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检索条件"丛书名=IFIP Advances in Information and Communication Technology 461"

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An Explorative Study of AI Applications in Composite Material Extrusion Additive Manufacturing 43rd

An Explorative Study of AI Applications in Composite Materia...

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

作者： Harper, Austin Wuest, Thorsten West Virginia Univ IMSE Dept Morgantown WV 26506 USA Univ South Carolina Dept Mech Engn Columbia SC 29208 USA

ISBN: (纸本)9783031716355;9783031716331;9783031716324

Additive Manufacturing (AM) is on the forefront of innovative advance manufacturing techniques leveraging Artificial Intelligence (AI) and Machine Learning (ML) to improve processing capabilities. We conducted a literature review to survey the current state of the art for AI/ML applications within Material Extrusion AM (MEX-AM). Furthermore, this study explored the intersection of AI applications and use of Carbon Fiber-Reinforced Polymers (CFRP) as a MEX-AM material. We found that while discontinuous CFRPs are covered in several experimental studies, there was a noticeable lack of research on continuous CFRPs among the collected papers. We found that the most common ML Solution for quality issues in MEX-AM was the artificial neural network feed forward supervised learning back propagation (ANN-FFNN-SL-BPN) Solution.

关键词： Material Extrusion Additive Manufacturing Machine Learning Artificial Intelligence Carbon-Fiber Reinforced Polymers

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The Impact of Augmentation Techniques on Icon Detection Using Machine Learning Techniques 1

引用

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

作者： Dicu, Madalina Chira, Camelia Babe Bolyai Univ Comp Sci Dept Cluj Napoca Romania

ISBN: (数字)9783031632150

ISBN: (纸本)9783031632174;9783031632150;9783031632143

This article examines the use of image augmentation techniques to improve icon detection in mobile interfaces, a critical task due to the small size of graphical user interface (GUI) elements and the insufficiency of comprehensive datasets. It evaluates whether diversifying the dataset or using specific augmentation methods alone can enhance detection performance. The study specifically compares the performance of two models, Faster R-CNN and YOLOv8, in detecting these elements, highlighting the challenges and potential solutions in automating complex process interactions through improved icon recognition. The findings from our computational experiments reveal that the application of classical image augmentation methods to enhance dataset diversity significantly improves the performance of these models. Remarkably, such augmentation techniques are capable of yielding results that are comparable to, or even exceed, those obtained from training the models on considerably larger datasets. It is particularly noteworthy that models demonstrate superior performance when they are initially provided with a substantial volume of annotations, surpassing the outcomes associated with models trained on extensive data collections. Among the various augmentation techniques evaluated, image rotation emerged as the most effective in enhancing the performance of both models. Nonetheless, it was observed that the Faster R-CNN model consistently outperformed the YOLOv8 model across all experiments.

关键词： Icon Detection Faster R-CNN YOLOv8

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Vision Transformer Based Tokenization for Enhanced Breast Cancer Histopathological Images Classification 1

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

作者： Abimouloud, Mouhamed Laid Bensid, Khaled Elleuch, Mohamed Aiadi, Oussama Kherallah, Monji Univ Sfax Natl Engn Sch Sfax Sfax Tunisia Univ KASDI Merbah Ouargla Lab Elect Engn LAGE Ouargla 30000 Algeria Univ Manouba Natl Sch Comp Sci ENSI Manouba Tunisia Kasdi Merbah Univ Artifcial Intelligence & Informat Technol Lab LIN Ouargla Algeria Fac Sci Sfax Tunisia Sfax Univ Adv Technol Environm & Smart Cities ATES Unit Sfax Tunisia

ISBN: (数字)9783031632112

ISBN: (纸本)9783031632136;9783031632112;9783031632105

Breast cancer remains a global concern, underscoring the crucial need for early diagnosis to ensure effective treatment. In recent years, convolutional neural networks (CNNs) have dominated medical vision tasks, but recent interest in computer-aided diagnosis (CAD) has shifted towards vision transformers (ViTs). However, the vision transformer (ViT) is recognized for its data-intensive nature and a substantial number of parameters, resulting in poorer performance compared to CNNs. The challenges associated with the data-intensive characteristics and extensive parameters of ViTs are especially pronounced in tasks involving medical image datasets characterized by data scarcity. To address this gap, our paper proposes the TokenLearner model for classifying breast tumours in histopathological images. This hybrid model fuses ViT and convolution layers operating directly on input patches and utilizing standard convolutions in the attention map block to dynamically highlight relevant regions in the input patches, minimizing the number of patches used in training for a Vision Transformer with lower training time and complexity compared to the ViT base architecture. The model was extensively evaluated using the BreakHis dataset, comprising 2496 benign and 5429 malignant masses, resulting in remarkable performance. It achieved an accuracy of 97.04%, precision of 96.99%, sensitivity of 96.11%, and F1 score of 96.54% for breast mass classification. Our study highlights the effective utilization of trained attention mechanisms in developing high-performance computer-aided systems for breast cancer diagnosis. Importantly, this approach achieves outstanding results while minimizing computational resource requirements and reducing processing time.

关键词： Breast cancer (BC) Convolutional networks (CNN) Computer-aided diagnosis (CAD) Vision transformers (VIT) histopathological images Brekhis

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An Introduction to Machine Learning Lifecycle Ontology and Its Applications 43rd

An Introduction to Machine Learning Lifecycle Ontology and I...

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

作者： Drobnjakovic, Milos Charoenwut, Perawit Nikolov, Ana Oh, Hakju Kulvatunyou, Boonserm Natl Inst Stand & Technol Engn Lab Gaithersburg MD 20899 USA

ISBN: (纸本)9783031716393;9783031716379;9783031716362

MachineLearning (ML) adoption is on the rapid rise, with a nearly40% compound annual growth rate over the next decade. In otherwords, companies will be flooded with ML models developed with different datasets and software. The ability to have information at one's fingertips about how these ML models were developed, what they were used for, what their performances and uncertainties are, what their internal structure looks like, and what datasets were used can have several benefits. These pieces of ML metadata are what we collectively call ML lifecycle information. This paper explains our current research into developing an ML Lifecycle Ontology (MLLO) to capture such information in a knowledge graph. The main objective of this paper is to describe the motivation through use cases and show that future research is warranted. To that end, basic and advanced use scenarios are described. MLLO is then introduced at a high level and validated with the basic use case to show its value. We then describe future work we are undergoing to demonstrate the hypotheses in the advanced use case in which MLLO not only serves as a standard queryable representation of ML metadata across different ML software but also as a connector to domain knowledge to assist in the ML model development and reuse.

关键词： Machine Learning Ontology Knowledge Graph Machine Learning Lifecycle Management Biomanufacturing

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Integration of Artificial Intelligence in Manufacturing Companies for Achieving Zero Waste - A Systematic Literature Review 25th

Integration of Artificial Intelligence in Manufacturing Comp...

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

作者： Miele, Ludovica Fraile, Francisco Esteso, Ana Rocca, Roberto Univ Politecn Valencia Camino Vera S-N Valencia 46022 Spain Politecn Milan Via Lambruschini4-B I-20156 Milan MI Italy

ISBN: (纸本)9783031717451;9783031717437;9783031717420

New conflicts, population growth, and global warming are only some of the reasons that have led to a slowdown in achieving the Sustainable Development Goals (SDGs) companies had set for 2030. In this context, resource efficiency can help manufacturing companies improve their environmental sustainability performance towards Zero Waste. Artificial Intelligence (AI) has been applied to improve resource efficiency;however, its wide scope and lack of a universal definition have made it challenging to identify this technology's potential clearly. This paper presents an overview of AI use in manufacturing to enhance resource efficiency. A systematic literature review (SLR) was conducted using the PRISMA guidelines. The articles have been assessed against the resource efficiency enhancement and strategic impact within the company and classified under AI task and AI algorithm used. The twenty-five articles explicitly aimed at improving resource efficiency were inserted in a classification framework. The research revealed the prominence of energy efficiency objectives, machine learning algorithms, and a limited number of explored manufacturing applications and fields. However, it uncovered significant gaps that should be assessed in the future.

关键词： Artificial Intelligence Resource Efficiency Energy Efficiency Environmental Sustainability Zero Waste Manufacturing Production Processes Systematic Literature Review

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Explainable Artificial Intelligence in Manufacturing Operations: A Bibliometric Analysis 43rd

Explainable Artificial Intelligence in Manufacturing Operati...

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

作者： Presciuttini, Anna Cantini, Alessandra Portioli-Staudacher, Alberto Politecn Milan Dept Management Econ & Ind Engn I-20156 Milan Italy

ISBN: (纸本)9783031716355;9783031716331;9783031716324

This paper presents a bibliometric analysis of explainable artificial intelligence (XAI) within the manufacturing sector, with a focus on the critical areas of quality, maintenance, and production. Despite the increasing integration of XAI in industrial applications, a bibliometric exploration of its impact across these specific dimensions remains uncharted. Our study fills this gap by employing bibliometric methods to map the landscape of XAI research in manufacturing, analyzing publication patterns and thematic evolutions. Understanding this landscape is crucial, as it not only highlights the current state and trajectory of XAI applications in manufacturing but also identifies key areas where further innovation and investigation can significantly enhance efficiency, transparency, and decision-making processes in the industry. Utilizing the Bibliometrix R-package and data from the Scopus database, we analyze 107 publications from 2019 to 2024. We chart the intellectual trajectory of XAI, delving into predominant themes and observing a research progression from foundational machine learning to its sophisticated applications, culminating in Industry 4.0 innovations. The analysis reveals an academic landscape where explainability is increasingly intertwined with the technological advances of smart manufacturing, spotlighting key topics and their evolution that reflect the field's dynamic nature. This investigation offers a novel lens on the bibliometric trends shaping the development of transparent, intelligent systems within the manufacturing sector.

关键词： Explainable Artificial Intelligence Manufacturing operations Literature Review Advanced Analytics Machine Learning

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Social Dimension of Sovereign Digital Citizenship: The SCOD Framework 25th

Social Dimension of Sovereign Digital Citizenship: The SCOD ...

引用

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

作者： Osorio, A. Luis Araujo, Emilia Urze, Paula Rodrigues, Jose Cavaleiro Polytech Univ Lisbon ISEL Inst Super Engn Lisboa Lisbon Portugal UnIRE Res Grp Lisbon Portugal Univ Minho ICS Inst Ciencias Sociais CECS Braga Portugal NOVA Univ Lisbon FCT Fac Ciencias & Tecnol CIUHCT Lisbon Portugal Polytech Univ Lisbon CRIA ESCS Escola Super Comunicacao Social Lisbon Portugal

ISBN: (纸本)9783031717451;9783031717437;9783031717420

Half a century from the dawn of the digital, after the last couple of decades of intense innovation in products and services, it is time to rethink the digital ecosystem. This paper delves into network effects that lead to the concentration of online business in a few providers, exploring some of its emerging challenges and risks within increasingly complex data collection and sharing environments. Drawing on various studies, we propose conceptualizing a model that contributes to democratizing network effect on understanding digitalization as a sociotechnical process involving human interests and interactions, which is added by public participation. The paper discusses the concept of sovereign citizen on digital citizenship (SCOD), structured as a collaborative network of public and private interests. It suggests that the need to give citizens control of their own data generated when accessing digital services is central. The paper explores primary potentialities, such as data privacy, control, and autonomy, and develops some core ideas and recommendations for its operationalization at the member-state and European levels.

关键词： Collaborative Networks Digital Citizenship Citizen Data Sovereignty Participation

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How Best Practices of SCOR DS Model Support Short Supply Chains Management: A Bibliometric Analysis 25th

How Best Practices of SCOR DS Model Support Short Supply Cha...

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

作者： Sassone, Gaia Arbaoui, Taha Botta-Genoulaz, Valerie Univ Lumiere Lyon 2 INSA Lyon Univ Claude Bernard Lyon 1 Univ Jean Monnet St EtienneDISP UR4570 F-69621 Villeurbanne France

ISBN: (纸本)9783031717413;9783031717390;9783031717383

Short Supply Chains are considered as one of the viable alternatives to reduce the environmental impact of economic activities. This work introduces a bibliometric analysis of short and classical supply chains using the 281 best practices from the new Supply Chain Operations Reference Digital Standard (SCOR DS) model. For each best practice, the number of papers related to short and classical supply chains is recorded from a literature analysis. Results show that the three most studied best practices are "Make Or Buy Analysis", "Supply Market Research", "Supply Chain Optimization". Nevertheless, some best practices require further investigation of their applicability in short supply chains.

关键词： Short Supply Chains Classical Supply Chains SCOR-DS Model Best Practices Bibliometric Analysis

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Experimentation and Evaluation of the Usability of an AR-Driven Zero Defect Manufacturing Solution in a Real Life Complex Assembly Setting 43rd

Experimentation and Evaluation of the Usability of an AR-Dri...

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

作者： Soares, Joao Martins, Jerome Arica, Emrah Schmitt, Robert Wacker, Jochen Rettig, Christoph Powell, Daryl Oliveira, Manuel KIT AR London England SINTEF Mfg Raufoss Norway Dentsply Sirona Bensheim Germany

ISBN: (纸本)9783031658969;9783031658945;9783031658938

This paper reports the outcomes of a case study on evaluating the usability of AR-driven quality control solution conducted in real life production setting with highly complex, high value production characteristics. The AR solution has been evaluated by the production personnel from every organizational level, with main emphasis given to operators and process engineers. The results clearly indicate a significant usability of the solution in avoiding the quality defects in the assembly line driven by manual operations.

关键词： Digitalization Manufacturing Industry 5.0 Zero defect manufacturing Quality Management Human-centred Manufacturing

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Exploring a Low-Cost Hardware Reverse Engineering Approach: A Use Case Experiment 39th

Exploring a Low-Cost Hardware Reverse Engineering Approach: ...

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

作者： Waltoft-Olsen, Andre Johnson, Phillip Overlier, Lasse Dyrkolbotn, Geir Olav Norwegian Inst Sci & Technol Gjovik Norway Colorado State Univ Ft Collins CO 80523 USA Statnett SF Oslo Norway

ISBN: (纸本)9783031651779;9783031651755;9783031651748

This research delves into Hardware Reverse Engineering, specifically focusing on hardware manipulation. It leverages the Supply Chain attack vector and conducts a practical experiment to manipulate sensor trustworthiness. Results demonstrate that identifying weaknesses and exploiting hardware vulnerabilities can be achieved without costly reverse-engineering platforms. The study further benefits novices venturing into Hardware Reverse Engineering research by advocating a low-cost approach using open-access knowledge.

关键词： Hardware Reverse Engineering Low-Cost Novice-friendly Tools

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