Design and integrity of deterministic system architectures.

Architectures represented by system construction 'building block' components and interrelationships provide the structural form. This thesis addresses processes, procedures and methods that support system design synthesis and specifically the determination of the integrity of candidate architectural structures. Particular emphasis is given to the structural representation of system architectures, their consistency and functional quantification. It is a design imperative that a hierarchically decomposed structure maintains compatibility and consistency between the functional and realisation solutions. Complex systems are normally simplified by the use of hierarchical decomposition so that lower level components are precisely defined and simpler than higher-level components. To enable such systems to be reconstructed from their components, the hierarchical construction must provide vertical intra-relationship consistency, horizontal interrelationship consistency, and inter-component functional consistency. Firstly, a modified process design model is proposed that incorporates the generic structural representation of system architectures. Secondly, a system architecture design knowledge domain is proposed that enables viewpoint evaluations to be aggregated into a coherent set of domains that are both necessary and sufficient to determine the integrity of system architectures. Thirdly, four methods of structural analysis are proposed to assure the integrity of the architecture. The first enables the structural compatibility between the 'building blocks' that provide the emergent functional properties and implementation solution properties to be determined. The second enables the compatibility of the functional causality structure and the implementation causality structure to be determined. The third method provides a graphical representation of architectural structures. The fourth method uses the graphical form of structural representation to provide a technique that enables quantitative estimation of performance estimates of emergent properties for large scale or complex architectural structures. These methods have been combined into a procedure of formal design. This is a design process that, if rigorously executed, meets the requirements for reconstructability

Knowledge formalization in experience feedback processes : an ontology-based approach

Because of the current trend of integration and interoperability of industrial systems, their size and complexity continue to grow making it more difficult to analyze, to understand and to solve the problems that happen in their organizations. Continuous improvement methodologies are powerful tools in order to understand and to solve problems, to control the effects of changes and finally to capitalize knowledge about changes and improvements. These tools involve suitably represent knowledge relating to the concerned system. Consequently, knowledge management (KM) is an increasingly important source of competitive advantage for organizations. Particularly, the capitalization and sharing of knowledge resulting from experience feedback are elements which play an essential role in the continuous improvement of industrial activities. In this paper, the contribution deals with semantic interoperability and relates to the structuring and the formalization of an experience feedback (EF) process aiming at transforming information or understanding gained by experience into explicit knowledge. The reuse of such knowledge has proved to have significant impact on achieving themissions of companies. However, the means of describing the knowledge objects of an experience generally remain informal. Based on an experience feedback process model and conceptual graphs, this paper takes domain ontology as a framework for the clarification of explicit knowledge and know-how, the aim of which is to get lessons learned descriptions that are significant, correct and applicable

A Software Architecture for Knowledge-Based Systems

. The paper introduces a software architecture for the specification and verification of knowledge-based systems combining conceptual and formal techniques. Our focus is component-based specification enabling their reuse. We identify four elements of the specification of a knowledge-based system: a task definition, a problem-solving method, a domain model, and an adapter. We present algebraic specifications and a variant of dynamic logic as formal means to specify and verify these different elements. As a consequence of our architecture we can decompose the overall specification and verification task of the knowledge-based systems into subtasks. We identify different subcomponents for specification and different proof obligations for verification. The use of the architecture in specification and verification improves understandability and reduces the effort for both activities. In addition, its decomposition and modularisation enables reuse of components and proofs. Ther..

Redesign of technical systems

The paper describes a systematic approach to support the redesign process. Redesign is the adaptation of a technical system in order to meet new specifications. The approach presented is based on techniques developed in model-based diagnosis research. The essence of the approach is to find the part of the system which causes the discrepancy between a formal specification of the system to be designed and the description of the existing technical system. Furthermore, new specifications are generated, describing the new behaviour for the `faulty¿ part. These specifications guide the actual design of this part. Both the specification and design description are based on YMIR, an ontology for structuring engineering design knowledge

Hardware/Software Co-Design via Specification Refinement

System-level design is an engineering discipline focused on producing methods, technologies, and tools that enable the specification, design, and implementation of complex, multi-discipline, and multi-domain systems. System-level specifications are as abstract as possible, defining required system behaviors while eliding implementation details. These implementation details must be added during the implementation process and the high effort associated with this locks system engineers onto the chosen implementation architecture. This work provides two contributions that ease the implementation process. The Rosetta synthesis capability generates hardware/software co-designed implementations from specifications that contain low level implementation details. The Rosetta refinement capability extends this by allowing a system's functional behavior and its implementation details to be described separately. The Rosetta Refinement Tool combines the functional behavior and the implementation details to form a system specification that can be synthesized using the Rosetta synthesis capability. The Rosetta refinement capability is exposed using existing Rosetta language constructs that have, previous to this work, never been exploited. Together these two capabilities allow the refinement of high level, architecture independent specifications into low level, architecture specific hardware/software co-designed implementations. The result is an effective platform for rapid prototyping of hardware/software co-designs and provides system engineers with the novel ability to explore different system architectures with low effort

An overview of the CellML API and its implementation

<p>Abstract</p> <p>Background</p> <p>CellML is an XML based language for representing mathematical models, in a machine-independent form which is suitable for their exchange between different authors, and for archival in a model repository. Allowing for the exchange and archival of models in a computer readable form is a key strategic goal in bioinformatics, because of the associated improvements in scientific record accuracy, the faster iterative process of scientific development, and the ability to combine models into large integrative models.</p> <p>However, for CellML models to be useful, tools which can process them correctly are needed. Due to some of the more complex features present in CellML models, such as imports, developing code <it>ab initio </it>to correctly process models can be an onerous task. For this reason, there is a clear and pressing need for an application programming interface (API), and a good implementation of that API, upon which tools can base their support for CellML.</p> <p>Results</p> <p>We developed an API which allows the information in CellML models to be retrieved and/or modified. We also developed a series of optional extension APIs, for tasks such as simplifying the handling of connections between variables, dealing with physical units, validating models, and translating models into different procedural languages.</p> <p>We have also provided a Free/Open Source implementation of this application programming interface, optimised to achieve good performance.</p> <p>Conclusions</p> <p>Tools have been developed using the API which are mature enough for widespread use. The API has the potential to accelerate the development of additional tools capable of processing CellML, and ultimately lead to an increased level of sharing of mathematical model descriptions.</p

Generating bridge geometric digital twins from point clouds

The automation of digital twinning for existing bridges from point clouds remains unsolved. Extensive manual effort is required to extract object point clusters from point clouds followed by fitting them with accurate 3D shapes. Previous research yielded methods that can automatically generate surface primitives combined with rule-based classification to create labelled cuboids and cylinders. While these methods work well in synthetic datasets or simplified cases, they encounter huge challenges when dealing with realworld point clouds. In addition, bridge geometries, defined with curved alignments and varying elevations, are much more complicated than idealized cases. None of the existing methods can handle these difficulties reliably. The proposed framework employs bridge engineering knowledge that mimics the intelligence of human modellers to detect and model reinforced concrete bridge objects in imperfect point clouds. It directly produces labelled 3D objects in Industry Foundation Classes format without generating low-level shape primitives. Experiments on ten bridge point clouds indicate the framework achieves an overall detection F1-score of 98.4%, an average modelling accuracy of 7.05 cm, and an average modelling time of merely 37.8 seconds. This is the first framework of its kind to achieve high and reliable performance of geometric digital twin generation of existing bridges