Communicating Processes with Data for Supervisory Coordination

We employ supervisory controllers to safely coordinate high-level discrete(-event) behavior of distributed components of complex systems. Supervisory controllers observe discrete-event system behavior, make a decision on allowed activities, and communicate the control signals to the involved parties. Models of the supervisory controllers can be automatically synthesized based on formal models of the system components and a formalization of the safe coordination (control) requirements. Based on the obtained models, code generation can be used to implement the supervisory controllers in software, on a PLC, or an embedded (micro)processor. In this article, we develop a process theory with data that supports a model-based systems engineering framework for supervisory coordination. We employ communication to distinguish between the different flows of information, i.e., observation and supervision, whereas we employ data to specify the coordination requirements more compactly, and to increase the expressivity of the framework. To illustrate the framework, we remodel an industrial case study involving coordination of maintenance procedures of a printing process of a high-tech Oce printer.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

Reflections on embedding safety throughout the process engineering program

Safety is an important part of being a well-rounded, responsible process engineer. It not only covers fundamental scientific knowledge but also a way of thinking and culture in how engineers approach their work, and is continually developed throughout the working life of a process engineer. However, how this safety learning can start to be imparted to engineering students in an academic environment is a challenge for educators. In this work the systems approach that has been taken as part of UCL's Integrated Engineering Program (IEP) teaching framework is examined. Within this framework, safety is embedded into the curriculum from the start in Year 1 and is continually extended and advanced throughout the process engineering program. As the first cohort of students graduate we reflect on how this has been implemented and received

A MDE-based optimisation process for Real-Time systems

The design and implementation of Real-Time Embedded Systems is now heavily relying on Model-Driven Engineering (MDE) as a central place to define and then analyze or implement a system. MDE toolchains are taking a key role as to gather most of functional and not functional properties in a central framework, and then exploit this information. Such toolchain is based on both 1) a modeling notation, and 2) companion tools to transform or analyse models. In this paper, we present a MDE-based process for system optimisation based on an architectural description. We first define a generic evaluation pipeline, define a library of elementary transformations and then shows how to use it through Domain-Specific Language to evaluate and then transform models. We illustrate this process on an AADL case study modeling a Generic Avionics Platform

Test Automation Framework for Embedded Systems

Embedded systems are everywhere! Electronic systems in just about every engineering market segment are classified as embedded systems, consumer electronics, medical, automotive, avionics, etc. Embedded systems differ from more conventional systems, such as computers, because they are limited to the embedded hardware, are designed to perform a dedicated function and have high quality and reliability requirements. Due to these characteristics, this type of system is strongly related to critical systems. Critical systems are systems that in the event of a failure can cause damage to living beings or the environment. Thus, it is necessary to ensure a high level of correctness in this type of systems. One way to increase the correctness of a system is through the process of testing. However, testing embedded systems presents a degree of difficulty because they are typically closed systems and work with real-time data that is difficult to reproduce and are non-deterministic. In this way, and with the collaboration of Altran Portugal, we intend to solve this problem by developing a framework that allows test automation for embedded systems. Automating the test data creation and execution of test case increases the quality of these systems by identifying defects to be fixed in a more efficient way. To this end, a survey of automation tools is done and each tool evaluated according to a set of criteria defined when designing the solution. The selected tool is Robot Framework, which is a widely used tool in the web and desktop application. Thus, integrating such a proficient tool in the embedded environment elevates the test automation in the embedded systems context. Then, we test the concept developed in this dissertation by executing functional tests in embedded systems that follow a model-driven development approach

DeReFrame: a design-research framework to study game mechanics and game aesthetics in an engineering design process

The main aim of this research is to study gaming techniques and elements that may potentially be beneficial to the future development of CAD systems for engineering design, in particular to maintain cognitive engagement. A design-research framework, called DeReFrame, was employed to construct an experimental game-based CAD framework exploring this. This research is based on reviews from the literature and experimental studies and include quantitative and qualitative data analysis methods measuring engineers’ performance and emotional responses. The thesis presents the construction process of the framework (DeReframe) to study a set of game mechanics and game aesthetics in an engineering design process and compare this with the traditional CAD. The framework was used to design and implement a game-based CAD system, called ICAD which was embedded with the following game mechanics of Directional Goals, Progression, Performance-Feedback and Rewards-Achievement. The DeReFrame and ICAD evolved through the experimental studies. In each case, selected game mechanics were at the core of each interaction and iteration which gave rise to feelings of progress, competence and mastery. The final results from the DeReFrame framework and ICAD indicated that gamified approaches should be included in engineering design with CAD: in particular the game mechanics of performance feedback and rewards-achievements influence engineers’ behaviour by supporting them within the problem-solving process creating an engaging-challenging interaction. In conclusion, this research has shown that a framework, that includes both engineering requirements and gamified aspects into consideration, cam serve as a basis for implementing game-based CAD to facilitate performance by providing engaging experiences for engineers

Multi-process modelling approach to complex organisation design

Present day markets require manufacturing enterprises (MEs) to be designed and run in a flexibly structured yet optimised way. However, contemporary approaches to ME engineering do not enable this requirement to capture ME attributes such that suitable processes, resource systems and support services can be readily implemented and changed. This study has developed and prototyped a model-driven environment for the design, optimisation and control of MEs with an embedded capability to handle various types of change. This so called Enriched-Process Modelling (E-MPM) Environment can support the engineering of strategic, tactical and operational processes and comprises two parts: (1) an E-MPM Method that informs, structures, and guides modelling activities required at different stages of ME systems design; and (2) an E-MPM Modelling Framework that specifies interconnections between modelling concepts necessary for the design and run time operation of ME systems. [Continues.

A Framework for Executable Systems Modeling

Systems Modeling Language (SysML), like its parent language, the Unified Modeling Language (UML), consists of a number of independently derived model languages (i.e. state charts, activity models etc.) which have been co-opted into a single modeling framework. This, together with the lack of an overarching meta-model that supports uniform semantics across the various diagram types, has resulted in a large unwieldy and informal language schema. Additionally, SysML does not offer a built in framework for managing time and the scheduling of time based events in a simulation. In response to these challenges, a number of auxiliary standards have been offered by the Object Management Group (OMG); most pertinent here are the foundational UML subset (fUML), Action language for fUML (Alf), and the UML profile for Modeling and Analysis of Real Time and Embedded Systems (MARTE). However, there remains a lack of a similar treatment of SysML tailored towards precise and formal modeling in the systems engineering domain. This work addresses this gap by offering refined semantics for SysML akin to fUML and MARTE standards, aimed at primarily supporting the development of time based simulation models typically applied for model verification and validation in systems engineering. The result of this work offers an Executable Systems Modeling Language (ESysML) and a prototype modeling tool that serves as an implementation test bed for the ESysML language. Additionally a model development process is offered to guide user appropriation of the provided framework for model building

A multiple case study of an interorganizational collaboration: Exploring the first year of an industry partnership focused on middle school engineering education

Background: Calls to improve learning in science, technology, engineering, and mathematics (STEM), and particularly engineering, present significant challenges for school systems. Partnerships among engineering industry, universities, and school systems to support learning appear promising, but current work is limited in its conclusions because it lacks a strong connection to theoretical work in interorganizational collaboration. Purpose/Hypothesis: This study aims to reflect more critically on the process of how organizations build relationships to address the following research question: In a public–private partnership to integrate engineering into middle school science curriculum, how do stakeholder characterizations of the collaborative process align with existing frameworks of interorganizational collaboration?. Design/Method: This qualitative, embedded multiple case study considered in-depth pre- and post-year interviews with teachers, administrators, industry, and university personnel during the first year of the Partnering with Educators and Engineers in Rural Schools (PEERS) program. Transcripts were analyzed using a framework of interorganizational collaboration operationalized for our context. Results: Results provide insights into stakeholder perceptions of collaborative processes in the first year of the PEERS program across dimensions of collaboration. These dimensions mapped to three central discussion points with relevance for school–university–industry partnerships: school collaboration as an emergent and negotiated process, tension in collaborating across organizations, and fair share in collaborating toward a social goal. Conclusions: Taking a macro-level look at the collaborative processes involved enabled us to develop implications for collaborative stakeholders to be intentional about designing for future success. By systematically applying a framework of collaboration and capitalizing on the rich situational findings possible through a qualitative approach, we shift our understanding of collaborative processes in school–university–industry partnerships for engineering education and contribute to the development of collaboration theory

Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models

In order to make data-driven models of physical systems interpretable and reliable, it is essential to include prior physical knowledge in the modeling framework. Hamiltonian Neural Networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems. Despite being suitable to estimate a wide range of physical system behavior from data, classical HNNs are restricted to systems without inputs and require noiseless state measurements and information on the derivative of the state to be available. To address these challenges, this paper introduces an Output Error Hamiltonian Neural Network (OE-HNN) modeling approach to address the modeling of physical systems with inputs and noisy state measurements. Furthermore, it does not require the state derivatives to be known. Instead, the OE-HNN utilizes an ODE-solver embedded in the training process, which enables the OE-HNN to learn the dynamics from noisy state measurements. In addition, extending HNNs based on the generalized Hamiltonian theory enables to include external inputs into the framework which are important for engineering applications. We demonstrate via simulation examples that the proposed OE-HNNs results in superior modeling performance compared to classical HNNs.Comment: Preprint submitted to IFAC 202