Large-Scale smart grids as system of systems

Smart Grids are advanced power networks that introduce intelligent management, control, and operation systems to address the new challenges generated by the growing energy demand and the appearance of renewal energies. In the literature, Smart Grids are presented as an exemplar SoS: systems composed of large heterogeneous and independent systems that leverage emergent behavior from their interaction. Smart Grids are currently scaling up the electricity service to millions of customers. These Smart Grids are known as Large-Scale Smart Grids. From the experience in several projects about Large-Scale Smart Grids, this paper defines Large-Scale Smart Grids as a SoS that integrate a set of SoS and conceptualizes the properties of this SoS. In addition, the paper defines the architectural framework for deploying the software architectures of Large-Scale Smart Grid SoS

Blockchain and sustainability disclosure: A scenario-based application for supply chains

This paper presents the implications of blockchain technologies on sustainability reporting and disclosure, and specifically proposes blockchain use-cases as a possible solution for problems experienced in the field of supply chain carbon information. This study addresses how the reliability of supply chains’ carbon-related information can become more transparent and reliable through a decentralized approach based on blockchain thinking (BT), issues that have been identified as a gap in the literature and in the practice. Scenario analysis and design science research (DSR) are used as a methodological driver to conceptualize over the nature of practical solutions using unified modeling language (UML) diagrams. The resulting use-case focuses on data retrieval in the supply chain. The paper also presents implications for the audit industry and their role in the assurance of such technological architecture implementations. The study is visionary as it offers a conceptualization based on scenario analysis. Developing a scenario enables researchers to depict a prospective situation, develop ability to solve future problems, and to back cast them in current policies, technologies, and actions

Intelligent business processes composition based on mas, semantic and cloud integration (IPCASCI)

[EN]Component reuse is one of the techniques that most clearly contributes to the evolution of the software industry by providing efficient mechanisms to create quality software. Reuse increases both software reliability, due to the fact that it uses previously tested software components, and development productivity, and leads to a clear reduction in cost. Web services have become are an standard for application development on cloud computing environments and are essential in business process development. These services facilitate a software construction that is relatively fast and efficient, two aspects which can be improved by defining suitable models of reuse. This research work is intended to define a model which contains the construction requirements of new services from service composition. To this end, the composition is based on tested Web services and artificial intelligent tools at our disposal. It is believed that a multi-agent architecture based on virtual organizations is a suitable tool to facilitate the construction of cloud computing environments for business processes from other existing environments, and with help from ontological models as well as tools providing the standard BPEL (Business Process Execution Language). In the context of this proposal, we must generate a new business process from the available services in the platform, starting with the requirement specifications that the process should meet. These specifications will be composed of a semi-free description of requirements to describe the new service. The virtual organizations based on a multi-agent system will manage the tasks requiring intelligent behaviour. This system will analyse the input (textual description of the proposal) in order to deconstruct it into computable functionalities, which will be subsequently treated. Web services (or business processes) stored to be reused have been created from the perspective of SOA architectures and associated with an ontological component, which allows the multi-agent system (based on virtual organizations) to identify the services to complete the reuse process. The proposed model develops a service composition by applying a standard BPEL once the services that will compose the solution business process have been identified. This standard allows us to compose Web services in an easy way and provides the advantage of a direct mapping from Business Process Management Notation diagrams

Scale aware modeling and monitoring of the urban energy chain

With energy modeling at different complexity levels for smart cities and the concurrent data availability revolution from connected devices, a steady surge in demand for spatial knowledge has been observed in the energy sector. This transformation occurs in population centers focused on efficient energy use and quality of life. Energy-related services play an essential role in this mix, as they facilitate or interact with all other city services. This trend is primarily driven by the current age of the Ger.: Energiewende or energy transition, a worldwide push towards renewable energy sources, increased energy use efficiency, and local energy production that requires precise estimates of local energy demand and production. This shift in the energy market occurs as the world becomes aware of human-induced climate change, to which the building stock has a significant contribution (40% in the European Union). At the current rate of refurbishment and building replacement, of the buildings existing in 2050 in the European Union, 75% would not be classified as energy-efficient. That means that substantial structural change in the built environment and the energy chain is required to achieve EU-wide goals concerning environmental and energy policy. These objectives provide strong motivation for this thesis work and are generally made possible by energy monitoring and modeling activities that estimate the urban energy needs and quantify the impact of refurbishment measures. To this end, a modeling library called aEneAs was developed in the scope of this thesis that can perform city-wide building energy modeling. The library performs its tasks at the level of a single building and was a first in its field, using standardized spatial energy data structures that allow for portability from one city to another. For data input, extensive use was made of digital twins provided from CAD, BIM, GIS, architectural models, and a plethora of energy data sources. The library first quantifies primary thermal energy demand and then the impact of refurbishment measures. Lastly, it estimates the potential of renewable energy production from solar radiation. aEneAs also includes network modeling components that consider energy distribution in the given context, showing a path toward data modeling and simulation required for distributed energy production at the neighborhood and district level. In order to validate modeling activities in solar radiation and green façade and roof installations, six spatial models were coupled with sensor installations. These digital twins are included in three experiments that highlight this monitoring side of the energy chain and portray energy-related use cases that utilize the spatially enabled web services SOS-SES-WNS, SensorThingsAPI, and FIWARE. To this author\u27s knowledge, this is the first work that surveys the capabilities of these three solutions in a unifying context, each having its specific design mindset. The modeling and monitoring activity and their corresponding literature review indicated gaps in scientific knowledge concerning data science in urban energy modeling. First, a lack of standardization regarding the spatial scales at which data is stored and used in urban energy modeling was observed. In order to identify the appropriate spatial levels for modeling and data aggregation, scale is explored in-depth in the given context and defined as a byproduct of resolution and extent, with ranges provided for both parameters. To that end, a survey of the encountered spatial scales and actors in six different geographical and cultural settings was performed. The information from this survey was used to put forth a standardized spatial scales definition and create a scale-dependent ontology for use in urban energy modeling. The ontology also provides spatially enabled persistent identifiers that resolve issues encountered with object relationships in modeling for inheritance, dependency, and association. The same survey also reveals two significant issues with data in urban energy modeling. These are data consistency across spatial scales and urban fabric contiguity. The impact of these issues and different solutions such as data generalization are explored in the thesis. Further advancement of scientific knowledge is provided specifically with spatial standards and spatial data infrastructure in urban energy modeling. A review of use cases in the urban energy chain and a taxonomy of the standards were carried out. These provide fundamental input for another piece of this thesis: inclusive software architecture methods that promote data integration and allow for external connectivity to modern and legacy systems. In order to reduce time-costly extraction, transformation, and load processes, databases and web services to ferry data to and from separate data sources were used. As a result, the spatial models become central linking elements of the different types of energy-related data in a novel perspective that differs from the traditional one, where spatial data tends to be non-interoperable / not linked with other data types. These distinct data fusion approaches provide flexibility in an energy chain environment with inconsistent data structures and software. Furthermore, the knowledge gathered from the experiments presented in this thesis is provided as a synopsis of good practices

Internet of Things data contextualisation for scalable information processing, security, and privacy

The Internet of Things (IoT) interconnects billions of sensors and other devices (i.e., things) via the internet, enabling novel services and products that are becoming increasingly important for industry, government, education and society in general. It is estimated that by 2025, the number of IoT devices will exceed 50 billion, which is seven times the estimated human population at that time. With such a tremendous increase in the number of IoT devices, the data they generate is also increasing exponentially and needs to be analysed and secured more efficiently. This gives rise to what is appearing to be the most significant challenge for the IoT: Novel, scalable solutions are required to analyse and secure the extraordinary amount of data generated by tens of billions of IoT devices. Currently, no solutions exist in the literature that provide scalable and secure IoT scale data processing. In this thesis, a novel scalable approach is proposed for processing and securing IoT scale data, which we refer to as contextualisation. The contextualisation solution aims to exclude irrelevant IoT data from processing and address data analysis and security considerations via the use of contextual information. More specifically, contextualisation can effectively reduce the volume, velocity and variety of data that needs to be processed and secured in IoT applications. This contextualisation-based data reduction can subsequently provide IoT applications with the scalability needed for IoT scale knowledge extraction and information security. IoT scale applications, such as smart parking or smart healthcare systems, can benefit from the proposed method, which  improves the scalability of data processing as well as the security and privacy of data.   The main contributions of this thesis are: 1) An introduction to context and contextualisation for IoT applications; 2) a contextualisation methodology for IoT-based applications that is modelled around observation, orientation, decision and action loops; 3) a collection of contextualisation techniques and a corresponding software platform for IoT data processing (referred to as contextualisation-as-a-service or ConTaaS) that enables highly scalable data analysis, security and privacy solutions; and 4) an evaluation of ConTaaS in several IoT applications to demonstrate that our contextualisation techniques permit data analysis, security and privacy solutions to remain linear, even in situations where the number of IoT data points increases exponentially

DYNAMIC ONTOLOGIES THAT ENCODE AND MANAGE RELEVANCE IN CONTEXT AWARE SYSTEMS

Context aware systems, to date, tend to fall into one of two categories: domain specific or generic across multiple domains. Domain specific systems are single-use instances – that is, establishing the ability to manage context for an additional domain necessitates the creation of an additional system. Authors of such systems should instead strive for generic ones. Generic context management systems require a generic modeling and context delivery system. Previous research has shown that generic context aware systems prove to be quite dynamic through their use of ontologies. These ontologies, however, are very rigid in nature, requiring additional software to mature and manage instantiated models, filter relevant information, or pre-cache information. The result is users who wish to use generic systems must encode relevance across ontological models, filters, and newly created external software with each re-use in order to manage context manipulation at run time. Through the design and implementation of Rover3, while leveraging the concept of an Automatic and Dynamic Information Model (ADIM) methodology, we outline what we believe how context aware systems should function. By providing a framework to encode relevance within ontologies, we minimize the way to present and consume relevant information. Our context management framework uses dynamic ontologies to deliver relevant information to users striving to achieve goals for any given situation. Walking through an accident response case study we showcase the aforementioned features of Rover3, showing how such incidents can benefit from context aware systems. The value of Rover3 is expressed through an extensibility study where efforts to expand existing ontological models are compared between Rover2 and Rover3. This dissertation presents: • The notion of relevant context and how it can be managed at runtime through a generic context aware system. • The required primitives and rules for modeling any generic situation. • The Automatic and Dynamic Information Model (ADIM) methodology, how one can encode relevance in a general information model, and exhaustive grammar and rules for this version of ADIM. • The Rover3 system and its application of ADIM, showcasing how it provides a generic framework to model and manage context that does not require any additional software

Ontoloji tabanlı çok-etmenli sanal fabrika sisteminin tasarımı ve geliştirilmesi.

Major developments in computers and information technologies, enable industrial and mechanical engineers to establish new net based, virtual collaboration platforms for enterprises. Benefiting from virtual enterprise platform enterprises will be able to combine their resources and capabilities on project based collaborations meanwhile protect their independent mainstream policies and secure their secret information. This concept is called virtual enterprise(VE). Virtual Enterprise (VE) is a collaboration model between multiple business partners in a value chain. The VE model is particularly feasible and appropriate for Small and Medium Enterprises (SME) and industry parks containing multiple SMEs that have different vertical competencies. One of the main targets of this research is to create an Ontology based Multi Agent Virtual Enterprise (OMAVE) System to prepare an appropriate platform for collaboration between technology start-ups in techno-parks and SMEs in Organized Industrial Zones in order to produce high value added high-tech products. OMAVE aims to help SMEs to shift from classic trend of manufacturing part pieces towards high-tech, innovative and research based products. In this way and to reach this goal a new semantic data infrastructure to enhance Re-Configurability and Flexibility of virtual enterprise systems has been developed. In order to support flexibility in Virtual Enterprise business processes and enhance its integration to enterprises' available manufacturing systems (e.g. MRP) an ontology based domain model of VE system has been established. OWL DL semantic data structure of VE by defining concepts, axioms, rules and functions in VE system has been developed. TDB data store to keep VE data and information in form of triples developed. SPARQL semantic RDF query language is used to handle and manipulate data on developed system data store. This architecture supports structure flexibility for developed VE infrastructure and improve reusability of data and knowledge in VE life cycle. To establish a multi agent based partner selection platform different agent types have been developed. These agents collaborate and compete with each other to select the most appropriate partner for the forthcoming VE project consortium. The agent based auctioning platform is coupled with a Fuzzy-AHP-TOPSIS multi criteria decision making algorithm to evaluate incoming bids from agents and rank proposals in each iteration. It is also important to notice that here, agents interaction's semantic is provided by an agent ontology. This agent ontology provides concepts, properties and all message formats for agents to settle a common language in interactions between agents. Implementing concurrent engineering, collaborative design and Product Life Cycle Management (PLM) concepts by integrating Dassault systems web based CATIA/ENOVIA V6 design and PLM tools to OMAVE system. To test and verify these achievements a case study to produce a test product by using developed OMAVE tools is established. This test product manufactured by contributions of SMEs from OSTIM organized Industrial Zone Aviation and Defense Cluster.Ph.D. - Doctoral Progra

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume