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8th international conference on Big data, knowledge and Control systems engineering, BdKCSE 2023

ISBN: (纸本)9798350313246

The proceedings contain 30 papers. The topics discussed include: from data to learning: the scientific approach to AI-enhanced online course design;implication and inference rules in multivalued logic with network configuration;modern hot water storage technologies for livestock farms;AIOps-driven enhancement of log anomaly detection in unsupervised scenarios;lean six sigma: integrating knowledge, data, and innovation for organizational excellence;key performance indicators in parking guidance information system;application of machine learning and natural language technologies in building semantic search systems: case study of a virtual legal assistant;an efficient hybrid volleyball premier league and antlion metaheuristic algorithm for feature selection;big data processing for Bulgarian healthcare - smart cards and some simulating decisions;and the role of big data in intelligent educational platform: a functional architecture.

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6th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical systems, IWCFS 2022, 4th international Workshop on Machine Learning and knowledge Graphs, MLKgraphs 2022, 2nd international Workshop on Time Ordered data, ProTime2022, 2nd international Workshop on AI System engineering: Math, Modelling and Software, AISys2022, 1st international Workshop on Distributed Ledgers and Related Technologies, DLRT2022 and 1st international Workshop on Applied Research, Technology Transfer and knowledge Exchange in Software and data Science, ARTE2022 held at 33rd international conference on database and Expert systems Applications, DEXA 2022

6th International Workshop on Cyber-Security and Functional ...

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6th international Workshop on Cyber-Security and Functional Safety in Cyber-Physical systems, IWCFS 2022, 4th international Workshop on Machine Learning and knowledge Graphs, MLKgraphs 2022, 2nd international Workshop on Time Ordered data, ProTime2022, 2nd international Workshop on AI System engineering: Math, Modelling and Software, AISys2022, 1st international Workshop on Distributed Ledgers and Related Technologies, DLRT2022 and 1st international Workshop on Applied Research, Technology Transfer and knowledge Exchange in Software and data Science, ARTE2022 held at 33rd international conference on database and Expert systems Applications, DEXA 2022

ISBN: (纸本)9783031143427

The proceedings contain 40 papers. The special focus in this conference is on database and Expert systems Applications. The topics include: Synthetic data in Automatic Number Plate Recognition;An Untold Tale of Scientific Collaboration: SCCH and AC 2 T;on the Creation and Maintenance of a Documentation Generator in an Applied Research Context;towards the Digitalization of Additive manufacturing;Twenty Years of Successful Translational Research: A Case Study of Three COMET Centers;data Integration, Management, and Quality: From Basic Research to Industrial Application;building a YouTube Channel for Science Communication;introduction of Visual Regression Testing in Collaboration Between Industry and Academia;vibration Analysis for Rotatory Elements Wear Detection in Paper Mill Machine;applying Time-Inhomogeneous Markov Chains to Math Performance Rating;introducing data Science Techniques into a Company Producing Electrical Appliances;a Technology Transfer Portal to Promote Industry-Academia Collaboration in South-Tyrol;fast and Automatic Object Registration for Human-Robot Collaboration in Industrial manufacturing;sending Spies as Insurance Against Bitcoin Pool Mining Block Withholding Attacks;risks in DeFi-Lending Protocols - An Exploratory Categorization and Analysis of Interest Rate Differences;battling the Bullwhip Effect with Cryptography;Reporting of Cross-Border Transactions for Tax Purposes via DLT;securing File System Integrity and Version History Via Directory Merkle Trees and Blockchains;taxation of Blockchain Staking Rewards: Propositions Based on a Comparative Legal Analysis;comparison Framework for Blockchain Interoperability Implementations;A Comparative Analysis of Anomaly Detection Methods for Predictive Maintenance in SME;towards Strategies for Secure data Transfer of IoT Devices with Limited Resources;application of Validation Obligations to Security Concerns;mode Switching for Secure Edge Devices;a Lifecycle Framework for Semantic Web Machine Learn

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ICAIW 2024 - Joint Proceedings of the 7th international conference on Applied Informatics Workshops: 5th international Workshop on Applied Artificial Intelligence, WAAI 2024, 6th international Workshop on Applied Informatics for Economy, Society, and Development, AIESD 2024, 6th international Workshop on data engineering and Analytics, WDEA 2024, 3rd international Workshop on Intelligent Transportation systems and Smart Mobility Technology, WITS 2024, 4th international Workshop on knowledge Mana

ICAIW 2024 - Joint Proceedings of the 7th International Conf...

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Joint of the 7th international conference on Applied Informatics Workshops, ICAIW 2024

The proceedings contain 28 papers. The topics discussed include: analysis of the relationship between productivity and the work environment in Ecuador by 2022;complex networks as a methodology for social studies: the case of ‘Son de Amores’;evaluation by an expert panel of agroecological practices in olive groves from an environmental, economic and technical feasibility perspective;export destination and firm performance: the case of Ecuador and the United States;georeferencing of savings banks and their role in financial inclusion in the Salinas Canton;GIS application for groundwater information processing and sustainable use in rural communities;media competencies and social development: a systematic review;and the processes of real estate expropriation and the fair price: case of the decentralized autonomous government of the Municipality of Ambato.

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Intelligent systems applied to anomaly detection and diagnosis in product design and manufacturing: aerospace industry case study

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international JOURNAL OF ADVANCED manufacturing TECHNOLOGY 2025年第11-12期137卷 5789-5807页

作者： Andrade, Matheus Herman Bernardim Szejka, Anderson Luis Mas, Fernando Pontif Catholic Univ Parana Ind & Syst Engn Grad Program Curitiba Brazil Univ Seville Mech & Mfg Engn Seville Spain CT Engn Grp Seville Spain

The manufacturing of an aircraft is a complex process, requiring exceptional precision and adherence to strict quality standards for every component. This process demands continuous sharing of information and knowledge, as any errors occurring during these stages may lead to problems that affect the entire product lifecycle. In this context, anomaly detection becomes a crucial domain of study, particularly in the early stages of product lifecycle management (PLM). Collecting and processing data in real-time facilitates early failure prediction in production processes, aids in decision-making for reworking defective parts, and enhances overall process optimisation. This paper aims to address the problem of lack of standardisation in the product design and manufacturing of parts by proposing an intelligent system to detect and diagnose anomalies in the product design and manufacturing of aerospace sheet metal parts. The proposed approach uses automated feature recognition (AFR) to classify the topological elements of the boundary model and recognise features through semantic rules. Then, machine learning techniques for anomaly detection, such as k-nearest neighbours (KNN), are applied to identify anomalies in the test data. Finally, ontologies are integrated as a method of knowledge formalisation and representation to assist the decision-making process for reworking the anomaly part, suggesting productive processes and corrective tools for anomaly correction. As a result, the approach can identify anomalies in 3D model parts based on their project requirements and geometric features. It allows anomaly visualisation and offers support in decision-making during manufacturing, determining corrective actions, and thus optimising the manufacturing process.

关键词： Aerospace industry Sheet metal parts Machine learning Anomaly detection knowledge representation Automated feature recognition

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Evaluation of the Contribution of knowledge Management to Efficiency in the manufacturing Industry Through Machine Learning 16

Evaluation of the Contribution of Knowledge Management to Ef...

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16th international conference on knowledge Management and Information systems, KMIS 2024 as part of 16th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2024

作者： Ibujés-Villacís, Juan Facultad de Ciencias Administrativas Escuela Politécnica Nacional Quito Ecuador

ISBN: (纸本)9789897587160

knowledge management (KM) has been instrumental for organizations to improve their efficiency. The objective of this research is to determine the contribution of knowledge management (KM) to manufacturing industry efficiency, using machine learning models to predict the relevant KM factors that should be taken into account to improve efficiency. Given the quantitative nature of the research, in the first phase, data on variables associated with KM factors and efficiency were collected and processed. In the second phase, four supervised machine learning models were developed to predict which manufacturing companies are efficient in their production process based on a set of KM factors. The study was based on information from 142 manufacturing companies in the province of Pichincha, Ecuador. The results show that the relevant KM factors that contribute to business efficiency are policies and strategies, organizational structure, technology, incentive systems and organizational culture. This pioneering study in Ecuador allows predicting the relevant KM factors that impact the efficiency of manufacturing firms. This article contributes to the field of knowledge management and provides information on the KM factors that manufacturing firms should focus on to achieve greater efficiency. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Smart manufacturing

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Towards Frugal Industrial AI: a framework for the development of scalable and robust machine learning models in the shop floor

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international JOURNAL OF ADVANCED manufacturing TECHNOLOGY 2025年第1期138卷 169-191页

作者： Martinez-Arellano, Giovanna Ratchev, Svetan Univ Nottingham Inst Adv Mfg Derby Rd Nottingham NG7 2GX England

Artificial intelligence (AI) among other digital technologies promise to deliver the next level of process efficiency of manufacturing systems. Although these solutions such as machine learning (ML) based condition monitoring and quality inspection are becoming popular, these work under very limited conditions. Solutions do not scale-up in the real environment, where there is a mix of manufacturing equipment, where the quality and quantity of data available changes from machine to machine, or where the process changes, changing the distribution of data (i.e. concept drift). This is particularly challenging in highly reconfigurable and flexible environments. Having to develop machine learning models from scratch every single time is not cost-effective, time-consuming, requires expert knowledge that is typically not available in the manufacturing environment as well as can be challenging when data is not available in high volumes. Model robustness, reusability, adaptability and life cycle management are the keys to scale-up this technology in the manufacturing industry. In this work, a conceptual framework to enable simple and robust ML model development for the shop floor is introduced. Referred here as Frugal Industrial AI, the approach takes advantage of existing models and their context to build more robust ones in a data-efficient manner. Using a semantic knowledge base of how to construct these models for different manufacturing applications and semi-automating the development or reuse of solutions through semantic similarity, it is demonstrated how models can be developed in a more streamlined way. In addition, it is demonstrated how capturing process context information is important for the effective reuse of existing models through continual learning. This is key to building more robust ML solutions that can deal with real changing manufacturing environments, avoiding retraining from scratch as well as enabling the non-expert to use AI effectively on the shop

关键词： Frugal machine learning Flexible manufacturing Continual learning Semantic modelling MLOps

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ICAIW 2022 - Joint Proceedings of the 5th international conference on Applied Informatics Workshops: 3rd international Workshop on Applied Artificial Intelligence, WAAI 2022, 4th international Workshop on Applied Informatics for Economy, Society, and Development, AIESD 2022, 5th international Workshop on data engineering and Analytics, WDEA 2022, 1st international Workshop on Intelligent Transportation systems and Smart Mobility Technology, WITS 2022, 2nd international Workshop on knowledge Management and Information Technologies, WKMIT 2022 and 1st international Workshop on systems Modeling, WSSC 2022, co-located with the 5th international conference on Applied Informatics, ICAI 2022

ICAIW 2022 - Joint Proceedings of the 5th International Conf...

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Joint 5th international conference on Applied Informatics Workshops, ICAIW 2022: 3rd international Workshop on Applied Artificial Intelligence, WAAI 2022, 4th international Workshop on Applied Informatics for Economy, Society, and Development, AIESD 2022, 5th international Workshop on data engineering and Analytics, WDEA 2022, 1st international Workshop on Intelligent Transportation systems and Smart Mobility Technology, WITS 2022, 2nd international Workshop on knowledge Management and Information Technologies, WKMIT 2022 and 1st international Workshop on systems Modeling, WSSC 2022

The proceedings contain 24 papers. The topics discussed include: analysis on early dropouts in engineering careers;depression diagnosis using text-based AI methods – a systematic review;satellite orbit prediction using a machine learning approach;bibliometric software: the most commonly used in research;bio-business of polymer derived from cassava waste for the industrial plastic sector;dynamic models to determine external factors and their impact on the shrimp price forecast;how to use Instagram to travel the world? an approach to discovering relevant insights from tourist media content;public safety perception in Ecuador: an approach from social networks over data analytics;relationship between corporate social responsibility, assets and income of companies in Ecuador;retrospective of scientific production on e-democracy;sticky floor and glass ceiling in Ecuador. the evolution of the gender wage gap, 2010-2021;and technical indicator for a better intraday understanding of uptrends or downtrends in the financial markets using volume transactions as a trigger.

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Enabling DevOps for Fog Applications in the Smart manufacturing domain: A Model-Driven based Platform engineering approach

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FUTURE GENERATION COMPUTER systems-THE international JOURNAL OF ESCIENCE 2024年 157卷 360-375页

作者： Cuadra, Julen Hurtado, Ekaitz Sarachaga, Isabel Estevez, Elisabet Casquero, Oskar Armentia, Aintzane Univ Basque Country UPV EHU Syst Engn & Automat Control Dept Bilbao 48013 Spain Univ Jaen Elect & Automat Engn Dept Jaen 23071 Spain

Cloud Computing is revolutionizing smart manufacturing by offering on-demand and scalable computer systems that facilitate plant data analysis and operational efficiency optimization. DevOps is a methodology, widely used for developing Cloud Computing systems, that streamlines software development by improving its integration, delivery, and deployment. Although cloud application designers within a DevOps team are assumed to have development and operational knowledge, this does not fall within the skills of experts that design analytics applications of plant data. The deployment environment is also relevant since, as such applications are often hosted in the Fog, the proliferation of application components may hinder their composition and validation. This work is aimed at embracing the Platform engineering approach to provide a tailored toolkit that guides the design and development of OpenFog compliant applications for the experts in the Smart manufacturing domain. The platform uses Model Driven engineering techniques and a flow-based visual editor to allow application designers to graphically compose applications from components previously delivered by component developers, abstracting them from the underlying technologies. As a result, containerized applications, ready to be deployed and run by a container orchestrator, are obtained. The feasibility of the proposal is proved through an industrial case study.

关键词： Fog Computing Model Driven engineering Node-RED Smart manufacturing DevOps Platform engineering

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knowledge-enhanced digital twin for industrial production process 22

Knowledge-enhanced digital twin for industrial production pr...

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22nd IEEE international conference on Industrial Informatics, INDIN 2024

作者： Yang, Chao Zheng, Yuan Hua, Yuan Ala-Laurinaho, Riku Atmojo, Udayanto Dwi Tammi, Kari Dept. of Mechanical Engineering Aalto University Espoo Finland Dept. of Civil Engineering Aalto University Espoo Finland Dept. of Information and Communications Engineering Aalto University Espoo Finland Dept. of Electrical Engineering and Automation Aalto University Espoo Finland

ISBN: (纸本)9798331527471

The manufacturing domain relies on Digital Twins (DTs) to mirror physical systems digitally, facilitating simulation, monitoring, and optimization. However, existing DTs may fail to capture the rich contextual knowledge essential for decision-making in complex manufacturing processes. The evolution to knowledge-enhanced DTs is essential, as it integrates domain-specific knowledge models, enabling a profound understanding of processes. To address this gap, this research introduces a knowledge-enhanced DT framework for the production process. This framework utilizes the ontology-based approach to aid the knowledge integration with the manufacturing DTs. The designed framework consists of three essential layers: The source layer, the Streaming data and knowledge coupling layer, and the Service layer. The proposed framework was further implemented in a lab-scale manufacturing setting and validated through several tests. The results demonstrated the seamless integration of knowledge and streaming data in the production process. © 2024 IEEE.

关键词： Smart manufacturing

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Practicing the engineering Fundamentals in a Project-Based Design and 3D Printing Course 26th

Practicing the Engineering Fundamentals in a Project-Based D...

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26th international conference on Interactive Collaborative Learning (ICL) - Towards a Hybrid, Flexible and Socially Engaged Higher Education / 52nd IGIP international conference on engineering Pedagogy

作者： Levin, Laura Verner, Igor Sami Shamoon Coll Engn IL-77245 Ashdod Israel Technion Israel Inst Technol IL-3200003 Haifa Israel

ISBN: (纸本)9783031530210;9783031530227

As 3D printing technology transforms product design and manufacturing, educational programs must incorporate its principles to prepare engineering students for the workforce. Establishing dedicated labs and integrating 3D printing into higher education curricula has become popular among universities and colleges. However, insufficient attention is paid to using this learning practice to apply knowledge of engineering fundamentals and develop skills for implementing efficient and reliable additive manufacturing processes. Therefore, new approaches are needed to increase the pedagogical value of this learning practice. This study explores the effectiveness of the project-based learning (PBL) approach for enhancing the understanding of mathematical, engineering, and 3D printing concepts and their application by mechanical engineering students. We developed a pilot course, "engineering and mathematical applications in 3D printing," for junior and senior-year students. The course included lectures on design for additive manufacturing (DfAM), 3D printing technologies, printability, and troubleshooting, as well as project tasks involving the design and fabrication of functional objects such as asymmetric spinning-tops, Pythagorean cups, and print-in-place mechanisms. In the study, we examined how participants applied their mathematical and engineering knowledge to solve complex design challenges using 3D printing technology, and their perceptions of the learning experience. Using an exploratory research design, data were collected through observations, student works, and post-course questionnaires. The findings indicate that the students successfully completed the project tasks, demonstrating their ability to apply engineering principles and optimize designs for 3D printing. The students highly appreciated the course's project-oriented format and its contribution to their understanding of 3D printing.

关键词： Mechanical engineering education Design education 3D printing Project-Based Learning (PBL)

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