Search Results (746)

Aiming at the issue of time-varying measurement noise with heavy-tailed characteristics and outliers generated by the polarization compass (PC) in the micro-electro-mechanical system–inertial navigation system (MEMS-INS) and PC-integrated navigation system when it is subject to internal and external disturbances, an improved Variational Bayesian Innovation Saturation Robust Adaptive Kalman filter (VISKF) algorithm is proposed. This algorithm utilizes the variational Bayesian (VB) method based on Student’s t-distribution (STD) to approximately calculate the statistical characteristics of the time-varying measurement noise of the PC, thereby obtaining more accurate measurement noise statistical parameters. Additionally, the algorithm introduces an innovation saturation function and proposes an adaptive update strategy for the saturation boundary. It mitigates the problem of innovation value divergence in PC caused by outliers through a two-layer structure that can track the changes in the innovation value to adaptively adjust the saturation boundary. To verify the effectiveness of the algorithm, static and dynamic experiments were conducted on an unmanned vehicle. The experimental results show that compared with adaptive Kalman filter (AKF), variational Bayesian robust adaptive Kalman filter (VBRAKF), and innovation saturate robust adaptive Kalman filter (ISRAKF), the proposed algorithm improves the dynamic orientation accuracy by 76.89%, 67.23%, and 84.45%, respectively. Moreover, compared with other similar target algorithms, the proposed algorithm also has obvious advantages. Therefore, this method can significantly improve the navigation accuracy and robustness of the INS/PC integrated navigation system in complex environments. Full article

This study presents the simulation of a standalone photovoltaic (PV) water pumping system that is made for use in rural areas and off-grid applications. The system contains a 174 W PV panel, a DC-DC boost converter, a DC motor, and a centrifugal pump. To optimize energy extraction, three maximum power point techniques (MPPT), Perturb and Observe (P&O), incremental conductance (INC), and a Hybrid P&O–INC algorithm, were implemented and evaluated. Unlike most prior studies focusing on large-scale systems, this work targets low-power configurations with load dynamics specific to motor–pump assemblies. The hybrid algorithm is finely tuned using conservative step sizes and adaptive switching thresholds. Simulation results under varying irradiance levels show that the hybrid MPPT achieves the best trade-off, combining high tracking efficiency with reduced power ripple, particularly under challenging low-irradiance conditions. Moreover, the approach offers a favorable balance between performance and implementation cost, positioning it as a viable and scalable solution for sustainable water supply in remote communities. Full article

In recent years, the application of deep learning technology in the field of food engineering has developed rapidly. As an essential food raw material and processing target, the number of kernels per maize plant is a critical indicator for assessing crop growth and predicting yield. To address the challenges of frequent target ID switching, high falling speed, and the limited accuracy of traditional methods in practical production scenarios for maize kernel falling count, this study designs and implements a real-time kernel falling counting system based on a Convolutional Neural Network (CNN). The system captures dynamic video streams of kernel falling using a high-speed camera and innovatively integrates the YOLOv8 object detection framework with the ByteTrack multi-object tracking algorithm to establish an efficient and accurate kernel trajectory tracking and counting model. Experimental results demonstrate that the system achieves a tracking and counting accuracy of up to 99% under complex falling conditions, effectively overcoming counting errors caused by high-speed motion and object occlusion, and significantly enhancing robustness. This system combines high intelligence with precision, providing reliable technical support for automated quality monitoring and yield estimation in food processing production lines, and holds substantial application value and prospects for widespread adoption. Full article

To address suboptimal throughput performance in conventional intelligent apple harvesting systems predominantly employing single manipulators, a dual-arm harvesting robot prototype was engineered. Leveraging the AUBO-i5 manipulator framework and kinematic characteristics, a coordinated workspace arrangement was established. Subsequently, the dual-manipulator harvesting platform was fabricated. A dynamic task allocation methodology and intelligent fruit sequencing approach were formulated, grounded in U-tube optimization principles. This framework achieved parallel operation ratios between 82.1% and 99%, with combined trajectory lengths spanning 9.24–11.90 m. Building upon established apple harvesting knowledge, a sequencing strategy incorporating dynamic manipulator zoning was developed. Validation was conducted through V-REP kinematic simulations where end-effector poses were continuously tracked, confirming zero limb interference during coordinated motion. Field assessments yielded parallel operation rates of 85.7–93.3%, total harvest durations of 17.8–22.3 s, and inter-manipulator path differentials of 267–541 mm. Throughout testing, collision-free operation was maintained while successfully harvesting all target fruits according to planned sequences. These outcomes validate the efficacy of U-tube-based dynamic zoning and sequencing methodologies for dual-manipulator fruit harvesting in intelligent orchard applications. Full article

Existing visual SLAM systems with neural representations excel in static scenes but fail in dynamic environments where moving objects degrade performance. To address this, we propose a robust dynamic SLAM framework combining classic geometric features for localization with learned photometric features for dense mapping. Our method first tracks objects using instance segmentation and a Kalman filter. We then introduce a cascaded, coarse-to-fine strategy for efficient motion analysis: a lightweight sparse optical flow method performs a coarse screening, while a fine-grained dense optical flow clustering is selectively invoked for ambiguous targets. By filtering features on dynamic regions, our system drastically improves camera pose estimation, reducing Absolute Trajectory Error by up to 95% on dynamic TUM RGB-D sequences compared to ORB-SLAM3, and generates clean dense maps. The 3D Gaussian Splatting backend, optimized with a Gaussian pyramid strategy, ensures high-quality reconstruction. Validations on diverse datasets confirm our system’s robustness, achieving accurate localization and high-fidelity mapping in dynamic scenarios while reducing motion analysis computation by 91.7% over a dense-only approach. Full article

For unmanned ground vehicles in squad mission support systems (SMSS-UGVs), tracking the entire squad as a group, rather than focusing on individual members, can effectively mitigate issues such as target loss caused by occlusion and environmental interference. However, most existing group target tracking methods are designed for extended targets, which typically assume a rigid and unchanging shape. In contrast, pedestrian groups in SMSS-UGV scenarios exhibit inconsistent motions among members, resulting in continuous changes in the overall group shape. To address this challenge, this paper proposes a group target tracking method specifically tailored for SMSS-UGVs in pedestrian tracking scenarios. We introduce a tracking framework that incorporates a data selection mechanism based solely on positional information, enabling robust handling of dynamic group composition through adaptive shape modeling. Furthermore, a novel group target tracking method based on multi-ellipse shape modeling (ME-CGT-UGV) is presented, which effectively captures complex and evolving group formations. The experimental results show that the proposed method reduces orientation error by 86.13% compared to single-target tracking and by 54.79% compared to shapeless modeling methods. It also maintains strong performance under challenging conditions, including occlusions, environmental disturbances, sharp turns, and formation changes. These findings indicate that the proposed approach significantly enhances the effectiveness and operational reliability of SMSS-UGVs in real-world applications. Full article

The utilization of knowledge management (KM) assists construction companies in planning for waste management. This study applies KM in the material recovery of a public sector building renovation, focusing on aluminum composite panels (ACPs). The cost/benefit analysis (CBA) method examines suitable scenarios, where costs and benefits cover economic, environmental, and social perspectives. The cost/benefit (C/B) ratios reveal that the repurposing scenario, where ACP waste is repurposed as signboards, is the most suitable scenario, with a C/B of 0.96. The refurbishing scenario, in which ACP waste is refurbished as new facades, may be considered if the labor cost could be reduced through training. The repurposing scenario is further examined with a sensitivity analysis and the Leadership in Energy and Environmental Design certification, and it is found that implementing this scenario serves as a beginning step toward green certification and aligns with Thailand’s national strategies for green building promotion and the long-term Net Zero 2065 target. The study results serve as a guideline for Thailand’s transition toward a low-carbon and resource-efficient construction sector. Future studies are recommended to examine the complex relationships between costs and benefits and to track dynamic changes in the C/B ratio over time. Full article

Human energy production and consumption have significantly contributed to the environmental crisis, impacting human health, wellbeing, and social justice. In this context, the concept of energy citizenship has emerged, referring to civic engagement in fostering sustainable and democratic energy systems and transitions. Under the Horizon Europe project EnergyPROSPECTS (PROactive Strategies and Policies for Energy Citizenship Transformation), we investigated the conditions and dynamics that promote or hinder energy citizenship and empower citizens to contribute to sustainable energy transformations. Through 44 in-depth interviews and four deliberative workshops in four European case study regions with individuals and organizations engaged in different forms of energy citizenship, we identified key psychological and organizational factors driving citizen empowerment. These findings informed the development of an interactive empowerment toolkit, a digital learning resource designed to enhance energy citizenship literacy and skills. This toolkit, although primarily targeting adults interested in energy citizenship, is adaptable for students and educators at various levels, offering two tracks: one for beginners with no prior involvement in the exercise of energy citizenship, and another for those with experience in energy activism. We highlight the scientific basis of the toolkit, detailing its components and demonstrating its application in fostering energy citizenship empowerment. The tool aims to equip users with the skills and knowledge necessary to actively participate in sustainable energy transitions. Full article

Distributed target tracking in multi-agent networks plays a critical role in cooperative sensing and autonomous navigation. However, it faces significant challenges in highly dynamic and adversarial setups. This study aims to enhance the resilience of decentralized target tracking algorithms against measurement faults and cyber–physical threats, especially false data injection attacks. We propose a consensus-based estimation algorithm that integrates a nearly constant velocity model with saturation-based filtering to suppress impulsive measurement variations and promote robust, distributed state estimation. To counteract adversarial conditions, we incorporate a dynamic false data injection detection and isolation mechanism that uses innovation thresholds to identify and disregard suspicious measurements before they can degrade the global estimate. The effectiveness of the proposed algorithms is demonstrated through a series of simulation-based case studies under both benign and adversarial conditions. The results show that increased network connectivity and higher consensus iteration rates improve estimation accuracy and convergence speed, while properly tuned saturation filters achieve a practical balance between fault suppression and accurate estimation. Furthermore, under localized, coordinated, and transient false data injection attacks, the detection mechanism successfully identifies compromised agents and prevents their data from corrupting the distributed global estimate. Overall, this study illustrates that the proposed algorithm provides a simplified fault-tolerant solution that significantly enhances the accuracy and resilience of distributed target tracking without imposing excessive communication or computational burdens. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

With the development of multi-sensor collaborative detection technology, radar track fusion has become a key means to improve target tracking accuracy. Traditional fusion methods based on Kalman filtering and weighted averaging have the problem of insufficient adaptability in complex environments. This paper proposes an end-to-end deep learning track fusion method, which achieves high-precision track reconstruction through residual extraction and parallel network fusion, providing a new end-to-end method for track fusion. The method combines the attention mechanism and the long short-term memory network in parallel and optimizes the computational complexity. Through the uncertainty weighting mechanism, the fusion weight is dynamically adjusted according to the reliability of the track features. Experimental results show that the mean absolute error of fusion accuracy of this method is 79% lower than the Kalman filter algorithm and about 87% lower than the mainstream deep learning model, providing an effective way for multi-radar track fusion in complex scenarios. Full article

Most existing Simultaneous Localization and Mapping (SLAM) systems rely on the assumption of static environments to achieve reliable and efficient mapping. However, such methods often suffer from degraded localization accuracy and mapping consistency in dynamic settings, as they lack explicit mechanisms to distinguish between static and dynamic elements. To overcome this limitation, we present BMP-SLAM, a vision-based SLAM approach that integrates semantic segmentation and Bayesian motion estimation to robustly handle dynamic indoor scenes. To enable real-time dynamic object detection, we integrate YOLOv5, a semantic segmentation network that identifies and localizes dynamic regions within the environment, into a dedicated dynamic target detection thread. Simultaneously, the data association Bayesian mobile probability proposed in this paper effectively eliminates dynamic feature points and successfully reduces the impact of dynamic targets in the environment on the SLAM system. To enhance complex indoor robotic navigation, the proposed system integrates semantic keyframe information with dynamic object detection outputs to reconstruct high-fidelity 3D point cloud maps of indoor environments. The evaluation conducted on the TUM RGB-D dataset indicates that the performance of BMP-SLAM is superior to that of ORB-SLAM3, with the trajectory tracking accuracy improved by 96.35%. Comparative evaluations demonstrate that the proposed system achieves superior performance in dynamic environments, exhibiting both lower trajectory drift and enhanced positioning precision relative to state-of-the-art dynamic SLAM methods. Full article

Foreign object intrusion in railway perimeter areas poses significant risks to train operation safety. To address the limitation of current visual detection technologies that overly focus on target identification while lacking quantitative risk assessment, this paper proposes a railway intrusion risk quantification method integrating track semantic segmentation and spatiotemporal features. An improved BiSeNetV2 network is employed to accurately extract track regions, while physical-constrained risk zones are constructed based on railway structure gauge standards. The lateral spatial distance of intruding objects is precisely calculated using track gauge prior knowledge. A lightweight detection architecture is designed, adopting ShuffleNetV2 as the backbone to reduce computational complexity, with an incorporated Dilated Transformer module to enhance global context awareness and sparse feature extraction, significantly improving detection accuracy for small-scale objects. The comprehensive risk assessment formula integrates object category weights, lateral risk coefficients in intrusion zones, longitudinal distance decay factors, and dynamic velocity compensation. Experimental results demonstrate that the proposed method achieves 84.9% mean average precision (mAP) on our proprietary dataset, outperforming baseline models by 3.3%. By combining lateral distance detection with multidimensional risk indicators, the method enables quantitative intrusion risk assessment and graded early warning, providing data-driven decision support for active train protection systems and substantially enhancing intelligent safety protection capabilities. Full article

Monitoring and tracking small moving objects in cluttered environments remain a major challenge for artificial-intelligence-based motion vision systems. This difficulty is not only due to the limited features presented by small objects themselves but also because of the numerous fake features present in complex dynamic environments. Drawing inspiration from the efficient small target motion detection mechanisms in insects’ brains, researchers have developed various visual networks for detecting tiny moving objects within complex natural environments. Although these networks perform well in detecting small-object motion by leveraging motion information, their ability to distinguish true targets from background noise remains severely limited under low-light conditions, where the contrast of small targets drops sharply and they are more easily overwhelmed by false motion in the background. To resolve the aforementioned limitation, this research proposes a new visual neural network. The network achieves effective discrimination between small moving targets and false targets in the background in low-light environments by leveraging the motion information for the targets and the differences in the response gradients between real moving targets and fake objects from the background. The designed network is composed of two main components: a motion perception module and a response gradient analysis module. The motion information perception module is responsible for acquiring the motion and position information for small targets, while the response gradient detection module extracts the response gradients between a tiny object and a background object and integrates the motion information, thereby effectively distinguishing small targets from fake background objects. The experimental results demonstrate that the proposed model can effectively distinguish small targets and suppress background false alarms in low-light environments. Comparisons of the experimental performance show that under a fixed false alarm rate, our model achieved a detection rate of 0.8. In addition, the proposed method recorded an average precision of 0.1 and an average F1-score of 0.1888. In contrast, the highest average precision achieved by the other methods was only 0.0075, and the highest F1-score was 0.0151. These results clearly indicate that our method substantially outperforms previous approaches in both its average precision and F1-score. These results collectively validate the effectiveness and competitiveness of the proposed model in small target detection tasks under low-light conditions. Full article

Background. Antimicrobial resistance (AMR) poses a significant global health threat, necessitating a deeper understanding of bacterial adaptation mechanisms. Introduction. This study investigates the genotypic and phenotypic evolutionary trajectories of Escherichia coli under meropenem and gentamicin selection, and it benchmarks these findings against florfenicol-evolved strains. Methodology. Utilizing a downsized, three-layer acrylic modified “Microbial Evolution and Growth Arena (MEGA-plate) system”—scaled to 40 × 50 cm for sterile handling and uniform 37 °C incubation—we tracked adaptation over 9–13 days, enabling real-time visualization of movement across antibiotic gradients. Results. Meropenem exposure elicited pronounced genetic heterogeneity and morphological remodeling (filamentous and circular forms), characteristic of SOS-mediated division arrest and DNA-damage response. In contrast, gentamicin exposure produced a uniform resistance gene profile and minimal shape changes, suggesting reliance on conserved defenses without major morphological adaptation. Comprehensive genomic analysis revealed a core resistome of 22 chromosomal loci shared across all three antibiotics, highlighting potential cross-resistance and the central roles of baeR, gadX, and marA in coordinating adaptive responses. Gene ontology enrichment underscored the positive regulation of gene expression and intracellular signaling as key themes in resistance evolution. Discussion. Our findings illustrate the multifaceted strategies E. coli employs—combining metabolic flexibility with sophisticated regulatory networks—to withstand diverse antibiotic pressures. This study underscores the utility of the MEGA-plate system in dissecting spatiotemporal AMR dynamics in a controlled yet ecologically relevant context. Conclusions. The divergent responses to meropenem and gentamicin highlight the complexity of resistance development and reinforce the need for integrated, One Health strategies. Targeting shared regulatory hubs may open new avenues for antimicrobial intervention and help preserve the efficacy of existing drugs. Full article