Search Results (196)

As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems and their virtual counterparts. The proposed framework advances toward a fully functional digital twin by integrating real-time perception and intuitive human–robot interaction capabilities. The framework is applied to a hospital test lab scenario, where a YuMi robot automates the sorting of microscope slides. The system incorporates a RealSense D435i depth camera for environment perception, Isaac Sim for virtual environment synchronization, and a locally hosted large language model (Mistral 7B) for interpreting user voice commands. These components work together to achieve bi-directional synchronization between the physical and digital environments. The framework was evaluated through 20 test runs under varying conditions. A validation study measured the performance of the perception module, simulation, and language interface, with a 60% overall success rate. Additionally, synchronization accuracy between the simulated and physical robot joint movements reached 98.11%, demonstrating strong alignment between the digital and physical systems. By combining local LLM processing, real-time vision, and robot simulation, the approach enables untrained users to interact with collaborative robots in dynamic settings. The results highlight its potential for improving flexibility and usability in industrial automation. Full article

Thermal control in rolling mills motors is gaining importance as more and more hard-to-deform steel grades are rolled. The capabilities of diagnostics monitoring also expand as digital IIoT-based technologies are adopted. Electrical drives in modern rolling mills are based on synchronous motors with frequency regulation. Such motors are expensive, while their reliability impacts the metallurgical plant output. Hence, developing the on-line temperature monitoring systems for such motors is extremely urgent. This paper presents a solution applying to synchronous motors of the upper and lower rolls in the horizontal roll stand of plate mill 5000. The installed capacity of each motor is 12 MW. According to the digitalization tendency, on-line monitoring systems should be based on digital shadows (coordinate observers) that are similar to digital twins, widely introduced at metallurgical plants. Modern reliability requirements set the continuous temperature monitoring for stator and rotor windings and iron core. This article is the first to describe a method for calculating thermal loads based on the data sets created during rolling. The authors have developed a thermal state observer based on four-mass model of motor heating built using the Simscape Thermal Models library domains that is part of the MATLAB Simulink. Virtual adjustment of the observer and of the thermal model was performed using hardware-in-the-loop (HIL) simulation. The authors have validated the results by comparing the observer’s values with the actual values measured at control points. The discrete masses heating was studied during the rolling cycle. The stator and rotor winding temperature was analysed at different periods. The authors have concluded that the motors of the upper and lower rolls are in a satisfactory condition. The results of the study conducted generally develop the idea of using object-oriented digital shadows for the industrial electrical equipment. The authors have introduced technologies that improve the reliability of the rolling mills electrical drives which accounts for the innovative development in metallurgy. The authors have also provided recommendations on expanded industrial applications of the research results. Full article

Ports are vital nodes in global trade networks but are also significant contributors to greenhouse gas emissions. Their transition toward sustainable, nearly zero-energy operations require comprehensive and structured strategies. This study proposes a practical and scalable framework to support the energy decarbonization of ports through the phased integration of hybrid renewable energy systems (HRES) and energy storage systems (ESS). Emphasizing a systems-level approach, the framework addresses key aspects such as energy demand assessment, resource potential evaluation, HRES configuration, and ESS sizing, while incorporating load characterization protocols and decision-making thresholds for technology deployment. Special consideration is given to economic performance, particularly the minimization of the Levelized Cost of Energy (LCOE), alongside efforts to meet energy autonomy and operational resilience targets. In parallel, the framework integrates digital tools, including smart grid infrastructure and digital shadow technologies, to enable real-time system monitoring, simulation, and long-term optimization. It also embeds mechanisms for regulatory compliance and continuous adaptation to evolving standards. To validate its applicability, the framework is demonstrated using a representative case study based on a generic port profile. The example illustrates the transition process from conventional energy models to a sustainable port ecosystem, confirming the framework’s potential as a decision-making tool for port authorities, engineers, and policymakers aiming to achieve effective, compliant, and future-proof energy transitions in maritime infrastructure. Full article

Background: Ensuring the safety of patients in the operating room, through the monitoring and prevention of adverse events is a central priority of healthcare delivery. In the professionalization of operating room nurses, the processes of identifying, assessing, developing, monitoring, and certifying transversal competencies are crucial. While national and international frameworks have attempted to define such competencies, they often vary in scope and remain inconsistently integrated into education and clinical practice. There is, therefore, a need for a comprehensive and structured identification of transversal competencies relevant to both perioperative and perianesthesiological nursing roles. Objectives: To formulate a validated and structured repertoire of transversal competencies demonstrated by operating room nurses in both perioperative and perianesthesiological contexts. Methods: A qualitative descriptive design was adopted, combining shadowed observation with Hierarchical Task Analysis (HTA). A convenience sample of 46 participants was recruited from a university and a public hospital in Italy. Data were collected between September 2021 and June 2023 and analyzed using content analysis and data triangulation. Results: Through a qualitative, inductive and iterative approach the study identified 15 transversal competencies, 50 sub-competencies, and 153 specific tasks and activities. Specifically, operating room nurses working in perioperative and perianesthesiological roles presented the following transversal competencies: communication and interpersonal relationships, situation awareness, teamwork, problem solving and decision-making, self-awareness, coping with stressors, resilience and fatigue management, leadership, coping with emotions, task and time management, ethical and sustainable thinking, adaptation to the context, critical thinking, learning through experiences, and data, information and digital content management. Each competency was associated with specific tasks observed. Conclusions: This framework complements the existing repertoire of technical-specialist competencies by integrating essential transversal competencies. It serves as a valuable tool for the assessment, validation, and certification of competencies related to patient and professional safety, emotional well-being, relational dynamics, and social competencies. The findings underscore the need for academic institutions to revise traditional training models and embed transversal competencies in both undergraduate and postgraduate nursing education. Full article

Digital content can bring many advantages to a sustainable world, such as higher accessibility, flexibility, reduction in natural resource consumption, or storage issues. Probably one of the biggest advantages is related to the possibility of preserving any type of document without the use of typography machines, as digital content does not suffer from deterioration. This approach can prove to be extremely useful in the case of important documents for human culture and history. Our paper presents some of the most successful cases of digitization of physical papers, including books, newspapers, and old manuscripts. The analysis of international models of digital libraries is followed by a case study of digitization in Romania, with an analysis of their future perspectives on sustainability. Full article

This article proposes and validates a novel methodology for automated simulation model generation of production systems based on monitoring sensors and actuator states controlled by Programmable Logic Controllers during regular operations. Although conventional Discrete Event Simulation is essential for material flow analysis and digital experimentation in Industry 4.0, it remains a resource-intensive and time-consuming endeavor—especially for small and medium-sized enterprises. The approach introduced in this research eliminates the need for prior system knowledge, physical inspection, or modification of existing control logic, thereby reducing human involvement and streamlining the model development process. The results confirm that essential structural and operational parameters—such as process routing, operation durations, and resource allocation logic—can be accurately inferred from runtime data. The proposed approach addresses the challenge of simulation model obsolescence caused by evolving automation and shifting production requirements. It offers a practical and scalable solution for maintaining up-to-date digital representations of manufacturing systems and provides a foundation for further extensions into Digital Shadow and Digital Twin applications. Full article

The Fourth Industrial Revolution has introduced “shared manufacturing” as a key concept that leverages digitalization, IoT, blockchain, and robotics to redefine the production and delivery of manufacturing services. This paper presents a novel approach to decentralized warehouse management integrating Large Language Models (LLMs) into the decision-making processes of autonomous agents, which serves as a proof of concept for shared manufacturing. A multi-layered system architecture consisting of physical, digital shadow, organizational, and protocol layers was developed to enable seamless interactions between parcel and warehouse agents. Shared Warehouse game simulations were conducted to evaluate the performance of LLM-driven agents in managing warehouse services, including direct and pooled offers, in a competitive environment. The simulation results show that the LLM-controlled agent clearly outperformed traditional random strategies in decentralized warehouse management. In particular, it achieved higher warehouse utilization rates, more efficient resource allocation, and improved profitability in various competitive scenarios. The LLM agent consistently ensured optimal warehouse allocation and strategically selected offers, reducing empty capacity and maximizing revenue. In addition, the integration of LLMs improves the robustness of decision-making under uncertainty by mitigating the impact of randomness in the environment and ensuring consistent, contextualized responses. This work represents a significant advance in the application of AI to decentralized systems. It provides insights into the complexity of shared manufacturing networks and paves the way for future research in distributed production systems. Full article

With its fast advancements, cloud computing opens many opportunities for research in various applications from the robotics field. In our paper, we further explore the prospect of integrating Cloud AI object recognition services into an industrial robotics sorting task. Starting from our previously implemented solution on a digital twin, we are now putting our proposed architecture to the test in the real world, on an industrial robot, where factors such as illumination, shadows, different colors, and textures of the materials influence the performance of the vision system. We compare the results of our suggested method with those from an industrial machine vision software, indicating promising performance and opening additional application perspectives in the robotics field, simultaneously with the continuous improvement of Cloud and AI technology. Full article

In the context of sea levels rising, monitoring spatial and temporal topographic changes along coastal dunes is crucial to understand their dynamics since they represent natural barriers against coastal flooding and large sources of sediment that can mitigate coastal erosion. Different technologies are currently used to monitor coastal dune topographic changes (GNSS, UAV, airborne LiDAR, etc.). Satellites recently emerged as a new source of topographic data by providing high-resolution images with a rather short revisit time at the global scale. Stereoscopic or tri-stereoscopic acquisition of some of these images enables the creation of 3D models using stereophotogrammetry methods. Here, the Ames Stereo Pipeline was used to produce digital elevation models (DEMs) from tri-stereo panchromatic and high-resolution Pleiades images along three 19 km long stretches of coastal dunes in SW France. The vertical errors of the Pleiades-derived DEMs were assessed by comparing them with DEMs produced from airborne LiDAR data collected a few months apart from the Pleiades images in 2017 and 2021 at the same three study sites. Results showed that the Pleiades-derived DEMs could reproduce the overall dune topography well, with averaged root mean square errors that ranged from 0.5 to 1.1 m for the six sets of tri-stereo images. The differences between DEMs also showed that Pleiades images can be used to monitor multi-annual coastal dune morphological changes. Strong erosion and accretion patterns over spatial scales ranging from hundreds of meters (e.g., blowouts) to tens of kilometers (e.g., dune retreat) were captured well, and allowed to quantify changes with reasonable errors (30%). Furthermore, relatively small averaged root mean square errors (0.63 m) can be obtained with a limited number of field-collected elevation points (five ground control points) to perform a simple vertical correction on the generated Pleiades DEMs. Among different potential sources of errors, shadow areas due to the steepness of the dune stoss slope and crest, along with planimetric errors that can also occur due to the steepness of the terrain, remain the major causes of errors still limiting accurate enough volumetric change assessment. However, ongoing improvements on the stereo matching algorithms and spatial resolution of the satellite sensors (e.g., Pleiades Neo) highlight the growing potential of Pleiades images as a cost-effective alternative to other mapping techniques of coastal dune topography. Full article

Monitoring volumetric glacier change requires cost-effective and accessible methods to generate multi-temporal digital elevation models (DEMs). We present Planet4Stereo, an open-source photogrammetry pipeline developed to generate DEMs from low-cost PlanetScope images, exploiting the high temporal repetition rate of the constellation for stereo reconstruction. Our approach enables multi-temporal 3D change detection using the freely available NASA Ames Stereo Pipeline (ASP), making the pipeline particularly valuable for geoscientists. We applied Planet4Stereo in two case studies: the Shisper glacier (Karakoram, Pakistan) for surge investigation and the Bøverbrean glacier (Smørstabb Massif, Norway) for change detection. The results from Shisper are in good agreement with previous studies using the same images but proprietary methods. The accuracy of the DEM of Bøverbrean was evaluated using high-precision LiDAR data, revealing varying deviations across terrain types, with higher errors in steep shadowed areas. Additionally, the change detection analysis confirmed the expected glacier retreat. Our results show that Planet4Stereo produces DEMs with comparable accuracy to commercial software and is freely accessible and easy to use. As both ASP and the PlanetScope satellites evolve, future work could refine the pipeline’s stereo-matching capabilities and evaluate its performance with next-generation satellite data. Full article

Cloud detection is a crucial task in satellite remote sensing, influencing applications such as vegetation indices, land use analysis, and renewable energy estimation. This study evaluates the performance of cloud masks generated for KOMPSAT-3 and KOMPSAT-3A imagery using the DeepLabV3+ deep learning model with a ResNet-101 backbone. To overcome the limitations of digital number (DN) data, Top-of-Atmosphere (TOA) reflectance was computed and used for model training. Comparative analysis between the DN and TOA reflectance demonstrated significant improvements with the TOA correction applied. The TOA reflectance combined with the NDVI channel achieved the highest precision (69.33%) and F1-score (59.27%), along with a mean Intersection over Union (mIoU) of 46.5%, outperforming all the other configurations. In particular, this combination was highly effective in detecting dense clouds, achieving an mIoU of 48.12%, while the Near-Infrared, green, and red (NGR) combination performed best in identifying cloud shadows with an mIoU of 23.32%. These findings highlight the critical role of radiometric correction and optimal channel selection in enhancing deep learning-based cloud detection. This study demonstrates the crucial role of radiometric correction, optimal channel selection, and the integration of additional synthetic indices in enhancing deep learning-based cloud detection performance, providing a foundation for the development of more refined cloud masking techniques in the future. Full article

In the digital 3D reconstruction of the shapes and surface reflectance of ancient paintings and drawings using Photometric Stereo (PS) techniques, normal integration is a key step. However, difficulties in locating light sources, non-Lambertian surfaces, and shadows make the results of this step inaccurate for such artworks. This paper presents a solution for PS to overcome this problem based on some enhancement of the normal integration process and the accurate measurement of Points of Interest (PoIs). The mutual positions of the LED lights, the camera sensor, and the acquisition plane in two custom-designed stands, are measured in laboratory as a system calibration of the 3D acquisition workflow. After an introduction to the requirements and critical issues arising from the practical application of PS techniques to artworks, and a description of the newly developed PS solution, the measurement process is explained in detail. Finally, results are presented showing how the normal maps and 3D meshes generated using the measured PoIs’ positions, and further minimized using image processing techniques, which significantly limits outliers and improves the visual fidelity of digitized artworks. Full article

The research presented in this article seeks to identify the possible causes of reinforcement shadows (RS) on the surface of concrete test specimen produced under laboratory conditions. Different hypotheses about RS were selected based on factory practices and simulated in the study. The test specimens were cast horizontally in contact with steel form-facing surfaces coated with a water-soluble release agent. In addition, two scenarios were analysed during specimen production: reinforcing mesh was fixed using plastic spacers or tie wire. The analysis of the reinforcement shadows was based on visual inspection, taking photos, surface moisture content measurements, and colour variation analysis using the Natural Colour System. It was concluded that RS, which are typically characterized by darker lines, can be defined by the percentage of black colour present in the shadowed area compared to the percentage of black colour in the surrounding area. This percentage can be quickly assessed on a factory scale using digital colour readers that provide timely information. The reduced concrete cover thickness from 35 mm to 10 mm revealed light horizontal dark lines on the exposed surface. It was hypothesised that the gap of less than 10 mm between the reinforcing bars and the steel form-facing plate, along with the sieving effect of the fresh concrete, can retard the cement paste hydration process, resulting in unhydrated ferrite phases that contribute to the dark colour of the unhydrated cement. The release agent sprayed on the steel form-facing surface straight through the reinforcing mesh created a RS effect of the reinforcement on the exposed concrete surface. The absence of a release agent under steel rebars decreased the wettability at the interface between the formwork and fresh concrete, resulting in dark lines during the curing process. It is important to avoid such cases when manufacturing precast reinforced concrete elements. Quantitatively assessing RS and proposing a standardized method for calculation and categorization could be a new research direction in the future. Full article

The preservation of intangible cultural heritage (ICH) has transitioned from “static” and “living” approaches to a “digital ecosystem”, becoming a significant topic of anthropological research. This study, adopting an anthropological perspective, integrates sentiment semantic analysis with user identity classification to propose the Identity and Sentiment-Centered Framework for Intangible Cultural Heritage (ISC-ICH). Drawing on four types of ICH museums in Eastern Sichuan, China—Nanchong Langzhong Wang Shadow Puppetry Museum, Bazhong Pingchang Fanshan Jiaozi Base, Guang’an Eastern Sichuan Folk Museum, and Dazhou ICH Exhibition Hall—as case studies, this research analyzes the core factors contributing to the audience’s sense of local identity, including its composition, emotional needs, and cultural interaction. The findings reveal that: (1) “Explorers” and “Experience Seekers” constitute the primary audience groups, with their emotional evaluations closely tied to cultural depth and interactivity. (2) The digital transformation of ICH museums faces challenges such as resource limitations, festival-centric phenomena, the rise of “internet celebrity” trends, and technological homogenization. This paper introduces a culturally tailored corpus and a comprehensive evaluation framework, highlighting the dynamic interaction between ICH and its audience. Additionally, it proposes effective digital strategies to enhance the social and cultural identity of ICH museums in peripheral regions. Full article

This research aims to demonstrate a machine learning (ML) algorithm-based indoor air quality (IAQ) monitoring and forecasting system for a public sector building use case. Such a system has the potential to automate existing heating/ventilation systems, therefore reducing energy consumption. One of Riga Technical University’s campus buildings, equipped with around 128 IAQ sensors, is used as a test bed to create a digital shadow including a comparison of five ML-based data prediction tools. We compare the IAQ data prediction loss using Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) error metrics based on real sensor data. Gated Recurrent Unit (GRU) and Kolmogorov–Arnold Networks (KAN) prove to be the most accurate models regarding the prediction error. Also, GRU proved to be the most efficient model regarding the required computation time. Full article