Search Results (11,599)

In petroleum drilling, conventional automatic drilling systems still rely heavily on manual intervention, which often leads to poor stability, limited multivariable coordination, and large fluctuations in drilling pressure. To address this problem, this study develops a hydraulic drawworks-based autodriller system with integrated power, drive, actuation, and control units, and establishes a mechanical-hydraulic-control co-simulation model for the coordinated regulation of drill-string hoisting speed and surface weight-on-bit (SWOB). Based on this platform, a dual-loop control framework is developed in which the inner loop uses linear active disturbance rejection control (LADRC) for rapid disturbance estimation and compensation, while the outer loop uses PID control for tracking regulation. Feedforward compensation is introduced to handle predictable load variation, and PSO-assisted fuzzy tuning is used to improve adaptability under varying operating conditions. Simulation results show that, compared with conventional cascaded PID control, the proposed controller reduces drawworks speed and SWOB overshoot by 12.5% and 40%, respectively, while the corresponding settling times are shortened by 1.805 s and 2.443 s. Prototype experiments on a scaled test platform further show that the proposed controller can be implemented on physical hardware and can maintain stable real-time regulation under laboratory conditions. These results support the feasibility of the proposed framework for coordinated hydraulic drawworks control under the simulated and laboratory-scale conditions considered in this study. Full article

To address the limitations of zero-shot generalization in Vision-Language Navigation (VLN), this paper proposes a novel knowledge graph-driven reinforcement learning approach. Our method constructs a hierarchical, dynamically updated knowledge graph online during the agent’s real-time interaction with the environment, seamlessly aligning external semantic priors with continuous visual perception. By leveraging a Chain-of-Thought (CoT) prompting mechanism, the agent performs multi-hop reasoning to precisely locate target objects. Furthermore, we design an end-to-end optimized reinforcement learning framework that fuses multi-modal features and employs a task-oriented composite reward function. Extensive experiments in the AI2-THOR simulation environment demonstrate that the proposed method significantly improves navigation success rates in zero-shot settings. The results validate its robust generalization capabilities, particularly for unseen object categories and complex scene layouts. Full article

To address the challenge of balancing regenerative braking efficiency and braking safety in rear-wheel-drive electric motorcycles, this study proposes a genetic algorithm-based electromechanical hybrid fuzzy braking control strategy. First, a three-dimensional fuzzy controller is designed with braking force, motorcycle speed, and battery state of charge (SOC) as input variables to adjust the regenerative braking ratio in real-time. To further improve the fuzzy logic, which typically relies on engineering experience, a genetic algorithm (GA) is employed to optimize the controller’s parameter space. Co-simulation results using BikeSim 2013.1 and MATLAB/Simulink R2022a demonstrate that, under WMTC and NEDC standard driving cycles, the proposed GA-optimized fuzzy control system increases energy recovery rates by 6.59% and 11.65%, respectively, compared with the unoptimized fuzzy control strategy. Full article

LiDAR is widely used in robotics because it provides reliable range data for navigation and mapping. On a small embedded robot, however, there is a practical conflict between scan resolution and reaction speed. Dense scans provide better environmental detail, but they take too long for fast obstacle avoidance, whereas sparse scans are faster but can miss obstacles if the spacing between adjacent rays is too large. This paper presents an Interleaved Sparse–Dense Scanning method for a servo-actuated single-point time-of-flight LiDAR mounted on an embedded mobile robot. A dense nested pan–tilt sweep is used for three-dimensional mapping, while a sparse forward scan is inserted between dense rows for obstacle detection and motion control. A geometric model is derived to relate sensing range, beam spacing, and minimum detectable object width. That model is then linked to zone-based safety constraints and to the distance the robot can travel before the next obstacle update. For the robot used in this study, the resulting sparse configuration is a 7-point forward scan over a 180${}^{\circ }$ field of view. Experiments in a real indoor environment showed that this configuration reliably detected target blocking obstacles and reduced decision latency by 6.2 times compared with waiting for a complete dense scan before each navigation update. The proposed method provides a practical balance between reactive obstacle avoidance and useful 3D mapping on a low-cost embedded platform, while making the system’s timing and safety limits explicit. Full article

Virtual Reality (VR) has emerged as a powerful tool for improving training strategies in advanced manufacturing through immersive experiences. Within this context, this study examines the impact of two training strategies, VR and Video-Based (VB) instructions, on system performance (execution time and human errors) in a cooperative Human–Robot Interaction (HRI) assembly task. Overall, 26 participants completed the task after receiving either VR or VB training, and a sub-sample of 6 people per group returned one month later to repeat the task, enabling an evaluation of performance over time. Objective and subjective metrics were collected, and statistical and effect size analyses were conducted to compare training effects across sessions. Results show that execution times and number of errors were comparable between VR and VB in the first real session. After one month, both groups exhibited improved performance, but VR-trained participants retained, on average, lower error rates, with a 71% reduction and the number of errors dropping to zero, and more stable error patterns, whereas VB-trained participants displayed greater variability and occasional accuracy degradation during repeated task execution. Moreover, within-group comparisons show that VR training is more effective for accuracy-critical cooperative HRI tasks. At the same time, VB remains a low-cost option for time-focused contexts, shedding light on how training modalities influence learning and forgetting in Industry 5.0. Full article

To address the urgent demand for real-time reconstruction in remote sensing satellite imaging, as well as the difficulty of extracting sparse target features from dark backgrounds under low-illumination conditions, this paper proposes a lightweight, scalable attention-guided super-resolution reconstruction framework (SASR). The framework adopts an efficient, scalable visual backbone with staged feature extraction to capture discriminative information at three hierarchical scales. A refined multi-scale channel attention module, improved from the classic MS-CAM structure, is further introduced to fuse high-level semantic features and low-level texture details comprehensively. Finally, stacked sub-pixel convolution operations are employed to achieve high-precision image super-resolution enhancement. The proposed method maintains superior lightweight characteristics and fast inference efficiency while embedding effective channel attention optimisation for accurate feature representation. Experimental validations are conducted on the GF-5 satellite datasets: at 2× magnification, the proposed model achieves 32.2346 dB PSNR and 0.8791 SSIM; at 3× magnification, 31.6040 dB PSNR and 0.8376 SSIM; at 4× magnification, PSNR remains above 30 dB, and SSIM exceeds 0.8. The framework also exhibits robust generalization performance on marine remote sensing image datasets. Comparative experiments with recent super-resolution methods on multiple public datasets further verify the effectiveness and practical superiority of the proposed approach. Full article

Accurate and robust UAV navigation in complex urban environments remains challenging due to dense buildings, dynamic obstacles, and unreliable GPS signals. To address this issue, this paper proposes GLP-VO, a hybrid visual odometry framework that combines geometric structure features with point features. An adaptive weighting strategy is introduced to balance the contributions of different feature types according to matching quality and scene complexity, while geometric constraints are incorporated into the optimization process to improve pose estimation accuracy and stability. Experiments on the TUM RGB-D dataset and real UAV flight sequences verify the effectiveness of the proposed method. GLP-VO achieves the best ATE results in five of the ten evaluated TUM sequences, including 0.91 cm on f1_xyz and 0.62 cm on f3_str_tex_far, and remains competitive on challenging sequences such as f2_360_kidnap with an ATE of 2.26 cm. In the ablation study, the full model reduces ATE and RPE by up to 44.9% and 43.1%, respectively. Moreover, the proposed system runs at approximately 35 FPS on the desktop platform and 11 FPS on the onboard platform, demonstrating a favorable balance between accuracy, robustness, and real-time performance. Full article

This article presents an experimental demonstration of an attitude-constrained adaptive guidance and control framework for safe autonomous docking, evaluated using a planar floating satellite platform testbed. The proposed approach combines a jerk-minimizing explicit guidance law, which enforces terminal constraints on position, velocity, and acceleration, with an adaptive tracking controller designed to handle modeling uncertainties, actuator limitations, and external disturbances. The guidance strategy generates a smooth, real-time trajectory for the chaser satellite, ensuring compatibility with limited onboard computation and maintaining high terminal accuracy. To ensure safe operation throughout the docking maneuver, a barrier-Lyapunov-based adaptive controller is augmented that imposes state constraints to enforce strict adherence to the desired trajectory with predefined nominal bounds. By virtue of the constraint, the tracking error is bounded within a pre-defined bound. The bound is not time-varying because the reference trajectory is designed to begin from the current state of the robot with minimum jerk. The complete framework is demonstrated through hardware-in-the-loop experiments using a planar floating satellite platform and a prototype docking station. Experimental results corroborate the efficacy of autonomously achieving docking while satisfying stringent terminal constraints on position, velocity, and orientation, demonstrating the framework’s robustness and practical applicability to on-orbit servicing missions. Full article

With the increasing number and enhanced sensing capabilities of satellites, the volume of satellite imagery has substantially surpassed the available bandwidth of satellite-to-ground links. Recently, with the adoption of commercial on-board GPUs, Learned Image Compression (LIC) offers the potential to mitigate this bottleneck by virtue of its superior rate–distortion performance over traditional codecs. However, existing LIC solutions operate in isolation on single satellites and underutilize the overlapping observations, which limits further gains in compression performance. In this paper, we propose Distributed Latent Representation Clustering (DLRC), which represents the first attempt to integrate real-time multi-satellite observation redundancy elimination into LIC. DLRC first introduces a local latent representation clustering mechanism. It discretizes the latent representation of LIC into compact cluster signatures on each satellite with lightweight computational overhead. Subsequently, DLRC presents a global cluster signature synchronization strategy. By exchanging signatures with negligible communication overhead, it enables multiple satellites to identify globally redundant local observations on a per-signature basis. By coding and downlinking only the latent representation corresponding to globally unique signatures, DLRC achieves non-redundant downlink in a training-free paradigm while remaining compatible with existing LIC architectures. Through extensive experiments, we demonstrate that DLRC achieves efficient bits per pixel reduction compared to independent LIC solutions while maintaining comparable reconstruction quality. Full article

In coaxial twin-rotor helicopters, the minimum blade tip distance may approach danger thresholds during rotor intersection under high-speed rotation and complex aerodynamic conditions, posing collision risks. This study proposes a multi-sensor fusion approach for measuring the blade collision warning parameter $d$, which maps the collision risk into a single evaluation metric and provides stable real-time outputs of phase, spatial position, and inter-blade distance under high-speed operational conditions. A collaborative measurement scheme integrating encoder-based phase detection, tip-tracking camera positioning, and millimeter-wave radar distance measurement was developed. A dynamic rotor motion simulation experimental platform with single-side rotation and rigid blades was constructed to validate the measurement performance under varying rotor speeds and blade tip distances. Experimental results indicate that measurement errors remain within ±1.87 mm, repeatability errors are below 0.67 mm, and the coefficient of variation is under 0.2%, confirming the accuracy and stability of the proposed method under dynamic conditions. Additional multi-speed experiments show that, over the tested rotational-speed range, the error of $d$ remains within (−5.86 mm, 6.57 mm), although the fluctuation of the results increases moderately at higher speeds as the blade intersection duration becomes shorter. The proposed approach provides a laboratory-validated technical basis for blade collision risk assessment and future warning implementation in coaxial twin-rotor helicopters. Full article

With the rapid development of precision agriculture and smart farming management, accurate crop disease detection has become a critical tool for optimizing agricultural resource allocation, controlling operational costs, and supporting scientific plant protection strategies. However, real-world field environments are often characterized by strong background interference, multiple concurrent diseases, and fine-grained lesion differences, posing significant challenges to existing detection methods in practical agricultural Internet of Things (IoT) applications. In this paper, we propose Freq-spatial Context Dynamic Network(FCDNet), an efficient and cost-effective detection model tailored for multi-category strawberry disease recognition in complex field management scenarios. The proposed model integrates a Freq-Spatial Feature Module (FSFM), a Context Guide Fusion Module (CGFM), and a Task Align Dynamic Detection Head (TADDH), enabling enhanced expression of high-frequency micro-lesions, adaptive filtering of field background noise, and spatial alignment of classification and regression tasks, while maintaining a lightweight architecture suitable for low-cost agricultural edge devices. Extensive experiments conducted on the newly constructed Strawberry Disease Dataset-7(S7DD) demonstrate that FCDNet consistently outperforms existing mainstream methods, achieving an F1-score of 91.0% and an mAP@0.5 of 94.6%. The model’s architectural robustness and capacity for generalization are further substantiated by evaluations across diverse agricultural datasets using PlantDoc and ALDOD. Ultimately, FCDNet became a practical and cost-effective tool for real-time detection of strawberry diseases, directly supporting more accurate yield forecasting and risk management in smart agriculture systems. Full article

Early detection and accurate segmentation of colorectal polyps during colonoscopy are crucial for the prevention of colorectal cancer. However, automated polyp segmentation remains challenging because of high inter-class variance, complex intestinal backgrounds, and blurred boundaries. To address these issues while maintaining computational efficiency, DSF-BRNet was developed for endoscopic polyp segmentation. In this framework, a Dual-Gated Semantic Fusion (DSF) module is introduced to reduce spatial misalignment between cross-level features and to provide a more reliable semantic basis for lesion localization. To further alleviate boundary ambiguity, a High-Frequency Boundary Refinement (HBR) module is used to sharpen segmentation contours under aligned semantic guidance. Together, these components form an Align-then-Refine framework in which semantic localization is strengthened before boundary refinement is performed. Experiments on four public benchmark datasets—Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB—showed competitive performance with favorable computational efficiency. Mean Dice scores of 0.943 on CVC-ClinicDB and 0.818 on ETIS-LaribPolypDB were achieved, with 25.55 M parameters and an inference speed of 80.08 FPS. These results indicate that accurate semantic localization and fine boundary preservation can be achieved simultaneously, suggesting that the method may be promising for real-time computer-aided diagnosis (CAD). Full article

Leukocyte telomere length (LTL) is shortened in adults with type 1 diabetes (T1D), but less data is available concerning pediatric cases. Multiple factors affect LTL, namely genes, epigenetics, environmental factors, oxidation, and psychological stress. Children with T1D and their families experience chronic stress. This study aimed to investigate LTL in children with T1D (n = 35) aged 6–13 years old, in comparison to age-matched healthy counterparts (n = 35), and assess any correlation of LTL with perceived stress. Relative LTL (rLTL) was assessed through real-time qPCR. Morning serum cortisol, high-sensitivity C-Reactive Protein (hsCRP), and glycated hemoglobin (HbA1c) were measured. Children completed the validated questionnaires “Stress in Children” and “Pediatric Quality of Life”. Parents answered the “Perceived Stress Scale”. Children with T1D had a lower rLTL (p = 0.02) compared to age-matched healthy controls, higher hsCRP (p = 0.031), and a lower estimated quality of life (p = 0.01). RLTL was found to be lower in females with T1D (p < 0.001) and was positively related to the ‘gender–social support’ factor (p = 0.002) and diabetes duration (p = 0.045), adjusted for children’s age, parental age, and sociodemographic characteristics. These pilot findings indicate early emergence of shorter rLTL in T1D, pointing to a sexual dimorphism pattern. Insights into preventing LTL shortening in pediatric T1D can be gained from large-scale studies examining the impact of gender and social support. Full article

Multi-baseline phase unwrapping is a critical procedure in interferometric synthetic aperture radar (InSAR) data processing, and cluster analysis (CA)-based algorithms have become an important research direction in this field. However, traditional CA algorithms suffer from cluster group loss, cluster centerline offset under high noise, and time-consuming search, leading to limited unwrapping performance. To address these issues, this article proposes a multi-baseline phase unwrapping algorithm based on the integrated processing of intercept pre-filtering and ambiguity number vector determination, achieving significant performance improvements through four core technical optimisations. First, the linear relationship model of ambiguity numbers is extended to be compatible not only with the traditional one-transmitter, multi-receiver architecture but also with distributed multi-baseline InSAR systems with independent transmit–receive links for each baseline. Second, through verification from both forward and reverse uniqueness perspectives, a strict one-to-one mapping relationship between reference intercepts and ambiguity number combinations is established and validated. Third, a double constraints screening strategy for ambiguity number combinations combining the single-baseline elevation range intersection constraint and the multi-baseline elevation space common intersection constraint is designed. Integrating the effective elevation range of the observation area, this strategy accurately filters out valid ambiguity number combinations with physical rationality, ensuring the reliability of the reference intercept vector. Fourth, an intercept pre-filtering method based on the reference intercept vector is proposed, which unifies actual intercept pre-filtering and ambiguity number vector determination. To verify the performance of the proposed algorithm, a simulation data experiment under varying noise levels and real data experiments are conducted. Results demonstrate that the algorithm can maintain intact cluster structures under complex noise conditions. It achieves a synergistic improvement in unwrapping accuracy and computational efficiency, and thus significantly outperforms comparative algorithms. The proposed algorithm achieves high precision and efficiency for multi-baseline InSAR processing in complex scenarios, with important application value in practical engineering. Full article

Building Information Modelling has become a common paradigm in the construction industry. To bridge the gap between end users and BIM data, some studies have adopted Natural Language Processing (NLP) in the BIM applications. Due to the incorrect segmentation of users’ natural language, most NLP-based BIM applications usually provide users with redundant or inaccurate BIM data. Sequence labeling has been widely studied in the area of NLP to find correct segments of a natural language sequence. However, the existing sequence labeling schemes perform poorly for specific BIM models. To address this issue, this study proposed a BIM model of an adaptive natural-language Sequence Labeling scheme using Machine learning, termed BIM-SeL. We first presented the problem definition of sequence labeling and the overall framework of the BIM-SeL. The BIM-SeL employs Conditional Random Field (CRF) to model the sequence labeling problem and Machine learning to train a sequence labeling model using a corpus of millions of data from the news and web domains. Then, a BIM dictionary extraction algorithm is developed to collect the exclusive vocabularies from the BIM models. A BIM dictionary-enhanced sequence labeling scheme is proposed to achieve the BIM model adaptive sequence labeling, by jointly utilizing the trained sequence labeling model and the BIM dictionary. To further enhance contextual representation and compare with state-of-the-art deep learning methods, we extend BIM-SeL with an advanced BERT*-BiLSTM-CRF model under the same framework. The effectiveness of the BIM-SeL was verified through two real-world projects, the BUCEA Library and a water pump house. The experiment results showed that the sequence accuracies of BIM-SeL in the BUCEA Library and the water pump house projects achieved 92.61% and 93.41%, respectively, and the vocabulary accuracies reach 96.77% and 97.32%, respectively. Compared with the original CRF-based sequence labeling algorithm, the BIM-SeL improved the sequence accuracies by 7.05 and 18.50 times, and the vocabulary accuracies by 1.33 and 2.48 times, in the two projects. Meanwhile, the BERT-BiLSTM-CRF variant obtains up to 99.93% vocabulary accuracy on real BIM test sequences, further validating the generality and advancement of the proposed framework. These observations proved that the BIM-SeL contributed to the natural language understanding of BIM applications using BIM data and could bridge the gap between users and BIM data. Full article