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Although Unified Modeling Language (UML) state machines constitute a convenient modeling formalism that is widely used in many applications, the lack of formal semantics impedes carrying out automatic processing, such as formal verification. In this article, we aim to achieve a proven translation from a subset of UML state machines to timed automata. A generic abstract syntax is defined for state machines that allows us to specify state machines as a tree-like structure, explicitly illustrating the hierarchical relationships within the model. Based on this syntax, a formal asynchronous semantics for state machines and systems of state machines is established. Additionally, the semantics of timed automata is specified. Then, a translation relation from the considered set of state machines to timed automata is defined and a strong equivalence relation — namely, a timed bisimulation between the source and target models — is formally proven. The proof is carried out inductively while considering continuous (time) and discrete transitions separately. This proof allows us to demonstrate a strong similitude between these models. [ABSTRACT FROM AUTHOR]

As Internet-based technology spreads to most areas of life, it becomes a challenge to grasp opportunities arising from enormous amounts of data being generated from various sources such as smart homes, smart cities, health care systems and industries. Efficient utilization of these data can enable us to improve many human practices, including those connected to health care. In the present study, we focus on the health care sector, as it consists of large-scale organizations that rely on the processing of big data and complex processing of information. Due to the dynamic nature and complexity of this domain, it is essential to develop sophisticated technologies for the efficient processing of vast amounts of information. There is, for example, a need for interactive tools that can visualise actual care processes being executed in the hospital. A tool visualising real-time data could give a dynamic view of the processes, with accurate quantitative information, which can be used to improve the quality and efficiency of health care provision. These tools should be built on the requirements of practitioners needs and requirements, to ensure their relevance and practical utility. In this paper, we present a user-driven design process for developing therapy data visualisation components of guided Internet-based cognitive behaviour therapy (iCBT) and their evaluation. In order to ensure the reusability of the visual components, we propose to utilise a model-based approach which allows data analysts to adapt domain models by means of model transformation and transform them into visualization. [ABSTRACT FROM AUTHOR]

Model transformation, which is a program targeting at transforming an input model to an output model, has been a critical basis for Model-Driven Engineering (MDE). The quality of model transformation programs directly affects the quality of software products built with MDE activities. Therefore, debugging model transformation programs has been crucial from the quality assurance point of view. One of the key impediments to the model transformation debugging is the high complexity and scale of the input models. In order to ameliorate the burden on model transformation debugging, this study proposes an effective approach to systematically reduce input models of model transformation programs. By combining the advantages of input simplification approaches for traditional programs and also the characteristics of model transformation, our approach leverages and adapts the delta debugging technique to model simplification. We conduct experiments to evaluate the proposed approach from two aspects: its effectiveness in model simplification, and its effects on model transformation debugging. Our experimental results confirm the positive contributions of the approach in both aspects. It delivers promising reduction effectiveness, and it can also well support the fault localization in model transformations. [ABSTRACT FROM AUTHOR]

Many powerful metamodeling platforms enabling model-driven software engineering (MDSE) exist, each with its strengths, weaknesses, functionalities, programming language(s), and developer community. Platform interoperability would enable users to exploit their mutual benefits. Such interoperability would allow the transformation of metamodels and models created in one platform into equivalent metamodels and models in other platforms. Language engineers could then freely choose the metamodeling platform without risking a lock-in effect. Two well-documented and publicly available metamodeling platforms are the eclipse modeling framework (EMF) and the modeling SDK for visual studio (MSDKVS). In this paper, we propose an M3-level-bridge (M3B) that establishes interoperability between EMF and MSDKVS on the abstract syntax level and on the graphical concrete syntax level. To establish such interoperability we (i) compare the two platforms, (ii) present a conceptual mapping between them, and (iii) implement a bidirectional transformation bridge including both the metamodel and model layer. We evaluate our approach by transforming a collection of publicly available metamodels and automatically generated or manually created models thereof. The transformation outcomes are then used to quantitatively and qualitatively evaluate the transformation's validity, executability, and expressiveness. [ABSTRACT FROM AUTHOR]

Both the SysML and Modelica standards are used in the field of Systems Engineering (SE) to model systems from different perspectives, on different abstraction levels. SysML is strong when modeling systems on a functional level because it provides different views. With the capabilities of other simulation specifications, the engineers can simulate the system architecture. On the other hand, an open standard, such as Modelica is a key enabler for representing multi‐physical systems described by differential, algebraic, and discrete equations. With the symbolic manipulation, the dynamics of the systems are represented in state space form, and solved by the numerical integration methods fixed or variable step. However, it is clear that the connection between systems engineering and system simulation, with their respective domain knowledge of the actual equipment in their system, is missing. By seeing these complementary values, the authors demonstrate language interaction to integrate SysML and Modelica to achieve complimentary values through bi‐directional transformation and simulation. [ABSTRACT FROM AUTHOR]

This study presents the development of an adaptive fuzzy control strategy for double-star PMSM-PWM inverters used in ship electrical propulsion. The approach addresses the current and speed tracking challenges of double-star permanent magnet synchronous motors (DSPMSMs) in the presence of parametric uncertainties. Initially, a modeling technique employing a matrix transformation method is introduced, generating decoupled and independent star windings to eliminate inductive couplings, while maintaining model consistency and torque control. The precise DSPMSM model serves as the foundation for an unknown nonlinear backstepping controller, approximated directly using an adaptive fuzzy controller. Through the Lyapunov direct method, system stability is demonstrated. All signals in the closed-loop system are ensured to be uniformly ultimately bounded (UUB). The proposed control system aims for low tracking errors, while also mitigating the impact of parametric uncertainties. The effectiveness of the adaptive fuzzy nonlinear control system is validated through tests conducted in hardware-in-the-loop (HIL) simulations, utilizing the OPAL-RT platform, OP4510. [ABSTRACT FROM AUTHOR]

In order to fully utilize the control degrees of freedom of a multi-three-phase-star smooth-pole permanent magnet synchronous motor (PMSM), this paper first develops a modeling approach using a new matrix transformation method. The proposed transformation produces decoupled and independent star windings, removing the inductive couplings and preserving the model and torque control's consistency as the number of phases increases. The model, together with a new vector control scheme, is superior for studying the effect of the winding phase angle shift on motor performance. Based on a numerical simulation, this paper focuses on the quality analysis of phase currents, non-sequential currents, and torque ripple with different phase angles for double- and triple-star PMSM drives. The control of a triple-star PMSM is validated, and the behavior analysis is investigated by OPAL-RT. [ABSTRACT FROM AUTHOR]

To apply the interference alignment (IA) technology to practical communication networks effectively, the low complexity IA design problem still needs to be solved well. In this paper, we study the low complexity closed-form IA design method, for the multiple-cell multiple-user multiple-input multiple-output (MIMO) cellular interference network. To eliminate the complex interference conveniently, the multiple-cell multiple-user cellular network model is transformed into several relatively simple network models. Based on the model transformation of the cellular network, the IA precoding is divided into two parts. Then the two-stage IA design scheme is proposed, which includes the outer IA design and inter one. The former is to eliminate the inter-cell interference, while the latter is to eliminate the inter-user interference. Simulation results show the higher system sum-rate performance and low computational complexity of the proposed IA design scheme. [ABSTRACT FROM AUTHOR]

Architecture-Driven Modernization (ADM) is a special kind of reengineering that employs models along the process. The main ADM metamodel is the Knowledge Discovery Metamodel (KDM), which is a platform-independent metamodel able to represent several views of a system. Although a lot of research is currently focused on the reverse engineering phase of ADM, little has been devoted to the forward engineering one, mainly on the generation of Platform-Specific Models (PSMs) from KDM. The forward engineering phase is essential because it belongs to the end of the horseshoe model, completing the reengineering process. Besides, the lack of research and the absence of tooling support in this phase hinder the industrial adoption of ADM. Therefore, in this paper, we present a process for creating Transformation Engines (TEs) capable of transforming KDM instances in a chosen PSM. We highlight two main contributions in this work. The first is a process that software engineers can follow for building TEs capable of generating PSM instances (e.g., Java model, Python model, etc.) from KDM instances. Having that on their hands, modernization engineers can then use generators for generating language-specific source code from the PSM. The second is delivering a specific TE called RUTE-K2J, which is able to generate Java models from KDM models. The transformation rules of RUTE-K2J have been tested considering sets of common code structures that normally appear when modernizing systems. The test cases have shown that in this version of RUTE the transformation rules are able to correctly generate 92% of the source code submitted to the transformation. [ABSTRACT FROM AUTHOR]

Copyright of Railway Standard Design is the property of Railway Standard Design Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Model Driven Engineering (MDE) adoption in the industry suffers from many technical and non-technical problems. One of the significant technical problems lies in the difficulty of building complex transformations from the composition of small and reusable transformations. Another problem resides in developing transformations from scratch in case they are missing. In this paper, we present an approach to how to handle these issues. The approach allows composing reusable transformations to build more complex ones by providing a catalog of prebuilt transformations targeting common architectures, frameworks, and design patterns. To give guidance and simplify the task of developing new transformations, we describe a platform description model of an entire system or a part of it in two views: a UML profile and a set of transformations. We also present three transformation types, each of which handles different abstraction design concerns. Generic transformations are small and reusable to build complex transformations, system-independent transformations are reusable and implement high-level design decisions, and system-specific transformations are not reusable and implement all design decisions needed for a given system. The approach is implemented as a plugin for a UML modeling tool and validated by developing a system that simulates the behavior of a gas station through model transformations built from the composition of reusable transformations.

The 38th issue of CSIMQ comprises four articles selected by guest issue editors on topics related to novel decision support methods or model-based frameworks for evolving or evaluating information systems. Design-oriented research is the dominant approach in these works, balancing technical design decisions insights with empirical evaluation cases. Three of the selected articles contribute with decision-support methods or frameworks – for ESG (Environment-Social-Governance) accounting, for democratized decision services, and for information security management. The fourth article revisits UML-based model-driven software development from a new perspective.

With digital patient data increasing due to new diagnostic methods and technology, showing the right data in the context of decision support at the point of care becomes an even greater challenge. Standard operating procedures (SOPs) modeled in BPMN (Business Process Model and Notation) contain evidence-based treatment guidance for all phases of a certain diagnosis, while physicians need the parts relevant to a specific patient at a specific point in the clinical process. Therefore, integration of patient data from electronic health records (EHRs) providing context to clinicians is needed, which is stored and communicated in HL7 (Health Level Seven) FHIR (Fast Healthcare Interoperability Resources). To address this issue, we propose a method combining an integration of stored data into BPMN and a loss-free transformation from BPMN into FHIR, and vice versa. Based on that method, an identification of the next necessary decision point in a specific patient context is possible. We verified the method for treatment of malignant melanoma by using an extract of a formalized SOP document with predefined decision points and validated FHIR references with real EHR data. The patient data could be stored and integrated into the BPMN element 'DataStoreReference'. Our loss-free transformation process therefore is the foundation for combining evidence-based knowledge from formalized clinical guidelines or SOPs and patient data from EHRs stored in FHIR. Processing the SOP with the available patient data can then lead to the next upcoming decision point, which will be displayed to the physician integrated with the corresponding data. [ABSTRACT FROM AUTHOR]

Context: This paper engages with the question of how one can arrive at more coherent explanations of social behavior in coupled socio-ecological systems. Objectives: The paper aims to give a counterargument to the assumption that social theory in coupled socio-ecological systems can only be developed in one way, as such showing that the premise of the special issue deserves further scrutiny. Methods: Current issues are identified and categorized based on a literature survey; likewise, the solutions derive from methodological literature, in particular from the realm of critical realism. Results: The paper shows how a set of assumptions about the nature of socio-ecological systems and about the nature of theory derived from observations prohibit a deeper understanding of how social theory may be developed at the nexus of coupled socio-ecological systems. Researchers should engage more clearly with movements between theories, models and data, and movements between ontology, epistemology and data. Conclusions: Landscape ecology can—and should—accommodate social theory in its analysis but from a different premise than is currently often the case. The proposed operations will contribute to a complexity-informed understanding of human behavior in coupled socio-ecological systems. [ABSTRACT FROM AUTHOR]

This paper studies disturbance estimation and compensation for discrete-time large-scale nonlinear descriptor systems with unknown measurement noises. Interval type-2 (IT-2) Takagi–Sugeno (T–S) fuzzy model is used to represent nonlinear dynamics, where fuzzy representation is assumed to be appearing not only in both the state and input matrices but also in the matrix of derivative state. First, by using a novel model transformation, the fuzzy representation in the derivative matrix is formulated into the linear one. Then, an augmented fuzzy observer is introduced to implement a synchronous estimation for the system state and the unknown measurement noise, which guarantees that reachable sets of the closed-loop system are bounded by a given ellipsoid. Moreover, a compensation-based controller is employed to remove or relieve the uncertainties induced by measurement noises. Finally, the validity of the obtained theories is testified by a numerical example. [ABSTRACT FROM AUTHOR]

The cross-line operation mode, based on interoperability technology, is becoming increasingly common in urban rail transits (URTs). Compared to trains running on a single line, cross-line trains can greatly facilitate transfer passengers. Taking the scenario of emergent large transfer passenger flow as an example, this paper explores the train rescheduling problem for serving transfer passengers by adding a cross-line backup train. To maximize the number of transfer passengers served by the cross-line backup train, a nonlinear optimization model is constructed by taking into account the operation parameters of planned trains on relevant lines, the deviation degree of the planned timetable, the utilization of the cross-line backup train, and the passenger flow calculation as constraints, and some linearization lemmas are proposed to transform it into a mixed integer programming (MIP) model with quadratic terms. A case study is conducted to discuss the impact of parameter changes on the objective function value and the applicability of different solution approaches. The results suggest that the operation trajectory of the cross-line backup train has an effect on the objective function value, which is related to the demand, the deviation tolerance of the planned timetable, and the running efficiency tolerance of the cross-line backup train. The corresponding methods help guide the organization of the cross-line backup train for large transfer passenger flow scenarios. [ABSTRACT FROM AUTHOR]

This study presents the development of an adaptive fuzzy control strategy for double-star PMSM-PWM inverters used in ship electrical propulsion. The approach addresses the current and speed tracking challenges of double-star permanent magnet synchronous motors (DSPMSMs) in the presence of parametric uncertainties. Initially, a modeling technique employing a matrix transformation method is introduced, generating decoupled and independent star windings to eliminate inductive couplings, while maintaining model consistency and torque control. The precise DSPMSM model serves as the foundation for an unknown nonlinear backstepping controller, approximated directly using an adaptive fuzzy controller. Through the Lyapunov direct method, system stability is demonstrated. All signals in the closed-loop system are ensured to be uniformly ultimately bounded (UUB). The proposed control system aims for low tracking errors, while also mitigating the impact of parametric uncertainties. The effectiveness of the adaptive fuzzy nonlinear control system is validated through tests conducted in hardware-in-the-loop (HIL) simulations, utilizing the OPAL-RT platform, OP4510.

The software industry is facing more complex computer systems, with short development and sustainability issues. To deliver good software with these constraints, software reuse has become a central concept for minimizing design and realization costs. This study improves upon Feature-Oriented Reuse Method with Business Component Semantics (FORM/BCS), a software development method that produces adaptable architectures from reusable domain components. This is a promising method for reusable software assets and model creation. The objective of the FORM/BCS is to bring the industrial production chain to the software. This study proposes a model to automatically transform the FORM/BCS business subsystem component into a process business component. Two metamodels for business subsystems and process business components were developed. In addition, this study establishes correspondences between the source metamodel and target metamodel classes, transformation rules, and the instance of the source metamodel and generates the target metamodel instance. Detailed findings can help practitioners reduce software design costs and development time, and contribute to the advancement of knowledge in software engineering.

By the 2010s, BIM is highly popular research area that gathers researchers from various domains. We tackle the major challenge of BIM and the main barrier for the application of BIM in smart environments: integration of BIM and wireless sensor networks. Existing studies in the literature have various dependencies and propose limited solutions. These solutions are dedicated to specific applications and domains. However, it is highly required to have a generic solution for real time indoor and outdoor environmental monitoring with full integration with the digital model of the building. To fulfill this need, in this study, we propose a novel approach to monitor and fulfill user requirements by a numerical model of the building, a novel framework and a BIMWSN integrated architecture based on the BIM principles for smart building environments. All the experiments are performed on a real testbed, physical sensor devices deployed on the classrooms of the university building. Such numerical model presents the digital model of the environment by gathering acquired data. Besides, data is gathered on a big data platform where it may be served to various other applications such as machine learning studies. [ABSTRACT FROM AUTHOR]

For multisensor networked systems with uncertain noise variances, multiplicative noises and multiple networked‐induced uncertainties including missing measurements, packets dropouts and two‐step random measurement delays, the robust weighted fusion estimation problem is addressed in this article. More precisely, the system noise variances are assumed to be uncertain but bounded, the other four uncertainties are compensated into fictitious white noise by the proposed model transformation method, which includes the augmented method and extended fictitious noise technique. Then local multi‐model system is obtained, for which robust local Kalman estimator is obtained based on the minimax robust estimation principle and unified estimation method. Based on this, the six robust weighted fusion time‐varying Kalman estimators are presented in a unified form, which include robust weighted fusers weighted by matrices, diagonal, scalars, and a robust covariance intersection (CI) fuser and two fast CI (FCI) fusers. The robustness proving method, including the extended Lyapunov equation approach with two kinds of generalized Lyapunov equations, non‐negative matrix factorization and elementary transformation of matrix, is presented to prove that the actual estimation error variances are guaranteed to have minimal upper bounds for all admissible uncertainties. The accuracy relations are proved. Further, the robust local and fused steady‐state Kalman estimators are presented. Finally, a simulation example applied to Internet‐based three tank water system is given to demonstrate effectiveness of the proposed results. [ABSTRACT FROM AUTHOR]

Traditional reliability analysis methods such as Reliability Block Diagram, Fault Tree Analysis, and Markov Analysis are all subjective methods whose results significantly depend on the analysts' skills and experiences. A model-based reliability method is proposed for the wheel brake system by using the architectural analysis and design language (AADL). The wheel brake system is modeled based on the AADL, and the AADL Error Model Annex is applied to describe the fault propagation of the system. An information extraction approach is proposed for the AADL-based model, and rules for transforming AADL-based models to colored Petri nets are given according to the information extracted. The reliability analysis of the wheel brake system is conducted in terms of the Colored Petri Nets. Through Monte Carlo simulation and linear regression, it is inferred that the lifetime of the wheel brake system follows a Weibull distribution with shape parameter 1.303 and scale parameter 9.992 × 103, and the accuracy of the method has been verified. In this study, the reliability analysis results are generated via the system model automatically; they do not depend on the analysts' experiences and skills, and ambiguity among different analysts can be avoided. [ABSTRACT FROM AUTHOR]

Model‐Driven Development (MDD) is one of the important approaches to develop complex software systems. This approach tries to model a system in high‐abstraction level. Then through applying multiple transformations step by step, the model abstraction level is reduced and finally yields to executable code. As a result, Model transformation (MT) plays a pivotal role on the realization of MDD goals. Due to the increasing complexity of software systems, MTs naturally become more complex. Hence, qualitative technical issues may be overlooked or forgotten in these model transformations. To alleviate these issues in terms of technical debts/code smells in MTs, we can apply MT patterns. A main drawback on applying patterns is that most of them are cataloged in informal language. Additionally, construction of a conceptual framework to help MT designers through applying patterns requires a precise specification of the underlying MT patterns. With a formal basis, this paper is trying to realize the proposed framework. Hence, some of the existing well‐known MT patterns are formalized. Then based on the identified common technical debts/code smells in MTs, a designer can be directed to apply the appropriate patterns and resolve the detected problems iteratively. For the applicability and functionality of the proposed framework, several examples of problematic model transformations in terms of quality flaws were considered and resolved using the appropriate patterns. We consider the Epsilon Transformation Language (ETL) cases of model transformations in this paper, and other similar MT languages could be treated using the same measures and patterns as well. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to fully utilize the control degrees of freedom of a multi-three-phase-star smooth-pole permanent magnet synchronous motor (PMSM), this paper first develops a modeling approach using a new matrix transformation method. The proposed transformation produces decoupled and independent star windings, removing the inductive couplings and preserving the model and torque control’s consistency as the number of phases increases. The model, together with a new vector control scheme, is superior for studying the effect of the winding phase angle shift on motor performance. Based on a numerical simulation, this paper focuses on the quality analysis of phase currents, non-sequential currents, and torque ripple with different phase angles for double- and triple-star PMSM drives. The control of a triple-star PMSM is validated, and the behavior analysis is investigated by OPAL-RT.

Model transformation is a core mechanism for Model-Driven Engineering (MDE). Writing complex programs such as model transformations (MT) is error-prone, and efficient testing techniques are required for their quality assurance. There are several challenges when it comes to testing MT, including the automatic generation of suitable input test models and the construction of test oracles based on verification properties. Many approaches to generating input models ensure coverage of a certain level of the source meta-model and some input/output model constraints. Furthermore, most transformation testing techniques are tailored to specific implementation languages or quality properties, which makes it difficult to reuse testing techniques for different languages due to their language-specific nature. The diversity of languages and verification properties raises the need for a black-box testing framework of MT that is independent of transformation implementation languages as well as supports systematic verification of the quality properties. In this paper, we clarify the basic elements of such a framework, and how to apply this framework for systematically testing MT. The main tasks of the model transformation testing process, including test design, test execution and evaluation, are defined and realized within this integrated framework. [ABSTRACT FROM AUTHOR]

The work presented in this paper focuses on the modeling and implementation of business intelligence processes, specifically how to apply model-driven architecture (MDA) throughout the entire development process of a data warehouse to automatically generate the multidimensional schema. As a result, this work specifies different rules for automating the process of obtaining an online analytical processing (OLAP) cube and implementing it using a collection of metamodels and automatic transformations. The data warehouse relational and OLAP cube models are then created using a model transformation from the conceptual model (class diagram). Finally, the XML file and SQL code are generated for creating the OLAP cubes and relational data warehouse, respectively. Using transformation languages like query-view-transformation (QVT) and Acceleo, all model transformations are performed. [ABSTRACT FROM AUTHOR]

An improved technique for the design of decentralized dynamic decoupled proportional-integral (PI) controllers to control many variables of column flotation was developed and implemented in this paper. This work was motivated by challenges when working with multiple inputs and multiple outputs (MIMO) systems that are not controllable by conventional linear feedback controllers. Conventional feedback control design consists of various drawbacks when it comes to complex industrial processes. The introduction of decentralization, decoupling, and many advanced controls design methods overcomes these drawbacks. Hence, the design and implementation of control systems that mitigate stability for MIMO systems are important. The developed closed-loop model of the flotation process is implemented in a real-time platform using TwinCAT 3.1 automation software and CX5020 Beckhoff programmable logic controllers (PLC) through the model transformation technique. The reasons for using the CX5020 as an implementation environment were motivated by the reliability, and is built according to new industry standards, allowing transformation, which makes it more advantageous to be used more than any other PLCs. This is done to validate the effectiveness of the recommended technique and prove its usability for any multivariable system. Comparable numerical results are presented, and they imply that industrial usage of this method is highly recommended. [ABSTRACT FROM AUTHOR]

This paper is concerned with the parameter estimation problem for the multivariate system disturbed by coloured noises. Since coloured noises will reduce the estimation accuracy, the model transformation technique is employed to whiten the original system without changing the input-output relationship. In order to alleviate the heavy computational burden caused by high-dimensional variables and different types of parameters, the transformed model is divided into several sub-models according to the numbers of outputs. However, after the decomposition, all the sub-models contain a same parameter vector, resulting in many redundant estimates. A model transformation based distributed stochastic gradient (MT-DSG) algorithm is derived to cut down the redundant estimates and exchange the information among the sub-models. Compared with the centralised multivariate generalised stochastic gradient algorithm, the MT-DSG algorithm has more accurate estimates and less computational complexity. Finally, an illustrative example is employed to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]