Leveraging Circular Economy through a Methodology for Smart Service Systems Engineering

Product Service Systems (PSS) and Smart Services are powerful means for deploying Circular Economy (CE) goals in industrial practices, through dematerialization, extension of product lifetime and efficiency increase by digitization. Within this article, approaches from PSS design, Smart Service design and Model-based Systems Engineering (MBSE) are combined to form a Methodology for Smart Service Architecture Definition (MESSIAH). First, analyses of present system modelling procedures and systems modelling notations in terms of their suitability for Smart Service development are presented. The results indicate that current notations and tools do not entirely fit the requirements of Smart Service development, but that they can be adapted in order to do so. The developed methodology includes a modelling language system, the MESSIAH Blueprinting framework, a systematic procedure and MESSIAH CE, which is specifically designed for addressing CE strategies and practices. The methodology was validated on the example of a Smart Sustainable Street Light System for Cycling Security (SHEILA). MESSIAH proved useful to help Smart Service design teams develop service-driven and robust Smart Services. By applying MESSIAH CE, a sustainable Smart Service, which addresses CE goals, has been developed

Patch-based models for visual object classes

This thesis concerns models for visual object classes that exhibit a reasonable amount of regularity, such as faces, pedestrians, cells and human brains. Such models are useful for making “within-object” inferences such as determining their individual characteristics and establishing their identity. For example, the model could be used to predict the identity of a face, the pose of a pedestrian or the phenotype of a cell and segment parts of a human brain. Existing object modelling techniques have several limitations. First, most current methods have targeted the above tasks individually using object specific representations; therefore, they cannot be applied to other problems without major alterations. Second, most methods have been designed to work with small databases which do not contain the variations in pose, illumination, occlusion and background clutter seen in ‘real world’ images. Consequently, many existing algorithms fail when tested on unconstrained databases. Finally, the complexity of the training procedure in these methods makes it impractical to use large datasets. In this thesis, we investigate patch-based models for object classes. Our models are capable of exploiting very large databases of objects captured in uncontrolled environments. We represent the test image with a regular grid of patches from a library of images of the same object. All the domain specific information is held in this library: we use one set of images of the object to help draw inferences about others. In each experimental chapter we investigate a different within-object inference task. In particular we develop models for classification, regression, semantic segmentation and identity recognition. In each task, we achieve results that are comparable to or better than the state of the art. We conclude that patch-based representation can be successfully used for the above tasks and shows promise for other applications such as generation and localization