The internal displacement change around shrinkage pores，stress and strain distribution，and microstructural evolution in the defect healing zone of ZTC4 titanium alloy during hot isostatic pressing (HIP) are investigated by numerical simulation combined with experimental methods. The results demonstrate that under the high-temperature and high-pressure conditions of HIP，high stress-strain zones form around the shrinkage pores. The stress magnitude shows an inverse relationship with the distance from pore surfaces，exhibiting higher stress levels in proximity to the pore boundaries. As the shrinkage pore size decreases，the strain concentration intensifies. After HIP，a radial pore-healing zone microstructure develops at the original shrinkage pore sites. Within this healed region，heterogeneous plastic deformation occurs among distinct α/β colonies. Colonies with more readily activated dislocation slip systems experience greater deformation magnitudes，ultimately leading to the formation of equiaxed grain structures.
Reaching for grasping, and manipulating objects are essential motor functions in everyday life. Decoding human motor intentions is a central challenge for rehabilitation and assistive technologies. This study focuses on predicting intentions by inferring movement direction and target location from multichannel electromyography (EMG) signals, and investigating how spatially and temporally accurate such information can be detected relative to movement onset. We present a computational pipeline that combines data-driven temporal segmentation with classical and deep learning classifiers in order to analyse EMG data recorded during the planning, early execution, and target contact phases of a delayed reaching task. Early intention prediction enables devices to anticipate user actions, improving responsiveness and supporting active motor recovery in adaptive rehabilitation systems. Random Forest achieves $80\%$ accuracy and Convolutional Neural Network $75\%$ accuracy across $25$ spatial targets, each separated by $14^\circ$ azimuth/altitude. Furthermore, a systematic evaluation of EMG channels, feature sets, and temporal windows demonstrates that motor intention can be efficiently decoded even with drastically reduced data. This work sheds light on the temporal and spatial evolution of motor intention, paving the way for anticipatory control in adaptive rehabilitation systems and driving advancements in computational approaches to motor neuroscience.
Teodor-Viorel Chelaru, Cristian Emil Constantinescu, Valentin Pană
et al.
This paper presents a stability analysis of single-channel, slow-rolling, Semi-Automatic Command to Line of Sight (SACLOS) missiles using a comparison of the Routh–Hurwitz and the Frank–Wall stability criteria and a nonlinear analysis. Beginning with a six-degree-of-freedom (6-DOF) model in the Resal frame, a linearized model for the commanded motion is developed. This linearized model, which features complex coefficients due to the coupling of longitudinal channels in rolling missiles, is used to define the structural scheme of the commanded object and its flight quality parameters. The guidance kinematic relations, guidance device equations, and actuator relations, incorporating a switching function specific to slow-rolling, single-channel missiles, are also defined and linearized within the Resal frame to construct a comprehensive structural diagram of the SACLOS missile. From this, the characteristic polynomial with complex coefficients is derived and analyzed by comparing the Routh–Hurwitz and the Frank–Wall stability criteria. This analysis determines a stability domain for the guidance gain and establishes a minimum limit for the guidance time. The stability domain defined through the linear model is then validated using a nonlinear model in the body frame.
Reliability analysis of aeroengine structures is a critical task in aerospace engineering, but traditional methods often face challenges of low computational efficiency and insufficient accuracy when dealing with complex, high-dimensional, and nonlinear problems. This paper proposes a novel reliability assessment method (AC-Kriging) based on the Actor–Critic network and Kriging surrogate models to address these issues. The Actor network optimizes the sampling strategy for design variables, making sampling more efficient. The Critic network assesses the reliability of these samples to ensure accurate results, while a Kriging surrogate model replaces expensive finite element simulations and cuts computational cost. Three case studies demonstrate that AC-Kriging significantly outperforms traditional methods in both sampling efficiency and reliability estimation accuracy. This research provides an efficient and reliable solution for reliability analysis of aeroengine structures, with important theoretical and engineering application value. Three case studies demonstrate that AC-Kriging significantly outperforms traditional methods in both sampling efficiency and reliability-estimation accuracy, requiring only 52–147 samples to achieve comparable accuracy while maintaining the relative failure probability error within 0.87–7.27%. This research provides an efficient and reliable solution for the reliability analysis of aeroengine structures.
Propellers are essential aerodynamic components widely used in aerospace engineering, marine vessels, and aerial platforms. With the growing demand for high-thrust electric unmanned aerial vehicles, greater emphasis is being placed on improving propeller aerodynamic performance and efficiency to enhance flight endurance and payload capacity. Traditional design methods, mostly based on blade element theory, simplify the blade into two-dimensional planar elements, making it difficult to accurately capture the three-dimensional streamline characteristics during rotation. This mismatch between geometric design and actual flow limits further improvements in propulsion efficiency. This paper proposes a two-dimensional airfoil body-fitted design method to address this limitation. This method is based on blade element theory and vortex theory to obtain the chord length and pitch angle distribution under specific operating conditions. Based on these distributions, each blade element is bent to fit a virtual cylindrical surface at the corresponding position. This ensures that all points on the two-dimensional airfoil are equidistant from the hub center. The proposed design method is validated through numerical simulations. The results show that the propeller designed with the body-fitted method improves efficiency by 4.2% compared with the one designed using blade element theory. This work provides a new technical approach for propeller design and has practical value for improving propeller efficiency.
Despite a slow neuromuscular system, humans easily outperform modern robot technology, especially in physical contact tasks. How is this possible? Biological evidence indicates that motor control of biological systems is achieved by a modular organization of motor primitives, which are fundamental building blocks of motor behavior. Inspired by neuro-motor control research, the idea of using simpler building blocks has been successfully used in robotics. Nevertheless, a comprehensive formulation of modularity for robot control remains to be established. In this paper, we introduce a modular framework for robot control using motor primitives. We present two essential requirements to achieve modular robot control: independence of modules and closure of stability. We describe key control modules and demonstrate that a wide range of complex robotic behaviors can be generated from this small set of modules and their combinations. The presented modular control framework demonstrates several beneficial properties for robot control, including task-space control without solving Inverse Kinematics, addressing the problems of kinematic singularity and kinematic redundancy, and preserving passivity for contact and physical interactions. Further advantages include exploiting kinematic singularity to maintain high external load with low torque compensation, as well as controlling the robot beyond its end-effector, extending even to external objects. Both simulation and actual robot experiments are presented to validate the effectiveness of our modular framework. We conclude that modularity may be an effective constructive framework for achieving robotic behaviors comparable to human-level performance.
Titanium alloys, with their low density, exceptional mechanical properties, and outstanding corrosion resistance, play a vital role in various aerospace applications. Our decision science-driven assessment focused on metastable <i>β</i>, near-<i>β</i>, <i>α</i> + <i>β</i>, and near-<i>α</i> Ti alloys for landing gear applications, integrating multiple-attribute decision-making (MADM) methods, principal component analysis (PCA), and hierarchical clustering (HC) is based on current literature. The ranks of the alloys evaluated by diverse MADM methods were consistent. The methodology identifies five top-ranked Ti alloys assists and verifies the guidelines for alloy design. The top-ranked alloy, Ti1300-BM-nano-α (alloy chemistry: Ti-5Al-4V-4Mo-3Zr-4Cr, solution treatment: 800 °C for 1 h followed by air cooling—solution treated below <i>β</i> transus, and aging: 500 °C for 4 h followed by air cooling), stands out with a percentage elongation (<i>%EL</i>) ~3.3 times greater than the benchmark or goal (density, <i>d</i> = ~4.6 g/cm<sup>3</sup>; yield strength <i>YS</i> = ~1250 MPa; <i>%El</i> = ~5), while maintaining similar density and yield strength. The analyses underline that metastable <i>β</i> Ti alloys comprising globular primary <i>α</i> + trans <i>β</i> matrix coupled with <i>α</i> precipitates in trans <i>β</i> are the base optimal microstructure to fine-tune using thermomechanical processing for aircraft landing gear applications.
Monitoring and mapping crop water stress and variability at a farm scale for cereals such as maize, one of the most common crops in developing countries with 200 million people around the world, is an important objective within precision agriculture. In this regard, unmanned aerial vehicle-obtained multispectral and thermal imagery has been adopted to estimate the crop water stress proxy (i.e., Crop Water Stress Index) in conjunction with algorithm machine learning techniques, namely, partial least squares (PLS), support vector machines (SVM), and random forest (RF), on a typical smallholder farm in southern Africa. This study addresses this objective by determining the change between foliar and ambient temperature (Tc-Ta) and vapor pressure deficit to determine the non-water stressed baseline for computing the maize Crop Water Stress Index. The findings revealed a significant relationship between vapor pressure deficit and Tc-Ta (R<sup>2</sup> = 0.84) during the vegetative stage between 10:00 and 14:00 (South Africa Standard Time). Also, the findings revealed that the best model for predicting the Crop Water Stress Index was obtained using the random forest algorithm (R<sup>2</sup> = 0.85, RMSE = 0.05, MAE = 0.04) using NDRE, MTCI, CCCI, GNDVI, TIR, Cl_Red Edge, MTVI2, Red, Blue, and Cl_Green as optimal variables, in order of importance. The results indicated that NIR, Red, Red Edge derivatives, and thermal band were some of the optimal predictor variables for the Crop Water Stress Index. Finally, using unmanned aerial vehicle data to predict maize crop water stress index on a southern African smallholder farm has shown encouraging results when evaluating its usefulness regarding the use of machine learning techniques. This underscores the urgent need for such technology to improve crop monitoring and water stress assessment, providing valuable insights for sustainable agricultural practices in food-insecure regions.
Gholamreza Davarpanah, Sajjad Mohammadi, James L. Kirtley
This paper focuses on designing new motors with high torque density, which is crucial for applications ranging from electric vehicles to robotics. We propose a double-teeth C-core switched reluctance motor with hybrid excitation, integrating permanent magnets and a novel drive technique to enhance motor torque density. We explore three magnet placement configurations to maximize torque. A common challenge with most self-starting methods used in two-phase SRMs is the generation of negative torque, which reduces the motor's torque density. Our adopted self-starting method minimizes negative torque, and we introduce a new drive strategy to control the switching on and off, effectively eliminating negative torque. Additionally, magnetic equivalent circuits are developed for the analytical design and theoretical analysis of all configurations. The SRMs under study are prototyped and tested, and their performances are evaluated in terms of torque-angle characteristics, current, and voltage. Both experimental and simulation results validate the effectiveness of the PM-assisted SRMs in enhancing torque density and efficiency.
Yueyang Wang, Aravinda Ramakrishnan Srinivasan, Yee Mun Lee
et al.
Understanding pedestrian behavior is crucial for the safe deployment of Autonomous Vehicles (AVs) in urban environments. Traditional pedestrian behavior models often fall into two categories: mechanistic models, which do not generalize well to complex environments, and machine-learned models, which generally overlook sensory-motor constraints influencing human behavior and thus prone to fail in untrained scenarios. We hypothesize that sensory-motor constraints, fundamental to how humans perceive and interact with their surroundings, are essential for realistic simulations. Thus, we introduce a constrained reinforcement learning (RL) model that simulates the crossing decision and locomotion of pedestrians. It was constrained to emulate human sensory mechanisms with noisy visual perception and looming aversion. Additionally, human motor constraint was incorporated through a bio-mechanical model of walking. We gathered data from a human-in-the-loop experiment to understand pedestrian behavior. The findings reveal several phenomena not addressed by existing pedestrian models, regarding how pedestrians adapt their walking speed to the kinematics and behavior of the approaching vehicle. Our model successfully captures these human-like walking speed patterns, enabling us to understand these patterns as a trade-off between time pressure and walking effort. Importantly, the model retains the ability to reproduce various phenomena previously captured by a simpler version of the model. Additionally, phenomena related to external human-machine interfaces and light conditions were also included. Overall, our results not only demonstrate the potential of constrained RL in modeling pedestrian behaviors but also highlight the importance of sensory-motor mechanisms in modeling pedestrian-vehicle interactions.
Aiming at the problem that the non-stationary of airborne bistatic radar clutter reduces the performance of traditional clutter suppression algorithm, this paper first formulates airborne bistatic radar clutter model, and then analyzes the bistatic radar clutter characteristics in four typical combat scenarios. In addition, in order to reduce the computation burden of the STAP algorithm, a three-dimensional cross-beam clutter suppression algorithm is proposed, which uses the transformation matrix to convert the space-time two-dimensional data into azimuth-elevation-Doppler three-dimensional data. By eliminating auxiliary beam clutter data, only the main beam clutter data which plays a major role in dual-basis clutter suppression is retained to form a three-dimensional cross-beam for local adaptive clutter suppression. The proposed algorithm can greatly reduce the amount of calculation and the demand of training samples, while trying to ensure that the clutter suppression performance does not degrade. The effectiveness of the proposed algorithm is verified by analyzing the clutter suppression effect and computation amount of various algorithms in typical combat scenarios. Therefore, the algorithm has great potential in the applications of airborne bistatic and non-stationary clutter suppression.
Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models.
José Enrique Rodríguez Marco, Manuel Sánchez Rubio, José Javier Martínez Herráiz
et al.
In terms of manned aircraft, pilots usually detect icing conditions by visual cues or by means of ice detector systems. If one of these cues is seen by the crew or systems detect icing conditions, they have to apply the evasive procedure as defined within the aircraft flight manual (AFM). However, as regards unmanned aircraft, there are not pilots on board and, consequently, nobody can act immediately when icing conditions occur. This article aims to propose new techniques of sending information to ground which make possible to know the aircraft performance correctly in icing conditions. For this goal, three contributions have been developed for the unmanned aircraft Milano. Since icing conditions are characterized quantitatively by the droplet size, the liquid water content, and the total air temperature, when these parameters are between certain limits ice formation on aircraft may occur. As a result of these contributions, in that moment, high-quality images of the wing leading edge, tail leading edge and meteorological probes will be captured and sent to ground making possible that remote pilots or artificial intelligent (AI) systems can follow the appropriate procedures, avoid encounters with severe icing conditions and perform real-time decision making. What is more, as information security is becoming an inseparable part of data communication, it is proposed how to embed relevant information within an image. Among the improvements included are image compression techniques and steganography methods.
This work investigates an application-driven co-design problem where the motion and motors of a six degrees of freedom robotic manipulator are optimized simultaneously, and the application is characterized by a set of tasks. Unlike the state-of-the-art which selects motors from a product catalogue and performs co-design for a single task, this work designs the motor geometry as well as motion for a specific application. Contributions are made towards solving the proposed co-design problem in a computationally-efficient manner. First, a two-step process is proposed, where multiple motor designs are identified by optimizing motions and motors for multiple tasks one by one, and then are reconciled to determine the final motor design. Second, magnetic equivalent circuit modeling is exploited to establish the analytic mapping from motor design parameters to dynamic models and objective functions to facilitate the subsequent differentiable simulation. Third, a direct-collocation-based differentiable simulator of motor and robotic arm dynamics is developed to balance the computational complexity and numerical stability. Simulation verifies that higher performance for a specific application can be achieved with the multi-task method, compared to several benchmark co-design methods.
Jacob Thorstensen, Tyler Henderson, Justin Kavanagh
Animal models indicate that motor behaviour is shaped by monoamine neurotransmitters released diffusely throughout the brain and spinal cord. We present strong evidence that human motor pathways are equally affected by neuromodulation through noradrenergic and serotonergic projections arising from the brainstem. To do so, we have identified and collated human experiments examining the off-label effects of well-characterised serotonergic and noradrenergic drugs on lab-based electrophysiology measures of corticospinal-motoneuronal excitability. Specifically, we focus on the effects that serotonin and noradrenaline associated drugs have on muscle responses to magnetic or electrical stimulation of the motor cortex and peripheral nerves, and other closely related tests of motoneuron excitability, to best segment drug effects to a supraspinal or spinal locus. We find that serotonin enhancing drugs tend to reduce the excitability of the human motor cortex, but that augmented noradrenergic transmission increases motor cortical excitability by enhancing measures of intracortical facilitation and reducing inhibition. Both monoamines tend to enhance the excitability of human motoneurons. Overall, this work details the importance of neuromodulators for the output of human motor pathways and suggests that commonly prescribed monoaminergic drugs have off-label motor control uses outside of their typical psychiatric/neurological indications.
In intracellular transports, motor proteins transport macromolecules as cargos to desired locations by moving on biopolymers such as microtubules. Recent experiments suggest that cargos that can associate motor proteins during their translocation have larger run-length, association time and can overcome the motor traffic on microtubule tracks. Here, we model the dynamics of a cargo that can associate at the most m free motors present on the track as obstacles to its motion. The proposed models display competing effects of association and crowding, leading to a peak in the run-length with the free motor density. This result is consistent with past experimental observations. For m=2 and 3, we show that this feature is governed by the largest eigenvalue of the transition matrix describing the cargo dynamics. In all the above cases, free motors are assumed to be present as stalled obstacles. We finally compare simulation results for the run-length for general scenarios where the free motors undergo processive motion in addition to binding and unbinding to or from the microtubule.
Recent years have seen an increase in events of drone incursion into airport terminal areas, leading to safety concerns and disruptions to airline operations. It is of great importance to identify the potential conflict, especially for those non-cooperative drones, as their intentions are always unknown. For the safe operation of air traffic, this paper proposes a conflict risk assessment method between non-cooperative drones and manned aircraft in the terminal area. First, the trajectory data of manned aircraft and drones are obtained. Real-time cylindrical protection zones are established around manned aircraft according to the separation interval for safe operation between the drone and the manned aircraft at different altitudes. Secondly, trajectory predictions for the manned aircraft and the drone are conducted, respectively. A quartile regression bidirectional gate recurrent unit neural network is proposed in this research for the trajectory prediction of the drones. The model integrates the bidirectional gated recurrent unit structure and the quartile regression structure. The performance indicators confirm the superiority of the proposed model. Based on the trajectory prediction results, it is then determined whether there is a conflict risk between the drone and manned aircraft by comparing the position distribution of the drone as well as the real-time cylindrical protection zone of the manned aircraft. The conflict probability between the drone and the manned aircraft is then calculated. The prediction accuracy of conflict probability is estimated by Monte Carlo simulation methods. The collision probability prediction accuracy of manned aircraft and drones at different flight stages and altitudes ranges from 73% to 97%, which shows the reliability of the proposed method. Finally, the collision probability between the drone and the manned aircraft at the closest encountering point and the estimated time to reach the closest encountering point are calculated. This paper predicts the conflict risk between the drone and manned aircraft, thus providing theoretical support for the safe operation of air transport in low-altitude environments.
This paper proposes an approach for impulsive formation maintenance tailored to distributed synthetic aperture radar, i.e., a spaceborne system composed by several antennas working together to provide enhanced remote sensing capabilities. The analyzed configuration is designed to guarantee the presence of a safety tube surrounding each satellite as the dynamics evolve. Formation requirements are related to general constraints on the acceptable along-track and radial/cross-track separations. The paper introduces an adaptive maintenance logic which fulfills these constraints. Specifically, the formation is adaptively redesigned around the chief every time geometry constraints are violated by means of a procedure developed by the authors in previous works and based on relative orbit parameters. Once these parameters are defined, the optimal impulsive burns required for orbit transfer are computed using state-of-the-art approaches. Performance in terms of delta-<i>v</i> and maneuver frequency is analyzed for a two-spacecraft formation exploiting a simulation environment based on MATLAB and GMAT. In ideal conditions, it is shown that maintenance costs are limited, while close proximity requires fine sensitivity on the applied impulses. A first assessment of the impact of relative navigation and maneuvering execution errors indicates that they play an important role in defining the overall control effort.
Fatigue evaluations are one of the main challenges to applying additively manufactured primary structural elements onto aircraft, especially for the gas-pore effects on fatigue strength. In this work, a bond decomposition strategy (BDS) in peridynamics (PD) is proposed; combined with our previously proposed model for fatigue damage, numerical simulations were performed to study the effect of Gas Pore (GP) on fatigue strength. Compared with the strategies in original paradigm of peridynamics, BDS achieves more elaborate description for bond status, predicts deformation fields around discontinuities with improved accuracy, and makes the spacing of material points become independent of discontinuity geometries. Two initiation modes are found in PD simulations, which exert an obvious impact on the final fatigue lifetimes; furthermore, it is revealed that GP not only leads to lower fatigue strength but also results in dispersity of fatigue strength data, in which dispersity is more severe if the GP size is larger, and the decline of fatigue strength is the most severe if the GP is located at subsurface for the same GP size.