| 1. |
- Javed, Salman, 1982-, et al.
(författare)
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A Smart Manufacturing Ecosystem for Industry 5.0 using Cloud-based Collaborative Learning at the Edge
- 2023
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Ingår i: NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium. - : IEEE. - 9781665477178 - 9781665477161
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Konferensbidrag (refereegranskat)abstract
- In the modern manufacturing industry, collaborative architectures are growing in popularity. We propose an Industry 5.0 value-driven manufacturing process automation ecosystem in which each edge automation system is based on a local cloud and has a service-oriented architecture. Additionally, we integrate cloud-based collaborative learning (CCL) across building energy management, logistic robot management, production line management, and human worker Aide local clouds to facilitate shared learning and collaborate in generating manufacturing workflows. Consequently, the workflow management system generates the most effective and Industry 5.0-driven workflow recipes. In addition to managing energy for a sustainable climate and executing a cost-effective, optimized, and resilient manufacturing process, this work ensures the well-being of human workers. This work has significant implications for future work, as the ecosystem can be deployed and tested for any industrial use case.
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| 2. |
- Javed, Saleha, 1990-, et al.
(författare)
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Cloud-based Collaborative Learning (CCL) for the Automated Condition Monitoring of Wind Farms
- 2022
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Ingår i: Proceedings 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS). - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Modeling Industrial Internet of Things (IIoT) architectures for the automation of wind turbines and farms(WT/F), as well as their condition monitoring (CM) is a growing concept among researchers. Several end-to-end automated cloud-based solutions that digitize CM operations intelligently to reduce manual efforts and costs are being developed. However, establishing robust and secure communication across WT/F is still difficult for the wind energy industry. We propose a fully automated cloud-based collaborative learning (CCL) architecture using the Eclipse Arrowhead Framework and an unsupervised dictionary learning (USDL) CM approach. The scalability of the framework enabled digitization and collaboration across the WT/Fs. Collaborative learning is a novel approach that allows all WT/Fs to learn from each other in real-time. Each turbine has CCL based CM using USDL as micro-services that autonomously perform feature selection and failure prediction to optimize cost, computation, and resources. The fundamental essence of the USDA approach is to enhance the WT/F’s learning and accuracy. We use dictionary distances as a metric for analyzing the CM of WT in our proposed USDL approach. A dictionary indicates an anomaly if its distances increased from the dictionary computed at a healthy state of that WT. Using CCL, a WT/F learns all types of failures that could occur in a similar WT/F, predicts any machinery failure, and sends alerts to the technicians to ensure guaranteed proactive maintenance. The results of our research support the notion that when testing a turbine with dictionaries of all the other turbines, every dictionary converges to similar behavior and captures the fault that occurs in that turbine.
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| 3. |
- Barton, Amy J., et al.
(författare)
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A Case Study of a Semantically Enhanced Public Health Digital Collection
- 2013
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Ingår i: Journal of Library Metadata. - : Taylor & Francis. - 1938-6389 .- 1937-5034. ; 13:4, s. 367-380
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Tidskriftsartikel (refereegranskat)abstract
- A historic public health digital collection, developed by the Ruth Lilly Medical Library's Digital Initiatives Group, includes full-text public health bulletin issues; historic photos, drawings, and images; and a vital statistics database. Each content component resides in its own digital space and each has to be separately searched. This paper will discuss the development of a prototype system that integrates and relates digital content within a dispersed collection using Semantic Web technologies. The search result sets are presented as a collection of interrelated content on a scatter graph that spatially indicates the degree of contextual relevancy.
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| 4. |
- Comer, Douglas, et al.
(författare)
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Applying open resilient cluster management (ORCM) to a multi-chassis core router
- 2012
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Ingår i: Proceedings of the 27th International Conference on Computers and Their Appliactions 2012. - : International Society for Computers and Their Applications. ; , s. 148-155
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Konferensbidrag (refereegranskat)abstract
- A high-end core router, such as the CiscoCRS-1, consists of multiple chassis, each of which is populated with multiple line cards that in turn have multiple high-speed network connections. The router’s control plane software must configure, control, and coordinate the set of interfaces to insure that the control software remains running at all times, that faults are detected and corrected, and that forwarding remains consistent across all interfaces. A key requirement is that changes in forwarding tables propagate to all parts of the router quickly without producing transient inconsistencies (the control software must be especially careful to avoid even short-terminternal routing loops). This paper considers the application of cluster management software to a core router. Specifically, we investigate OpenMPI and the ORCM system that uses OpenMPI. After a review of basics and definition of terms, the paper considers fault tolerance and describes ORCM capabilities and limitations. It then presents measurements of latency and throughput that characterize performance and overhead. We conclude with possible extensions and future work.
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| 5. |
- Javed, Salman, 1982-, et al.
(författare)
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An approach towards demand response optimization at the edge in smart energy systems using local clouds
- 2023
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Ingår i: Smart Energy. - : Elsevier. - 2666-9552. ; 12
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Tidskriftsartikel (refereegranskat)abstract
- The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.
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| 6. |
- Javed, Salman, 1982-
(författare)
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Approach Towards Engineering Microservice-Oriented Composable Ecosystems for Smart Industries
- 2023
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The emergence of smart and integrated industrial ecosystems is replacing traditional manufacturing, where operations are more digitalized and automated. For communication and cooperation, these operations require seamless integration of the devices to ensure interoperability in a secure, reliable, and adaptable environment. Designing a solution with these capabilities raises concerns about automation and engineering optimization. More recently, the rapid progression toward Industry 5.0 (I5.0) is further reshaping the landscape of smart industry ecosystems, necessitating innovative engineering and management solutions based on its core values of resilience, sustainability, and human-centricity.This thesis investigates these challenges and requirements by emphasizing adaptable, secure, reliable, composable, and scalable communication in complex industrial ecosystems. Central to this work is a local cloud-based collaboration approach to the design and development of composable ecosystems using microservices, which facilitate the integration of various information technology and operational technology (IT/OT) systems prevalent in smart industries. These encompass industrial and smart home Internet of Things (IoT)-based smart industry ecosystems and smart energy systems. Using the microservice-oriented Eclipse Arrowhead framework, this research provides scalable and adaptable solutions that adhere to the core values of I5.0. This research also bridges the integration gap between smart manufacturing ecosystems and smart home IoT technologies, laying the foundation for interconnected smart factories and improved energy management systems.Collaboration between IT/OT components and stakeholders in smart industry and smart energy ecosystems improves competitiveness, productivity, and informed decision making, thereby filling a critical research gap. The thesis presents a cloud-based collaborative learning (CCL) approach for automated condition monitoring in smart industry ecosystems. The thesis exemplifies the use cases of wind farms and smart manufacturing ecosystems that use CCL to address the issues of dynamic learning and real-time data sharing between various IoT-based IT/OT systems. Unlike traditional smart manufacturing models that focus primarily on automation and cost efficiency, CCL-based and I5.0 core value-driven ecosystems support human-centricity, sustainability, and resilience. Lastly, the thesis investigates the optimization of demand response based on collaboration among stakeholders in smart energy systems using edge-based automation clouds. The proposed approach promotes resilient and sustainable smart city demand response strategies by ensuring human comfort, security, data privacy, and all stakeholder integration in smart energy systems.
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