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Literature Review on the Current State-of-the-Art in Research and Technological Advancements in the Field of Machine Learning Applied to Predictive Maintenance 43rd

Literature Review on the Current State-of-the-Art in Researc...

引用

43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Resanovic, Daniel Balc, Nicolae Univ Appl Sci CAMPUS 02 Graz Austria Tech Univ Cluj Napoca Cluj Napoca Romania

ISBN: (纸本)9783031716355;9783031716331;9783031716324

This literature review examines the technologies of Machine Learning (ML) in Predictive Maintenance (PdM), highlighting the necessity for industries to boost production efficiency for competitive advantage amid growing global demands. It underscores the pivotal roles of technological advancements, especially the Internet of Things (IoT) and Big Data, in enabling smarter automation and intelligent production processes. By leveraging equipment sensor data, Predictive Maintenance serves as a crucial proactive maintenance strategy, enhancing reliability and accuracy through its diagnostic and prognostic stages. However, challenges in financial, organizational, data, and repair complexities hinder its full-scale implementation, necessitating a deeper understanding of ML techniques in PdM. The review further discusses AI/ML's role in enhancing predictive maintenance, detailing applications of AI, ML, and Deep Learning (DL) in predictive analytics and identifying emerging trends like transformers and self-supervised learning, which promise to improve PdM outcomes. Through a structured analysis, this report underscores the evolving landscape of ML applications in PdM, highlighting both challenges and opportunities, and suggesting further exploration of ML algorithms in this field.

关键词： Predictive Maintenance Machine Learning Artificial Intelligence State of ML in Predictive Maintenance Data Management

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Optimizations for Learning from Linear Feedback Shift Register Variations with Artificial Neural Networks 20th

Optimizations for Learning from Linear Feedback Shift Regist...

引用

20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Boanca, Sara Babes Bolyai Univ Cluj Napoca Romania

ISBN: (纸本)9783031632259;9783031632235;9783031632228

Recent years have seen an increase in Machine Learning techniques being applied to learn from Pseudorandom Number Generators (PRNGs). Currently, the best results have been obtained for learning on Linear Feedback Shift Registers (LFSRs). Due to the deterministic nature of LFSRs, Decision Trees (DTs) and Artificial Neural Networks (ANNs) were able to reach up to 100% test accuracy for next bit prediction tasks. Despite important advances, a number of directions have been neglected. The current work sets to investigate such directions and bring a more comprehensive understanding of the ANNs capabilities in this context, cementing them as the most reliable technique for learning on LFSRs and their variations. More precisely, the study presents the results of learning from the previously uninvestigated Galois form a LFSR. Moreover, an optimization is proposed with respect to the number of bits needed for learning to predict the outputs of the Geffe generator through the introduction of an ANN pipeline model. Performed experiments display the strength of the approach that is able to maintain up to 100% accuracy for predicting Geffe outputs while reducing the amount of training bits to the degree of magnitude 103 for each LFSR in the proposed pipeline.

关键词： Linear Feedback Shift Registers Galois Geffe Artificial Neural Networks

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Putting Authorization Servers on User-Owned Devices in User-Managed Access 1

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39th International Conference on ICT Systems Security and Privacy Protection (SEC)

作者： Hirai, Masato Kotani, Daisuke Okabe, Yasuo Kyoto Univ Kyoto Japan

ISBN: (数字)9783031651755

ISBN: (纸本)9783031651779;9783031651755;9783031651748

When a user uses a third-party application that relies on data from another platform, the platform must authorize access by that application based on the user's consent for privacy. Although OAuth 2.0 is widely used for such authorization, it is a troublesome task for the users to manage authorization. User-Managed Access (UMA) is a framework for asynchronous user-centric authorization management. UMA aims to reduce the burden of users for handling authorization requests from third-party applications. In UMA, transparency of the authorization server, usually operated by a (believed to be) trusted third-party, is important because the authorization server may behave maliciously, such as authorizing access against the authorization policy set by users. Some proposals use blockchain for improving transparency, but blockchain may not be suitable because it makes information public although authorization policies and decisions will be sensitive to user privacy. In this paper, we propose an architecture that improves UMA's transparency by putting the UMA authorization server on user-owned devices. By making authorization decisions on the devices, our architecture improves transparency while reducing the users' burden. We also use STRIDE-per-Element to discuss discovered threats and how to mitigate risks.

关键词： User-Managed Access Federated Authorization Self-Sovereign

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Pollutant Concentration Prediction by Random Forest to Estimate a Contaminant Source Position 1

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Alaoui, Sidi Mohammed Djemal, Khalifa Feiz, Amir Ali Ngae, Pierre Univ Paris Saclay Univ Evry IBISC F-91020 Evry Courcouronnes France Univ Paris Saclay Univ Evry LMEE F-91020 Evry France

ISBN: (数字)9783031632150

ISBN: (纸本)9783031632174;9783031632150;9783031632143

The goal of Source Term Estimation (STE) is to accurately identify the parameters that describe the source of a release, namely the position and strength. This requires a reliable dispersion model. Recent advancements have integrated machine learning with the Gaussian dispersion model, enhancing the prediction of pollutant concentrations while mitigating the impact of complex terrains. However, pollutant dispersion varies significantly across different atmospheric stability classes. Addressing this, we introduce a novel strategy termed the Multiple Learning Model (MLM), which segments predictions based on stability classes: Neutral (MLMN), Unstable (MLMU), and Stable (MLMS). This approach, by building models to specific atmospheric conditions, promises more precise source estimations. In comparative study, MLM not only refined prediction accuracy from 0.04 to 0.06 but also improved source location estimates, narrowing the discrepancy to 7 m-25 m from the actual source, a marked improvement over traditional random forest models. This methodological advancement underscores the potential of stability-class-specific models in enhancing the accuracy and reliability of pollutant source estimations.

关键词： Source term estimation Gaussian dispersion model Random forest Multiple Learning Model Bayesian inference Markov Chain Monte Carlo

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Emotions in Human-AI Collaboration 25th

Emotions in Human-AI Collaboration

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25th ifip WG 5.5 Working Conference on Virtual Enterprises (PRO-VE)

作者： Ferrada, Filipa Camarinha-Matos, Luis M. Univ Nova Lisboa DEEC UNINOVA CTS NOVA Sch Sci & Technol P-2829516 Caparica Portugal Univ Nova Lisboa DEEC LASI P-2829516 Caparica Portugal

ISBN: (纸本)9783031717413;9783031717390;9783031717383

This exploratory paper addresses the role of emotions in the management of collaborative networks (CNs) amid the rise of hybrid teams consisting of humans and AI agents. Building on previous research that emphasizes the critical role of emotions in fostering trust and preventing conflicts within CNs, we propose expanding these emotional frameworks to accommodate hybrid collaborative networks. The paper reviews the significance of human-AI collaboration, highlighting the complementary strengths of both and identifying three research streams: affective/sentient AI agents, human emotions modelling, and collective hybrid network emotions. Emphasizing the underexplored area of collective emotions, we suggest leveraging these insights to enhance the management and sustainability of hybrid networks. A framework for emotions estimation in CNs is described. Our aim is to identify challenges and guide future research in the integration of emotional intelligence within human-AI collaborative environments.

关键词： Human-AI collaboration Hybrid collaborative networks Emotions Collective emotions Resilience

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Graph Neural Networks in PyTorch for Link Prediction in Industry 4.0 Process Graphs 20th

Graph Neural Networks in PyTorch for Link Prediction in Indu...

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Kafeza, Eleanna Drakpopoulos, Georgios Mylonas, Phivos Zayed Univ Coll Interdisciplinary Res Dubai U Arab Emirates Ionian Univ Dept Informat Hellas Greece Univ West Attica Informat & Comp Engn Dept Hellas Greece

ISBN: (纸本)9783031632211;9783031632198;9783031632181

Process mining constitutes an integral part of enterprise infrastructure as its adaptability and evolution potential enhance the digital awareness of stakeholders. In the context of Industry 4.0 a mainstay of process mining is the integrity verification of process graphs. Since manufacturing typically consists of numerous operations, it follows that process mining techniques, including link prediction, must possess learning capabilities powerful enough to accurately evaluate the deviation degree from the respective template using a wide array of structural and functional attributes, including semantics in the form of labels denoting operations such as data request or human operator notification. In turn, this relies heavily on discerning higher order patterns because of the distributed nature of industrial processes. Graph neural networks (GNNs) are ideally suited for performing link prediction since they offer scalability, versatility, and geometric intuition. Two attribute sets were tested, one containing only structural patterns and one combining them with functional ones. Results with synthetic benchmark process graphs of varying complexity show that GNNs exploit the extra functional information in the form of labels to recover missing edges, themselves part of the graph structure, even when the functional attributes are noisy.

关键词： Graph neural networks Link prediction Industry 4.0 Geometric analytics Process graphs Higher order patterns PyTorch

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Collaborative Ecosystems for Increasing Automation in Accounting Processes in Small Firms 25th

Collaborative Ecosystems for Increasing Automation in Accoun...

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25th ifip WG 5.5 Working Conference on Virtual Enterprises (PRO-VE)

作者： Kortesalmi, Hell Aunimo, Lili Karkinen, Eija-Leena Haaga Helia Univ Appl Sci Helsinki Finland Univ Vaasa Vaasa Finland Centria Univ Appl Sci Kokkola Finland

ISBN: (纸本)9783031717413;9783031717390;9783031717383

Automation of accounting processes promises various benefits for firms, such as enhanced productivity, improved customer service and job satisfaction. Despite the enhancements, small firms are often reluctant to undertake accounting process automation projects. This research studies how collaborative ecosystems support small firms in adopting accounting automations. The study draws on the technology Acceptance Model (TAM) and studies the perceived usefulness and ease of use of automation technology in the context of small firms. In this qualitative research, 12 firms joined a collaborative ecosystem to develop automated accounting processes. A year later, 12 semi-structured interviews were conducted to examine the technology uptake by the firms. Our findings indicate that a collaborative ecosystem provides support in the first steps of technology uptake, especially by adding to the perceived usefulness of the automations. However, it did not increase automation-related skills enough to increase the perceived ease of use of the technologies.

关键词： Intelligent automation collaborative ecosystems collaborative networks accounting processes accounting process automation technology adoption model small firms skills RPA society 5.0

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Designing and Implementing Second Life for Electric Vehicle Batteries: An Integrated Framework to Navigate Ecosystem Actors Towards Circularity 43rd

Designing and Implementing Second Life for Electric Vehicle ...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Chirumalla, Koteshwar Dahlquist, Erik Kulkov, Ignat Stefan, Ioana Johansson, Glenn Malardalen Univ Digital & Circular Ind Serv DigiCircle Res Grp Eskilstuna Sweden

ISBN: (纸本)9783031716249;9783031716225;9783031716218

The electrification of vehicles has become a critical means to achieve climate-neutral transportation. As more electric vehicles (EV) are adopted, an increasing number of lithium-ion batteries will be utilized, inevitably experiencing capacity degradation over time. Retaining the value of these retired batteries through remanufacturing, reusing, and repurposing to create a second life holds significant environmental and economic benefits. However, many companies within the battery ecosystem struggle to capitalize on this opportunity due to a lack of business insight and suitable business models tailored to their operational contexts. The purpose of this paper is to propose an integrated framework for designing and implementing second life for the EV batteries that could guide and navigate ecosystem actors towards circularity. This study employed an explorative qualitative inquiry approach, utilizing interviews and workshop methods, involving 15 companies in the EV battery ecosystem. Data collection involved 24 semi-structured interviews and 22 workshops. The framework includes four building blocks, including 1) barriers and enablers, 2) circular business models archetypes and design principles, 3) ecosystem management, and 4) battery performance monitoring. Further, the paper explains criteria influencing the selection of design and implementation of strategies for battery second life. This research contributes to the theory of circular business models and ecosystem management in general, with specific relevance to battery second life and circularity.

关键词： Second-life Batteries Battery Second Life Battery Circularity Circular Business Models Performance Monitoring Circular Ecosystems

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Gamified Crowd Management Utilizing AR and Computer Vision on the Edge 20th

Gamified Crowd Management Utilizing AR and Computer Vision o...

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20th International Conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Koulouris, Dionysios Menychtas, Andreas Maglogiannis, Ilias Univ Piraeus Dept Digital Syst Piraeus Greece

ISBN: (纸本)9783031632259;9783031632235;9783031632228

The experience of visitors in exhibition areas is a key factor for the success of the endeavor and it can be considerably enhanced by using modern digital technologies and tools. Among others, the experience is affected by the number of visitors in the exhibition area, making the indoor crowd management a challenging topic which cross cuts several scientific areas. Various methods have been developed to overcome this issue especially in museum exhibitions where even a few visitors beyond a certain threshold may completely obstruct the view of the exhibits and ruin the experience. Latest technological advancements include the ability of mobile devices and microprocessors to perform even heavier tasks in executing complex algorithms and data processing by utilizing the state of the art sensors and hardware components of the mobile devices. In this work we present a crowd management algorithm and a prototype system for a museum exhibition which operates at the edge of the network utilizing the mobile devices as execution environments. As part of the prototype, we propose the integration of a series of serious games that are supplementary to the exhibits and have the goal of delaying the user before continuing to a next sector of the exhibition, acting in parallel as digital tools for education and information sharing for the various exhibits. The crowd monitoring is achieved by utilizing the localization ability of Augmented Reality-AR with Cloud functionality for mobile phones. Moreover, AR can be used in the aforementioned serious games to offer a seamless experience by combining the exhibition site with additional content. Algorithms for "visitors flow management", "visitor routing" and "room exploration" are introduced and extensively described and a Cloud AR Management Platform is developed for manipulating all the system aspects.

关键词： Augmented reality

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Developing a Circular and Resilient information System: A Design Science Approach 43rd

Developing a Circular and Resilient Information System: A De...

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43rd ifip WG 5.7 International Conference on advances in Production Management Systems (APMS)

作者： Farmakis, Timoleon Koukopoulos, Anastasios Zois, Georgios Mourtos, Ioannis Lounis, Stavros Kalaboukas, Kostas Athens Univ Econ & Business Dept Management Sci & Technol ELTRUN E Business Res Ctr Patiss 76 Athens 10434 Greece Grp Maggioli Greek Branch 19 Andrea Papandreou St Maroussi 15124 Greece

ISBN: (纸本)9783031716249;9783031716225;9783031716218

Nowadays, circularity and resiliency are crucial for manufacturing. There is a need for collaboration across the value chain, deployment of critical enablers, and connection of traceability to sustainability and business objectives to accelerate the shift towards circular and resilient production processes. This study reviews circular economy and resilient manufacturing by further analyzing the literature on circular and resilient information systems (IS). We identify key performance indicators for circularity and resiliency and utilize a design science research approach to design the circular and resilient information system (CRIS) conceptual architecture. We further propose leveraging cutting-edge technologies and tools to enable real-time decision-making, monitoring, and certification of materials and products, facilitating sustainable and resilient manufacturing practices. The deployment of CRIS as part of digital transformation efforts represents a strategic move to meet the growing demands for sustainability and resilience.

关键词： Secondary Raw Materials Circular Economy Sustainability Resilience information Systems Digital Transformation Manufacturing

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