Search Results (1,324)

This paper proposes a digital twin (DT)-enabled edge computing framework for healthcare task offloading in smart medical environments. The DT maintains a continuously synchronized virtual representation of the physical edge system, capturing queue states, resource utilization, and energy dynamics. Based on this virtual state, a deterministic optimization model is formulated to support real-time offloading and scheduling decisions under latency, energy, and fairness constraints. Unlike prediction-only approaches, the proposed DT operates in a closed-loop manner, where state estimation and synchronization directly influence scheduling feasibility and system performance. The offloading problem is formulated as a mixed-integer linear programming (MILP) model to jointly optimize task allocation, delay minimization, and energy efficiency. The simulated workload includes 10,000 heterogeneous healthcare applications. At each time slot, one to five tasks are generated and uniformly selected from ECG, video-processing, or medical-imaging workloads, with average task sizes of 90 KB, 280 KB, and 150 KB, respectively. This setup aims to emulate diverse real-world healthcare edge workloads with varying communication and computation demands. The proposed approach significantly reduces average latency by up to 57%, eliminates task drops in all evaluated scenarios, and improves load balancing compared with hospital-only and round-robin baselines. Although total energy consumption increases moderately, energy efficiency per completed task improves due to more effective scheduling by stability, reducing deadline violations and enhancing resource utilization. We further analyze the impact of DT freshness and updated frequency and show that outdated or misaligned DT updates can degrade performance by increasing delay and leading to suboptimal decisions, while overly frequent updates introduce additional coordination overhead. These results highlight the importance of jointly designing DT synchronization mechanisms and optimization-based scheduling strategies for reliable and cost-efficient healthcare edge systems. Full article

Real-time monitoring of operator fatigue is critical for ensuring operational safety and reliability in dynamic industrial environments, especially under swing conditions such as offshore floating nuclear power platforms. This study proposes a multimodal fatigue monitoring framework based on surface electromyography (sEMG) and heart rate variability (HRV) sensors for real-time fatigue recognition. Experiments were conducted on a six-degree-of-freedom motion platform with three swing levels, involving 23 participants performing simulated emergency operation tasks. Four machine learning models (Naive Bayes, K-Nearest Neighbor, Multilayer Perceptron, and Random Forest) were employed for fatigue state classification. The results show that the Random Forest model achieves the best performance, with an overall accuracy of 98.2%, 100% true positive rate for the normal state and fatigue, and 66.7% true precision for severe fatigue. The proposed multimodal fusion method effectively suppresses motion artifacts and improves recognition robustness under swing interference. Rigorous subject-level stratified cross-validation eliminates sample leakage risks; bootstrap confidence intervals and pairwise significance tests statistically verify model performance differences; class imbalance mitigation strategies are deployed to quantify uncertainty for the scarce severe-fatigue category; literature-supported Borg CR-10 grading thresholds are validated via retrospective cutoff sensitivity analysis to guarantee reliable fatigue labeling. This sensor-based intelligent monitoring system provides a reliable solution for real-time fatigue detection of operators in dynamic digital industrial scenarios, supporting accident prevention and sustainable operation of high-risk industrial systems. Full article

The full-scope simulator plays a critical role in nuclear power plant emergency drills, personnel training, and accident analysis. Traditional system programs lack sufficient computational performance to meet real-time requirements when simulating complex accident scenarios in reactor systems. This study focuses on the small-break loss-of-coolant accident (SBLOCA) in nuclear power plants, generating large-scale datasets through digital simulations. After data preprocessing and normalization, a light gradient boosting decision tree (LightGBM) regression model was developed using machine learning algorithms. SHAP (SHapley Additive exPlanations) analysis identified the contributing factors, enabling the model to predict key parameters such as peak fuel cladding temperature, primary reactor coolant pressure, and pressurizer water level. The model achieved a mean square error (MSE) below 0.002 and a coefficient of determination (R²) exceeding 0.98, with a prediction speed approximately 32,500 times faster than traditional system programs, requiring less than $4\times {10}^{-4}$ seconds per data point. This study provides a novel solution for complex condition simulations in nuclear power plant full-scope simulators. Full article

The transition to Beyond fifth generation of wireless networks (B5G) and sixth generation of wireless networks (6G) exposes the severe interference and coverage limitations of conventional cell-centric architectures. To overcome these bottlenecks, this paper presents a scalable four-layer radio network planning framework that jointly optimizes the deployment of distributed active antenna arrays and passive reconfigurable intelligent surfaces (RISs). The proposed framework integrates a digital twin (DT) loop within an Open-RAN (O-RAN) architecture, employing multi-agent deep reinforcement learning (MADRL) and fractional programming (FP) for real-time joint active and passive beamforming optimization. Extensive Monte Carlo simulations in a dense urban environment demonstrate a 45% increase in spectral efficiency, a 30% reduction in uplink interference, and an 84% reduction in coverage holes compared to legacy 5G networks. Ultimately, these results provide network operators with a cost-effective, standards-compliant blueprint to extend non-line-of-sight (NLOS) coverage by 40% without incurring the prohibitive capital expenditure (CAPEX) of dense active hardware deployments. Furthermore, the proposed architecture demonstrates a competitive 10–15% margin of improvement in spectral efficiency over recent state-of-the-art DRL-based RIS frameworks. Full article

Rapidly developing, mid-sized Canadian municipalities often suffer from a deficit in dedicated modernization capacity, leaving public infrastructure lagging behind growth and reliant on historically “grandfathered” legacy solutions. To overcome the lack of empirical, data-backed planning in these regions, this paper proposes an agile, data-driven smart city framework centered around a localized digital twin (DT) environment. The framework is evaluated through a case study of a proposed new public transit route in Abbotsford, British Columbia, a rapidly expanding city grappling with decentralized commercial zones and low-density sprawl. Our approach synthesizes heterogeneous, multi-source spatial data, including regional commuter trajectories, real-time Abbotsford International Airport (YXX) flight schedules, and points of interest (POI) business densities, to map high-resolution hourly temporal variations in traffic conditions. These streams feed into a virtual simulation framework that evaluates operational cost–benefit trade-offs for proposed transit routes. Crucially, this framework serves as a living, continuously updated system that enables resource-constrained cities to dynamically simulate transit networks as commercial footprints and transit volumes evolve. Finally, we discuss the roadmap for this framework, detailing how integrating predictive AI models and gamified interfaces can democratize urban planning, enabling municipal stakeholders and non-technical operators to interactively co-design public transit systems. Full article

The development of ferrous metallurgy, as with most industrial sectors, is progressing toward the adoption of IIoT technologies and the development of digital automatic control systems for electrotechnical and mechatronic complexes. This direction is implemented within the paradigm of digital twins (DTs), which enable the use of advanced design methods, virtual commissioning, and maintenance. The concept of relatively simple object-oriented DTs created using available software and applicable at individual stages of the equipment lifecycle has been substantiated. The relationship between the object-oriented approach and M. Grieves’ classification system has been determined. The contribution of this paper lies in the fact that this problem is addressed for the first time using the example of electrotechnical systems of rolling mills. Definitions of DTs are provided, along with a brief overview of digital platforms developed by leading manufacturers of metallurgical equipment. The development of object-oriented DTs based on Simulink Real-Time modules and domains of the Simscape library is substantiated. A methodology for their virtual tuning using Hardware-in-the-Loop (HIL) simulation is proposed. The results of developing an aggregated DT of interconnected electric drives of the upper and lower rolls (UMD and LMD) of the horizontal stand of the 5000 plate rolling mill are presented. An example of DT implementation in a programmable logic controller (PLC) based on a multicore processor using CODESYS 3.5 software is provided. The advantages and prospects of this approach are discussed. Validation of the results is performed by comparing processes during virtual tuning with oscillograms obtained from the actual mill. Satisfactory accuracy is confirmed, and recommendations for the broader application of the developed object-oriented digital twins are given. Full article

Combine harvesters operate under harsh, time-varying field conditions, where unplanned downtime causes significant timeliness losses. This review systematically examines advances in structural fault diagnosis for combine harvesters, tracing the evolution from passive structural redundancy to active state perception and simulation-driven health management. Following a systematic search and screening methodology, the review analyzes the boundaries of structural optimization under fluctuating field conditions, evaluates traditional machine learning (ML) methods with handcrafted features, and surveys deep learning (DL) and multi-sensor fusion advances across vibration, acoustic, and visual modalities. A structured comparison across feature learning, generalization, diagnostic coverage, computational cost, and interpretability highlights the complementary strengths of traditional ML and DL paradigms. Key deployment challenges are identified: weak fault features under strong field noise, data distribution shift under multi-condition coupling, extreme sample scarcity and class imbalance, limited onboard computing and real-time latency constraints, interpretability gaps, hydraulic and pneumatic diagnostic neglect, functional safety compliance (ISO 25119), and the heightened reliability demands of unmanned autonomous operation. To address these challenges, future directions include physics-informed hybrid models, self-supervised pre-training and few-shot learning, lightweight edge inference, staged pre-deployment verification, model updating and lifelong learning strategies, cross-energy-domain diagnosis with fault-tolerant control, human-factors-aware interface design, and digital twin (DT) augmentation—the latter regarded as a strategic research vision requiring incremental validation rather than a near-term deployable solution. This review provides a reference for enhancing combine harvester mission reliability through the integration of AI-enabled perception, multi-source fusion, and simulation-driven health management. Full article

Testing advanced driver assistance systems (ADAS) under rare or safety-critical conditions is impractical at full scale: track campaigns are expensive, time-intensive, and cannot easily reproduce low-probability events. Scaled cyber–physical testbeds offer a more accessible path by coupling miniature vehicle platforms with virtual simulation environments, but the field has no unified review that covers co-simulation architectures, indoor localization, and ground truth strategies in a single treatment. This paper addresses that gap with a PRISMA 2020-compliant systematic review of 92 primary sources selected from 984 records identified across IEEE Xplore and Scopus. Three topic areas are examined: real-time co-simulation architectures built on AirSim, CARLA, Gazebo, and LGSVL, compared for ROS 2 integration, synchronisation model, and edge hardware suitability; three indoor localization methods, namely AprilTag fiducial tracking, Visual Simultaneous Localization and Mapping (VSLAM), and Ultra-Wideband (UWB) radio positioning, evaluated against shared accuracy, latency, infrastructure, and robustness criteria; and existing ground truth strategies for indoor localization benchmarking. A consistent finding across the corpus is that no controlled cross-method localization comparison exists for scaled testbeds. To address this, we introduce the Programmable Ground Truth Reference System (PGTRS), which renders spatial references on a programmable LED floor panel at a pixel pitch of approximately 3.9 mm, targeting sub-centimetre ground truth accuracy without dedicated motion-capture infrastructure. The concept is demonstrated within a 1:14 scale Digital Driving Twin (DDT) testbed built at the University of Applied Sciences Wildau at a hardware cost of approximately €6576. Design guidelines and open research challenges are discussed. Full article

Digital Twins (DT) are increasingly positioned as key enablers of sustainable urban development, yet many implementations remain fragmented, technology-driven, and weakly connected to clearly defined decision-making needs. The present study develops a structured information governance framework for DTs, drawing on the principles of ISO 19650. The framework establishes a traceable hierarchy linking organizational objectives, DT use cases, information requirements, the Level of Information Need, information exchange processes and machine-readable Information Delivery Specifications. Its purpose is to ensure that information is clearly defined, exchanged, validated, and maintained in a consistent and verifiable manner before it is used for monitoring, simulation, predictive analytics, or decision support. The proposal is illustrated through an urban air-quality Digital Shadow demonstrator integrating BIM, GIS, weather services, and a real-time visualization environment. Candidate information-quality indicators are also introduced and demonstrated through synthetic calculations intended to explain their application. Neither the demonstrator nor the calculated KPI values constitute validation of the framework or evidence of improved operational performance. Instead, they establish a structured basis for future testing in operational Urban Digital Twin (UDT) implementations. The contribution lies in integrating established BIM concepts into a single DT-oriented traceability chain rather than introducing them as new standards or methods. Full article

To address the two fundamental contradictions in distribution network digital simulation systems: “diversified simulation requirements versus standardized configuration” and “massive simulation outputs versus sparse decision-making information”, this paper builds upon existing work on Common Information Model (CIM)-based automatic simulation model generation and dynamic voltage regulation simulation and further constructs a user-oriented intelligent simulation service layer. This layer is collaboratively composed of an Orchestration_Agent (simulation orchestration agent) and an Analysis_Agent (result analysis agent), tasked with three responsibilities: based on multi-level simulation granularity (L0–L3) and a simulation template library, leveraging a large language model (LLM) to achieve natural language requirements parsing and automatic workflow orchestration; based on a simulation knowledge graph, implementing parameter recommendation, verification, and anomaly-adaptive recovery for process control; and based on a hybrid architecture of rule templates, statistical analysis, and causal graph models, achieving automatic result analysis, root cause reasoning, and structured report generation, with case feedback driving knowledge base iteration. Validation was conducted on data from a real 10 kV feeder with 91 distribution transformer areas over 30 consecutive days (2880 time cross-sections): comprehensive requirements-parsing accuracy of 96.3%, automatic parameter configuration coverage rate of 94.7%, anomaly identification recall/precision of 94.0%/96.9%, root cause reasoning accuracy of 92.1%, and the median end-to-end time per simulation shortened from approximately 36 min under the manual mode to 4.7 min. The results demonstrate that the proposed service layer provides a viable engineering technology pathway for the evolution of distribution network simulation from tool-oriented to service-oriented. Full article

This paper presents the design, implementation, and validation of a mobile Real-Time Location System (RTLS) based on Ultra-Wideband (UWB) technology for acquiring and transforming production and logistics data in an industrial environment. The aim is to obtain accurate real-time information on the movement of workers and material flows to support data-driven optimization within the Industry 4.0 framework. The proposed solution introduces a mobile RTLS architecture enabling flexible deployment without permanent infrastructure changes. The system was experimentally validated in a manufacturing enterprise, where UWB anchors and wearable tags were used to monitor six operators and handling equipment during a 12 h production shift. The collected data were analyzed using trajectory mapping, heatmaps, and worker activity analysis to identify inefficiencies in the existing production layout. The identified bottlenecks and unnecessary worker movements were subsequently used to redesign the production layout in Tecnomatix Process Simulate. Simulation results demonstrated a 51.9% reduction in worker travel distance, while transportation and waiting activities, which accounted for approximately 24% of the original production lead time, were significantly reduced in the proposed layout. The results confirm that UWB-based RTLS provides reliable input data for simulation-driven manufacturing layout redesign and supports objective decision-making in digital manufacturing environments. Full article

High renewable penetration and large-scale green hydrogen production are accelerating the formation of the new-type power system (NTPS), in which electrical dispatch, electrolysis, hydrogen storage, fuel-cell reconversion, and flexible demand must be coordinated under nonlinear network physics and uncertain renewable, load, and hydrogen-demand trajectories. This study develops a physics-informed distributionally robust multi-agent reinforcement learning (PI-DRO-MARL) framework for coordinated NTPS operation with integrated electricity–hydrogen coupling. The operational objective is to minimize worst-case expected operating cost, including generation and grid-exchange cost, electrolysis and hydrogen-delivery cost, storage degradation, renewable curtailment, and load- or hydrogen-shedding penalties, while satisfying AC power-flow balance, voltage limits, line-loading limits, ramping limits, battery state-of-charge constraints, hydrogen-storage dynamics, and electrolysis/fuel-cell conversion constraints. The framework embeds physics-informed residuals and projection operators into a centralized-training decentralized-execution architecture; represents renewable, electrical-load, hydrogen-demand, and price uncertainty through statistically calibrated Wasserstein ambiguity sets; and trains agents with robust value estimation and feasibility-aware action correction. Validation is conducted on a modified IEEE 33-bus distribution network coupled with a 12-node hydrogen system, with additional scalability checks on modified IEEE 69-bus and IEEE 123-node reference systems. Across ten random seeds, the primary case shows an operating cost of USD 8850 with a 95% confidence interval of USD 8770–8940, a mean constraint-violation rate of 0.37%, and a shifted-scenario cost increase of 12.6%, outperforming deterministic optimization, stochastic programming, standard reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), multi-agent deep deterministic policy gradient (MADDPG), constrained RL, safe RL, and robust RL baselines. Ablation, Wasserstein-radius, time-step, and stress-test analyses further show that distributional robustness, physics-informed projection, and multi-agent coordination provide distinct and complementary benefits. The results support PI-DRO-MARL as a simulation-validated architecture for real-time, uncertainty-aware NTPS dispatch, while field deployment still requires digital-twin calibration, hardware-in-the-loop testing, and site-specific operational validation. Full article

The real-time optimization of indoor thermal comfort and ventilation efficiency in offices is limited by the high computational cost of traditional Computational Fluid Dynamics (CFD) simulations (single simulation taking hours to days). Furthermore, the strong nonlinearity and coupling characteristics of temperature fields further increase the prediction difficulty of surrogate models. This study proposes a two-stage CFD surrogate model that maps five-dimensional operating condition parameters (supply temperature, supply velocity and position (x, y, z)) to POD modal coefficients through a deep neural network, followed by linear reconstruction of the flow field based on POD theory. The main contributions are: (1) a multi-branch temperature network (weighted fusion architecture of main branch + auxiliary branch + residual connection); (2) a Temperature-Aware Attention Mechanism (generating adaptive attention weights in the 2D temperature modal coefficient space); (3) a combination of hierarchical regularization with an intelligent data augmentation strategy. Experiments based on 510 office CFD scenarios demonstrate that the model achieves a Mean Absolute Error (MAE) of 0.210 K for temperature field prediction (34.4% improvement compared to the baseline with MAE of 0.320 K) and 0.0075 m/s for velocity field prediction (24.2% improvement compared to the baseline with MAE of 0.0099 m/s); the coefficient of determination (R²) reaches 0.98 (temperature) and 0.92 (velocity), respectively. The single prediction time is approximately 0.0008 s, 3–4 orders of magnitude faster than traditional CFD. This model provides an effective approach for temperature field prediction in office ventilation scenarios and provides a practical framework for real-time control, optimization, and digital twin applications. Full article

High-performance sensorless operation in multiphase electric drives requires speed estimation techniques capable of providing fast dynamic response, reduced oscillatory behavior, and low implementation complexity. In this context, a sliding-mode observer (SMO) based on an exponential reaching law (ERL) is proposed for rotor speed estimation in asymmetrical six-phase induction machines operating under indirect rotor field-oriented control. Unlike conventional SMO implementations, the proposed approach avoids auxiliary low-pass filtering (LPF) stages by employing an ERL-based adaptive gain mechanism, thereby preventing the phase delay and bandwidth reduction commonly associated with LPF-based observers. As a result, the proposed observer preserves fast transient dynamics, attenuates chattering near the sliding surface, and improves the smoothness of the estimated signals. The proposed technique is particularly suitable for multiphase drive applications, where sensorless operation reduces hardware complexity and improves system reliability by eliminating mechanical speed sensors and associated wiring. A Lyapunov-based stability analysis is presented to demonstrate the convergence properties of the observer and discuss the influence of the ERL parameters on the estimation dynamics. Simulation and experimental results obtained on a real-time test bench validate the digital implementation of the proposed SMO + ERL, demonstrating improved transient tracking, smoother estimated signals, stable low-speed operation, satisfactory speed reversal performance, and effective operation under loaded conditions. Full article

Designing modern disassembly systems requires the integration of industrial automation equipment. Due to the support of various communication protocols, PLCs not only perform control tasks but also act as intelligent data centers in distributed production systems. PLC solutions increasingly combine traditional approaches to automation with modern digital technologies, enabling predictive maintenance, real-time data analysis, as well as remote process management and integration with digital twin simulation. The takt time of manual disassembly may vary due to human and technical factors; therefore, its estimation is a problem in many processes including, for example, Bluetooth speakers. This article discusses the issue of PLC-based control systems for a sorting process of dismantled parts, and the methodology of a digital twin framework in FlexSim software. A prototype of a sorting line based on a conveyor belt with an S7-1200 series PLC controller and a full digital twin development cycle were presented. The explicit assessment of takt-related temporal fidelity in PLC-mediated event streams remains less developed. Therefore, this article addresses this gap by using a Digital-Twin-in-the-Loop (DTiL) configuration as a digital twin validation setup in which a source process model generates PLC-mediated events and a separate resulting digital twin model is evaluated against this source. The article focuses on temporal fidelity, PLC-mediated event transfer, and takt-time estimation. Thus, the gathered empirical time data were then fed into the digital twin model and analyzed to obtain information about the time delay of the PLC signals. This article separates the general digital twin architecture from one specific validation scenario implemented in a digital twin in-the-loop configuration with FlexSim, Siemens TIA Portal, PLCSim Advanced, and a local network communication chain. Delay analysis is based on photocell event timestamps and inter-event time differences, which reduce the effect of initial clock mismatch. The results indicate that, under the tested local-network DTiL configuration, absolute event delays are visible, while inter-event timing and aggregated takt statistics remain highly consistent between the source and resulting models. These findings support the preliminary feasibility of PLC-mediated takt-oriented monitoring for long manual operations. Nevertheless, broader validation under different controller configurations, communication conditions, and operating scenarios is required before generalizing the proposed approach. Full article