Zaineb Ajra, Grégoire Vergotte, Stéphane Perrey
et al.
The incorporation of neuroimaging techniques such as electroenchephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has provided new opportunities for the analysis of dynamic brain processes involved in cognitive and motor functions. Despite the great contribution of the open-access neuroimaging datasets to neuroscience studies, they have mainly remained on a single modality and isolated task paradigms performed in a controlled environments. These limitations restrict the analysis of multi-task effects in real-world applications, thus creating a gap in the understanding of how cognitive and motor processes interact in daily life activities. To address these limitations, we present a multi-modal dataset containing neurophysiological (EEG, fNIRS), physiological (ECG), behavioral, and subjective measures collected from 30 healthy participants over three sessions. This dataset includes a hierarchical series of seven tasks ranging from single cognitive and motor activities, such as N-back, motor, passive motor, mental arithmetic and motor imagery, to combined cognitive-motor interactions simulating real life scenarios. This raw dataset provides a resource for developing advanced preprocessing methods and analysis pipelines, with potential applications in brain-computer interfaces, neurorehabilitation, and other fields requiring an understanding of multi-tasks brain dynamics. https://doi.org/10.18112/openneuro.ds007554.v1.0.0
Abstract Under dry friction conditions, friction heat accumulation is a key issue that leads to thermal fatigue, increased wear, and even failure of materials. To address this issue, this study proposes a friction-reducing and lubricating surface based on the synergistic effect of paraffin and surface texture. A ball-on-disk friction tester was utilized to test the textured surface and the composite surface, and the dynamic changes of temperature, friction coefficient, and wear amount of the two surfaces were compared during the friction process. Meanwhile, the effect of texture shape on the friction characteristics of the composite surface was explored, and the morphology of the worn surface was observed by the scanning electron microscope. In addition, a numerical model of the composite surfaces was established based on the fluent software to reveal the influence of texture shape on the lubrication performance. The results found that the phase change of paraffin could absorb the friction heat and lower the temperature at the friction interface by the degree of 11.2%. Meanwhile, the paraffin could provide the lubricating effect, which stabilized friction behavior and reduced friction coefficient and wear mass by the degrees of 69.1–76.8% and 79.5–86.2%, respectively. Among five texture shapes, the circle texture coupled with paraffin presented the lowest friction coefficient of 0.054 and mass loss of 1.6 mg. This study provides a solution for the simultaneous improvement of temperature rise, friction, and wear for the bearing raceway.
In this study, we introduce an Unmanned Aerial Vehicle (UAV) centric visual semantic simultaneous localization and mapping (SLAM) framework that integrates RGB–D cameras, inertial measurement units (IMUs), and a 5G–enabled remote interaction module. Our system addresses three critical limitations in existing approaches: (1) Distance constraints in remote operations; (2) Static map assumptions in dynamic environments; and (3) High–dimensional perception requirements for UAV–based applications. By combining YOLO–based object detection with epipolar–constraint-based dynamic feature removal, our method achieves real-time semantic mapping while rejecting motion artifacts. The framework further incorporates a dual–channel communication architecture to enable seamless human–in–the–loop control over UAV–Unmanned Ground Vehicle (UGV) teams in large–scale scenarios. Experimental validation across indoor and outdoor environments indicates that the system can achieve a detection rate of up to 75 frames per second (FPS) on an NVIDIA Jetson AGX Xavier using YOLO–FASTEST, ensuring the rapid identification of dynamic objects. In dynamic scenarios, the localization accuracy attains an average absolute pose error (APE) of 0.1275 m. This outperforms state–of–the–art methods like Dynamic–VINS (0.211 m) and ORB–SLAM3 (0.148 m) on the EuRoC MAV Dataset. The dual-channel communication architecture (Web Real–Time Communication (WebRTC) for video and Message Queuing Telemetry Transport (MQTT) for telemetry) reduces bandwidth consumption by 65% compared to traditional TCP–based protocols. Moreover, our hybrid dynamic feature filtering can reject 89% of dynamic features in occluded scenarios, guaranteeing accurate mapping in complex environments. Our framework represents a significant advancement in enabling intelligent UAVs/UGVs to navigate and interact in complex, dynamic environments, offering real-time semantic understanding and accurate localization.
Background. The inertia switches are traditionally used in mobile vehicles for commutating the electric circuits of the engineering systems. Triggering of such devices takes place mainly when having taken integral along linear acceleration while the vehicle’s space motion. To integrate the linear acceleration, both magnetic induction and hydraulic dampers are customary used. In a number of cases the hydraulic dampers, simplifying design of inertia switchers, are preferred. Given work presents the results of development of the inertia switch design, its mathematical model: differential equations, describing motion of mobile design elements, and the initial motion conditions, as well. For differential equating as follows assumptions are taken: the liquid is incompressible; no account to inertia switch components variation in dimension due to environmental variation in temperature; no account to dumping liquid viscosity variation due to environmental temperature variation. Materials and methods. The major feature of inertiaswitch designed to distinguish it from similar inertia devices is its feasible actuation at acceleration along either of the two axial directions. The contact system switches from the initial state at releasing and following turning of the jumper strap. Design of the inertia switch enables reliable retention of the initial state of a contact system at any operation conditions of the mobile vehicles as well as fail-safe switching while vehicle motion during specified period of time (speedup, braking) with acceleration of no less than certain (specified) value. Presented results demonstrate possibility of development of the reliable and technologically effective inertia switch, designed for engineering systems of the independent mobile vehicles.
The tandem tilt-wing UAV features an advanced aerodynamic layout design and is regarded as a solution for small-scale urban air mobility. However, the tandem wing configuration exhibits complex aerodynamic interactions between the front and rear wings during cruise flight and the wing tilt transition process. The objective of this paper is to investigate the aerodynamic coupling characteristics between the front and rear wings of the tandem tilt-wing UAV under level flight and tilt transition conditions while also assessing the influence of the propellers on the aircraft’s aerodynamic performance. Through CFD numerical analysis, the aerodynamic characteristics of various aircraft components are examined at different angles of attack and wing tilt angles, and the underlying reasons for the observed differences and variations are explored. The results indicate that, during level flight, the aerodynamic interference between the wings is primarily dominated by the detrimental influence of the front wing on the rear wing. During the tilt transition process, mutual interactions between the front and rear wings occur as wing tilt angle changes, leading to more drastic variations in lift coefficients and increased control difficulty. However, the propeller’s effect contributes to smoother changes in lift and drag, thereby enhancing aircraft stability.
To increase the efficiency of future electric vehicles, it is crucial to reduce drivetrain losses in battery-powered vehicles. This enables either an increase in driving range or overall cost savings by reducing battery capacity while maintaining the same range. Harmonic motor losses account for an avoidable share of more than 30% of the total eDrive losses in standard B6-2L 300 kW iPMSM configurations. These losses result from high-frequency voltage distortion across the motor windings, which can be reduced through various approaches. Of great importance is the classification of cost-neutral and low-cost concepts for loss reduction. The following presents and categorizes approaches to loss reduction that have been developed by research and industry in recent years. In particular, novel part-load-capable motor and inverter concepts are introduced, which enable motor switching or multilevel operation to reduce harmonic losses in the part-load range.
A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path electrostatic signal noise, which challenges the gas-path component condition monitoring and feature extraction techniques. Firstly, the integration of CEEMDAN addresses modal aliasing and intermittent signal challenges, while the proposed low-pass filtering method autonomously selects valuable signal components. Additionally, the application of the WT in the unselected components enhances the extraction of useful information, presenting a unique and advanced approach to electrostatic signal denoising. Moreover, the proposed method is applied to simulated signals with different input signal-to-noise ratios and experimental fault electrostatic signals of a micro-turbojet engine. The comparison with several traditional approaches in a denoising test for the simulated signals and experimental signals reveals that the proposed method performs better in extracting the effective components of the signal and eliminating noise.
Earthquakes pose significant risks to national stability, endangering lives and causing substantial economic damage. This study tackles the urgent need for efficient post-earthquake relief in search and rescue (SAR) scenarios by proposing a multi-UAV cooperative rescue task allocation model. With consideration the unique requirements of post-earthquake rescue missions, the model aims to minimize the number of UAVs deployed, reduce rescue costs, and shorten the duration of rescue operations. We propose an innovative hybrid algorithm combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), called the PSOGWO algorithm, to achieve the objectives of the model. This algorithm is enhanced by various strategies, including interval transformation, nonlinear convergence factor, individual update strategy, and dynamic weighting rules. A practical case study illustrates the use of our model and algorithm in reality and validates its effectiveness by comparing it to PSO and GWO. Moreover, a sensitivity analysis on UAV capacity highlights its impact on the overall rescue time and cost. The research results contribute to the advancement of vehicle-routing problem (VRP) models and algorithms for post-earthquake relief in SAR. Furthermore, it provides optimized relief distribution strategies for rescue decision-makers, thereby improving the efficiency and effectiveness of SAR operations.
Martin Jirásek, Tomáš Sieger, Gabriela Chaloupková
et al.
Objective: To assess the effect of overall, between- and within-day subjectively rated fluctuations in motor and non-motor symptoms in people with functional motor disorder (FMD) on the health-related quality of life (HRQoL). Background: FMD is a complex condition characterized by fluctuating motor and non-motor symptoms that may negatively impact HRQoL. Methods: Seventy-seven patients (54 females, mean age 45.4 (SD 10.4) years) with a clinically established diagnosis of FMD, including weakness, completed symptom diaries, rating the severity of motor and non-motor symptoms (i.e., pain, fatigue, mood, cognitive difficulties) on a 10-point numerical scale three times daily for seven consecutive days. HRQoL was assessed using the SF-36 questionnaire. For the analysis, fluctuation magnitude was defined in terms of the variability in self-reported symptom scores. Results: The mental component of SF-36 was jointly predicted by the overall severity scores (t(74) = -3.61, P < 0.001) and overall general fluctuations (t(74) = -2.98, P = 0.004). The physical SF-36 was found to be related only to the overall symptom severity scores (t(74) = -7.09, P < 0.001), but not to the overall fluctuations. The assessment of the impact of different components showed that the mental component of SF-36 was significantly influenced by the combined effect of average fatigue (t(73) = -3.86, P < 0.001), between-day cognitive symptoms fluctuations (t(73) = -3.22, P = 0.002), and within-day mood fluctuations (t(73) = -2.48, P = 0.015). Conclusions: This study demonstrated the impact of self-reported symptom fluctuations across multiple motor and non-motor domains on mental but not physical HRQoL in FMD and highlighted the importance of assessing and managing fluctuations in clinical practice.
Ankur Kamboj, Rajiv Ranganathan, Xiaobo Tan
et al.
Conventional approaches to enhancing movement coordination, such as providing instructions and visual feedback, are often inadequate in complex motor tasks with multiple degrees of freedom (DoFs). To effectively address coordination deficits in such complex motor systems, it becomes imperative to develop interventions grounded in a model of human motor learning; however, modeling such learning processes is challenging due to the large DoFs. In this paper, we present a computational motor learning model that leverages the concept of motor synergies to extract low-dimensional learning representations in the high-dimensional motor space and the internal model theory of motor control to capture both fast and slow motor learning processes. We establish the model's convergence properties and validate it using data from a target capture game played by human participants. We study the influence of model parameters on several motor learning trade-offs such as speed-accuracy, exploration-exploitation, satisficing, and flexibility-performance, and show that the human motor learning system tunes these parameters to optimize learning and various output performance metrics.
Yasuhiro Miyazawa, Dahun Lee, Seonghyun Kim
et al.
A long-standing challenge in impact mitigation is the development of versatile and omnifarious protective structures capable of encompassing a wide spectrum of scenarios, for example, ranging from low-speed pedestrian impacts to high-speed vehicle collisions. However, most existing impact mitigation strategies rely on fixed geometries or pre-tuned material properties targeting specific impact speed, lacking the ability to adapt in real time. Here, we draw inspiration from origami to design impact mitigation structures that exhibit multi-modal and self-adaptive behavior. We introduce a Resch-patterned origami structure that hosts two distinctive deformation modes: a monostable folding mode and a bistable unfolding mode featuring snap-through. Impact experiments reveal a speed-dependent dynamic bifurcation, wherein the structure autonomously switches between folding and unfolding in response to the applied impact velocity. This dynamic bifurcation, intrinsically distinct from kinematic or static origami bifurcations, enables real-time selection of deformation pathways that enhance energy dissipation across a broad range of impact conditions. We further demonstrate the scalability and practical relevance of this mechanism by fabricating tessellations in a bumper-like configuration and evaluating their performance using a pendulum-based mannequin impact test. Together, these results establish dynamic bifurcation in origami-based structures as an adaptive impact mitigation strategy. This approach enables scalable and programmable protective systems that autonomously select deformation modes in real time, with broad relevance to adaptive robotics, smart protective armor, and aerospace damping technologies.
With the requirements that a type of platform can perform diversified combat tasks and the needs for cost control, the airborne mission pod is bound to play a more important role in future operations. Therefore, research is carried out on the reconnaissance and surveillance, target indication, electronic countermeasure and other multi type airborne mission pods carried by foreign manned and unmanned combat aircraft, and the mainstream models, technical characteristics, development ideas, development paths, and adaptive carriers of airborne mission pods are analyzed. It is found that the development of foreign pods has gone from based on aircraft platforms to independent of platforms, from customization to generalization, and data processing and information generation have gradually become intelligent and autonomous. Finally, the development trend of intelligent, generalization, modularity and system architecture opening of airborne mission pod in the future is summarized.
T. M. J. T. Baltussen, M. Goutham, M. Menon
et al.
Autonomous mobile robots enable increased flexibility of manufacturing systems. The design and operating strategy of such a fleet of robots requires careful consideration of both fixed and operational costs. In this paper, a Monte-Carlo Tree Search (MCTS)-based metaheuristic is developed that guides a Branch & Bound (B&B) algorithm to find the globally optimal solution to the Fleet Size and Mix Vehicle Routing Problem with Time Windows (FSMVRPTW).The metaheuristic and exact algorithms are implemented in a parallel hybrid optimization algorithm where the metaheuristic rapidly finds feasible solutions that provide candidate upper bounds for the B&B algorithm. The MCTS additionally provides a candidate fleet composition to initiate the B&B search. Experiments show that the proposed approach results in significant improvements in computation time and convergence to the optimal solution.
The powertrains of today's hybrid electric vehicles (HEVs) are developed for human drivers and, therefore, may not be the optimum choice for future Autonomous vehicles (AVs), given that AVs can accurately manipulate their velocity profile to avoid unnecessary energy loss. In this work, we closely examine the necessary degree of hybridization for AVs compared to human drivers by deploying real-world urban driving profiles and generating equivalent AV drive cycles in a mixed autonomy scenario. We solve the optimal energy management problem for HEVs with various motor sizes from the automotive market, and demonstrate that while human drivers typically require a motor size of around 30 kW to fully benefit from hybridization, AVs can achieve similar gains with only a 12 kW motor. This greater benefit from a smaller motor size can be attributed to a more optimal torque request, allowing for higher gains from regenerative braking and a more efficient engine operation. Furthermore, We investigate the benefits of velocity smoothing for both traditional cars and HEVs and explore the role of different mechanisms contributing to fuel consumption reduction. Our analysis reveals that velocity smoothing provides greater benefits to HEVs equipped with small motors compared to non-hybrid vehicles and HEVs with larger motors.
Silvia Romero-Azpitarte, Cristina Luna, Alba Guerra
et al.
Space exploration and establishing human presence on other planets demand advanced technology and effective collaboration between robots and astronauts. Efficient space resource utilization is also vital for extraterrestrial settlements. The Collaborative In-Situ Resources Utilisation (CISRU) project has developed a software suite comprising five key modules. The first module manages multi-agent autonomy, facilitating communication between agents and mission control. The second focuses on environment perception, employing AI algorithms for tasks like environment segmentation and object pose estimation. The third module ensures safe navigation, covering obstacle avoidance, social navigation with astronauts, and cooperation among robots. The fourth module addresses manipulation functions, including multi-tool capabilities and tool-changer design for diverse tasks in In-Situ Resources Utilization (ISRU) scenarios. Finally, the fifth module controls cooperative behaviour, incorporating astronaut commands, Mixed Reality interfaces, map fusion, task supervision, and error control. The suite was tested using an astronaut-rover interaction dataset in a planetary environment and GMV SPoT analogue environments. Results demonstrate the advantages of E4 autonomy and AI in space systems, benefiting astronaut-robot collaboration. This paper details CISRU's development, field test preparation, and analysis, highlighting its potential to revolutionize planetary exploration through AI-powered technology.