Search Results (1,318)

This study investigates the dual-task control challenge of path following and obstacle avoidance for deep-sea mining robots operating in complex, unstructured environments. To address the limitations of traditional training strategies, we propose an optimized training framework that integrates environmental design enhancements and algorithmic advancements. Specifically, we develop a Dual-Task Training Environment by combining the Random Obstacle Environment with a newly proposed Obstructed Path Environment, ensuring a balanced learning approach. While agents trained solely in the Random Obstacle Environment exhibit unilateral obstacle avoidance strategies and achieve a 0% success rate in randomized obstacle scenarios, those trained in the Dual-Task Environment demonstrate 85.4% success under identical test conditions and acquire more complex bilateral avoidance strategies. Additionally, we introduce a Dynamic Multi-Step Update mechanism, which integrates immediate rewards with long-term returns to enhance deep reinforcement learning (Twin Delayed Deep Deterministic Policy Gradient, TD3) performance without increasing computational complexity. Under the optimal multi-step setting (n = 5), the Dynamic Multi-Step Update mechanism significantly improves path following accuracy, reducing trajectory deviations to 0.128 m on straight paths and 0.195 m on S-shaped paths, while achieving nearly 100% success in multi-directional obstacle avoidance tests. These improvements collectively enhance the adaptability, robustness, and operational performance of deep-sea mining robots, advancing intelligent control strategies for autonomous deep-sea exploration and resource extraction. Full article

Fault diagnosis is essential in industrial production. With the advancement of IoT technology, real-time data acquisition and storage have become feasible, enabling deep learning-based fault diagnosis methods to achieve remarkable results. However, existing approaches often overlook the temporal characteristics of fault occurrences and struggle with data imbalance between normal and faulty conditions, impacting diagnostic performance. To address these challenges, this paper proposes an integrated fault diagnosis method that incorporates data balancing, feature extraction, and temporal information analysis. The approach consists of two key components: (1) dataset construction using digital twin technology and (2) an integrated fault diagnosis model (CNN-BLSTM-attention). Digital twin technology generates virtual data under various operating conditions, mitigating the small-sample issue. The proposed model leverages a sliding window mechanism to capture both feature and temporal information, enhancing fault pattern recognition. Experimental results demonstrate that, compared to traditional methods, this approach effectively reduces noise interference and achieves a high diagnostic accuracy of 96.46%, validating its robustness in complex industrial settings. This research provides valuable theoretical and practical insights for improving fault diagnosis in industrial equipment such as screw presses. Full article

The topic of state estimation using multibody models combined with Kalman filters has been an active field of research for more than 15 years now. Through state estimation, virtual sensors can be used to increase the knowledge of a system, measuring variables that cannot be obtained through conventional sensors. This is useful for control purposes or updating the state of a digital twin of a system. One of the most tricky questions with the different approaches tested in the literature is the parameter tuning of the filters, in particular, the covariance matrix of the plant noise. This work presents a new method which includes a shaping filter to whiten the plant noise combined with an adaptive algorithm to adjust the plant noise parameters. This new method is tested and compared with methods already described in the literature using the three-simulation method. The new method is at least as accurate as the best hand-tuned filters in most of the situations evaluated, and improves the accuracy of previously presented adaptive methods. All the methods and mechanisms tested in this paper are available in an open source library written in matlab called MBDE. Full article

Rolling bearings, as core components in mechanical systems, directly influence the overall reliability of equipment. However, continuous operation under complex working conditions can easily lead to gradual performance degradation and sudden faults, which not only result in equipment failure but may also trigger a cascading failure effect, significantly amplifying downtime losses. To address this challenge, this study proposes an intelligent diagnostic method that integrates variational mode decomposition (VMD) optimized by the improved subtraction-average-based optimizer (ISABO) with transformer–twin extreme learning machine (Transformer–TELM) ensemble technology. Firstly, ISABO is employed to finely optimize the initialization parameters of VMD. With the improved initialization strategy and particle position update method, the optimal parameter combination can be precisely identified. Subsequently, the optimized parameters are used to model and decompose the signal through VMD, and the optimal signal components are selected through a constructed two-dimensional evaluation system. Furthermore, diversified time-domain features are extracted from these components to form an initial feature set. To deeply mine feature information, a multi-layer Transformer model is introduced to refine more discriminative feature representations. Finally, these features are input into the constructed TELM fault diagnosis model to achieve precise diagnosis of rolling bearing faults. The experimental results demonstrate that this method exhibits excellent performance in terms of noise resistance, accurate fault feature capture, and fault classification. Compared with traditional machine learning techniques such as kernel extreme learning machine (KELM), extreme learning machine (ELM), support vector machine (SVM), and Softmax, this method significantly outperforms other models in terms of accuracy, recall, and F1 score. Full article

To address the challenges of dynamic adversarial scenario modeling distortion, insufficient cross-institutional data privacy protection, and simplistic evaluation systems in collegiate basketball tactical education, this study proposes and validates an immersive instructional system integrating digital twin and federated learning technologies. The four-tier architecture (sensing layer, digital twin layer, federated layer, and interaction layer) synthesizes multimodal data (motion trajectories and physiological signals) with Multi-Agent Reinforcement Learning (MARL) to enable virtual–physical integrated tactical simulation and real-time error correction. Experimental results demonstrate that the experimental group achieved 35.2% higher tactical execution accuracy (TEA) (p < 0.01), 1.8 s faster decision making (p < 0.05), and 47% improved team coordination efficiency compared to the controls. The hierarchical federated learning framework (trajectory ε = 0.8; physiology ε = 0.3) maintained model precision loss at 2.4% while optimizing communication efficiency by 23%, ensuring privacy preservation. A novel three-dimensional “Skill–Creativity–Load” evaluation system revealed a 22% increase in unconventional tactical applications (p = 0.013) through the Tactical Creativity Index (TCI). By implementing lightweight federated architecture with dynamic cognitive offloading mechanisms, the system enables resource-constrained institutions to achieve 87% of the pedagogical effectiveness observed in elite programs, offering an innovative solution to reconcile educational equity with technological ethics. Future research should focus on long-term skill transfer, multimodal adaptive learning, and ethical framework development to advance intelligent sports education from efficiency-oriented paradigms to competency-based transformation. Full article

This study presents a multi-user virtual reality (VR) tool designed to enhance hands-on learning in engineering education through real-time sensorized digital twins. The motivation stems from the limitations of traditional laboratory settings, such as time constraints and restricted access to physical equipment, which can hinder practical learning. The developed environment allows multiple students, wearing VR headsets, to interact simultaneously with a real-time synchronized virtual model of an engine, replicating its physical counterpart at the Mechanical Engineering Laboratory of the Pontificia Universidad Católica de Valparaíso, Chile. This novel integration of VR and digital twin technology offers students a unique opportunity to observe engine behavior in operation within a safe, controlled virtual space. By bridging theoretical knowledge with practical experience, this approach deepens understanding of complex mechanical concepts while fostering the development of key technical skills. Additionally, the use of real-time data visualization and digital twins provides a safer, more interactive, and efficient alternative to traditional laboratory practices, overcoming constraints like time limitations and equipment availability. This innovative method introduces students to Industry 4.0 principles, encouraging data-driven analysis and informed decision making. Full article

The primary challenge in robotic navigation lies in enabling robots to adapt effectively to new, unseen environments. Addressing this gap, this paper enhances the Twin Delayed Deep Deterministic Policy Gradient (TD3) model’s adaptability by introducing randomized start and goal points. This approach aims to overcome the limitations of fixed goal points used in prior research, allowing the robot to navigate more effectively through unpredictable scenarios. This proposed extension was evaluated in unseen environments to validate the enhanced adaptability and performance of the TD3 model. The experimental results highlight improved flexibility and robustness in the robot’s navigation capabilities, demonstrating the ability of the model to generalize effectively to unseen environments. Additionally, this paper provides a concise overview of TD3, focusing on its core mechanisms and key components to clarify its implementation. Full article

Quantum key distribution (QKD) is a cornerstone of secure communication in the quantum era, yet most existing protocols are designed for point-to-point transmission, limiting their scalability in networked environments. In this work, we introduce Loop-Back QKD, a novel QKD protocol that supports both two-party linear configurations and scalable multiuser ring topologies. By leveraging a structured turn-based mechanism and bidirectional pulse propagation, the protocol enables efficient key distribution while reducing the quantum bit error rate (QBER) through a multi-pulse approach. Unlike trusted-node QKD networks, Loop-Back QKD eliminates intermediate-node vulnerabilities, as secret keys are never processed by intermediate nodes. Furthermore, unlike Measurement-Device-Independent (MDI-QKD) and Twin-Field QKD (TF-QKD), which require complex entanglement-based setups, Loop-Back QKD relies solely on direct polarization transformations, reducing vulnerability to side-channel attacks and practical implementation challenges. Additionally, our analysis indicates that multi-pulse Loop-Back QKD can tolerate higher QBER thresholds. However, this increased robustness comes at the cost of a lower key rate efficiency compared to standard QKD schemes. This design choice enhances its robustness against real-world adversarial threats, making it a strong candidate for secure multiuser communication in local and metropolitan-scale quantum networks. Full article

Background: The emergency transport of thoraco-omphalopagus conjoined twins is a rare occurrence in the medical field of emergency medicine with no specific guidelines on which medical personnel can rely upon. We present to you the case of the transport of thoraco-omphalopagus conjoined twins. Methods: Important changes were made to the configuration of the aircraft as to provide adequate ventilation (one twin ventilated manually with an ABV while the other benefiting from mechanical ventilation) and hemodynamic stability. Results: The team successfully orchestrated the transfer with no complications arriving during the transfer with the twins benefiting from specialized care in the intended tertiary care center. Conclusions: Our paper highlights the importance for innovative approaches in adapting neonatal transport equipment, including ventilation and monitoring for two critically ill patients in a space designed for one, all that underscores the importance of tailored neonatal transport solutions. Full article

The increasing complexity of urban logistics in smart cities requires innovative solutions that leverage real-time data, predictive analytics, and adaptive learning to enhance efficiency. This study presents a predictive analytics framework integrating digital twin technology, IoT-enabled logistics data, and cybernetic feedback loops to improve last-mile delivery accuracy, congestion management, and sustainability in smart cities. Grounded in Systems Theory and Cybernetic Theory, the framework models urban logistics as an interconnected network, where real-time IoT data enable dynamic routing, demand forecasting, and self-regulating logistics operations. By incorporating machine learning-driven predictive analytics, the study demonstrates how AI-powered logistics optimization can enhance urban freight mobility. The cybernetic feedback mechanism further improves adaptive decision-making and operational resilience, allowing logistics networks to respond dynamically to changing urban conditions. The findings provide valuable insights for logistics managers, smart city policymakers, and urban planners, highlighting how AI-driven logistics strategies can reduce congestion, enhance sustainability, and optimize delivery performance. The study also contributes to logistics and smart city research by integrating digital twins with adaptive analytics, addressing gaps in dynamic, feedback-driven logistics models. Full article

Polymer nanocomposites (PNCs) are a versatile class of materials known for their enhanced mechanical, thermal, electrical, and barrier properties, with the latter referring to resistance against the permeation of gases and liquids. Achieving optimal nanoparticle dispersion within the polymer matrix is essential to fully realizing these advantages. This study investigates strategies for improving nanoparticle dispersion and examines the impact of controlled dispersion on the resulting nanocomposite properties. Various methods, including in situ polymerization, twin screw extrusion, sol–gel processes, nanoparticle surface modification, solution casting, and advanced compounding techniques such as additive manufacturing and self-healing composites were explored to enhance dispersion and improve the compatibility between nanoparticles and polymers. The synergy between improved dispersion and enhanced functionalities—such as increased mechanical strength, thermal stability, conductivity, and chemical resistance—makes these nanocomposites highly valuable for industrial applications in sectors such as the automotive, aerospace, electronics, pharmaceuticals, and packaging industries. The key recommendations based on our findings highlight how customized nanocomposites can address specific industrial challenges, fostering innovation in materials science and engineering. Full article

Some studies have reported the microstructure evolution of nickel-based superalloys during isothermal forging (IF). However, most of them have not taken into account the microstructure evolution during the preheating stage in manufacturing processes. Investigating the microstructure evolution mechanisms during preheating of nickel-based superalloy can provide a more accurate characterization of the initial microstructures prior to IF. In this study, the evolution of grain structure, participation phase, and twins in a hot extruded nickel-based superalloy are examined during heat treatment at the temperature range of 1050~1140 °C and 5~180 min. Also, the interaction mechanisms among the above microstructures are analyzed. Experimental results demonstrate that higher temperature significantly accelerates the dissolution of the primary γ′ (γ′_p) phase and grain growth. At 180 min, the average grain size rapidly grows from 4.59 μm at 1080 °C to 14.09 μm at 1110 °C. In contrast, the impact of holding time on the microstructure diminishes after 30 min. At 1080 °C, the average grain size grows from 2.52 μm at 5 min to 4.95 μm at 30 min, after which it remains relatively stable. Initially, the γ′_p phase hinders grain boundary migration and inhibits grain growth. However, its complete dissolution at high temperatures significantly promotes grain growth. Careful selection of preheating temperature can mitigate rapid grain growth before forging. Additionally, twins not only refine grains through nucleation and segmentation, but also hinder grain boundary migration in regions with high dislocation density, thereby alleviating grain growth. A model detailing the dissolution of the γ′_p phase during preheating is developed, with a correlation coefficient and average absolute relative error of 0.9947 and 9.15%, respectively. This model provides theoretical support for optimizing preheating temperatures and estimating initial microstructures prior to IF. Full article

The tissue paper converting system involves critical stages, including unwinding, embossing, folding, stacking, cutting, and packaging. Despite its maturity, challenges such as production interruptions during roll changes, equipment coordination issues, and unstable packaging speeds persist, affecting overall efficiency. While existing methods like fast roll change mechanisms and automated speed regulation systems have improved individual equipment performance, they fall short in achieving system-wide optimization. This paper proposes a digital-twin-based framework for the tissue paper converting system. High-precision simulation models are developed for key equipment, including the base paper roll stand, folding machine, paper stacker, and packaging machine, enabling real-time system monitoring and prediction. Key performance indicators such as base paper availability, folding speed, stacking status, and packaging progress are effectively predicted, preventing interruptions and reducing downtime. The proposed framework was validated in a real-world application at a Chinese paper manufacturing company. Results show that it increases machine runtime by 3 h per day, improving production efficiency by 15%. This leads to an additional daily output of 35,000 packs, contributing approximately CNY 85,000 in revenue growth. Additionally, the system helps prevent 300 kg of base paper waste daily, saving CNY 600 in manufacturing costs per day. Full article

Composite materials are increasingly used in various vehicles and construction parts, necessitating a comprehensive understanding of their behavior under varying thermal conditions. Measuring the thermo-mechanical properties with traditional methods such as tensile testing or dynamical mechanical analysis is often time-consuming and requires costly apparatus. This paper introduces an innovative non-destructive method for identifying the orthotropic engineering constants of composite test sheets as a function of temperature. The proposed technique represents an advancement of the conventional impulse excitation technique, incorporating an automated pendulum exciting mechanism and creating digital twins of the test sheets. The automated measurement of the impulse response function yields resonance frequencies and damping ratios at specified temperatures. These values are subsequently utilized in digital twins for identification of the engineering constants. The method is fully automated across predefined temperature intervals and can be seamlessly integrated into existing climate chambers equipped with remote control facilities. The results obtained from the described measurement technique were applied to a bi-directionally glass-reinforced thermoplastic PA6 matrix in a tested temperature range of −20 °C to 60 °C, revealing that the complex engineering constants are significantly affected by temperature. Full article

As the demand for sustainable and environmentally friendly materials has increased, renewable resources have been explored for the development of biobased composites. Two biobased composite materials were developed from thermoplastic starch (TPS), short fibers from plantain pseudostems sheaths and the starch from the plantain pseudostem core, using twin-screw extrusion and compression molding. Based on the findings, there is evidence of a biobased composite material with reduced water absorption of up to 9.9%, keeping thermal stability at a degradation temperature between 300 and 306 °C and increasing tensile properties by over 506%, although hardness showed slight increases (4.6%). In addition, the capacity of the sheath to generate a water vapor barrier is highlighted by reducing the magnitude of losses in mechanical properties during storage for a period of 8 days. This study contributes to the use of agricultural residues to create sustainable products, offering a pathway toward reducing dependency on synthetic polymers and mitigating environmental impact. Full article