AADLib, A Library of Reusable AADL Models

The SAE Architecture Analysis and Design Language is now a well-established language for the description of critical embedded systems, but also cyber-physical ones. A wide range of analysis tools is already available, either as part of the OSATE tool chain, or separate ones. A key missing elements of AADL is a set of reusable building blocks to help learning AADL concepts, but also experiment already existing tool chains on validated real-life examples. In this paper, we present AADLib, a library of reusable model elements. AADLib is build on two pillars: 1/ a set of ready-to- use examples so that practitioners can learn more about the AADL language itself, but also experiment with existing tools. Each example comes with a full description of available analysis and expected results. This helps reducing the learning curve of the language. 2/ a set of reusable model elements that cover typical building blocks of critical systems: processors, networks, devices with a high level of fidelity so that the cost to start a new project is reduced. AADLib is distributed under a Free/Open Source License to further disseminate the AADL language. As such, AADLib provides a convenient way to discover AADL concepts and tool chains, and learn about its features

Interoperability and Standards: The Way for Innovative Design in Networked Working Environments

Organised by: Cranfield UniversityIn today’s networked economy, strategic business partnerships and outsourcing has become the dominant paradigm where companies focus on core competencies and skills, as creative design, manufacturing, or selling. However, achieving seamless interoperability is an ongoing challenge these networks are facing, due to their distributed and heterogeneous nature. Part of the solution relies on adoption of standards for design and product data representation, but for sectors predominantly characterized by SMEs, such as the furniture sector, implementations need to be tailored to reduce costs. This paper recommends a set of best practices for the fast adoption of the ISO funStep standard modules and presents a framework that enables the usage of visualization data as a way to reduce costs in manufacturing and electronic catalogue design.Mori Seiki – The Machine Tool Compan

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Distributed product development approaches and system for achieving optimal design.

The research in this dissertation attempts to provide theoretic approaches and design systems to support engineers who are located in different places and belong to different teams or companies to work collaboratively to perform product development.The second challenge is addressed by developing a collaborative design process modeling technique based on Petri-net. Petri-net is used to describe complex design processes and to construct different design process alternatives. These alternative Petri-net models are then analyzed to evaluate design process alternatives and to select the appropriate process.In this dissertation, three major challenges are identified in realization of a collaborative design paradigm: (i) development of design method that supports multidisciplinary xi design teams to collaboratively solve coupled design problems, (ii) development of process modeling techniques to support representation and improve complex collaborative design process, and (iii) implementation of a testbed system that demonstrates the feasibility of enhancing current design system to satisfy with the needs of organizing collaborative design process for collaborative decision making and associated design activities.New paradigms, along with accompanying approaches and software systems are necessary to support collaborative design work, in a distributed design environment, of multidisciplinary engineering teams who have different knowledge, experience, and skills. Current research generally focuses on the development of online collaborative tools, and software frameworks that integrate and coordinate these tools. However, a gap exists between the needs of a distributed collaborative design paradigm and current collaborative design tools. On one side, design methodologies facilitating engineering teams' decision making is not well developed. In a distributed collaborative design paradigm, each team holds its own perspective towards the product realization problem, and each team seeks design decisions that can maximize the design performance in its own discipline. Design methodologies that coordinate the separate design decisions are essential to achieve successful collaboration. On the other side, design of products is becoming more complex. Organizing a complex design process is a major obstacle in the application of a distributed collaborative design paradigm in practice. Therefore, the principal research goal in this dissertation is to develop a collaborative multidisciplinary decision making methodology and design process modeling technique that bridges the gap between a collaborative design paradigm and current collaborative design systems.To overcome the first challenge, decision templates are constructed to exchange design information among interacting disciplines. Three game protocols from game theory are utilized to categorize the collaboration in decision makings. Design formulations are used to capture the design freedom among coupled design activities.The third challenge, implementation of collaborative design testbed, is addressed by integration of existing Petri-net modeling tools into the design system. The testbed incorporates optimization software, collaborative design tools, and management software for product and process design to support group design activities.Two product realization examples are presented to demonstrate the applicability of the research and collaborative testbed. A simplified manipulator design example is used for explanation of collaborative decision making and design process organization. And a reverse engineering design example is introduced to verify the application of collaborative design paradigm with design support systems in practice

Optimal Gateway Placement in Low-cost Smart Cities

Rapid urbanization burdens city infrastructure and creates the need for local governments to maximize the usage of resources to serve its citizens. Smart city projects aim to alleviate the urbanization problem by deploying a vast amount of Internet-of-things (IoT) devices to monitor and manage environmental conditions and infrastructure. However, smart city projects can be extremely expensive to deploy and manage partly due to the cost of providing Internet connectivity via 5G or WiFi to IoT devices. This thesis proposes the use of delay tolerant networks (DTNs) as a backbone for smart city communication; enabling developing communities to become smart cities at a fraction of the cost. A model is introduced to aid policy makers in designing and evaluating the expected performance of such networks and results are presented based on a public transit network data-set from Chapel Hill, North Carolina and Louisville, Kentucky. We also demonstrate that the performance of our network can be optimized using algorithms associated on set-cover and Influence maximization problems. Several optimization algorithms are then developed to facilitate the effective placement of gateways within the network model and these algorithms are shown to outperform traditional centrality-based algorithms in terms of cost-efficiency and network performance. Finally, other innovative ways of improving network performance in a low-cost smart city is discussed

Statistical arbitrage powered by Explainable Artificial Intelligence

Machine learning techniques have recently become the norm for detecting patterns in financial markets. However, relying solely on machine learning algorithms for decision-making can have negative consequences, especially in a critical domain such as the financial one. On the other hand, it is well-known that transforming data into actionable insights can pose a challenge even for seasoned practitioners, particularly in the financial world. Given these compelling reasons, this work proposes a machine learning approach powered by eXplainable Artificial Intelligence techniques integrated into a statistical arbitrage trading pipeline. Specifically, we propose three methods to discard irrelevant features for the prediction task. We evaluate the approaches on historical data of component stocks of the S&P500 index and aim at improving not only the prediction performance at the stock level but also overall at the stock set level. Our analysis shows that our trading strategies that include such feature selection methods improve the portfolio performances by providing predictive signals whose information content suffices and is less noisy than the one embedded in the whole feature set. By performing an in-depth risk-return analysis, we show that the proposed trading strategies powered by explainable AI outperform highly competitive trading strategies considered as baselines