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Industry 5.0. Digital Twins in the Process Industry. A Bibliometric Analysis 12th

Industry 5.0. Digital Twins in the Process Industry. A Bibli...

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12th Conference on Cloud Computing, Big Data and Emerging Topics

作者： Walas Mateo, Federico De Giusti, Armando Univ Nacl Arturo Jauretche UNAJ Buenos Aires DF Argentina Univ Nacl Plata UNLP Fac Informat III LIDI La Plata Argentina

ISBN: (纸本)9783031708060;9783031708077

In the context of industrial digitalization, the Industry 5.0 model incorporates digital twins as an innovative tool. This study aims to delve deeper into the concept of digital twins, their integration with the Industrial Internet of Things (IIoT), and how these solutions contribute to bring intelligence into industrial environments. Digitalization in industry enables connected products and processes to enhance the productivity and efficiency of people, plants, and equipment. The outcomes of these improvements should have widespread impacts on both the economy and the environment. As connected products and processes generate data, this data is increasingly viewed as a fundamental source of competitive advantage, posing new challenges in industrial environments. The article examines digital twin technology, its integration with IIoT and the intelligence of this devices in the Industry 5.0 or smart manufacturing framework. The focus lies on discussing about the contribution of digital twins for optimizing industrial processes. The paper reviews relevant articles and conducts a bibliometric analysis of key topics surrounding digital twins as a value-added solution for process optimization within the Industry 5.0 paradigm. The primary findings highlight the growing significance of this subject since 2018, as evidenced by the number of published articles in the Scopus Database. Additionally, the study underscores the complexity of digital twins addressing this issue within the industrial environment.

关键词： Industry 5.0 Digital Twin (DT) Industrial Internet of Things (IIoT) process industries Industrial Process Optimization

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An Innovative Fault Detection Robotic Tool for Overhead Cranes in Industries: Magnetic Wheel Modelling and Experimental Validation 5th

An Innovative Fault Detection Robotic Tool for Overhead Cran...

引用

5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Yadav, Arun Kumar Szpytko, Janusz AGH Univ Krakow Krakow Poland

ISBN: (纸本)9783031807596;9783031807602

In this paper, we present the innovative robotic tool design and modelling, validation of a new family of climbing robots that are capable of adhering to vertical surfaces through permanent magnetic wheels. The robotic system is composed of two modules, a sensing module, and magnetic wheel module which are arranged in a sandwich configuration, with the surface to climb interposed between them. To perform the inspection task, few magnetic wheeled climbing robots have been proposed for many industrial applications of ferromagnetic structures. To achieve a reliable system, good payload abilities, and minimize the power consumption of robot, the design of magnetic circuit and calculation of adhesion forces of magnetic wheel are drive factor to achieve all these. In this paper a four permanent wheeled robot is proposed to climb on the bridges and girders of the overhead cranes for health monitoring. An improved design of magnetic wheel is presented in this paper. Finite element method is performed on the wheel to get the magnetic flux distribution and calculate the attractive forces between the wheels and inspection area. By the simulation distribution of magnetic flux lines were compared on the plane inspection area and a curved concave and convex surface. By keeping the main objective of recognizing the fault into the ferromagnetic structures a wireless robotic system is proposed for the overhead cranes.

关键词： Crack detection Nondestructive testing Climbing robot Wheeled robot Magnetic adhesion GMR sensor

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Small Vehicle Damage Detection with Acceleration Spectrograms: An Autoencoder-Based Anomaly Detection Approach 12th

Small Vehicle Damage Detection with Acceleration Spectrogram...

引用

2023 International Conference on Smart Cities and Green ICT Systems and International Conference on Vehicle Technology and Intelligent Transport Systems

作者： Khan, Sara Faria, Bruno Ferreira, Andre Robert Bosch GmbH Renningen Germany Robert Bosch Car Multiemdia SA Braga Portugal

ISBN: (纸本)9783031709654;9783031709661

This paper presents a Machine Learning (ML) based methodology for a real-time small-vehicle damage detection system. While dents and scratches may seem trivial for individual car owners, they hold significant implications for insurance companies and car-rental/taxi service providers. In addition to these interested parties, car manufacturers and car service providers are also keen to obtain accurate vehicle damage information for vehicle behaviour analysis. Therefore, vehicle damage detection remains a key and challenging problem in the automotive research community. Our novel approach mainly uses spectrograms of inertial sensors like accelerometers which measure accurate vehicle acceleration. Inertial sensors are cost-effective and tailored for real-time data capture which makes them a competitive candidate for our choice. The designed system employs auto-encoders as automatic feature extractors from input acceleration spectrograms. These feature representations derived from sensor data are then classified into damage or non-damage categories using an anomaly detection approach. We achieved an impressive approximately 90% F1-score. The outcomes of this work contribute to the progress of intelligent damage detection systems. Furthermore, it assists in understanding the relationships between autoencoders and signal data in this context. Our approach can be used in many applications in the automotive sector, such as automated vehicle inspection, enhanced airbag response systems, and improving the safety of autonomous driving.

关键词： Accelerometer Autoencoders Anomaly detection Machine learning Damage detection

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Optimizing Cost Overhead in Low TPS Microservices with Frequent Writes to Storage with Small Data Size 4th

Optimizing Cost Overhead in Low TPS Microservices with Frequ...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Pandey, Avanish Karn, Aryan Goswami, Subrata Samsung R&D Inst India Bangalore Karnataka India

ISBN: (纸本)9783031837821;9783031837838

With the increasing adoption of the microservices approach for developing applications in the software industry, there is a crucial need to analyze the resources that lead to an increase in the cost of a microservice and look for ways to minimize the unnecessary cost overhead. This paper discusses in detail the factors on which the cost of a microservice depends and what techniques can be used to reduce the cost occurred due to these factors, particularly when using third-party cloud providers such as Amazon Web Services(AWS). In this paper, we analyze and compare different serverless and storage services to determine their suitability for applications with specific requirements. Specifically, our focus is on the microservices with low transactions per second(TPS), high-frequency write operations, and small data sizes. We will propose an architecture design with an aim to optimize cost efficiency for such use cases, while ensuring performance and scalability.

关键词： Serverless Architecture Low TPS Microservices Amazon Web Services

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Design of a Crowdlending System for SMEs Based on Smart Contract in Metropolitan Lima 4th

Design of a Crowdlending System for SMEs Based on Smart Cont...

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4th International Conference on Advanced Research in Technologies information Innovation and Sustainability-ARTIIS-Annual

作者： Cabrera Campos, Alvaro Fernando Tornero Cortes, Fabriccio Manuel Burga Durango, Daniel Tello Saenz, Carlos Alberto Peruvian Univ Appl Sci Lima Peru Univ Estadual Paulista Sao Paulo Brazil

ISBN: (纸本)9783031840777;9783031840784

Small and medium-sized enterprises (SMEs) are crucial for economic growth and job creation, especially in developing economies. In Metropolitan Lima, SMEs face obstacles in accessing traditional financing due to stringent requirements, lengthy processes, and high interest rates. To address this, we propose a blockchain-based crowdlending system using smart contracts to facilitate secure, transparent, and efficient financing for SMEs. This system eliminates intermediaries and inefficiencies of traditional banking, allowing SMEs to access capital from a wider pool of investors on more favorable terms. Smart contracts automate and streamline the lending process, ensuring transaction transparency and immutability, which fosters trust among borrowers, lenders, and stakeholders. The decentralized nature of blockchain reduces transaction costs and simplifies processes. Our extensive user validation with SMEs and potential investors in Metropolitan Lima showed a 99% compliance rate on key indicators, including transaction completion, user satisfaction, and perceived security. This innovative system has the potential to transform SME financing in Metropolitan Lima, empowering SMEs to drive economic growth, create jobs, and foster a more inclusive financial ecosystem.

关键词： smart contract blockchain technology crowdlending sme financing access to finance financial inclusion decentralized finance metropolitan lima

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An Examination of Pre-school Children's Usage Behavior of Augmented Reality: Traditional vs. AR-Assisted LEGO^® Building 1

引用

8th International Conference on computer-Human Interaction Research and Applications-CHIRA-Annual

作者： Yigitbas, Enes Dell'Aquila, Alessio Paderborn Univ Zukunftsmeile 2 D-33102 Paderborn Germany

ISBN: (数字)9783031826337

ISBN: (纸本)9783031826320;9783031826337

Many children are introduced to LEGO((R)) bricks for the first time at a young age. LEGO((R)) models are usually built according to a set of building instructions, each instruction step being printed onto paper as an orthographic projection. However, as children's spatial reasoning skills are yet to be fully developed, they tend to misinterpret positions and shapes from these types of instructions, resulting in suboptimal performance in LEGO((R)) construction tasks. With Augmented Reality (AR), users can perceive and interact with three-dimensional, virtual content as if it was present in the real world, eliminating the need to convert two-dimensional instructions into three-dimensional models. In this work, an AR-Assisted LEGO((R)) Building Instruction (ARALBI) application was developed, providing three-dimensional reference LEGO((R)) models. ARALBI was specifically designed to be used by preschool children, with them constructing the real model step by step next to the virtual model. It was tested against typical paper-building instructions in a user study based on the General Assembly Task Model (GATM). Using AR instructions, preschool children performed better in terms of effectiveness (i.e. made fewer mistakes) but not efficiency (i.e. required more time to finish assembling the model) when compared to paper instructions. The results of this study indicated that preschool children, who never used AR technology before, enjoyed the use of the AR application and preferred it over paper-building instructions.

关键词： AR Children LEGO((R)) Modeling Assistance Usability

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Transcultural Leadership and Sustainable Development in the Digital Era: Navigating the 4IR in South Africa 4th

Transcultural Leadership and Sustainable Development in the ...

引用

4th International Conference on Society 5.0

作者： Kruger, Sean Univ Pretoria Ctr Future Work ZA-0002 Pretoria GT South Africa

ISBN: (纸本)9783031714115;9783031714122

This study investigates transcultural leadership's role in navigating organisations through the Fourth Industrial Revolution (4IR) towards sustainable development, with a focus on the South African context. It examines the intersection of digital transformation with key sectors like energy transition, circular economy, and sustainability, advocating for leaders to blend global sustainability aims with local cultural insights. Through a mixed-methods approach, combining qualitative focus group discussions with a meta-analysis of literature, the research captures the nuanced interplay between digital maturity and sustainable practices. The findings highlight transcultural leadership as vital for fostering transparency, reform, and digital progress, essential for aligning with Society 5.0 goals. This approach offers strategic insights into digital transformation that promotes sustainable and equitable growth. By engaging a diverse group of participants from various sectors, the study complements its empirical data, ensuring a comprehensive exploration of strategies for achieving sustainability in the 4IR era. This synthesis of leadership strategies and stakeholder insights provides valuable guidance for policymakers, business leaders, and scholars in addressing the 4IR's challenges and leveraging its opportunities for sustainable development.

关键词： Transcultural leadership Digital transformation Sustainable development Fourth Industrial Revolution (4IR) Future of Work

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A Comprehensive Framework Integrating ML, Automation Pyramid, and KPIs for Industry 5.0 5th

A Comprehensive Framework Integrating ML, Automation Pyramid...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Ponce, Pedro Anthony, Brian Bradley, Russel Xu, Wenhao Isabel Mendez, Juana Molina, Arturo Tecnol Monterrey Inst Adv Mat Sustainable Mfg Monterrey 64849 Nuevo Leon Mexico MIT Dept Mech Engn Cambridge MA 02139 USA Inst Tecnol Tlahuac III Tecnol Nacl Mexico Mexico City DF Mexico

ISBN: (纸本)9783031807596;9783031807602

The manufacturing industry continually seeks advanced technologies to enhance performance per evolving customer requirements. Machine learning (ML) emerges as a pivotal assistive technology essential for strategic integration with Key Performance Indicators (KPIs). Traditionally, KPIs monitor and measure industrial system performance. This paper proposes a framework leveraging KPIs to integrate ML across the automation pyramid in Industry 5.0. The framework enables early detection of malfunctions and areas for improvement, preventing productivity loss. Validated across various industries, the framework demonstrates enhanced operational efficiency, sustainability, and human-centric benefits. information and Communication Technologies advancements facilitate real-time data collection and analysis, aligning with ISO 22400 standards for manufacturing operations management. ML techniques generate actionable insights crucial for sustainable development in industries such as automotive, which require holistic goal assessments. Industry 4.0 marked a significant shift towards automation and data exchange, leveraging IoT, cloud computing, and big data analytics. Industry 5.0 emphasizes human-machine collaboration, customization, and sustainability, evolving KPIs to include worker satisfaction, customization capabilities, and social and environmental impact metrics. This evolution spans various sectors: manufacturing, pharmaceuticals, retail, e-commerce, high-energy-use industries, and consumer goods. ML minimizes downtime, enhances product quality, optimizes supply chains, and improves worker safety by analyzing data from wearables and sensors. Integrating ML with KPIs in Industry 4.0 and 5.0 enables industries to be more efficient, adaptive, and responsive to market and environmental changes, improving decision-making, operational efficiency, and alignment with business and sustainability goals.

关键词： Industry 4.0 Industry 5.0 Machine learning Automation pyramid KPIs

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The AURORAL Privacy Approach for Smart Communities Based on ODRL 1st

The AURORAL Privacy Approach for Smart Communities Based on ...

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1st Global Internet of Things and Edge Computing Summit

作者： Cimmino, Andrea Cano-Benito, Juan Garcia-Castro, Raul Univ Politecn Madrid Ontol Engn Grp Madrid Spain

ISBN: (纸本)9783031785719;9783031785726

Over the past decade, smart cities have developed significantly, while rural areas have lagged. The AURORAL project aims to bridge this gap by offering a decentralised, semantically interoperable platform tailored for rural concerns. This platform not only facilitates the exchange of data but also adds features such as data validation or privacy policies for the users. This paper explores how AURORAL uses the ODRL language to allow users to establish and enforce privacy policies, thereby enhancing data privacy control.

关键词： Data privacy ODRL policies Semantic interoperability

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Comprehensive Water Quality Analysis and Prediction Using Ensemble Machine Learning Models 4th

Comprehensive Water Quality Analysis and Prediction Using En...

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Sujatha, E. Ranjith, R. Malathi, K. Poongulali, E. Saveetha Engn Coll Autonomous Dept Comp Sci & Engn Chennai Tamil Nadu India Saveetha Engn Coll Autonomous Dept Artificial Intelligence & Machine Learning Chennai Tamil Nadu India

ISBN: (纸本)9783031837890;9783031837906

Ensuring water quality is crucial for public health and ecological balance. With increasing urbanization and industrialization, water sources are often contaminated, necessitating regular monitoring and predictive modelling. This study focuses on predicting water quality in Chennai's Puzhal Lake, Retteri Lake, and Chembarambakkam Lake using nine machine learning algorithms: Logistic Regression, Ridge Regression, Stochastic Gradient Descent (SGD) Classifier, Support Vector Classifier (SVC), Nu-Support Vector Classifier (NuSVC), Decision Tree, Random Forest Classifier, AdaBoost Classifier, and XGBoost Classifier. Water quality parameters such as pH, hardness, chloramines, sulphate, conductivity, organic carbon, trihalomethanes, turbidity, potability, Total Suspended Solids (TSS), Total Dissolved Solids (TDS), and Total Solids (TS) were measured using a water test kit. The dataset was used to train and evaluate the machine learning models based onWorld Health Organization (WHO) guidelines. Among the algorithms, the Random Forest Classifier achieved the highest accuracy with a score of 0.8179, followed by Support Vector Classifier (0.7132) and XGBoost Classifier (0.5925). A novel Ensemble Integration and Aggregation Algorithm (EIAA) was proposed to enhance predictive performance by aggregating the predictions of the individual models through majority voting and averaging methods. The EIAA demonstrated improved accuracy and reliability in predicting water quality. This study provides valuable insights into the most effective machine learning techniques for water quality prediction, contributing to improved water management and safety in Chennai.

关键词： Water Quality Prediction Machine Learning Ensemble Algorithm Chennai Lakes Environmental Monitoring

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