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Search Results (1,229)

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Keywords = model predictive speed control

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23 pages, 1631 KiB  
Article
Detecting Malicious Anomalies in Heavy-Duty Vehicular Networks Using Long Short-Term Memory Models
by Mark J. Potvin and Sylvain P. Leblanc
Sensors 2025, 25(14), 4430; https://doi.org/10.3390/s25144430 - 16 Jul 2025
Viewed by 65
Abstract
Utilizing deep learning models to detect malicious anomalies within the traffic of application layer J1939 protocol networks, found on heavy-duty commercial vehicles, is becoming a critical area of research in platform protection. At the physical layer, the controller area network (CAN) bus is [...] Read more.
Utilizing deep learning models to detect malicious anomalies within the traffic of application layer J1939 protocol networks, found on heavy-duty commercial vehicles, is becoming a critical area of research in platform protection. At the physical layer, the controller area network (CAN) bus is the backbone network for most vehicles. The CAN bus is highly efficient and dependable, which makes it a suitable networking solution for automobiles where reaction time and speed are of the essence due to safety considerations. Much recent research has been conducted on securing the CAN bus explicitly; however, the importance of protecting the J1939 protocol is becoming apparent. Our research utilizes long short-term memory models to predict the next binary data sequence of a J1939 packet. Our primary objective is to compare the performance of our J1939 detection system trained on data sub-fields against a published CAN system trained on the full data payload. We conducted a series of experiments to evaluate both detection systems by utilizing a simulated attack representation to generate anomalies. We show that both detection systems outperform one another on a case-by-case basis and determine that there is a clear requirement for a multifaceted security approach for vehicular networks. Full article
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22 pages, 6565 KiB  
Article
Hybrid NARX Neural Network with Model-Based Feedback for Predictive Torsional Torque Estimation in Electric Drive with Elastic Connection
by Amanuel Haftu Kahsay, Piotr Derugo, Piotr Majdański and Rafał Zawiślak
Energies 2025, 18(14), 3770; https://doi.org/10.3390/en18143770 - 16 Jul 2025
Viewed by 66
Abstract
This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed [...] Read more.
This paper proposes a hybrid methodology for one-step-ahead torsional torque estimation in an electric drive with an elastic connection. The approach integrates Nonlinear Autoregressive Neural Networks with Exogenous Inputs (NARX NNs) and model-based feedback. The NARX model uses real-time and historical motor speed and torque signals as inputs while leveraging physics-derived torsional torque as a feedback input to refine estimation accuracy and robustness. While model-based methods provide insight into system dynamics, they lack predictive capability—an essential feature for proactive control. Conversely, standalone NARX NNs often suffer from error accumulation and overfitting. The proposed hybrid architecture synergises the adaptive learning of NARX NNs with the fidelity of physics-based feedback, enabling proactive vibration damping. The method was implemented and evaluated on a two-mass drive system using an IP controller and additional torsional torque feedback. Results demonstrate high accuracy and reliability in one-step-ahead torsional torque estimation, enabling effective proactive vibration damping. MATLAB 2024a/Simulink and dSPACE 1103 were used for simulation and hardware-in-the-loop testing. Full article
(This article belongs to the Special Issue Drive System and Control Strategy of Electric Vehicle)
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25 pages, 5451 KiB  
Article
Research on the Stability and Trajectory Tracking Control of a Compound Steering Platform Based on Hierarchical Theory
by Huanqin Feng, Hui Jing, Xiaoyuan Zhang, Bing Kuang, Yifan Song, Chao Wei and Tianwei Qian
Electronics 2025, 14(14), 2836; https://doi.org/10.3390/electronics14142836 - 15 Jul 2025
Viewed by 136
Abstract
Compound steering technology has been extensively adopted in military logistics and related applications, owing to its superior maneuverability and enhanced stability compared to conventional systems. To enhance the steering efficiency and dynamic response of distributed-drive unmanned platforms under low driving torque conditions, this [...] Read more.
Compound steering technology has been extensively adopted in military logistics and related applications, owing to its superior maneuverability and enhanced stability compared to conventional systems. To enhance the steering efficiency and dynamic response of distributed-drive unmanned platforms under low driving torque conditions, this study investigates their unique compound steering system. Specifically, a compound steering dynamics model is established, and a hierarchical stability control strategy, along with a model predictive control-based trajectory tracking algorithm, are innovatively proposed. First, a compound steering platform dynamics model is established by combining the Ackermann steering and skid yaw moment methods. Then, a trajectory tracking controller is designed using model predictive control algorithm. Finally, the additional yaw moment is calculated based on the lateral velocity error and yaw rate error, with stability control allocation performed using a fuzzy control algorithm. Comparative hardware-in-the-loop experiments are conducted for compound steering, Ackermann steering, and skid steering. The experimental results show that the compound steering technology enables unmanned platforms to achieve trajectory tracking tasks with a lower torque, faster speed, and higher efficiency. Full article
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19 pages, 5255 KiB  
Article
Health Status Assessment of Passenger Ropeway Bearings Based on Multi-Parameter Acoustic Emission Analysis
by Junjiao Zhang, Yongna Shen, Zhanwen Wu, Gongtian Shen, Yilin Yuan and Bin Hu
Sensors 2025, 25(14), 4403; https://doi.org/10.3390/s25144403 - 15 Jul 2025
Viewed by 95
Abstract
This study presents a comprehensive investigation of acoustic emission (AE) characteristics for condition monitoring of rolling bearings in passenger ropeway systems. Through controlled laboratory experiments and field validation across multiple operational ropeways, we establish an optimized AE-based diagnostic framework. Key findings demonstrate that [...] Read more.
This study presents a comprehensive investigation of acoustic emission (AE) characteristics for condition monitoring of rolling bearings in passenger ropeway systems. Through controlled laboratory experiments and field validation across multiple operational ropeways, we establish an optimized AE-based diagnostic framework. Key findings demonstrate that resonant VS150-RIC sensors outperform broadband sensors in defect detection, showing greater energy response at characteristic frequencies for inner race defects. The RMS parameter emerges as a robust diagnostic indicator, with defective bearings exhibiting periodic peaks and higher mean RMS values. Field tests reveal progressive RMS escalation preceding visible damage, enabling predictive maintenance. Furthermore, we develop a novel Paligemma LLM model for automated wear detection using AE time-domain images. The research validates the AE technology’s superiority over conventional vibration methods for low-speed bearing monitoring, providing a scientifically grounded approach for safety-critical ropeway maintenance. Full article
(This article belongs to the Special Issue Sensor-Based Condition Monitoring and Non-Destructive Testing)
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15 pages, 6454 KiB  
Article
xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
by Chung-I Huang, Jih-Sheng Chang, Jun-Wei Hsieh, Jyh-Horng Wu and Wen-Yi Chang
Appl. Sci. 2025, 15(14), 7859; https://doi.org/10.3390/app15147859 - 14 Jul 2025
Viewed by 104
Abstract
Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police [...] Read more.
Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article
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33 pages, 7266 KiB  
Article
Temperature Prediction and Fault Warning of High-Speed Shaft of Wind Turbine Gearbox Based on Hybrid Deep Learning Model
by Min Zhang, Jijie Wei, Zhenli Sui, Kun Xu and Wenyong Yuan
J. Mar. Sci. Eng. 2025, 13(7), 1337; https://doi.org/10.3390/jmse13071337 - 13 Jul 2025
Viewed by 215
Abstract
Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. Abnormal temperature variations in the gearbox high-speed shaft (GHSS) serve as reliable indicators of potential faults. This study proposes a Spatio-Temporal Attentive (STA) synergistic architecture for GHSS fault detection [...] Read more.
Gearbox failure represents one of the most time-consuming maintenance challenges in wind turbine operations. Abnormal temperature variations in the gearbox high-speed shaft (GHSS) serve as reliable indicators of potential faults. This study proposes a Spatio-Temporal Attentive (STA) synergistic architecture for GHSS fault detection and early warning by utilizing the in situ monitoring data from a wind farm. This comprehensive architecture involves five modules: data preprocessing, multi-dimensional spatial feature extraction, temporal dependency modeling, global relationship learning, and hyperparameter optimization. It was achieved by using real-time monitoring data to predict the GHSS temperature in 10 min, with an accuracy of 1 °C. Compared to the long short-term memory (LSTM) and convolutional neural network and LSTM hybrid models, the STA architecture reduces the root mean square error of the prediction by approximately 37% and 13%, respectively. Furthermore, the architecture establishes a normal operating condition model and provides benchmark eigenvalues for subsequent fault warnings. The model was validated to issue early warnings up to seven hours before the fault alert is triggered by the supervisory control and data acquisition system of the wind turbine. By offering reliable, cost-effective prognostics without additional hardware, this approach significantly improves wind turbine health management and fault prevention. Full article
(This article belongs to the Section Ocean Engineering)
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23 pages, 6067 KiB  
Article
Daily-Scale Fire Risk Assessment for Eastern Mongolian Grasslands by Integrating Multi-Source Remote Sensing and Machine Learning
by Risu Na, Byambakhuu Gantumur, Wala Du, Sainbuyan Bayarsaikhan, Yu Shan, Qier Mu, Yuhai Bao, Nyamaa Tegshjargal and Battsengel Vandansambuu
Fire 2025, 8(7), 273; https://doi.org/10.3390/fire8070273 - 11 Jul 2025
Viewed by 452
Abstract
Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source [...] Read more.
Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source remote sensing data to enhance predictive capabilities in eastern Mongolia. Utilizing fire point data from eastern Mongolia (2012–2022), we fused multiple feature variables and developed and optimized three models: random forest (RF), XGBoost, and deep neural network (DNN). Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. Shapley additive explanations (SHAP) interpretability analysis revealed that daily-scale meteorological factors—daily average relative humidity, daily average wind speed, daily maximum temperature—and the normalized difference vegetation index (NDVI) were consistently among the top four contributing variables across all three models, identifying them as key drivers of fire occurrence. Spatiotemporal validation using historical fire data from 2023 demonstrated that fire points recorded on 8 April and 1 May 2023 fell within areas predicted to have “extremely high” fire risk probability on those respective days. Moreover, points A (117.36° E, 46.70° N) and B (116.34° E, 49.57° N) exhibited the highest number of days classified as “high” or “extremely high” risk during the April/May and September/October periods, consistent with actual fire occurrences. In summary, the integration of multi-source data fusion and Bayesian-optimized machine learning has enabled the first high-precision daily-scale wildfire risk prediction for the eastern Mongolian grasslands, thus providing a scientific foundation and decision-making support for wildfire prevention and control in the region. Full article
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23 pages, 8011 KiB  
Article
Efficient Prediction of Shallow-Water Acoustic Transmission Loss Using a Hybrid Variational Autoencoder–Flow Framework
by Bolin Su, Haozhong Wang, Xingyu Zhu, Penghua Song and Xiaolei Li
J. Mar. Sci. Eng. 2025, 13(7), 1325; https://doi.org/10.3390/jmse13071325 - 10 Jul 2025
Viewed by 169
Abstract
Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency. Deep learning approaches, based on data-driven [...] Read more.
Efficient prediction of shallow-water acoustic transmission loss (TL) is crucial for underwater detection, recognition, and communication systems. Traditional physical modeling methods require repeated calculations for each new scenario in practical waveguide environments, leading to low computational efficiency. Deep learning approaches, based on data-driven principles, enable accurate input–output approximation and batch processing of large-scale datasets, significantly reducing computation time and cost. To establish a rapid prediction model mapping sound speed profiles (SSPs) to acoustic TL through controllable generation, this study proposes a hybrid framework that integrates a variational autoencoder (VAE) and a normalizing flow (Flow) through a two-stage training strategy. The VAE network is employed to learn latent representations of TL data on a low-dimensional manifold, while the Flow network is additionally used to establish a bijective mapping between the latent variables and underwater physical parameters, thereby enhancing the controllability of the generation process. Combining the trained normalizing flow with the VAE decoder could establish an end-to-end mapping from SSPs to TL. The results demonstrated that the VAE–Flow network achieved higher computational efficiency, with a computation time of 4 s for generating 1000 acoustic TL samples, versus the over 500 s required by the KRAKEN model, while preserving accuracy, with median structural similarity index measure (SSIM) values over 0.90. Full article
(This article belongs to the Special Issue Data-Driven Methods for Marine Structures)
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27 pages, 2425 KiB  
Article
Performance of Various Artificial Intelligence Models for Predicting Temperature in an Industrial Building—A Case Study
by Johan Roussel, Zoubeir Lafhaj, Pascal Yim, Thomas Danel and Laure Ducoulombier
Buildings 2025, 15(14), 2428; https://doi.org/10.3390/buildings15142428 - 10 Jul 2025
Viewed by 199
Abstract
This article presents a comparative analysis of the performance of various artificial intelligence models for predicting temperature in an industrial building. The main objective is to identify an optimal algorithm that enables efficient thermal management, which is essential for ensuring product quality, maintaining [...] Read more.
This article presents a comparative analysis of the performance of various artificial intelligence models for predicting temperature in an industrial building. The main objective is to identify an optimal algorithm that enables efficient thermal management, which is essential for ensuring product quality, maintaining process safety, and optimizing energy consumption. Through an in-depth study, this research evaluates the accuracy, learning speed, and adaptability of the models in a dynamic industrial environment. Gradient Boosting algorithms, notably XGBoost and LightGBM, have demonstrated a capacity to give promising results, particularly for short- and mid-term predictions. The main results were obtained with XGBoost, achieving a mean absolute error of only 0.32 °C for a 5 min prediction horizon—below the 0.5 °C accuracy of the sensors used. The article also highlights the practical implications of these findings for Industry 4.0. By integrating high-performing predictive models into equipment control systems, it is possible to reduce energy costs, improve thermal comfort, and ensure efficient process stability. This research underscores the importance of an algorithmic approach tailored to both the thermal characteristics of the building and the computational capabilities of industrial infrastructure, with feature importance analysis confirming that external temperature and heating system states are the primary predictive factors, thereby helping to optimize industrial operations. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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22 pages, 2113 KiB  
Article
Tracking Control of Quadrotor Micro Aerial Vehicles Using Efficient Nonlinear Model Predictive Control with C/GMRES Optimization on Resource-Constrained Microcontrollers
by Dong-Min Lee, Jae-Hong Jung, Yeon-Su Sim and Gi-Woo Kim
Electronics 2025, 14(14), 2775; https://doi.org/10.3390/electronics14142775 - 10 Jul 2025
Viewed by 126
Abstract
This study investigates the tracking control of quadrotor micro aerial vehicles using nonlinear model predictive control (NMPC), with primary emphasis on the implementation of a real-time embedded control system. Apart from the limited memory size, one of the critical challenges is the limited [...] Read more.
This study investigates the tracking control of quadrotor micro aerial vehicles using nonlinear model predictive control (NMPC), with primary emphasis on the implementation of a real-time embedded control system. Apart from the limited memory size, one of the critical challenges is the limited processor speed on resource-constrained microcontroller units (MCUs). This technical issue becomes critical particularly when the maximum allowed computation time for real-time control exceeds 0.01 s, which is the typical sampling time required to ensure reliable control performance. To reduce the computational burden for NMPC, we first derive a nonlinear quadrotor model based on the quaternion number system rather than formulating nonlinear equations using conventional Euler angles. In addition, an implicit continuation generalized minimum residual optimization algorithm is designed for the fast computation of the optimal receding horizon control input. The proposed NMPC is extensively validated through rigorous simulations and experimental trials using Crazyflie 2.1®, an open-source flying development platform. Owing to the more precise prediction of the highly nonlinear quadrotor model, the proposed NMPC demonstrates that the tracking performance outperforms that of conventional linear MPCs. This study provides a basis and comprehensive guidelines for implementing the NMPC of nonlinear quadrotors on resource-constrained MCUs, with potential extensions to applications such as autonomous flight and obstacle avoidance. Full article
(This article belongs to the Section Systems & Control Engineering)
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41 pages, 7199 KiB  
Article
Entropy, Irreversibility, and Time-Series Deep Learning of Kinematic and Kinetic Data for Gait Classification in Children with Cerebral Palsy, Idiopathic Toe Walking, and Hereditary Spastic Paraplegia
by Alfonso de Gorostegui, Massimiliano Zanin, Juan-Andrés Martín-Gonzalo, Javier López-López, David Gómez-Andrés, Damien Kiernan and Estrella Rausell
Sensors 2025, 25(13), 4235; https://doi.org/10.3390/s25134235 - 7 Jul 2025
Viewed by 232
Abstract
The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some [...] Read more.
The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some cases to misdiagnosis. Existing methods often isolate the analysis of gait variables, overlooking the whole complexity of biomechanical patterns and variations in motor control strategies. While previous studies have explored the use of statistical physics principles for the analysis of impaired gait patterns, gaps remain in integrating both kinematic and kinetic information or benchmarking these approaches against Deep Learning models. This study evaluates the robustness of statistical physics metrics in differentiating between normal and abnormal gait patterns and quantifies how the data source affects model performance. The analysis was conducted using gait data sets from two research institutions in Madrid and Dublin, with a total of 81 children with ITW, 300 with CP, 20 with HSP, and 127 typically developing children as controls. From each kinematic and kinetic time series, Shannon’s entropy, permutation entropy, weighted permutation entropy, and time irreversibility metrics were derived and used with Random Forest models. The classification accuracy of these features was compared to a ResNet Deep Learning model. Further analyses explored the effects of inter-laboratory comparisons and the spatiotemporal resolution of time series on classification performance and evaluated the impact of age and walking speed with linear mixed models. The results revealed that statistical physics metrics were able to differentiate among impaired gait patterns, achieving classification scores comparable to ResNet. The effects of walking speed and age on gait predictability and temporal organisation were observed as disease-specific patterns. However, performance differences across laboratories limit the generalisation of the trained models. These findings highlight the value of statistical physics metrics in the classification of children with different toe walking conditions and point towards the need of multimetric integration to improve diagnostic accuracy and gain a more comprehensive understanding of gait disorders. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis: 2nd Edition)
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22 pages, 6123 KiB  
Article
Real-Time Proprioceptive Sensing Enhanced Switching Model Predictive Control for Quadruped Robot Under Uncertain Environment
by Sanket Lokhande, Yajie Bao, Peng Cheng, Dan Shen, Genshe Chen and Hao Xu
Electronics 2025, 14(13), 2681; https://doi.org/10.3390/electronics14132681 - 2 Jul 2025
Viewed by 362
Abstract
Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors [...] Read more.
Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors like cameras or LiDAR, which are susceptible to occlusions, poor lighting, and environmental interference. To address these limitations, this paper proposes a novel sensor-enhanced hierarchical switching model predictive control (MPC) framework that integrates proprioceptive sensing with a bi-level hybrid dynamic model. Unlike existing methods that either rely on handcrafted controllers or deep learning-based control pipelines, our approach introduces three core innovations: (1) a situation-aware, bi-level hybrid dynamic modeling strategy that hierarchically combines single-body rigid dynamics with distributed multi-body dynamics for modeling agility and scalability; (2) a three-layer hybrid control framework, including a terrain-aware switching MPC layer, a distributed torque controller, and a fast PD control loop for enhanced robustness during contact transitions; and (3) a multi-IMU-based proprioceptive feedback mechanism for terrain classification and adaptive gait control under sensor-occluded or GPS-denied environments. Together, these components form a unified and computationally efficient control scheme that addresses practical challenges such as limited onboard processing, unstructured terrain, and environmental uncertainty. A series of experimental results demonstrate that the proposed method outperforms existing vision- and learning-based controllers in terms of stability, adaptability, and control efficiency during high-speed locomotion over irregular terrain. Full article
(This article belongs to the Special Issue Smart Robotics and Autonomous Systems)
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33 pages, 3235 KiB  
Article
Intelligent Assurance of Resilient UAV Navigation Under Visual Data Deficiency for Sustainable Development of Smart Regions
by Serhii Semenov, Magdalena Krupska-Klimczak, Olga Wasiuta, Beata Krzaczek, Patryk Mieczkowski, Leszek Głowacki, Jian Yu, Jiang He and Olena Chernykh
Sustainability 2025, 17(13), 6030; https://doi.org/10.3390/su17136030 - 1 Jul 2025
Viewed by 319
Abstract
Ensuring the resilient navigation of unmanned aerial vehicles (UAVs) under conditions of limited or unstable sensor information is one of the key challenges of modern autonomous mobility within smart infrastructure and sustainable development. This article proposes an intelligent autonomous UAV control method based [...] Read more.
Ensuring the resilient navigation of unmanned aerial vehicles (UAVs) under conditions of limited or unstable sensor information is one of the key challenges of modern autonomous mobility within smart infrastructure and sustainable development. This article proposes an intelligent autonomous UAV control method based on the integration of geometric trajectory modeling, neural network-based sensor data filtering, and reinforcement learning. The geometric model, constructed using path coordinates, allows the trajectory tracking problem to be formalized as an affine control system, which ensures motion stability even in cases of partial data loss. To process noisy or fragmented GPS and IMU signals, an LSTM-based recurrent neural network filter is implemented. This significantly reduces positioning errors and maintains trajectory stability under environmental disturbances. In addition, the navigation system includes a reinforcement learning module that performs real-time obstacle prediction, path correction, and speed adaptation. The method has been tested in a simulated environment with limited sensor availability, variable velocity profiles, and dynamic obstacles. The results confirm the functionality and effectiveness of the proposed navigation system under sensor-deficient conditions. The approach is applicable to environmental monitoring, autonomous delivery, precision agriculture, and emergency response missions within smart regions. Its implementation contributes to achieving the Sustainable Development Goals (SDG 9, SDG 11, and SDG 13) by enhancing autonomy, energy efficiency, and the safety of flight operations. Full article
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23 pages, 2350 KiB  
Article
Comparative Evaluation of the Effects of Variable Spark Timing and Ethanol-Supplemented Fuel Use on the Performance and Emission Characteristics of an Aircraft Piston Engine
by Roussos Papagiannakis and Nikolaos Lytras
Energies 2025, 18(13), 3440; https://doi.org/10.3390/en18133440 - 30 Jun 2025
Viewed by 176
Abstract
Nowadays, there are many studies that have been conducted in order to reduce the emissions of modern reciprocating engines without, at the same time, having a negative impact on the performance characteristics. One method to accomplish that is by using ethanol-supplemented fuels instead [...] Read more.
Nowadays, there are many studies that have been conducted in order to reduce the emissions of modern reciprocating engines without, at the same time, having a negative impact on the performance characteristics. One method to accomplish that is by using ethanol-supplemented fuels instead of conventional gasoline. On the other side of the spectrum, spark timing is one of the most important parameters that affects the combustion mechanism inside a reciprocating engine and is basically controlled by the ignition advance of the engine. Therefore, the main purpose of this study is to investigate the effect of spark timing alteration on the performance characteristics and emissions of a modern reciprocating, naturally aspirated, aircraft SI engine (i.e., ROTAX 912s), operated under four different engine operating points (i.e., combination of engine speed and throttle opening), by using ethanol-supplemented fuel. The implementation of the aforementioned method is achieved through the use of an advanced simulating software (i.e., GT-POWER), which provides the user with the possibility to completely design a piston engine and parameterize it, by using a comprehensive single-zone phenomenological model, for any operating conditions in the entire range of its operating points. The predictive ability of the designed engine model is evaluated by comparing the results with the experimental values obtained from the technical manuals of the engine. For all test cases examined in the present work, the results are affiliated with important performance characteristics, i.e., brake power, brake torque, and brake-specific fuel consumption, as well as specific NO and CO concentrations. Thus, the primary objectives of this study were to examine and evaluate the results of the combination of using ethanol-supplemented fuel instead of gasoline and the alteration of the spark timing, to asses their effects on the basic performance characteristics and emissions of the aforementioned type of engine. By examining the results of this study, it is revealed that the increase in the ethanol concentration in the gasoline–ethanol fuel blend combined with the increase in the ignition advance might be an auspicious solution in order to meliorate both the performance and the environmental behavior of a naturally aspirated SI aircraft piston engine. In a nutshell, the outcoming results of this research show that the combination of the two methods examined may be a valuable solution if applied to existing reciprocating SI engines. Full article
(This article belongs to the Special Issue Internal Combustion Engine Performance 2025)
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25 pages, 6409 KiB  
Article
Dynamic Response Mitigation of Offshore Jacket Platform Using Tuned Mass Damper Under Misaligned Typhoon and Typhoon Wave
by Kaien Jiang, Guangyi Zhu, Guoer Lv, Huafeng Yu, Lizhong Wang, Mingfeng Huang and Lilin Wang
Appl. Sci. 2025, 15(13), 7321; https://doi.org/10.3390/app15137321 - 29 Jun 2025
Viewed by 266
Abstract
This study addresses the dynamic response control of deep-water jacket offshore platforms under typhoon and misaligned wave loads by proposing a Tuned Mass Damper (TMD)-based vibration suppression strategy. Typhoon loading is predicted using the Weather Research and Forecasting (WRF) model to simulate maximum [...] Read more.
This study addresses the dynamic response control of deep-water jacket offshore platforms under typhoon and misaligned wave loads by proposing a Tuned Mass Damper (TMD)-based vibration suppression strategy. Typhoon loading is predicted using the Weather Research and Forecasting (WRF) model to simulate maximum wind speed and direction, a customized exponential wind profile fitted to WRF results, and a spectral model calibrated with field-measured data. Correspondingly, typhoon wave loading is calculated using stochastic wave theory with the Joint North Sea Wave Project (JONSWAP) spectrum. A rigorous Finite Element Model (FEM) incorporating soil–structure interaction (SSI) and water-pile interaction is implemented in the Opensees platform. The SSI is modeled using nonlinear Beam on Nonlinear Winkler Foundation (BNWF) elements (PySimple1, TzSimple1, QzSimple1). Numerical simulations demonstrate that the TMD effectively mitigates dynamic platform responses under aligned typhoon and wave conditions. Specifically, the maximum deck acceleration in the X-direction is reduced by 26.19% and 31.58% under these aligned loads, with a 17.7% peak attenuation in base shear. For misaligned conditions, the TMD exhibits pronounced control over displacements in both X- and Y-directions, achieving reductions of up to 29.4%. Sensitivity studies indicated that the TMD’s effectiveness is more significantly impacted by stiffness detuning than mass detuning. It should be emphasized that the effectiveness verification of linear TMD is limited to the load levels within the design limits; for the load conditions that trigger extreme structural nonlinearity, its performance remains to be studied. This research provides theoretical and practical references for multi-directional coupled vibration control of deep-water jacket platforms in extreme marine environments. Full article
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