterative design process is supported by a set of model transformation rules. The rules manage the refinement process (by adding or removing subsystems or by adding or removing details on a given subsystem) as a model transformation. Our approach is supported by a rule‐based generator that implements the automatic transformation of UML diagrams into Event‐B specifications allowing formal verification of their correctness properties, and relieving software architects of mastering formal techniques. To experiment and validate our approach, we consider a case study dedicated to the smart cities.

A concise funnel robust model-free control mechanism for hypersonic space vehicles based on error driving

Based on non-affine models of hypersonic space vehicles, the tracking control problem of hypersonic vehicles is studied and analyzed in this article using funnel robust model-free control mechanism considering parametric uncertainty and external disturbances. First, the control system is decomposed into altitude subsystem and velocity subsystem. For altitude subsystem, we propose a concise funnel robust model-free control mechanism based on error driving, and a novel model transformation approach is applied to the controller design. The new model-free controller only contains a Hurwitz stable term and a filtering term, and does not need precise motion model and too much calculation, so it can improve the calculation speed of the system. For velocity subsystem, only a concise proportional-integral controller is needed to meet the tracking requirements. Moreover, the devised controller is capable of guaranteeing funnel performance on the altitude and velocity tracking errors. Finally, numerical simulation results are presented to verify the efficiency of the design.

State estimator design for genetic regulatory networks with leakage and discrete heterogeneous delays: A nonlinear model transformation approach

We address the state estimation problem for a class of genetic regulatory networks (GRNs) with leakage and discrete heterogeneous delays in this paper. In order to simplify the complexity of designing the state estimator for nonlinear delayed model of GRNs, a state estimate algorithm based on a model transformation method is presented, which goal is to get the entire system state. First, in light of the Lagrange’s mean-value theorem, the nonlinear model transformation method is applied to describe the nonlinear error system as a linear time-varying model. Second, by constructing the Lyapunov–Krasovskii functional dependent on the composite function variables of state estimate errors, a new technique lemma is provided and applied to the stability analysis. Third, the sufficient condition of state estimator design is obtained in term linear matrix inequalities, under which, the certification of the uniform asymptotic stability for state estimate error system is derived. Finally, a simulation example illustrates the rationality and effectiveness of the proposed theoretical results.

Tensor product‐based model transformation approach to tower crane systems modeling

AbstractThis paper presents the application of the tensor product (TP)‐based model transformation approach to produce Tower CRrane (TCR) systems models. The modeling approach starts with a nonlinear model of TCR systems as representative multi‐input–multi‐output controlled processes. A linear parameter‐varying model is next derived, and the modeling steps specific to TP–based model transformation are proceeded to obtain the TP model. The TP model is tested on TCR laboratory equipment in two open‐loop scenarios considering chirp signals and pseudorandom binary step signals applied to the three model inputs (control inputs). The nonlinear and TP model outputs in the two scenarios are the payload position, the cart position, and the arm angular position. The nonlinear and TP model outputs are collected, measured, and compared. The simulation results prove that the derived TP model approximately mimics the behavior of the nonlinear model; both system responses and numerical approximation errors are illustrated.

Tensor product‐based model transformation approach to cart position modeling and control in pendulum‐cart systems

AbstractThe paper presents the application of the tensor product (TP)‐based model transformation technique to model and control the cart position of single‐input multi‐output pendulum‐cart systems (PCSs). The modeling is first carried out. The derived TP model, the nonlinear model of PCS, and the laboratory equipment are tested in the same open‐loop scenario, and their corresponding outputs are collected, measured, and compared. The parallel distributed compensation (PDC) technique is next applied to TP‐based cart position controller design. A linear state feedback control design is also designed in order to conduct a comparative analysis. The two proposed control system structures are experimentally validated and tested in the same scenarios on the laboratory equipment, and their performance indices are analyzed and compared.

Improved Stability Criteria on Linear Systems with Distributed Interval Time-Varying Delays and Nonlinear Perturbations

The problem of delay-range-dependent stability analysis for linear systems with distributed time-varying delays and nonlinear perturbations is studied without using the model transformation and delay-decomposition approach. The less conservative stability criteria are obtained for the systems by constructing a new augmented Lyapunov–Krasovskii functional and various inequalities, which are presented in terms of linear matrix inequalities (LMIs). Four numerical examples are demonstrated for the results given to illustrate the effectiveness and improvement over other methods.

A Model Transformation Approach for Detecting Distancing Violations in Weighted Graphs

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. A wireless 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.

Robust fusion steady‐state estimators for networked stochastic uncertain systems with packet dropouts and missing measurements

AbstractIn this article, the robust fusion steady‐state filtering problem is investigated for a class of multisensor networked systems with mixed uncertainties. The uncertainties include state‐dependent and noise‐dependent multiplicative noises, missing measurements, packet dropouts, and uncertain noise variances, the phenomena of missing measurements and packet dropouts occur in a random way, and are described by two Bernoulli distributed random variables with known conditional probabilities. Using a model transformation approach, which consists of augmented approach, de‐randomization approach and fictitious noise approach, the original multisensor system under study is converted into a multi‐model multisensor system with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case subsystems with conservative upper bounds of uncertain noise variances, the robust local steady‐state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the optimal fusion algorithm weighted by matrices, the robust distributed weighted state fusion steady‐state Kalman estimators are derived for the considered system. The robustness of the proposed estimators is proved by using a combination method consisting of augmented noise approach, decomposition approach of non‐negative definite matrix, matrix representation approach of quadratic form, and Lyapunov equation approach, such that for all admissible uncertainties, the actual steady‐state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady‐state Kalman estimators are proved. An example with application to autoregressive moving average (ARMA) signal processing is proposed, which shows that the robust local and fusion signal estimation problems can be solved by the state estimation problems. Simulation example verifies the effectiveness and correctness of the proposed results.

A security policy model transformation and verification approach for software defined networking

Software-defined networking (SDN) has been increasingly utilized to enforce the security of complex networks. However SDN-based security enforcement mechanisms rely heavily on some specific security policies containing underlying network information. Facing the increasingly complex and huge SDN networks, we urgently need a novel security policy management mechanism which can be completely transparent to any underlying network information. That is it can permit network managers to define the high-level security policy model without containing any underlying information, and by means of model transformation, high-level security policy model can be automatically transformed into its corresponding lower-level security policy model containing underlying information. Moreover, we must ensure the system model of data plane updated by the low-level security policy model can hold all of security properties defined in high-level security policy model. Based on these insights, we propose a security policy model transformation and verification approach for SDN in this paper. We first specify the security policies used in SDN networks as a formal security policy model (SPM). Then we establish the system model of SDN’s data plane and the mapping rules between the policy objects of SPM and the system objects of system model of data plane. Based on these mapping rules, we propose a security policy model transformation mechanism which transforms SPM into the low-level security policy model, RSPM. In order to verify the system model of data plane updated by RSPM can hold all of security properties defined in SPM, we propose a security policy verification mechanism based on model checking techniques and a group of validation conditions. Finally, we utilize a comprehensive case to illustrate the feasibility of this approach.

EasyModel: A Refinement-Based Modeling and Verification Approach for Self-Adaptive Software

Self-adaptive software (SAS) is gaining popularity as it can reconfigure itself in response to the dynamic changes in the operational context or itself. However, early modeling and formal analysis of SAS systems becomes increasingly difficult, as the system scale and complexity is rapidly increasing. To tackle the modeling difficulty of SAS systems, we present a refinement-based modeling and verification approach called EasyModel. EasyModel integrates the intuitive Unified Modeling Language (UML) model with the stepwise refinement Event-B model. Concretely, EasyModel: 1) creates a UML profile called AdaptML that provides an explicit description of SAS characteristics, 2) proposes a refinement modeling mechanism for SAS systems that can deal with system modeling complexity, 3) offers a model transformation approach and bridges the gap between the design model and the formal model of SAS systems, and 4) provides an efficient way to verify and guarantee the correct behaviour of SAS systems. To validate EasyModel, we present an example application and a subject-based experiment. The results demonstrate that EasyModel can effectively reduce the modeling and formal verification difficulty of SAS systems, and can incorporate the intuitive merit of UML and the correct-by-construction merit of Event-B.

Robust measurement fusion steady-state estimator design for multisensor networked systems with random two-step transmission delays and missing measurements

In this paper, we study the centralized fusion (CF) and weighted measurement fusion (WMF) robust steady-state Kalman filtering problem for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, two-step random delays, missing measurements, and uncertain noise variances. By using a model transformation approach consisting of augmented approach, de-randomization approach and fictitious noise approach, the original multisensor system under study is converted into a multi-model multisensor system with only uncertain noise variances. By introducing an augmented state vector, and applying the weighted least squares (WLS) algorithm, the CF and WMF systems are obtained. According to the minimax robust estimation principle, based on the worst-case fusion systems with conservative upper bounds of uncertain noise variances, the CF and WMF robust steady-state Kalman estimators (predictor, filter, and smoother) are presented in a unified framework. Their robustness is proved by using a combination method consisting of augmented noise approach, matrix representation approach of quadratic form, and Lyapunov equation approach, the so-called robustness is concerned with the design of a filter such that for all admissible uncertainties, the actual fused steady-state estimation error variances of the estimators are guaranteed to have the corresponding minimal upper bounds. The accuracy relations among the robust local and fused steady-state Kalman estimators are proved. An example with application to autoregressive (AR) signal processing is proposed, which shows that the robust fusion signal estimation problems can be solved by the robust fusion state estimation method. Simulation example shows the effectiveness and correctness of the proposed method.

Multi-criteria test cases selection for model transformations

Model transformations play an important role in the evolution of systems in various fields such as healthcare, automotive and aerospace industry. Thus, it is important to check the correctness of model transformation programs. Several approaches have been proposed to generate test cases for model transformations based on different coverage criteria (e.g., statements, rules, metamodel elements, etc.). However, the execution of a large number of test cases during the evolution of transformation programs is time-consuming and may include a lot of overlap between the test cases. In this paper, we propose a test case selection approach for model transformations based on multi-objective search. We use the non-dominated sorting genetic algorithm (NSGA-II) to find the best trade-offs between two conflicting objectives: (1) maximize the coverage of rules and (2) minimize the execution time of the selected test cases. We validated our approach on several evolution cases of medium and large ATLAS Transformation Language programs.

Improving the Security of UML Sequence Diagram Using Genetic Algorithm

A sequence diagram is a modeling approach for visualizing the behavioral execution of a system. The objective of this research is to investigate the problem of security in a behavioral model (sequence diagram) through the application of model refactoring. We propose detection and correction techniques, empirical evaluation of the proposed techniques and assessment of security improvement in sequence diagrams. The detection of security bad smells is achieved through the adaptation of a genetic algorithm, while correction is accomplished by the model transformation approach. The results show significant detection recall and correction efficacy of the proposed detection and correction approaches, respectively. Our results show that the proposed approach is effective in detecting and correcting bad smells and can improve the security of UML Sequence Diagram.