Article
Computer Science, Information Systems
Lukas Kaupp, Kawa Nazemi, Bernhard Humm
Summary: This article introduces the Flourish dashboard for context-aware fault diagnosis. It conducts a bilingual evaluation through a questionnaire and interviews, and collects feedback from users. The article concludes that the Flourish dashboard is an essential component for context-aware fault diagnosis.
Article
Computer Science, Artificial Intelligence
Pablo Oliveira Antonino, Rafael Capilla, Patrizio Pelliccione, Frank Schnicke, Daniel Espen, Thomas Kuhn, Klaus Schmid
Summary: The concept of Industry 4.0 aims to improve the integration of production through automation and intelligent capabilities. To support this concept, we have developed a tailored version of the ISO 25010 quality model specifically for the needs of Industry 4.0, providing actionable support for quality concerns.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Computer Science, Information Systems
Zhila Esna Ashari, Naomi S. Chaytor, Diane J. Cook, Hassan Ghasemzadeh
Summary: We propose a novel active learning framework for activity recognition using wearable sensors, which takes into account the limitations of the oracle. We introduce the concept of mindful active learning and propose a computational framework to maximize the active learning performance. Our experimental results demonstrate that this approach performs well in terms of activity recognition accuracy and performance.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Multidisciplinary Sciences
Armando Walter Colombo, Stamatis Karnouskos, Christoph Hanisch
Summary: The digital transformation in industry requires the establishment of an industrial network based on service cooperation between digital assets and humans, leading to a fundamental shift in mindset. The digitization process along the three dimensions of the Industry 4.0 reference architecture model is crucial for achieving digital transformation.
PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES
(2021)
Article
Automation & Control Systems
Martin A. Sehr, Marten Lohstroh, Matthew Weber, Ines Ugalde, Martin Witte, Joerg Neidig, Stephan Hoeme, Mehrdad Niknami, Edward A. Lee
Summary: Programmable logic controllers (PLCs) are a well-established platform widely used in industrial automation, but poorly understood by researchers. This article provides an overview of the current state of practice and offers a critical analysis of the strengths and weaknesses of the dominant programming styles for PLC-based automation systems. Opportunities for improvements are identified, with proposed deterministic, distributed programming models that incorporate explicit timing, event-triggered computation, and enhanced security measures.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2021)
Article
Computer Science, Artificial Intelligence
Ariel Bar, Bracha Shapira, Lior Rokach
Summary: The Markov chain models have been influential in various domains for over a century, but limited research has been conducted on incorporating contextual conditions into the modeling phase. This paper proposes five novel approaches for learning contextual Markov chain models, and demonstrates their effectiveness and scalability through experiments.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Interdisciplinary Applications
Paolo Bellavista, Nicola Bicocchi, Mattia Fogli, Carlo Giannelli, Marco Mamei, Marco Picone
Summary: Digital factories are on the brink of achieving unprecedented levels of resilience and flexibility, in response to increasingly demanding customer and market requirements. Digital twins serve as a vital component in this vision, offering software counterparts to industrial assets that enable control, simulation, analytics, and servitization functionalities. To ensure their effectiveness, digital twins must incorporate adaptive, autonomous, and context-awareness functionalities.
COMPUTERS IN INDUSTRY
(2023)
Article
Green & Sustainable Science & Technology
Mariano Alarcon, Fernando Manuel Martinez-Garcia, Felix Cesaraeo Gomez de Leon Hijes
Summary: This study proposes the low-cost integration of Energy Management Systems and Maintenance Management Systems within the main management systems of a company to improve energy efficiency and reduce maintenance costs in industry. Utilizing network analyzers in electric machinery plays a central role in understanding real operating conditions and identifying abnormal behavior, leading to significant reductions in energy consumption and maintenance costs. The proposed measures have been successfully implemented by a multinational corporation, resulting in improved energy efficiency and cost savings at a chemical plant in El Palmar, Spain.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Article
Automation & Control Systems
Xiaolong Xu, Ji Gu, Hanzhi Yan, Wentao Liu, Lianyong Qi, Xiaokang Zhou
Summary: The development of intelligent supply chains is crucial in improving enterprise efficiency and customer satisfaction in Industry 4.0. Currently, using blockchain to optimize the supply chain ensures data security and interaction between suppliers, but the loss in time efficiency and supplier cooperation is still a significant issue. To address this, reputable supplier selection is necessary to reduce loss in quality of service (QoS) under the premise of blockchain security. Additionally, choosing a reputable supplier as the primary peer in the blockchain consensus process can effectively decrease transaction latency. This study proposes a reputation-aware supplier assessment system (SAS) that implements Canopy and K-medoids for supplier classification and utilizes a backpropagation neural network for supplier reputation evaluation, showing its effectiveness through extensive experiments.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Yige Zhang, Weixiong Rao, Mingxuan Yuan, Jia Zeng, Pan Hui
Summary: This paper focuses on improving Telco localization accuracy by proposing a context-aware Telco localization technique called RLoc, which consists of machine-learning-based localization algorithm, detection algorithm to find flawed samples, and repair algorithm to replace outlier positions. Experimental results show that RLoc can greatly improve Telco location accuracy by taking into account spatio-temporal locality of MR locations and exploit trajectory context to detect and repair flawed positions.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2022)
Article
Operations Research & Management Science
Abhijit Gosavi, Vy K. Le
Summary: The emergence of Internet of Things and artificial intelligence in the era of Industry 4.0 has changed the decision-making process in production systems. The use of digital twins (DTs) enables many decisions to be made automatically with minimal human intervention. This article proposes a neural network (NN) approach to handle the issue of input data for reliability computations not satisfying a known distribution, and applies a machine learning-inspired algorithm to solve the underlying semi-Markov decision process. Numerical experiments demonstrate the effectiveness of the approach, and the convergence properties of the algorithm are analyzed mathematically.
ANNALS OF OPERATIONS RESEARCH
(2022)
Review
Health Care Sciences & Services
Stella C. Christopoulou
Summary: The application of Context Aware Computing (CAC) in healthcare has great potential to advance medical research and provide health services. Implemented by Evidence-Based Health Informatics (EBHI), CAC systems have shown improvements in healthcare provision, confirming their value and reliability. Evaluation of CAC systems in EBHI has positive effects on health status and the management of long-term diseases.
Review
Computer Science, Information Systems
Sascha Julian Oks, Max Jalowski, Michael Lechner, Stefan Mirschberger, Marion Merklein, Birgit Vogel-Heuser, Kathrin M. Moslein
Summary: This study conducts a large-scale literature review and develops a novel categorization for industrial cyber-physical systems (CPS), providing insights into future research needs and potentials.
INFORMATION SYSTEMS FRONTIERS
(2022)
Review
Chemistry, Analytical
Miroslaw Rucki
Summary: The paper provides a review of research reports on air gauging from 2012 to 2022, placing it in the context of Industry 4.0. Despite a decrease in published papers, researchers continue to improve air gauges in static and non-steady states. Digitalization, uncertainty estimation, calibration, and linearization were key areas of focus, with applications including real-time monitoring and in-process control. Integration with computer systems aligns air gauges with the Industry 4.0 concept.
Article
Automation & Control Systems
David Alfavo-Viquez, Mauricio-Andres Zamora-Hernandez, Jorge Azorin-Lopez, Jose Garcia-Rodriguez
Summary: This research proposes a method for analyzing fatigue in assembly operations by considering indicators such as eye aspect ratio, operator pose, and operating time. A dataset is generated and recorded from different perspectives to facilitate the analysis. A deep learning system is trained to recognize the sequence of operator actions, and a model is proposed for determining the level of fatigue by processing multimodal information.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Borja Bordel, Ramon Alcarria, Diego Martin, Diego Sanchez-de-Rivera
INTEGRATED COMPUTER-AIDED ENGINEERING
(2020)
Article
Computer Science, Theory & Methods
Borja Bordel, Ramon Alcarria, Tomas Robles
Summary: Blockchain technology provides trust among multiple independent agents in reaching consensus, making it ideal for critical data operations in future next-generation internet applications. However, some current Blockchain-supported proposals have security vulnerabilities, particularly susceptible to new cyberattacks, which require theoretical and experimental studies.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Borja Bordel, Ramon Alcarria, Tomas Robles
Summary: This paper presents the most recent solutions for user authentication in Industry 4.0 scenarios, which are based on capturing unique biological characteristics and using artificial intelligence and machine learning technologies for recognition. To monitor users in an unobtrusive manner, sensing and processing modules are connected through wireless communication technologies. However, resource-constrained sensors cannot implement common cryptographic techniques to protect users' information. Therefore, the paper proposes a new lightweight cryptographic approach to address the security issues in short-range wireless communications.
INTEGRATED COMPUTER-AIDED ENGINEERING
(2022)
Article
Chemistry, Analytical
Borja Bordel, Ramon Alcarria, Tomas Robles
Summary: Industry 4.0 is aimed at transforming the economical ecosystem by integrating new paradigms like cyber-physical systems or AI into production systems. A key benefit is improved efficiency, although software-level innovative algorithms are sensitive to data quality. This paper proposes a solution based on numerical algorithms with a predictor-corrector architecture to enhance data quality and reduce error probability in Industry 4.0 systems.
Article
Computer Science, Information Systems
Calimanut-Ionut Cira, Martin Kada, Miguel-angel Manso-Callejo, Ramon Alcarria, Borja Bordel Sanchez
Summary: In this study, a conditional Generative Adversarial Network is used to reconstruct road geometries and improve initial semantic segmentation results. The model achieved a performance improvement of 1.3% on unseen data compared to the initial results. Qualitative perceptual validation demonstrated significant improvements in large-scale post-processing using unsupervised generative learning.
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
(2022)
Article
Education, Scientific Disciplines
Borja Bordel, Ramon Alcarria, Tomas Robles, Diego Martin
Summary: After the sudden closure of higher education institutions in Spain, particularly in Madrid, due to the outbreak of COVID-19 in 2020, professors in engineering education quickly adapted different teaching methods, impacting students differently and revealing a gender gap in computer engineering education.
INTERNATIONAL JOURNAL OF ENGINEERING PEDAGOGY
(2021)
Article
Computer Science, Artificial Intelligence
Alvaro Sanchez-Picot, Diego Martin, Borja Bordel, Ramon Alcarria
Summary: This research proposes the application of a prosumer model to allow domain experts to create simple services without programming knowledge, in order to facilitate access to information and creation of new knowledge. The empirical study and case study demonstrate the feasibility of this approach.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Borja Bordel, Ramon Alcarria, Tomas Robles
Summary: 6G networks must operate in the Ka-band, which is also used by scientific instruments and can cause interference. In this paper, a new solution is proposed to adjust the transmission power and ensure the desired QoS level by analyzing the positions of user devices using a three-dimensional model. When the transmission power is high enough to interfere with scientific instruments, a scheduling algorithm based on swarm intelligence is triggered to optimize the distribution of time slots and radio channels. Experimental validation shows that the proposed solution can achieve complete coexistence with an interference level of -26 dBm and a QoS above 95% of the expected level.
Article
Computer Science, Information Systems
Llinet Benavides Cesar, Miguel Angel Manso Callejo, Calimanut-Ionut Cira, Ramon Alcarria
Summary: This paper presents a methodology for solar irradiance prediction using artificial intelligence and introduces a large-scale dataset, CyL-GHI, which contains refined data from 37 stations in the Spanish region of Castile and Leon. The dataset covers 18 years with a temporal resolution of 30 minutes. Popular artificial intelligence algorithms were optimized and tested on the dataset, providing performance values for comparison with other forecasting implementations.
Proceedings Paper
Computer Science, Information Systems
Borja Bordel, Ramon Alcarria
Summary: Cyberprotection in the context of IoT includes security, privacy and trust. While security and privacy technologies have been extensively explored, new challenges in terms of trust and reputation arise. This paper proposes a distributed architecture based on Blockchain technologies to provide trust and reputation services in IoT systems.
MOBILE INTERNET SECURITY, MOBISEC 2021
(2022)
Article
Engineering, Electrical & Electronic
Miguel-Angel Manso-Callejo, Calimanut-Ionut Cira, Ramon Pablo Alcarria Garrido, Francisco Javier Gonzalez Matesanz
Summary: Deep learning applied to feature extraction and mapping from high-resolution images shows potential in improving terrain mapping processes. Experiences have been applied on a small scale with high expectations for country-wide applicability. The methodology can also be adapted for large-scale efficient extraction of geospatial elements with automated procedures.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
(2021)
Article
Computer Science, Information Systems
Borja Bordel, Ramon Alcarria, Tomas Robles
Summary: This study employs supervisory control techniques to ensure the correct execution of distributed and choreographed processes in Industry 4.0 scenarios. The innovative use of soft models and deformation indicators allows the control solution to be implemented not only in traditional industrial scenarios, but also in future engineered applications.
Article
Computer Science, Information Systems
Borja Bordel, Ramon Alcarria, Tomas Robles, Marcos Sanchez Iglesias
Summary: Future IoT systems will rely on emerging 5G networks, but also face new security challenges. Traditional encryption techniques will no longer be safe and efficient in resource-constrained IoT systems in the future. Therefore, a new mechanism combining digital watermarking techniques and lightweight cryptographic technologies has been proposed to protect, authenticate, and anonymize data.
Article
Environmental Studies
Calimanut-Ionut Cira, Miguel-angel Manso-Callejo, Ramon Alcarria, Teresa Fernandez Pareja, Borja Bordel Sanchez, Francisco Serradilla
Summary: The study introduces a technique based on generative learning and image-to-image translations, trained on a novel dataset to improve the accuracy of semantic segmentation predictions of road surface areas in high-resolution aerial imagery. The model shows significant improvement in the Intersection over Union (IoU) score on a novel testing set and visually assessed effectiveness with great improvements over initial predictions.